Last month, we had the pleasure of hosting a special session at the University of Calgary featuring Dr. Terence P. McNulty on 25 Years of Commissioning and Ramp-Up Performance: McNulty Curves Revisited.

The session brought together about 100 attendees, including a strong turnout from Calgary’s downtown professional community, and it sparked a thoughtful discussion on one of the most important yet often underestimated phases of major projects: the transition from commissioning to stable operations.

The session built on Dr. McNulty’s long-standing work on ramp-up performance and the updated McNulty Curves, which continue to provide a powerful lens for understanding why some projects achieve design capacity quickly while others struggle for months or even years.

Several messages from the session stood out clearly. First, strong ramp-up performance is rarely accidental. Second, the quality of commissioning and start-up planning matters enormously. Third, technical readiness alone is not enough. As the lecture emphasized, practical judgment, or phronesis, remains essential in navigating real project conditions, leading to my personal favorite slide:

For those who would like to revisit the material, the presentation slides will be available for a limited time here:

Session Slides – Limited-Time Link

Despite some last-minute difficulties caused by severe weather, which led to Terry’s flight being cancelled, we were still able to successfully hold the session online and ensure the event went ahead. We continued the evening with the reception dinner afterward, which gave attendees a valuable opportunity to connect, reflect on the discussion, and enjoy the evening together.

We would also like to sincerely thank the organizations that supported this event: the Canadian Institute of Mining, Metallurgy & Petroleum (CIM), Hatch, and the AACE Calgary Section.

Their support helped make the session possible, and we are grateful for their continued engagement with the broader engineering and project management community.

Graduate Student Opportunities in Generative AI

We are also pleased to share a new opportunity for prospective graduate students. We are inviting applications for Master’s and PhD positions connected to an emerging research program on Generative AI in construction management, with a particular focus on schedule development and related decision-support challenges.

We are looking for candidates with:

• Strong academic background in engineering, applied mathematics, computer science, or related disciplines

• Experience or interest in AI-based scheduling and construction planning

• Experience or interest in fine-tuning, domain adaptation, and domain knowledge enhancement for generative AI models.

Click here to get to the online form, remember to send your resume and CV through by email as instructed on the form:

Online Application Form

Please share this opportunities with interested students within your network.

A shortlist of candidates will be interviewed by our team, together with our research partners, through at least two rounds of interviews.

Upcoming Department of Civil Engineering Event

Finally, we would love you to join an upcoming event hosted by the Department of Civil Engineering at the University of Calgary: Inspiring Voices: Women in Civil Engineering Leadership Dinner.

This will be taking place on Thursday, April 9, 2026 at 6:00 PM at the Last Defense Lounge (LDL) at the University of Calgary Main Campus.

See Event Webpage and Register

The program includes two inspiring featured talks that will bring both depth and personal reflection to the evening. Jess Theroux’s talk, Taking My Time, An Act of Rebellion, invites reflection on resilience, intention, and the power of moving through life and work at a pace that is thoughtful and self-defined.

Later, Jessica Bekker will share Personal Journey in Learning and Discovering the Blackfoot Tipi as an Indigenous Woman and Engineer, offering a deeply personal perspective on culture, identity, learning, and engineering.

Together, these talks promise to make the evening not only engaging, but also meaningful and memorable.

Looking Ahead

This has been a busy and productive year for us as we continue building the seeds of several new research initiatives. We are planning to share more of our latest research in the months ahead. Please stay tuned, and do let us know what you think. We would be very happy to hear from you and to explore how we might work together.

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On Thursday, March 12, 2026, renowned metallurgical engineer Dr. Terence P. (“Terry”) McNulty will present a lecture titled “25 Years of Commissioning & Ramp-Up Performance: McNulty Curves Revisited.”

• Speaker: Dr. Terence P. McNulty

• Date: Thursday, March 12, 2026

• Time: 4:30 PM (MDT)

• Location: University of Calgary, N.W. Campus (Please register for more info)

• Registration is available here.

Only a few spots remain, so we encourage interested participants to register soon.

Why Ramp-Up Performance Matters

One of the least understood, but most critical, phases of major capital projects occurs after construction is completed: the period when facilities transition from commissioning to stable operations.

Across the mining, energy, and industrial sectors, many projects struggle during this stage. Even when construction is completed successfully, achieving design throughput and stable performance often takes far longer than expected. In some cases, it can take months or even years before operations stabilize.

Dr. McNulty’s well-known McNulty Curves offer one of the most widely referenced empirical frameworks for understanding this phenomenon. Developed through decades of case studies across the minerals and process industries, the curves illustrate how few projects achieve full capacity quickly and how varied ramp-up trajectories can be across facilities.

In this lecture, Dr. McNulty will revisit and update the curves based on additional case histories and share insights on:

• Why many projects miss early throughput targets

• The operational and managerial factors that influence ramp-up success

• What distinguishes fast, stable start-ups from prolonged underperformance

• Practical strategies project teams can use to reduce start-up risk

For professionals involved in project management, commissioning, operations readiness, and asset performance, these lessons are particularly valuable.

Learning from a Pioneer in the Industry

Dr. McNulty brings more than sixty years of experience across research, operations, and executive leadership in the minerals industry.

Over his career he has worked with organizations such as Anaconda Company, Kerr-McGee Chemical, and Hazen Research, and later founded T.P. McNulty and Associates, a global consulting firm specializing in metallurgy, mining, and process engineering.

His work has influenced generations of engineers and project managers seeking to understand the operational realities of large industrial facilities. With more than 50 publications, multiple patents in copper metallurgy, and recognition from leading professional societies, Dr. McNulty’s insights combine rigorous technical understanding with extensive real-world experience.

Event Sponsors

This event has been made possible through the generous support of:

• Canadian Institute of Mining, Metallurgy & Petroleum (CIM)

• EPM Research Group at the University of Calgary.

• Hatch

• AACE Calgary Section

Takeaways,

• Risk management failures are often rooted in faulty communication.

• Ask AI to produce multiple versions and choose one deliberately.

• Value clarity, define glossaries, and lock the semantics.

• Maintain AI-generated risk registers separately with clear rules for escalation.

• Train young engineers to challenge AI outputs.

Artificial intelligence is flooding into project management tools, knowledge bases and decision support systems.

For engineers and project leaders it is easy to see the appeal: a tireless assistant that drafts schedules, writes progress reports and crunches massive data sets in seconds.

However, the very ease with which AI is being woven into everyday workflows should give risk professionals pause.

With any powerful technology, the earliest waves of adoption are the most risky. Initial successes can create a sense of confidence long before underlying vulnerabilities have been truly tested.

This emerging landscape is increasingly showing a familiar pattern: over-trusted outputs, underspecified models, and opaque data pipelines that quietly compound into poor decisions, or, at times, significant failures.

At the EPM Research Lab at the University of Calgary, our research focuses on learning the challenges and uncertainties around AI adoption from our from industry partners. Drawing on these shared lessons, I aim to outline several practical ideas and the critical issues teams should watch for when integrating AI into project and risk management workflows.

Communication Failures in Risk Management

Human communication sits at the core of project and risk management. When it fails, the consequences can be severe.

Many well-known engineering disasters were not rooted solely in technical errors, but in misunderstandings, ambiguous language, and misaligned interpretations. This is the lens through which the adoption of AI in risk management must be viewed.

For those of us in Calgary, a striking example of how miscommunication can trigger major project challenges lies at the very heart of our city’s most recognized landmarks.

Calgary’s Peace Bridge

Calgary’s Peace Bridge highlights how semantic and standards misalignment, not a purely technical defect, can derail a major project.

The welds fabricated in Spain met European standards, yet failed under Canadian testing protocols. The project relied on an unverified assumption that international specifications were interchangeable. That gap in interpretation became a critical interface risk.

When Canadian inspectors applied domestic code-based probe testing, dozens of welds were deemed non-compliant, prompting extensive re-inspection, grinding, and re-welding of the tubular steel structure [1].

The rework increased the total weld length from roughly 500,000 inches to over 1.5 million inches, requiring months of additional labour and specialist oversight, effectively tripling the welding effort. The opening date slipped repeatedly, from an initial 2010 target to early 2011, then mid-2011, and ultimately to March 2012, demonstrating how a single unmanaged interface risk between design codes can cascade into substantial schedule overrun [1].

“In a timely fashion”

The 2021 collapse of the Champlain Towers South condominium in Surfside, Florida illustrates how ambiguous technical language can become a fatal failures.

A 2018 engineering report warned of “major structural damage” and advised that deterioration be repaired “in a timely fashion.” In hindsight these phrases were dire, but non-engineers on the condo board struggled to interpret how “major” the damage was or what timeframe “timely” implied.

Crucially, the report never stated explicitly that the structure was at risk of collapse. The intended urgency did not translate, decisions were delayed, and 98 people died when the building partially collapsed [3].

Other well-known disasters show similar patterns. The 1986 Challenger shuttle explosion has been described as a technical communication failure: engineers raised concerns about O-ring performance at low temperatures, but these concerns were not forcefully communicated into the final launch decision [4]. Communication breakdown also played a central role in the Deepwater Horizon blowout, where early warning signs and technical concerns were not effectively escalated in real time [5]. The Hyatt Regency walkway collapse resulted from a critical design change that was misunderstood between the steel fabricator and the engineering firm [6]. More recently, Boeing’s 737 MAX crashes exposed how software dependencies, documentation, and training gaps can create semantic blind spots between designers, regulators, and pilots [7].

Across these examples, the pattern is consistent and reflects the main lens through which the adoption of generative AI in risk management must be viewed.

Avoid Anchoring and Biases to AI

Behavioral science has shown that human judgments are systematically biased [8].

In a classic experiment, two groups of participants were asked to estimate the product of a sequence of numbers. When the sequence started with larger numbers (8 × 7 × 6…), median estimates were far higher than when the sequence started with smaller numbers. The early figures anchored people’s judgments [9].

In project planning, anchoring occurs when stakeholders fixate on original schedules or budgets and resist adjustment even as new information emerges [8,9]. In risk management, AI-generated risk descriptions can create a similar effect by anchoring how teams interpret and understand a risk.

Generative AI systems exacerbate anchoring because their outputs feel precise. Under the hood, large language models (LLMs) are stochastic: each response is one sample from a probability distribution over possible completions. Yet the language is fluent and confident, which tempts teams to treat the first answer as “the answer.”

Aside from anchoring, the NIST Generative AI Risk Management Framework highlights how human-AI configurations can create automation bias and over-reliance [10]. Users may accept AI outputs even when there are warning signs, particularly when they do not understand the model’s limitations.

Over-reliance also manifests as sycophancy bias: chatbots that align with user cues, echoing opinions to please or reassure the user. Chatbots affirm users’ actions more often than human interlocutors and rarely challenge harmful framings, thereby reinforcing existing beliefs rather than interrogating them [11].

At the same time, evaluations of AI-generated summaries show that models frequently omit qualifiers and over-generalise findings. When explicitly instructed to “be accurate,” some models paradoxically increased the rate of over-generalisation [12].

Self‑consistency and best‑of‑N sampling

AI research has started to address these issues through multiple-output decoding strategies such as self-consistency and best-of-N sampling.

The idea of self-consistency is proposed to generate several independent reasoning paths (using chain-of-thought prompting) and then selects the answer that is most consistent across those paths [13]. Best-of-N sampling extends this idea: the model generates multiple candidate outputs and then selects the one with the highest internal confidence or best fit to predefined criteria [14].

Both strategies reduce hallucinations and improve reasoning by averaging across several “runs” rather than trusting a single sample. Practitioners can adopt a simple analogue of these techniques:

For project teams, treat AI outputs like Monte Carlo draws. Request several versions, review the spread and pick the one that best fits your domain knowledge. This simple practice not only mitigates anchoring but also surfaces oversights and faulty assumptions.

The Rising Cost of Ambiguity

The growing reliance on generative AI introduces a new and more pervasive form of under-specification.

Because LLMs have reduced the effort of producing confident, coherent language, project teams now face an environment saturated with plausible explanations and risk narratives that differ only subtly in meaning.

A study of 14 widely used LLMs found substantial intra-model variability: the same LLM, with the same prompt, may produce outputs ranging from poor to highly creative [15].

In this context, any ambiguity in the definition of terms creates space for the model to silently reinterpret them, and as the volume of AI-generated text expands, the organizational cost of semantic ambiguity rises proportionally.

This dynamic is particularly evident in LLM-assisted workflows, where under-specification manifests as semantic drift. When prompts are loosely defined, models fill the gaps with their own latent assumptions, producing multiple internally coherent but conceptually incompatible outputs [16].

The challenge going forward will be ensuring that the documentation reflects a stable, shared interpretation. The only antidote is investing in clarity: explicitly defining ontologies, articulating precise thresholds, constraining prompts, and requiring structured outputs that force the model to operate within the organization’s established vocabulary.

In conventional machine learning pipelines, under-specification arises because many different parameter configurations can achieve similar performance on the training and validation distributions while behaving differently under new conditions [17].

LLMs reproduce a similar phenomenon, but through language rather than model parameters. Both cases highlight the same lesson for project management: without rigor in definitions, structure, and semantic alignment, AI-enabled workflows risk divergent interpretations into the decision-making.

Risk Identification Paradox

Construction projects have long aspired to “thorough” risk identification, yet practitioners face a persistent paradox: the more exhaustive the pursuit of risks becomes, the less effective it often is [18].

Excessively long risk registers obscure underlying patterns, dilute managerial attention, and overwhelm already stretched delivery teams, allowing weak signals of consequential risks to disappear within a series of low-value items [19].

The opportunity cost is substantial. Every hour spent documenting remote hypotheticals is an hour not invested in analyzing, mitigating, or rehearsing responses to high-impact threats. High-reliability sectors have long recognized that there is a rational stopping point.

The ALARP principle (As Low As Reasonably Practicable) operationalizes this idea by acknowledging that once risks fall below a tolerability threshold, additional mitigation effort offers diminishing returns [18]. The Pareto principle offers a similar insight: a small subset of risks typically accounts for most of the potential impact.

Modern enterprise-risk frameworks embed these concepts directly. COSO’s ERM framework and ISO 31000 address risk appetite and tolerance explicitly, giving organizations structured cut-off points for attention and action [21,22].

When combined with the reality that projects unfold in dynamic and uncertain environments, best practice reframes risk identification as an iterative, context-responsive activity, not a one-time attempt at omniscience.

This highlights the communication dimension within the broader risk-management process: risk registers are, above all, communication instruments. Their purpose is to keep teams collectively aware of emerging issues, coordinate controls, and maintain shared situational awareness [23].

Tiered or multi-level risk registers reinforce this role by distinguishing between material or urgent risks that require leadership attention and other less crucial risks that can be monitored locally.

Escalation processes then serve two complementary functions: signaling (alerting leaders to cross-boundary or systemic issues) and screening (filtering noise before it reaches governance forums). When designed well, these architectures help maintain organizational mindfulness without overwhelming project teams or steering committees [24].

AI-Generated Exhaustive Registers

Generative AI and large language models now make it technically feasible to produce highly exhaustive, dynamic, and continuously refreshed risk registers.

These systems can ingest real-time project data, design revisions, schedule updates, and change requests, generating a broad constellation of potential risks far faster than human teams.

Early research indicates that LLMs can outperform human groups in the breadth of risk identification, provided their outputs are curated [25]. Recent studies show that LLMs can surface rich risk sets for sustainable operations in onshore wind projects, again with the caveat that human filtering remains essential [26].

In this sense, AI is emerging as a high-recall “risk discovery engine,” particularly valuable in megaprojects where interfaces and stakeholder dynamics evolve rapidly.

However, exhaustiveness cannot come at the expense of human judgment or efficiency. Behavioral research demonstrates that when information volumes grow without structure, individuals revert to heuristics or defer to seemingly authoritative systems [8,17,23].

A more practical architecture, therefore, is to maintain a separate AI-generated risk register that operates in parallel to the formal project register.

The AI tiered system can continually populate this space with emerging signals, weak indicators, and speculative risks without burdening the project team. From there, organizations should define explicit escalation criteria.

Only those AI-generated risks that meet the agreed criteria are then elevated into the formal project risk register, where they receive structured analysis, ownership, and monitoring.

This approach preserves the advantages of broad, AI-driven exploration while safeguarding the constraints of human attention and decision-making. It mirrors principles from resilience engineering: sense widely at the periphery, where anomalies first emerge, but escalate selectively at the core, where decisions and resources must remain focused [18,23].

Human Oversight Means Training People to Challenge AI

Many experienced professionals can readily distinguish between plausible AI reasoning and obvious AI errors.

But the next generation of engineers may not be able to do so without proper mentoring and support. It is not guaranteed that this intuition will persist without explicit preparation [27].

As AI-generated text increasingly permeates the project environment, risk registers, engineering memos, meeting minutes, it will become harder to spot flawed logic embedded within these artefacts, especially for future professionals who have never performed these tasks without AI assistance.

For this reason, simply stating that “humans are in the loop” is insufficient. The NIST Generative AI Profile emphasizes that human oversight can fail when users become over-confident in AI systems or when organizational routines implicitly defer to automated outputs [10]. Similarly, a recent CSET report on automation bias documents how over-reliance on automated systems has contributed to accidents and near-misses, underscoring that effective oversight must be deliberately designed and continually trained [28].

The industry must therefore invest in cultivating AI literacy among young engineers, planners, and inspectors. This includes embedding training on critical thinking, ethics, and model limitations into graduate and early-career programmes, and creating mentorship structures where experienced project managers actively explain why certain AI-generated recommendations are implausible or unsafe. Put plainly, organizations must hire and train young engineers to challenge AI outputs, not defer to them.

Question then Becomes

As generative AI embeds itself deeper into engineering and project management, the central challenge increasingly shifts away from computation and towards communication.

To navigate this era safely, the construction industry should treat AI as an accelerator of both strengths and vulnerabilities.

Organizations that invest now in semantic discipline, reliable AI workflows, and training young engineers to interrogate AI will be better positioned than those that simply add ‘AI’ to their tool stack.

For decades, the saying in construction disputes has been that “the party with more documentation wins.” But as AI becomes ubiquitous, and every interaction, revision, and decision is automatically captured, will the advantage shift to the party with clearer semantics and shared understanding?

As the generation of engineers who can challenge AI outputs ages and retires, will the next generation, raised on AI-assisted reasoning, retain the ability to question, verify, and audit these systems?[27] Or is the trajectory more likely to get worse before it gets even worse?

And as AI assumes more of the administrative load in project and risk management, will it free practitioners to focus on the human communication that actually mitigates risk? Or will it widen the gap between documentation and understanding?

Notes

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If you wish to share your opinion, provide insights, correct any details in this article, or if you have any questions, please email editor@epmresearch.com.

Refer to this article using the following citation format:

• Zangeneh, P. (2025), “Industry Should Brace for the First Wave of AI-Driven Risk Management Failures ”, EPM Research Letters.

References

1. Journal of Commerce. Welding problems cause Calgary Peace Bridge delays. Journal of Commerce. 2011 Apr 6.

2. CBC. Welding issues delay Peace Bridge project. 2010 Nov 16.

3. Forbes. When words kill: lessons from the Champlain Towers collapse. Forbes. 2021 Jun 28.

4. NASA. Lessons From Challenger. 2021.

5. De Wolf, Daniel. Crisis communication failures: The BP case study. International Journal of Advances in Management and Economics. 2013.

6. National Academy of Engineering. The Hyatt Regency walkway collapse. Online Ethics Center.

7. Herkert, J., Borenstein, J. & Miller, K. The Boeing 737 MAX: Lessons for Engineering Ethics. Sci Eng Ethics.

8. Kahneman, Daniel. Thinking, fast and slow. macmillan, 2011.

9. Decision Lab. Anchoring bias: the psychology of clinging to initial estimates. The Decision Lab. 2023.

10. National Institute of Standards and Technology (NIST). Artificial Intelligence Risk Management Framework: Generative AI Profile (NIST AI 600-1). 2024.

11. Hern A. Sycophantic AI chatbots tell users what they want to hear, research finds. The Guardian. 2025.

12. Peters U, Chin-Yee B. Generalization bias in large language model summarization of scientific research. R. Soc. Open Sci. 2025.

13. Wang X, Wei J, Scales N, et al. Self-consistency improves chain-of-thought reasoning in language models. In: Proceedings of ICLR 2023 [Internet].

14. Kang, Zhewei, Xuandong Zhao, and Dawn Song. Scalable best-of-n selection for large language models via self-certainty. arXiv. 2025.

15. Haase, Jennifer, Paul HP Hanel, and Sebastian Pokutta. “Has the Creativity of Large-Language Models peaked? An analysis of inter-and intra-LLM variability.” Journal of Creativity. 2025.

16. Yang, Chenyang, et al. What Prompts Don’t Say: Understanding and Managing Underspecification in LLM Prompts. arXiv. 2025.

17. D’Amour A, Heller K, Moldovan D, et al. Underspecification presents challenges for credibility in modern machine learning. J Mach Learn Res. 2022.

18. Fenton N, Neil M. Risk assessment and decision analysis with Bayesian networks. 2nd ed. Boca Raton: Chapman & Hall/CRC; 2019.

19. Turner BA, Pidgeon NF. Man-made Disasters. 2nd ed. Oxford: Butterworth-Heinemann; 1997.

20. Health and Safety Executive (HSE). Reducing risks, protecting people: HSE’s decision-making process (R2P2). HSE. 2001.

21. Committee of Sponsoring Organizations of the Treadway Commission (COSO). Enterprise Risk Management—Integrating with Strategy and Performance. COSO. 2017.

22. International Organization for Standardization. ISO 31000:2018 – Risk management: Guidelines. ISO . 2018.

23. Weick KE, Sutcliffe KM. Managing the unexpected: resilient performance in an age of uncertainty. 2nd ed. San Francisco: Jossey-Bass; 2007.

24. Zangeneh, P. (2025), The Art of Strategy - Part 2: A Review of Dixit and Nalebuff’s Classic Within the Context of Project Management, EPM Research Letters.

25. Nyqvist M, Landberg M, Josephson PE. Can ChatGPT exceed humans in construction project risk management?. Safety Insights. 2024.

26. Wen H, AbouRizk S, Mohamed Y. Using large language models to identify project risks for sustainable operations: a case study of onshore wind farms. In: CIB Conferences. 2025.

27. Cosentino R. Navigating Major Programmes podcast: human-specific skills vs AI in major project controls. 2025.

28. Center for Security and Emerging Technology (CSET). AI safety and automation bias. CSET. 2023.

This year, I have the privilege of co-chairing the conference with Dr. Fuzhan Nasiri from Concordia University. Together, and with the remarkable support of our organizing committee, we are shaping this specialty track into a vibrant meeting ground for both academics and practitioners who see engineering management not just as an analytical discipline but as a driver of real-world transformation.

I wish to encourage you to participate in this important Canadian event, and hope to see you and the broader EPM Research community in Québec City next June!

Submit your abstract by October 22, 2025, and join us in sharing your ideas, and shaping the future of engineering management in Canada.

Submit Your Abstract

Call for Abstracts: Now Extended

I’m happy to learn that the local organizing committee has extended the deadline for abstract submissions to October 22, 2025.

We invite contributions in three flexible formats of full papers, extended abstracts, and case studies. Each format serves a distinct purpose in advancing the conversation between research and practice:

• Full Papers – For authors aiming to present rigorous research, analytical frameworks, or methodological advancements. These contributions typically include data, models, and detailed findings that push the theoretical or technical boundaries of engineering management.

• Case Studies – For practitioners and researchers seeking to share lessons from real projects. These submissions highlight decisions, trade-offs, and reflections from implementation, successes, failures, or unexpected outcomes that can inform future practice.

• Extended Abstracts – Especially well-suited for industry participants. This format allows professionals to discuss emerging issues, operational challenges, and innovation ideas without the need to develop a full paper. Extended abstracts encourage early sharing of insights, enabling dialogue around problems that merit joint academic-industry attention.

Themes for 2026

This year’s topics reflect the evolving frontiers of engineering management in Canada and beyond that was put together by the our great members of the engineering management technical committee:

1. Management of Generative AI in Civil Engineering Applications

2. Project Delivery, Contracts, and Collaborative Models

3. Civil Engineering Workforce and Trades Management

4. Public Policy and Civil Engineering in Public Sector

5. Innovation, Technology Management, and New Business Models in Civil Engineering

6. Procurements and Supply Chain Management

7. Risk, Uncertainty, and Complexity Management and Modelling

8. Leadership and Stakeholders Management in Civil Engineering

9. Buildings, Facilities, and Infrastructure Management

10. Construction and Project Management

11. Management of Climate Change Adaptation & Resilience Projects

Submit your abstract by October 22, 2025, and join us in sharing your ideas, and shaping the future of engineering management in Canada.

Submit Your Abstract

Building a Stronger Dialogue with Industry

One of our major efforts this year is to expand participation from Canadian public and private industry. With the help of our committee members, we are reaching out to project owners, utilities, contractors, and consultancies who make engineering decisions at scale.

We aim to make this conference a place where research meets real constraints, and where industry voices help shape the next generation of tools, methods, and engineering leaders of Canada.

Looking Ahead: Bi-Weekly Articles on EPM Research

Starting this November, our academic year series will start with bi-weekly updates featuring short project case studies and curated articles on emerging topics in project and engineering management.

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This review draws on key concepts from game theory and negotiation literature to support better decision-making in project settings. Here we review:

1. Thomas Schelling and Strategic Moves

2. Conflicts as Bargaining in Variable-sum Games

3. Deterrence

4. Rational Irrationality

5. Bargaining

6. Negotiations

7. Credibility of Strategic Moves in Project Environments

8. Question then Becomes

I first revisit the core concepts of strategic moves and their critical components: commitments, threats, promises, and credibility from the reviews we did of the Art of Strategy by Dixit and Nalebuff (Part 1 and Part 2) (1).

Then I explore how these principles apply to project environments, and delve into broader idea of conflict deterrence, and limiting rationality, first formalized by Thomas Schelling in his seminal book The Strategy of Conflict that dates back to 1960 (2).

The significance of Schelling’s work lies in how it lays the foundation for a strategic approach to conflict, bargaining, and negotiation. Despite the seemingly cold-blooded title of his book, Schelling clarifies that the theory is not about the efficient application of violence. It is not fundamentally a theory of aggression, resistance, or war. Rather, it is a theory of interdependent decisions and nonuse of violence and the conditioning of one’s behavior based on the behavior of others.

We then review another seminal work, Getting to Yes: Negotiating Agreement Without Giving In by Roger Fisher, William Ury, and Bruce Patton, to discuss core concepts such as the Best Alternative to a Negotiated Agreement (BATNA) and its critical role in negotiation strategy (3).

As Schelling points out, the strategy of conflict applies across many areas of daily life. Whether dealing with children, navigating right-of-way in traffic, or interacting with friends and partners, most of our social behavior involves elements of conflict, bargaining, and negotiation.

Thomas Schelling and Strategic Moves

Schelling was a pioneer in exploring the idea of not merely playing the game but changing the game through strategic moves.

As discussed in the first and second parts of the review of the Art of Strategy by Dixit and Nalebuff, strategic moves involve actively shaping the game itself to influence outcomes and the behavior of others. These moves can be categorized into three core types: commitments, threats, and promises.

Strategic moves consist of two critical components: the move itself and the course of action that establishes its credibility. The effectiveness of any strategic move hinges on both the action and the credibility behind it.

Commitments are unconditional actions that restrict one’s own choices to compel a response. They are powerful tools that can bind opponents to a specific course of action. However, undermining an opponent’s commitment—if done strategically—can open pathways for de-escalation and negotiation.

Hernán Cortés famously "burned his ships" during the 1519 conquest of the Aztec Empire. While the historical accuracy of this account is debated, it serves as a powerful metaphor for total commitment—eliminating the option of retreat. On the flip side, as Sun Tzu noted, undermining an opponent’s commitment may require offering them—if not a literal one—a golden bridge to retreat across.

In contrast, threats and promises are conditional moves with distinct dynamics. Threats aim to deter undesirable actions by imposing negative consequences. They are typically open-ended and often seek to preserve the status quo. Promises, on the other hand, incentivize desired actions through rewards and are usually intended to disrupt or improve the status quo.

Conflicts as Bargaining in Variable-sum Games

Schelling starts his book by establishing that pure conflict—where interests are entirely opposed—is an exceptional case. It arises only in scenarios of total destruction, a rarity even in warfare. More often, conflict involves a complex interplay of competing interests, where success is not defined by absolute victory over an adversary but by achieving favorable outcomes within one’s own value system.

In this context, "winning" means securing gains that align with one's goals and priorities rather than defeating the opponent outright. This can occur through bargaining, compromise, or by avoiding mutually destructive actions altogether.

Conflict management, therefore, is not solely about overpowering an opponent but about skillfully navigating the potential for conflict. Strategies such as deterrence, limited engagement, and negotiation highlight the importance of mutual dependence and shared interests. Whether in warfare, business negotiations, or labor strikes, the objective is often to find solutions that prevent costly and damaging confrontations.

Most conflict situations resemble bargaining scenarios more than zero-sum contests. Unlike constant-sum games, where one party’s gain equates to another’s loss, many conflicts are variable-sum games in which mutually beneficial outcomes are possible.

This perspective underscores the importance of understanding the other party’s motivations and decisions, as success often hinges on finding common ground rather than imposing unilateral solutions.

For example, a successful labor strike is not necessarily one that devastates the employer; it may be one that is never happened. As Sun Tzu stated, "The greatest victory is that which requires no battle."

Deterrence

Framing conflict as an essential type of bargaining, the concept of Deterrence then hinges on the credible threat of force, not its actual use.

It is the art of persuading an adversary that certain actions will incur unacceptable costs, not just to the adversary but to those making the threat too. For this to work, the threat must be believable, which depends on the perceived willingness and capability of the deterring party to follow through.

A key distinction lies between using force and exploiting its potential. Effective deterrence demands more than military strength, it requires strategic communication, psychological insight, and an understanding of the adversary’s calculus.

Deterrence operates where conflict and common interest coexist. It fails in cases of complete alignment or irreconcilable hostility. This dynamic applies not just to adversaries but also to allies, consider how nations deter partners from defecting on agreements, much like a state might deter an enemy.

Deterrence becomes critical in settings where no higher authority exists to enforce rules. Ancient feuds and modern criminal networks illustrate this dynamic: gangs rely on threats—not courts—to uphold agreements. Often limited conflict, retaliation, and fragile truces emerge as substitutes for formal enforcement among nations.

Rational Irrationality

Rationality, as a guiding principle in decision-making, often implies logical consistency, optimization of outcomes, and alignment with defined objectives. However, in conflict scenarios, this notion of rationality is not universally advantageous.

A purely rational actor is predictable, calculable, and sometimes exploitable. This creates a paradox: in certain strategic interactions, it may be advantageous to *limit* one’s own rationality.

Strategic interactions often reveal that strict adherence to rational decision-making can be a disadvantage. In certain contexts, especially where unpredictability or deterrence is required, the appearance of irrationality can be a valuable asset.

Rationality encompasses not only cognitive processes but also the systems, structures, and agents through which decisions are enacted. These include technological tools, communication channels, legal frameworks, and even the rationality of one’s collaborators. From a strategic standpoint, disrupting or limiting these rational components may be beneficial. For example, impairing communication systems or legally constraining access to resources may serve as deliberate tactics to counter external coercion or exploitation.

Bargaining

Most real-world conflicts resemble variable-sum games where mutual gains are possible. Yet, achieving favorable outcomes is not only a matter of efficiency but also of distributional strategy—determining who gains more from the agreement.

The conventional wisdom that effective negotiators should have full authority and flexibility to make concessions is misleading. Often, the power of a negotiator lies precisely in their apparent inability to yield or compromise. A negotiator who can credibly claim to lack authority to make concessions possesses a strategic advantage. By limiting their own flexibility, they force the other party to adjust its position.

Schelling’s insights into commitment strategies highlight how deliberately restricting one’s options may enhance bargaining power.

For commitments to be effective, they must be credible, visible, and costly to break. The mechanisms that enforce these commitments range from formal contracts and technological solutions to cultural norms and reputational stakes. In certain contexts, verbal oaths or public statements may suffice; in others, formalized agreements or technological tools are required to ensure credibility. The effectiveness of these commitments often hinges on institutional enforcement or social expectations that render renegotiation prohibitively costly.

The strategic use of commitments also involves timing and communication. In scenarios where commitments are made sequentially, the first mover often holds a decisive advantage by setting terms that subsequent actors must either accept or challenge under constrained conditions.

However, when communication is delayed or obstructed, as in negotiations conducted through indirect channels, parties with limited receptiveness to counteroffers may inadvertently strengthen their bargaining position. By appearing or genuinely being unable to reconsider their stance, they create an impression of unyielding resolve.

Ultimately, Schelling posits that the paradox of bargaining power lies in the deliberate cultivation of weakness. By surrendering certain options or reducing one’s flexibility, a negotiator can alter the expectations and strategies of their opponent. This counterintuitive principle applies across a wide spectrum of bargaining scenarios, from labor disputes and business negotiations to geopolitical standoffs.

Negotiations

The concept of BATNA—Best Alternative To a Negotiated Agreement—was developed by Roger Fisher and William Ury in their book Getting to Yes: Negotiating Agreement Without Giving In.

Negotiations are seldom balanced; differences in power—whether financial resources, political connections, or organizational influence—can significantly impact outcomes. While no negotiation method can guarantee success against a stronger opponent, strategic preparation can greatly enhance your position. The core strategy here is your BATNA.

A common error is to rely on a rigid "bottom line"—a fixed threshold beyond which an offer will not be accepted. While bottom lines can protect negotiators from poor deals, they also severely limit flexibility. Such rigidity prevents negotiators from identifying innovative solutions that better address underlying interests.

In contrast, clearly defining your BATNA enables more strategic and dynamic negotiation. A BATNA represents your most favorable option if negotiations fail. Instead of setting a rigid boundary, you measure proposed agreements against this best alternative. This approach safeguards against overly optimistic or pessimistic expectations about potential outcomes.

To illustrate, consider a family selling their home. Rather than simply stating a minimum acceptable price, they should explore their BATNA: Would renting the property or delaying the sale be preferable to accepting a lower offer? Identifying and clearly understanding their BATNA empowers them to evaluate offers effectively and avoid agreements that are detrimental to their interests.

Effectively using BATNA requires intentional development through three critical steps:

1. Identify Alternatives: Generate a comprehensive list of possible actions if negotiation fails.

2. Improve Feasibility: Refine the most promising alternatives into actionable, realistic options.

3. Select the Best Alternative: Choose the strongest alternative to serve as your benchmark during negotiations.

A robust BATNA strengthens your negotiating power. Consider job interviews: entering negotiations with multiple job offers significantly shifts the power dynamic in your favor compared to having none.

The strategic disclosure of your BATNA should be carefully evaluated. Revealing a strong BATNA can enhance your leverage; however, disclosing a weak BATNA can weaken your negotiating position. Similarly, understanding the other party's BATNA provides crucial insight. If their alternative is overly optimistic, helping them assess it more realistically can create opportunities for mutually beneficial agreements.

The BATNA framework is essential in project management contexts. Leaders who rigorously analyze and strengthen their BATNA position their teams effectively, mitigating vulnerabilities during negotiations and optimizing outcomes. Whether negotiating contracts, partnerships, or internal resource allocation, clearly defining and continuously improving your BATNA is a cornerstone of strategic negotiation.

Credibility of Strategic Moves in Project Environments

Dixit and Nalebuff classified the approaches to establishing credibility for strategic moves into four categories of tactics:

1. Changing Payoffs: Restructuring the game to make compliance more attractive or deviation prohibitively costly.

2. Restricting Choices: Limiting options to reverse commitments, effectively creating credible threats or promises.

3. Incremental Moves: Using repeated, smaller engagements to build trust incrementally.

4. Using Agents and Teams: Employing external entities or teams to enhance credibility and accountability.

Among these tactics, restricting choices through automated systems, contractual terms, or similar measures is the most readily available and practical option for project managers. In contrast, tactics such as employing third-party arbitrators or auditors (agents and teams), significantly changing payoff structures, or incorporating incremental moves usually require higher levels of arrangements than of the daily plans of project managers, making them less immediately accessible.

Projects, by definition, are temporary endeavors aimed at achieving specific, unique outcomes within clearly defined timeframes and resources. Unlike ongoing business operations, project settings often lack the luxury of repeated interactions between the same stakeholders, fundamentally altering how credibility and trust are established.

While reputation within the project-based industry is critical—given the interconnected nature of project stakeholders and the limited pool of clients, contractors, and consultants—the traditional tactic of incremental moves, which relies heavily on repeated engagements to foster trust, is typically less accessible.

Nonetheless, contracting strategies such as progressive contracting are essentially aiming to bridge this gap. Progressive contracting involves dividing the overall scope into sequential phases or milestones, each with clearly defined deliverables and decision points. At each stage, parties assess performance and decide whether and how to proceed.

Question then Becomes

As this review has shown, managing conflict in project environments requires more than interpersonal skills. It demands a strategic mindset rooted in an understanding of bargaining, credibility, and negotiation dynamics.

Drawing from Schelling’s work on strategic moves and deterrence, and complemented by Fisher and Ury’s practical insights on BATNA, we see that conflicts in projects are rarely zero-sum. They more often resemble variable-sum games, where value can be created or lost depending on how threats, promises, and commitments are made and enforced.

Yet applying these principles in real project settings raises important questions. For instance: How can project managers strategically limit their own flexibility to strengthen their bargaining position without also giving in to the paradox of bargaining power and appearing weak?

In what ways and to what extent does the absence of a central enforcement authority manifest in multi-stakeholder megaprojects, particularly between contractors? Does the presence of a client always serve as an effective arbiter, or are there conditions where deterrence becomes necessary?

Notes

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Refer to this article using the following citation format:

• Zangeneh, P. (2025), “Conflict, Bargaining, and Negotiation in Project Management - Exploring Core Concepts and Thomas Schelling’s Insights in Project Management Context.” EPM Research Letters.

This article is a reading material for the “Strategy in Projects: Game Theoretic Approach” course at the University of Calgary, Department of Civil Engineering.

References

• Dixit, A. K., & Nalebuff, B. J. (2008). The art of strategy: A game theorist's guide to success in business and life. W. W. Norton & Company.

• Schelling, T. C. (1980). The strategy of conflict (2nd ed.). Harvard University Press. (Original work published 1960)

• Fisher, R., Ury, W., & Patton, B. (2011). Getting to yes: Negotiating agreement without giving in (3rd ed.). Penguin Books. (Original work published 1981)

It was his obsession, however, that nudged open the door to one of the universe’s most profound laws of reasoning.

What began as a quiet attempt to reason from effect back to cause would, centuries later, evolve into an entire framework for understanding complexity, risk, AI, and adaptive decision-making.

In this article, we begin with the early history of Bayes' theorem and trace its evolution to modern-day applications in risk modeling. We review:

1. What was wrong with Reverend Bayes?

2. Bayes’ Pool Table Thought Experiment

3. Mathematics of Risk

4. From Thomas Bayes to Bayesian Networks

5. Three Fundamental Patterns of Probabilistic Interdependence

6. Risk Modeling with Bayesian Networks

7. Bayesian Networks in Projects and Operations

8. Bayesian Networks in Digital Twins

9. Question then Becomes

Bayesian Networks (BNs), build on Bayes’ original insight by modeling the probabilistic dependencies between variables using directed acyclic graphs (DAGs). These networks capture how real-world phenomena influence one another, not through isolated causes, but through structured chains of probabilistic dependencies (1–4).

Over the years, Bayesian methods have proven to be powerful tools for tackling real-world challenges in projects and operations risk modeling, and systems engineering. This article is part of a course developed at the University of Calgary, focused on practical applications of Bayesian methods.

What was Wrong with Reverend Bayes?

From limited information we have, it appears that Reverend Thomas Bayes (1702–1761) was a quiet intellectual, driven by both religious faith and a rigorous sense of logic.

In his later years, he turned to a deceptively simple question: How should we update our beliefs in light of new information? Perhaps he recognized that the human brain does this instinctively, given wet sidewalks, we infer rain. But his goal was to mathematically determine the likelihood of a hidden cause given an observed effect.

Unlike classical probability, which predicts outcomes based on known conditions, Bayes sought to reverse the lens: to reason backwards from data to underlying causes.

Bayes, however, never articulated his findings using today’s formalism. After his death, Richard Price, his friend and colleague found an unfinished paper in his files titled An Essay Towards Solving a Problem in the Doctrine of Chances (1763). Realizing its importance, Price edited, completed, and prepared the work for publication. He also wrote an introduction to explain and contextualize the significance of Bayes’ result. The torch was later carried forward by Pierre-Simon Laplace, who extended Bayes’ ideas into a broader and more influential framework.

Bayes’ Pool Table Thought Experiment

Imagine a pool table stretching from 0 to 1m. Now let us consider the following three acts.

First - Somewhere along this table, a wooden bar is placed at a secret location, call it X. The exact position is hidden from us; all we know is that it's somewhere between 0 and 1, chosen uniformly at random. This unseen position represents a probability we want to uncover.

Second - A ball is dropped. But this one land according to a rule—it falls randomly somewhere between 0 and X, again chosen uniformly, call it Y.

Third Act – The wooden bar is removed. The only thing we can observe is Y.

And now the essential question arises: given this observed Y, what can we reasonably infer about the unknown X? In other words, how do we revise our belief about where the wooden bar might have been placed?

Engaging with the Experiment

First, the hidden value X—the location of the wooden bar—is assumed to be uniformly distributed between 0 and 1. This means, before observing anything, we treat all positions as equally likely.

Second, the observed value Y, the landing spot of the ball, and we know is also uniformly distributed over the interval between 0 and X. So once X is fixed (though unknown to us), Y is equally likely to land anywhere from 0 up to that value.

When we observe Y, one thing becomes immediately clear: the unknown value X must be greater than Y. The ball couldn’t have landed outside the range from 0 to X, so Y < X.

But beyond that, something more subtle happens. We can reason those smaller values of X—those just slightly larger than Y—are more likely than much larger ones. Why? Because for Y to have landed near itself within a large interval (say from 0 to 0.9), it would have to be a rather rare, although not impossible, outcome.

This asymmetry shifts our belief. After seeing Y, we no longer consider all values of X equally likely. We now prefer those that are closer to Y, while still greater than it. In Bayesian terms, we’ve updated our prior belief (from X uniformly distributed between 0 and 1) into a posterior belief—one that leans toward values of X just above the observed Y.

What we now call Bayes’ rule—or Bayes’ theorem—is a direct consequence of the basic rule of conditional probability that Reverend Thomas Bayes used to calculate the probability of a hidden cause X, given an observed outcome Y, also known as the Posterior distribution of P(X|Y).

In the context of the table and ball experiment, applying Bayes’ rule for values where X > Y leads to a specific form of the posterior distribution that is shown in Figure 2.

For example, let’s say Y = 0.3. Before observing anything, we assume X follows a uniform distribution between 0 and 1—represented as the red line in Figure 1. But after seeing Y = 0.3, the updated belief (the blue curve) tilts in favor of values just above 0.3. As expected, smaller values of X closer to 0.3 are now more probable than those closer to 1.

Importantly, the area under the posterior curve still sums to 1, preserving the total probability. What changes is not the amount of belief, but how that belief is distributed.

In the decades following Bayes’ quiet mathematical insight, it was Pierre-Simon Laplace who expanded this modest idea into a grand framework for reasoning under uncertainty. Between 1774 and 1812, Laplace independently developed and generalized what we now call Bayesian probability.

Laplace introduced the formal concepts of prior (here P(X)), and posterior (probability, giving structure to the idea of updating beliefs in light of new data. He was also the first to express Bayes’ Theorem in its modern algebraic form and apply it rigorously to real-world problems, particularly in astronomy and statistical estimation.

Inverting Probabilities, Updating Beliefs

Bayes’ rule has become the fundamental mathematics of probabilities and an indispensable part of risk modeling. It helps us update what we believe based on what we observe.

For example, if you’re vaccinated and want to know the chance, you’ll still get a disease, Bayes’ rule lets you calculate that by inverting data of how many vaccinated people got sick.

This same approach is central to Bayesian statistics. In traditional (frequentist) statistics, we usually start with a model and ask: If this model is true, how likely is it that we would observe this data? This is useful, but it doesn’t directly tell us how confident we should be in the model itself.

Bayesian statistics inverts this proposition. Using Bayes’ rule, we ask: Given the data we've observed, how likely is it that this model is true? Instead of just asking how likely the data are under a given model? Bayes rules allow us to answer given the data, how likely is the model?

This shift is powerful. It means we can compare different models or hypotheses directly, update our confidence in them as new data arrives. It turns statistics into an evolving dialogue between belief and evidence. We will delve deep into Bayesian statistics in the second part of the course.

From Reverend Bayes to Bayesian Networks

Once we understand the relationship between X and Y, the natural next step can be to follow the chain further: what about a third variable, or a fourth?

If the table and ball experiment taught us how to infer X from Y, we can imagine continuing the sequence. Suppose we now place a wooden bar at position Y, and drop a second ball that lands somewhere position Z < Y. This gives us a new question: Given Z, what can we say about Y? And in turn, what does that tell us about X?

This idea of connected, conditional relationships, where knowledge flows from one variable to another, laid the foundation for connecting large networks of variables in what is known now as Bayesian Networks.

Bayesian Networks

While several scholars had continued exploring this idea, including the notable Professor Andrey Markov in the early 20th century, the formal foundation of Bayesian Networks was proposed more recently, in the 1980s, by Judea Pearl.

Pearl transformed Bayes’ rule from a two-variable equation into a framework for reasoning across entire webs of interconnected uncertainty. In his book, Probabilistic Reasoning in Intelligent Systems (1988), Pearl introduced Bayesian Networks as graphical models that use directed acyclic graphs (DAGs), as shown in Figure 4, to represent dependencies among variables (5).

In his recent research biography, The Book of Why, Pearl suggests that he drew inspiration for message passing through layers and nodes from early advances in neural networks (3).

Pearl, along with other key scholars, played a central role in advancing inference in Bayesian Networks through the development of belief propagation, also known as message passing. This method made it possible to perform efficient probabilistic inference, even in complex systems with many interdependent variables.

Building on this foundation, Philip Dawid (1992) formalized the clique tree representation, which provided a structured way to organize computations across interconnected variables(6). Ross D. Shachter (1986) introduced the tree decomposition method for exact inference, enabling networks to be restructured into simpler forms suitable for computation (7). In a key advance, Steffen Lauritzen and David Spiegelhalter (1988) applied the junction tree framework to Bayesian inference, allowing for local computations instead of costly global ones(8). Finally, Finn Jensen (1994) contributed by developing practical implementations of the algorithm, helping bring these theoretical advances into widespread use (9).

Wider Adoption and Impact

By the 1990s, Bayesian Networks had found their place in AI, expert systems, and decision-support tools.

Microsoft, for instance, used them in its Office troubleshooting assistant. The networks enabled machines to reason in uncertain environments, just as humans do—by updating beliefs, weighing alternatives, and inferring hidden causes from visible effects.

In a 1990s Los Angeles Times article, Bill Gates, then Microsoft Chairman, stated that Microsoft's competitive advantage, particularly in areas like understanding human speech and building internet services, was its expertise in "Bayesian networks".

From a single hidden ball on a table to vast networks modeling disease, risk, or intent, the journey from Bayes’ rule to Bayesian Networks reveals a deep truth: knowledge doesn't exist in isolation—it spreads through structured, conditional relationships. The real power lies in tracing those threads.

Three Fundamental Patterns of Probabilistic Interdependence

As we expand from two to three variables, a new layer of complexity emerges.

There are only three distinct ways to structure three nodes in a directed acyclic graph (DAG), each with a different pattern of causal influence. These three configurations, shown in Figure 5, form the fundamental building blocks of all Bayesian Networks(1).

The first structure is the sequential chain (Figure 5) where A affects C through mediator B. Therefore, conditioned on B, A and C are independent. For example, remoteness, affects the need for new infrastructure, which in turn affects cost. By knowing the exact need for infrastructures, remoteness becomes independent of cost (through that pathway).

The diverging structure in Figure 5 is the declaration of a common cause. The C and B are probabilistically dependent through A, and independently conditioned on A. For example, remoteness, affects the need for new infrastructure, and through another pathway, it affects the labor shortage. In absence of knowledge about the project’s remoteness, it is possible to “induce” that a project with labor shortage, may as well require more infrastructure spending, as it may be remote. However, knowing the degree of remoteness blocks this dependency by creating a conditional independence. Therefore, labour shortage is independent of infrastructure spending when the remoteness is already known. This form of inductive reasoning allows for modeling the latent risk factor (i.e., risk driver) through their effects on measurable risk indicators.

The converging structure in Figure 5 is the declaration of a common effect. The structure exerts that A, and B are probabilistically independent, but both affect C. However, conditioning on C creates probabilistic dependency between A and B. For example, the project profitability is a common effect of both the payback period, and the rate of return. In the absence of knowledge about profitability, the payback period and the rate of return are independent. However, knowing that the project is not profitable creates a conditional dependence between the two. If the rate of return is high, the low profitability of the project can only be explained away by a long and undesirable payback period. Unlike the previous two structures where conditioning on the cause or the mediator creates probabilistic independence, conditioning here on the effect creates dependence.

Together, these three structures—chains, forks, and colliders—are the DNA of Bayesian reasoning. They show how causality, dependence, and inference can shift dramatically depending on where in the network we look—and what we condition on.

Mathematics of Risk

In many instances, risk can be a qualitative and multi-faceted concept, it may involve social judgment, ethical concerns, and strategic trade-offs.

However, when we need to quantify risk for decision-making and modeling, we can break it down into three core elements: a scenario or event that might occur, the likelihood of that event, and the consequence if it happens.

This decomposition gives us the classic risk triplet:

• Si​: a specific scenario or set of events that leads to exposure or harm

• Pi​: the likelihood or frequency of that scenario

• Ci​: the consequence, such as financial loss or operational impact

To assess whether these consequences matter, we introduce another layer: thresholds, denoted Tj. These are the limits of acceptable impact across different domains, 1 to j, such as finance, safety, environment, or reputation. A risk becomes significant, to various degrees, when the expected consequence Pi​ Ci exceeds a relevant threshold Tj​.

At a preliminary maturity level in risk modeling, we might treat the likelihood Pi​ as a fixed probability, and the consequence, Ci, and the threshold, Tj​, as deterministic values (single numbers).

At higher maturity levels, we recognize that all three elements—likelihood, consequence, and threshold—carry uncertainty. These are not always known with precision; they often depend on expert judgment, assumptions, or incomplete and scarce previous data.

Just like the position of the first and second balls on the pool table, Z, and Y, inform us about the uncertainty of the location of the disappeared wooden bar, X. Bayesian methods allow us to work with uncertainties at both levels.

Bayesian methods provide a mathematical framework that allows for two fundamental reasoning capabilities.

1. To model real world phenomena as part of connected systems with probabilistic interdependencies, and,

2. to represent our current understanding and expert judgement as probability distributions and update them as new evidence and data emerges.

Risk Modeling with Bayesian Networks

The classic bow-tie diagrams are useful conceptualizations of risk.

As shown in Figure 6, the bow-tie diagram illustrates a hazardous event at the center, flanked by causes on the left and consequences on the right. It helps visualize how threats may lead to loss of control, and how recovery measures can mitigate consequences.

In conventional risk and reliability engineering, this conceptual bow-tie is often transposed into a combination of Fault Tree Analysis (FTA) and Event Tree Analysis (ETA), as seen in Figure 7. FTA traces backward from a system failure to its root causes through logical gates, while ETA projects forward from a triggering event to multiple outcomes by mapping safety function successes and failures.

While both are foundational tools—logical, binary, and elegantly structured—they struggle to capture probabilistic dependencies beyond the "AND" and "OR" logic gates. They assume that components fail independently unless explicitly connected by logic, and they lack the flexibility to express more subtle interdependencies across time and space.

But dependencies do arise, and they often do so in ways that challenge such linear thinking.

These can manifest through the Three Fundamental Patterns of Probabilistic Interdependence as mentioned in the previous section. This Probabilistic Interdependencies include common causes (e.g., several factors triggering MUE), common effects (multiple failure paths converging on a single point), or serial cause-effect chains (where a risk event triggers a process that in turn creates new vulnerabilities).

Bayesian Networks allow us to introduce probabilistic dependencies across multiple layers of systems and operations.

This graphical structure allows us to do more than just trace risk pathways, it enables both inference and diagnosis by inversing Bayesian probabilities. If we observe a failure, we can use Bayes’ rule to reason backward and identify the most likely cause. Or, if we observe multiple upstream warnings, we can anticipate the probability of a future breakdown.

Bayesian Networks can turn bow-tie diagrams into living systems—ones that learn, update, and support real-time decision-making in uncertain environments.

Bayesian Networks in Projects and Operations

The large-scale adoption of BN in probabilistic risk modeling is mainly because of BNs capability to incorporate incompatible and disparate sources of information, such as data and expert knowledge, as probabilistic beliefs.

Several noteworthy literature reviews have been published for BN applications in risk analysis (10), human reliability assessment (11) , water resources management (12), and ecological risk assessment (13).

McCabe first proposed to model risk in the construction domain in the late 1990s, and used BNs to create probabilistic performance measurements for construction simulation (14–17). Later, researchers used BNs to model project cost risk (18), schedule risk, or both (19).

Noteworthy applications in risk, reliability, and resilience appeared in the literature starting from early 2000s (20). Researchers used BNs to model regulatory compliance risk of different systems (21). BNs were used to model the concept of resilience by expanding reliability to include vulnerability and maintainability (22). An interesting and transferable use to the project domain was modeling the risk of cascading or progressive failure (domino effects) (23).

Concurrently, BNs were used as an actuarial method to model operational risks in financial institutions (24). BNs were successfully utilized to model operational risk with particular emphasis on its ability to conduct scenario modeling for operational risks (25).

The applications of BNs in operational risk overlapped with inclusion of organizational risk, human risks, and eventually socio-technical risks. Researchers proposed frameworks to establish socio-technical risk indicators for BNs (26). The challenge to quantify socio-technical risks was first to combine the social and technical sources of information.

BNs were successfully proposed for nuclear power generator software systems and were adopted by other researcher to create models for the nuclear industry (27–29)

An interesting application of BNs to model organizational risks was proposed to model the probability of collision in maritime transportation (30). Researchers proposed standardized methodologies for probabilistic risk analysis of socio-technical systems (31). BNs were proposed to model diverse sources of risk for complex construction projects (32).

Bayesian Networks in Digital Twins

Certain types of digital twins, especially process, system, and infrastructure-level twins, rely heavily on simulation to model complex behaviors, interactions, and uncertainties.

However, real-time decision-making poses a challenge when relying on simulation. Simulations are computationally intensive and time-consuming, making them unsuitable for instant responses in live systems. By training BNs on the outputs of offline simulations, digital twins can embed probabilistic reasoning models that offer immediate, data-driven insights and predictions.

Dynamic Bayesian network (DBN) framework were used for aircraft digital twin to help in crack growth monitoring, enabling probabilistic diagnosis and prognosis (33). Other researchers extend this by developing a nonparametric Bayesian network-based digital twin, employing Dirichlet process mixture models (DPMM) and Gaussian particle filters to autonomously update model structure and parameters, thus improving accuracy in the health assessment of electro-optical systems (34).

Together, these studies illustrate the value of Bayesian inference in enabling adaptive, interpretable, and computationally efficient digital twins across safety-critical domains.

Question then Becomes

Bayesian Networks are now ubiquitous, silently powering the intelligent systems that surround us.

In generative AI, they enable models to estimate uncertainty, generate realistic data, and adapt intelligently to new evidence. From speech recognition on your phone to diagnostics in complex machinery, BNs are quietly reasoning beneath the surface, helping systems make sense of uncertainty.

• In complex engineering projects, Bayesian Networks are used to model cascading risks, prioritize mitigation strategies, and inform stakeholder decisions under uncertainty.

• In operations, they provide predictive foresight by linking observable indicators to hidden system states, enhancing reliability and responsiveness.

• In digital twins, Bayesian models serve as dynamic reasoning engines—constantly updating beliefs as new data flows in—transforming static simulations into adaptive decision-support systems.

In this article, we traced the journey from Bayes’ original idea to its modern-day applications. We reviewed key concepts like the pool table thought experiment, patterns of probabilistic interdependence, and how BNs help us move beyond simple logic-based risk tools. We explored their use in three major areas: projects; operations; and digital twins.

Despite all this, Bayesian Networks are not a standard part of risk management practices. Why is that?

Is it because today’s project, and operations start with too qualitative, and too complex types of risks, entangled with social and political judgments, to be modeled?

Or is it because building and maintaining Bayesian models requires a mix of technical skill and domain knowledge that’s hard to find and maintain outside of advanced analytics teams of computer scientists?

What will it take for Bayesian thinking to become a practical, everyday tool in managing project and operational risk, just as it already is in the intelligent systems we rely on every day?

Notes

Join the EPM Network to access insights, influence our research, and connect with a community shaping the industry's future.

Subscribe now

Support us by sharing this article with your friends and colleagues, or over social media.

If you wish to share your opinion, provide insights, correct any details in this article, or if you have any questions, please email editor@epmresearch.com.

Refer to this article using the following citation format:

• Zangeneh, P. (2025), “From Reverend Bayes to Modern Risk Modeling in Projects, Operations, and Digital Twins.” Bayesian Methods for Risk Modeling - Part 1, EPM Research Letters.

This article is a reading material for the “Bayesian Methods for Risk Modeling (BMRM)” and “ENCI 604 - Risk, Uncertainty and Reliability” courses at the University of Calgary, Department of Civil Engineering.

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Highlights

• The construction and project management industry is rapidly evolving due to transformative technologies such as Gen-AI and Extended Reality.

• A survey of twenty industry experts identified three categories of functions impacted by AI: efficiency functions, workflow functions, and human-centric functions.

• Participants believe Gen-AI shows the highest impact in structured tasks such as scheduling, budgeting, and risk management, while Extended Reality enhances collaboration and training.

• The industry anticipates continued AI and XR integration, automation in construction, and the rise of hybrid AI models in 2025.

• The study aims to expand into an annual survey in collaborations with universities and industry leaders worldwide.

The world is undergoing a period of unprecedented transformation, and the construction and project management industry is no exception.

Rapid advancements in technology, coupled with evolving societal and environmental demands, are reshaping how projects are conceptualized, and delivered. Amidst this flux, understanding the role of transformative technologies in enhancing business processes is critical for construction and project management firms, and professionals alike.

We consulted with twenty industry practitioners and experts from our EPM Network, representing different regions across the globe. This report reflects the collective insights and expertise of professionals deeply engaged in the fields of construction and project management, who are using transformative technologies at the forefront of engineering projects.

The results revealed three distinct categories of construction and project management functions being affected. Interestingly, these categories of functions can be explained by their reliance on human activity into efficiency functions, workflow functions, and human-centric functions.

Our experts suggest a future of faster, more streamlined project delivery and development with AI; however, its impact varies across different functions.

This article explores key insights from our study, covering the following topics:

1. Objectives and Audience

2. Transformative Forces in 2024

3. Survey Design

4. Results

   1. Impacts of Generative AI

   2. Impacts of Extended Reality

   3. What to Expect in 2025

5. Major Projects Trends in Canada

6. Discussion: Perception and Reality

7. Conclusion and This Year’s Plan

At the EPM Research Group, we aim to combine rigorous academic research with practical industry insights. We are part of the University of Calgary’s Civil Engineering Department and backed by the Engineering Project Management Endowment.

Objectives and Audience

This study was commissioned as a proof of concept to gauge interest, assess feasibility, and address validity concerns for an annual industry survey program on the impact of transformative technologies on construction and project management.

We asked experts about the impacts of transformative technologies in construction and project management and their opinions on questionnaire design.

Our vision is to create an annual survey to capture the pulse of the industry and its technological transformations year after year. We hope to co-create a resource that informs, inspires, and empowers stakeholders across the industry. The objectives of this survey program are:

• Assessing the current state of industry adoption of transformative technologies.

• Understanding technology adoption across various sub-sectors of the construction industry and across different countries and regions.

• Exploring professional perspectives on the potential, limitations, and challenges of these technologies.

• Examining the industry’s outlook on disruptive transformations.

• Guiding academic research by highlighting industry’s emerging needs.

We defined the sample population as industry practitioners (both governmental agencies and private industry) in construction and project management from around the world who are at the forefront of leveraging transformative technologies in their projects.

The study benefits a diverse audience in construction and project management, from engineers and project managers to business leaders, academics, policymakers, and young engineers looking to navigate their careers.

This range of audiences and stakeholders reflects the interconnected nature of our industry.

Transformative Forces in 2024

We focused on two transformative technologies: Generative AI and Extended Reality.

These technologies were selected for their significant emergence in 2024 and their potential to reshape the construction and project management landscape.

Generative AI

Generative Artificial Intelligence (Gen-AI)—encompassing technologies such as Large Language Models (LLMs) and Large Image-Generative Models (LIGMs)—has emerged as a transformative force, redefining industries at an unprecedented pace.

The rapid ascent of Generative AI is affecting both academic research and technology development across many industrial sectors.

Gen-AI distinguishes itself by its ability to create new content—text, images, and code—that mirrors its training data. Unlike traditional AI, which primarily analyzes or classifies, Gen-AI extends beyond existing data by generating novel outputs.

The adoption of Gen-AI in construction and project management remains in its early stages, often limited to proof-of-concept applications [1]. Several challenges hinder widespread adoption, including validating AI-generated outputs, fine-tuning models for domain-specific applications, mitigating biases, ensuring data security, and addressing ethical implications.

In 2024, OpenAI introduced GPT-4o, a multimodal model capable of processing and generating text, images, and audio in real-time, setting new benchmarks in voice recognition and translation. The year also marked a dual surge in the rise of open-source models and the adoption of smaller, more efficient AI systems [2].

Open-source releases, such as Meta’s LLaMA 3 and Databricks’ DBRX, aimed to democratize AI and challenge the dominance of proprietary giants like OpenAI [3,4]. Alibaba joined this movement by releasing over 100 open-source models, including text-to-video technology, expanding AI’s applications in automotive, gaming, and scientific research [5].

In parallel, companies such as DeepSeek are reducing training costs through innovative methods, including the Mixture-of-Experts (MoE) approach, which routes tasks to specialized expert models for greater efficiency. This technique enables AI models to be trained at a fraction of the cost compared to traditional methods [6].

Simultaneously, the industry is embracing compact, task-specific models designed for efficiency and affordability, prioritizing practical deployment and accessibility across a range of applications [7].

Extended Reality

Extended Reality (XR)—encompassing Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR)—was another transforming force in 2024, aiming to change the way we work, learn, and connect by merging physical and digital spaces.

Despite hardware limitations, XR remains at the forefront of technological development due to its potential to revolutionize collaboration, training, and problem-solving. By enabling shared virtual spaces, XR allows teams to brainstorm, simulate, and interact with digital objects in real time, fostering seamless collaboration across virtual landscapes. XR provides hyper-realistic simulation environment for skill development and risk-free scenario testing [8].

In construction and project management, XR can transform training, remote operations, and stakeholder collaboration [9]. Immersive simulations allow workers to practice operating machinery, navigate complex sites, and respond to emergencies within risk-free, hyper-realistic environments [10,11]. Virtual offices enable stakeholders to explore digital twins of projects, refine designs, and collaboratively resolve physical and logistical challenges in real time [12].

The year 2024 marked a significant leap in XR advancements. Apple introduced the Vision Pro [13], a mixed-reality headset that seamlessly blends digital and physical worlds, offering professionals a powerful tool for reimagining project visualization and collaboration.

Not to be outdone, Meta unveiled the Orion glasses [14], lightweight AR eyewear that overlays virtual objects onto the real environment, enhancing on-site decision-making and stakeholder engagement. Google entered the arena with Android XR [15], an operating system tailored for XR devices, paving the way for a new wave of applications that could revolutionize how we approach construction projects.

Survey Design

The survey structure for this study consisted of three main components: Gen-AI Impact, Extended Reality Impact, and Industry Outlook.

Before answering the questions, participants were provided with descriptions of the strengths and weaknesses of Gen-AI and Extended Reality based on a systematic literature review we conducted.

1- Generative AI Impact

Participants evaluated the potential for productivity gains that Generative AI could bring to 14 functions and processes within construction and project management.

Each area was rated on a scale from 1 to 9, with 9 indicating the highest potential for productivity gains. The 14 functions and processes within construction and project management included are as follows:

1. Project Planning and Scheduling: Streamlining timelines, resource allocation, and schedule resilience.

2. Budgeting and Cost Estimation: Automating cost forecasts and refining financial accuracy.

3. Risk Management: Enhancing risk identification and mitigation strategies using AI-driven insights.

4. Project Controls: Improving monitoring, execution, and corrective actions.

5. Procurement and Supply Chains: Optimizing vendor selection and contract management processes.

6. Contract Management and Tendering: Streamlining bid evaluations and contract negotiations.

7. Layout Planning and Logistics: Advancing site layout designs and resource movement efficiency.

8. Quality Control and Compliance: Automating quality assurance processes and ensuring regulatory adherence.

9. Stakeholder Engagement: Personalizing communication strategies and improving stakeholder alignment.

10. Lessons Learned and Continuous Improvement: Enabling systematic project close out reports, reviews, and knowledge-sharing.

11. Human Resources and Staffing: Enhancing workforce planning and skills development.

12. Occupational Health and Safety: Leveraging AI to monitor and improve safety measures.

13. Trend and Change Management: Managing project change orders and their impact dynamically.

14. Conflict Resolution: Facilitating proactive identification and resolution of disputes.

These fourteen construction and project management functions were described in greater detail in the survey compared to this report. The descriptions helped to establish a common baseline of understanding.

2- Extended Reality Impact

The survey included targeted questions about the familiarity of participants with Extended Reality (XR) technologies. This set of questions aimed to understand the current level of adoption and firsthand experience with XR technologies in the industry.

Next, participants were asked which of the previous functions in construction and project management would most benefit from the convergence of Generative AI and XR. The question further encouraged respondents to reflect on the practical applications of combining AI-driven insights with immersive XR tools.

3- Industry Outlook

Lastly, the participants were asked to reflect on their outlook for the coming year. This question was further discussed with some of participants over conference calls to grasp their point of view on the technological trends.

Results

In total, twenty industry experts participated, offering perspectives on the areas where Generative AI might exert the greatest influence. The respondents were asked to rank fifteen critical domains in construction and project management on a scale from 1 to 9, with 9 indicating the highest potential and 1 reflecting minimal impact.

Impacts of Generative AI

The results, visualized in the spider plot of Figure 1, provide a clear snapshot of how industry experts perceive the varying impacts of Generative AI in productivity gains across different construction and project management functions.

We refrained from employing more time-intensive pairwise comparisons in favor of simple Likert scales to keep the survey efficient for participants. Despite this simplification, the results revealed distinct patterns in the data, showing clear variations in the perceived impact of generative AI across functions.

Clustering of Generative AI Impact Areas

We identified three distinct groupings based on respondents' perceptions of the relative potential for productivity gains.

These groupings allowed us to classify construction and project management functions into three categories:

• Efficiency Functions, which saw the highest perceived impact (e.g., planning, scheduling, and cost estimation);

• Workflow Functions, reflecting moderate potential (e.g., logistics, procurement, and compliance); and,

• Human-Centric Functions, where the impact was perceived to be lower due to the reliance on interpersonal skills and decision-making (e.g., stakeholder engagement and conflict resolution).

Figure 2 depicts this categorization and the areas where generative AI can complement and enhance current practices.

1. Efficiency Functions: Generative AI demonstrated the highest potential in domains relying on structured workflows, repetitive updating, and large datasets. Participants saw immediate value in leveraging AI to increase productivity on these activities.

   • Project Planning and Scheduling: Automating schedule templates, identifying potential bottlenecks, and analyzing historical data.

   • Budgeting and Cost Estimation: Accelerating conceptual cost estimating, cost breakdown generation, and anomaly detection.

   • Risk Management and Project Controls: Generating predictive insights, improving risk analysis, and creating automated dashboards.

   • Contract Management and Tendering: Automating contract drafting, vendor analysis, and tender evaluations.

   • Lessons Learned: Compiling and analyzing project histories to identify best practices and inform future strategies.

2. Workflow Functions: The synergy between AI tools and human judgment was emphasized due to the often time intensive workflow and case specific context of these functions. These domains benefited from AI's ability to handle routine, data-intensive tasks while relying on humans to navigate the process:

   • Procurement and Supply Chains: Automating RFPs and supplier evaluations while preserving human oversight for negotiations.

   • Layout Planning and Logistics: Proposing site layouts and logistics scenarios, adaptable by human planners.

   • Quality Control and Compliance: Standardizing checklists and identifying gaps, with human-led physical inspections.

   • Human Resources and Staffing: Streamlining applicant processing while retaining human interactions for cultural fit assessments.

   • Project Trends and Change Management: Identifying trends and suggesting changes while requiring expert integration.

   • Occupational Health and Safety: Drafting safety policies with humans ensuring environment-specific adaptation.

3. Human-Centric Functions: These domains—heavily influenced by interpersonal skills and real-time decision-making—showed moderate or lower potential for AI adoption. While AI can assist with tools like sentiment analysis and automated reports, the human element remains crucial:

   • Stakeholder Engagement: Supporting sentiment summaries but maintaining trust-building as a human-led activity.

   • Conflict Resolution: Analyzing conflict data to suggest strategies, yet relying on empathy and nuanced judgment for resolutions.

While this preliminary survey respondent pool was limited to only twenty experts, it highlighted an interesting delineation between tasks where AI can lead, collaborate, or support.

Impacts of Extended Reality

After exploring the perceived impact of Generative AI, we assessed participants’ familiarity with Virtual Reality (VR) and Augmented Reality (AR) technologies.

About 40% of the participants reported having used these tools, highlighting relatively limited adoption of XR in construction and project management.

Despite limited exposure to XR technologies, respondents demonstrated significant optimism about the potential for combining XR with Generative AI to address critical industry challenges. This convergence offers unique opportunities in areas such as project controls, 4D/5D scheduling, risk management, layout planning, and procurement. Figure 3 highlights the perception of participants as to which function stands to gain the most from integration of XR and Generative AI.

A common theme in our conversations was around how by harnessing the immersive capabilities of XR, professionals can simulate AI-generated scenarios, enabling them to visually explore and interact with project data in real-time.

What to Expect in 2025

Lastly, participants were asked to share their outlook for the coming year, with follow-up discussions conducted through one-on-one calls to better understand their perspectives on technological trends.

At the Engineering Project Management (EPM) research group, our research is focused on three of these transformative themes shaping the future of construction and project management, marked below with an asterisk: the integration of AI and XR technologies, Hybrid ML-LLM AI models, and the adoption of Small Language Models (SLMs) with edge computing.

1- AI and XR Integration*

The integration of AI and XR has emerged as a key trend, identified by survey respondents and widely recognized across the industry.

Meta has addressed the demand for user-friendly devices with the Ray-Ban Meta Smart Glasses, a lightweight option featuring a camera and Generative AI capabilities, though it lacks full XR functionality [16]. This trade-off prioritizes wearability and everyday use. Looking ahead, Meta is developing Orion AR glasses, which promise advanced AR and AI integration in a sleek form [14]. Expected by late 2027, these glasses reflect Meta's focus on overcoming current limitations in wearable technology [4].

2- AI in Automation and Robotics

By 2025, AI-driven automation and robotics are set to transform the construction industry, streamlining processes and enhancing safety. Advanced robotics will take on complex tasks with unmatched precision and speed, reducing physical strain on workers and mitigating risks in hazardous environments [17].

Autonomous drones equipped with AI will revolutionize site management by conducting inspections, monitoring progress, and identifying potential issues like structural weaknesses or material shortages in real time. These insights will enable project managers to optimize resources, maintain schedules, and make data-driven decisions [18].

Tesla is advancing AI robotics with innovations like 'Optimus,' which could be adapted for construction tasks. Such robots, capable of performing diverse manual activities, have the potential to reduce labor-intensive work and further expand the scope of automated construction technologies [19].

3- Hybrid ML-LLM AI Models*

LLMs excel in capturing linguistic patterns, generating coherent text, and generalizing across tasks. Their ability to perform hierarchical task planning through analogical reasoning has become central to LLM research. However, traditional AI and machine learning models surpass LLMs in tasks requiring accuracy and prediction. By integrating LLMs' task decomposition with ML data-driven forecasting, a hybrid ML-LLM AI approach offers transformative solutions in construction and project management.

ML can analyze historical project data to predict risks, optimize processes, and identify patterns that lead to inefficiencies, enabling proactive management [20]. LLMs communicate these results with diverse teams, automate documentation, and generate compliance reports and proposals, while reducing administrative effort [21,22].

4- Open Source / Small Language Models (SLM) and Edge Computing*

Open Source and Small Language Models (SLMs) represent a transformative opportunity for construction and project management, offering cost-effective, targeted solutions to manage complex data and communication flows.

Models like GPT-4o-mini and Gemini-flash provide a streamlined alternative to Large Language Models (LLMs), enabling organizations to deploy powerful natural language processing applications with significantly reduced investment [7].

Beyond cost efficiency, the value of SLMs lies in their adaptability; they can be refined and deployed on local servers disconnected from the broader internet which is an immense advantage when dealing with proprietary information and sensitive project data.

SLMs are also well-suited for integration with XR (Extended Reality) technologies, which often operate on portable devices with limited processing power. This synergy facilitates real-time, context-sensitive support through augmented reality overlays and virtual environments, enhancing operational efficiency as well as improving training and safety protocols [23].

5- Generative AI Design and Architecture

A few experts in our survey population were eagerly monitoring the impact of Generative AI on architectural design in 2025.

The Large Image Generation Models (LIGM) are set to significantly expanding the way architects approach the creation and refinement of building concepts. This cutting-edge technology enables the input of design parameters and constraints into AI systems, which then generate a wide array of innovative design options [24].

6- Avatars Everywhere

The concept of avatars—digital personas that represent users in virtual environments—has evolved significantly, becoming a versatile tool in various industries. In 2025, these digital representations are not only personal avatars in social media or gaming but have expanded into professional fields, offering a unique way to interact within digital spaces. Avatars can be customized to mirror the user’s physical appearance or embody a completely designed persona, equipped with realistic expressions and movements [25].

Major Projects Trends in Canada

Understanding major transformative trends in construction and project management benefits from a parallel awareness of the broader landscape of construction project prevalence.

Figure 4 and 5 show the annual trend in project numbers and capital costs for both the resource project and infrastructure projects in Canada as disclosed by Government of Canada open data initiative [26,27].

In terms of the number of projects in Canada, both resource and infrastructure projects have been on the rise in 2024. However, while capital expenditure for resource projects has shown a steady increase, infrastructure investment has declined from 2023 to 2024.

As shown in Figure 4, the energy sector, which remains a cornerstone, has seen consistent activity in electricity generation, oil and gas, and other projects such as biomass, biofuel, and geothermal production, with 340 projects contributing CA$510 billion by 2024. Mining, has exhibited robust growth, with investments growing from CA$89 billion to CA$117.1 billion in four years, driven by metals and an expanding focus on non-metals and critical resources.

Canada's infrastructure investment trends highlight a strategic shift between volume and value. In 2021, the number of projects (excluding those related to COVID 19 response) peaked at 1,377, supported by a record CA$20.94 billion in eligible costs, reflecting a major post-pandemic recovery push. While project counts dropped in subsequent years—reaching 643 in 2022 and 486 in 2023—investment levels remained significant. By 2024, project counts rose again to 802, supported by CA$5.77 billion in funding.

Discussion: Perception and Reality

The rapid evolution of AI continues to reshape industries.

The recent emergence of powerful open-source models by DeepSeek in early 2025 challenged the consensus on proprietary models, highlighting the volatility of the Gen-AI scene and the infancy of this technological race [28].

AI's trajectory remains one of continuous advancement, with no definitive winner yet, as new models and capabilities emerge at an accelerating pace.

As suggested by a curious paradox known as Jevons Paradox, technological efficiency does not always lead to reduced demand [29]. On the contrary, as seen with the steam engine and many other era-defining inventions, improvements in efficiency often result in increased overall demand and consumption.

In construction and project management, the introduction of Gen-AI does not appear to be replacing workers yet. However it has helped in eliminating bottlenecks and increasing the quality and speed of project delivery.

The limited consultations we had with industry experts through this study suggest that instead of seeing lower employment, firms are facing a greater need for skilled professionals. The overall rise in the number of major projects announced in Canada in 2024 may indicate a trend that aligns with efficiency gains.

The reliability of Generative AI models remains a key consideration. Many of the most sensitive functions in construction and project management carry significant liabilities and require human oversight. Gen-AI models can drastically reduce the cost of first drafts, but final work products require accountability that must remain with experienced professionals.

For XR technologies, hardware limitations, including integration challenges and high costs, remain key bottlenecks. Addressing these issues is essential for realizing XR's full potential and achieving seamless adoption across business processes.

Looking ahead, the challenge is not whether AI will reshape the industry—it already is—but how organizations will adapt to these changes.

Conclusion and Next Year’s Plan

Looking ahead to 2025, transformative advancements in AI and XR are expected to drive significant changes in the construction and project management industry, reshaping workflows and enhancing the integration of digital and physical environments.

Our goal for 2025 is to expand the scope of this study, both in reach and depth, into an industrywide survey of the construction and project management industry. We have established contacts with several universities around the world to partner on this survey.

We aim to engage a wider audience across diverse geographies and disciplines to ensure the findings accurately reflect the industry's dynamic and interconnected nature.

Building on the insights gained from this study, we are refining our approach and designing a more precise and expressive questionnaire to capture nuanced perspectives on emerging trends within the construction and project management sector.

If you are interested in supporting this effort and sharing your insights on this year’s or next year’s survey, we would greatly value your contribution. Your perspectives are important for informing the design of next year’s survey. We invite you to connect with us.

Notes

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Refer to this article using the following citation format:

• Zangeneh, P. and Ghorab, Kh. (2025), “Transformative Technologies in Construction and Project Management: 2025 Outlook and Survey Results”, EPM Research Letters.

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29. York, R., & McGee, J. A. (2015). Understanding the Jevons paradox. Environmental Sociology, 2(1), 77–87. https://doi.org/10.1080/23251042.2015.1106060

NextStar is the largest investment in Canadian automotive history. With a projected annual capacity of 49.5 GWh, enough to power 450,000 EVs. This facility represents a major shift in the Canada’s approach to sustainable transportation and it marks an important milestone in the transition toward cleaner mobility (1).

The global move toward electric vehicles is reshaping industries and economies, and at the heart of this transition lies battery technology. Windsor, Canada’s “automotive capital,” has faced its share of challenges over the years, from shifts in manufacturing to the impacts of economic downturns. Yet, through resilience and adaptability, the city is positioning itself at the forefront of the EV revolution (2).

The NextStar project is expected to create up to 2,500 jobs, offering new opportunities in advanced manufacturing and reinforcing Windsor’s role in the evolving automotive landscape. More than just an economic boost, this initiative reflects a broader commitment to innovation and sustainability in Canadian industry.

This article explores the several dimensions of the NextStar project:

1. Project Highlights, Timeline, and Team

2. Financing Structure

3. Risks and Complexities

4. Lithium-Ion Pouch Type Batteries

5. Project’s Social Impacts

6. Sustainable Development

7. Windsor’s Automotive Legacy

Project Highlights, Timeline, and Team

The NextStar Energy facility spans up to 4.5 million square feet on a 220-acre site. At its core, the facility consists of two primary structures:

• Cell Building (2.7 million SF): Dedicated to manufacturing lithium-ion battery cells, ensuring high-capacity energy storage for EVs.

• Module Building (445,000 SF): Responsible for assembling battery cells into modules, completing the final product before distribution.

The supporting infrastructure includes a master substation, an electrolyte supply system, safety and waste management units, and solvent recovery systems—all designed to enhance efficiency, safety, and sustainability in battery production.

The main production facilities feature high-precision cleanroom and dry room environments to maintain strict humidity and temperature controls, alongside integrated module assembly lines that ensure efficient production and quality assurance.

The project was reported to include at least 38,000 tons of structural steel supplied, along with 3,100 tons of steel joists and 420,000 square feet of decking. The mechanical, HVAC, and utility systems are equally extensive, including 5 million pounds of sheet metal for ventilation and air control, more than 75 miles of hydronic and plumbing pipes, and over 900 HVAC units to maintain precise climate conditions essential for battery production.

Project Timeline

The project was reported to have reached approximately 30% completion by December 2023, with the Module Building fully enclosed and structural steel installation completed. At that time, equipment installation had begun, while the Cell Building was nearing completion of its structural framework, with around 40% of its exterior enclosed (4).

The first phase of operations, focusing on battery module production, was reported to be on track for launch in the first half of 2024. Cell manufacturing, a critical component of the facility’s output, was projected to begin in 2025, with full production expected by 2026 (5).

NextStar reported to have officially commenced battery module production in October 2024, marking a milestone in the project’s transition from construction to operation. The project has also been making strides in workforce development, with over 450 of the projected 2,500 jobs reportedly filled (6).

Project Team

The execution relied on a diverse network of stakeholders, including industry leaders, contractors, engineers, and suppliers. At its core, NextStar Energy, a joint venture between Stellantis and LG Energy Solution drives the plant’s development and oversees its construction and operations.

Sponsors:

• Stellantis: Global automaker and co-founder of NextStar Energy, responsible for overseeing battery production and integration into its EV supply chain(7).

• LG Energy Solution: Leading battery manufacturer, co-founder of NextStar Energy, bringing expertise in lithium-ion battery production (8).

• NextStar Energy: The joint venture entity responsible for the construction, management, and operation of the Windsor EV battery plant (3).

Contractors, Engineers, and Suppliers:

• Alberici-Barton Malow (A-BM): A joint venture comprised of Alberici and Barton Malow is the Design-builder responsible for the construction of the 4-million-square-foot facility, including preconstruction, steel fabrication, and concrete work (9).

• Kasian Architecture Interior Design and Planning Ltd.: Architect of record for the Module Building, responsible for architectural design in collaboration with Gresham Smith, Stantec (Formerly Morrison Hershfield), and A-BM (10).

• Gresham Smith: Architecture firm collaborating on the Module Building design for NextStar Energy. Structural Engineers (11).

• Stantec (Formerly Morrison Hershfield): Engineering firm involved in various aspects of the facility’s development. (12).

• SMRT Architects and Engineers: Providing cleanroom and dry room envelope design and engineering services in collaboration with Hodess Cleanroom Construction (13).

• Hodess Cleanroom Construction: Specializing in cleanroom construction for battery manufacturing processes (13,14).

• Black & McDonald: Responsible for mechanical and HVAC systems, including installing 5 million pounds of sheet metal, 75+ miles of hydronic and plumbing pipe, and 900 HVAC units (15).

• Walters Group: Tasked with installing 3,100 tons of steel, joists, and 420,000 square feet of decking within a four-month timeframe (16).

• Hillsdale Fabricators: A division of Alberici, supplying 38,000 tons of structural steel for the facility (9,17).

Financing Structure

NextStar Energy reflects a strategic collaboration between industry and government, aligning private investment with public incentives.

At the core of the funding structure is a public-private partnership, where government contributions are tied to production milestones. Stellantis is investing CA$3.4 billion in plant construction, while the federal and Ontario governments have each pledged CA$500 million toward development costs. However, much of the government subsidies are conditional on the facility meeting specific production targets, ensuring that public funding is linked to tangible economic and environmental returns (18).

Based on LG Energy Solution's financial statements as of June 30, 2024, the company has committed to investing US$1.464 billion in the joint venture with Stellantis and has already contributed US$765 million. The agreement includes a clause that allows either party to purchase the other's shares in the event of a default, ensuring mutual commitment to the project's success (8).

Risks and Complexities

Project Completion and Ramp Up

Executing a project of this scale comes with a unique set of challenges, requiring advanced design strategies, global coordination, and sophisticated logistics to ensure smooth construction and production.

Innovative Design and Construction: To meet the project's ambitious timeline, prefabrication and digital project management tools have played a crucial role in optimizing efficiency. One of the project stakeholders highlighted the use of Navis, Revit, BIM 360, Procore, Blue Beam, and P6 in their workflow (15). By prioritizing off-site prefabrication, the project has been able to reduce onsite labor demands and accelerate field installation, a crucial factor in meeting the tight construction schedule.

Global Coordination: Given the number of architecture firms, consultants, and stakeholders involved—many spreads across multiple countries—seamless communication and precise coordination are critical to maintaining progress. As the facility ramps up operations, NextStar Energy is collaborating with LG’s facilities in Poland to supply interim battery cells, ensuring production continuity while local manufacturing scales up.

Sophisticated Logistics and Manufacturing Risks: The facility is designed with advanced logistical systems to optimize the movement of raw materials and finished battery cells. However, manufacturing EV batteries at scale presents stringent quality control challenges.

Supply Chain Vulnerabilities

The EV battery supply chain is highly complex and geographically concentrated, making it susceptible to disruptions.

A significant portion of battery-grade materials and components are processed in a limited number of countries, notably China, which controls a substantial share of the global battery supply chain.

In early 2025, the Trump administration proposed a 25% tariff on imports from Canada and Mexico, including automotive parts and metals essential for EV manufacturing (19). This policy aimed to encourage domestic production but introduced challenges for the North American EV industry, which relies on a seamless cross-border supply chain. The tariffs will lead to increased production costs, potential delays, and uncertainty within the industry.

Automakers and suppliers are now reassessing their supply chain strategies, considering relocating production to the U.S. or other regions to mitigate the impact of these tariffs. The lack of regulatory stability is a major risk to continued economic success and thriving of EV battery projects in north America.

Economic and Financial Challenges

The financial viability of EV battery plants is influenced by fluctuating demand for electric vehicles, high production costs, and intense global competition.

The collapse of major European EV battery producer Northvolt in November 2024 underscores the financial risks inherent in the industry, where even well-funded companies can face insolvency due to production challenges and market dynamics (20).

Technology Obsolescence

The rapid evolution of battery technology presents a fundamental challenge for EV battery plants, where large-scale investments in current production methods may risk becoming obsolete as new, more efficient, and cost-effective solutions emerge.

Innovations such as solid-state batteries and lithium-sulfur chemistries are being actively explored to enhance energy density and reduce reliance on scarce raw materials. Companies like Lyten are making significant strides in lithium-sulfur battery production, aiming to disrupt traditional supply chains and improve performance. However, while these next-generation technologies hold promise, they also face hurdles, including technical barriers, high development costs, and the need for new manufacturing processes—all of which could affect the long-term viability of existing battery production facilities (21).

CEO Danies Lee recently emphasized the NextStar Energy plant’s flexibility within lithium-ion technology, highlighting LG Energy Solution’s track record in navigating advancements in battery chemistry over the past two decades. “We’re pretty flexible under the umbrella of lithium-ion battery technology,” Lee noted. “When it comes to the lithium process and chemistry, we can respond to change” (5).

The Windsor plant has been designed with adaptability in mind, allowing for simultaneous production of different battery types while ensuring that its cells remain versatile across multiple vehicle platforms. This strategic approach reflects an industry-wide recognition that resilience and innovation must go hand in hand—balancing the demands of today’s market with the breakthroughs of tomorrow (5).

Lithium-Ion Pouch Type Batteries

NextStar Energy’s decision to manufacture pouch-type lithium-ion batteries marks a shift in the electric vehicle (EV) battery sector, leveraging their unique advantages in space efficiency, energy density, and safety (22).

Unlike traditional cylindrical or prismatic lithium-ion batteries, pouch batteries utilize a flexible aluminum foil casing rather than a rigid metal enclosure, allowing for customizable shapes that optimize space within EV battery packs. This design enhances energy efficiency and reduces weight, a crucial factor for improving EV range.

The manufacturing process, although complex, includes a cutting-edge “degassing” method developed by LG Energy Solution, which improves efficiency and reduces the common issue of electrolyte leakage. In 2022, pouch-type batteries accounted for 26% of the market share in the EV battery industry and are projected to increase to 31% by 2030 due to their high energy density and flexible applications (23).

Unlike lithium-sulfur (Li-S) batteries, which utilize sulfur-based cathodes for higher energy density but suffer from a shorter cycle life, pouch-type batteries rely on conventional lithium-ion chemistry with a liquid electrolyte.

Additionally, all-solid-state batteries, a next-generation alternative, replace the liquid electrolyte with a solid electrolyte, enhancing safety by eliminating leakage risks and improving longevity. While pouch-type batteries still contain liquid electrolytes and require advanced safety measures, their design flexibility, high energy density, and thermal efficiency make them a preferred choice for next-generation EVs, supporting the industry's shift toward lighter, more efficient, and sustainable energy storage solutions (23).

Manufacturing of Lithium-Ion batteries

Lithium-ion batteries generate electricity by moving lithium ions between the cathode and anode during charging and discharging.

The cathode determines capacity and voltage, while the anode, typically graphite, influences lifespan due to structural wear over cycles.

Research into silicon anodes aims to improve capacity and efficiency. The electrolyte, available in liquid, solid, or gel forms, enables ion transport and must maintain high conductivity and stability. A separator, usually made of polyethylene or polypropylene, prevents direct contact between the cathode and anode while allowing ion flow and shutting down at high temperatures. Efforts to develop thinner separators seek to enhance energy density and reduce battery size. Manufacturing process of Lithium-Ion batteries entails four main steps:

1. Electrode Manufacturing: Mixing, coating, and rolling cathode and anode materials with binders and solvents to enhance adhesion and conductivity. The coated materials are then compressed into electrodes.

2. Cell Assembly: Integrating separators and electrolytes with electrodes, assembling them into mono-cells or bi-cells using stacking or winding techniques.

3. Formation Process: Charging and discharging cells to stabilize performance, followed by aging and degassing to remove trapped gases.

4. Pack Process: Configuring assembled cells into battery packs, connecting multiple cells, and enclosing them in protective casings for final use.

Project’s Social Impacts

The economic impact of NextStar Energy extends beyond its initial capital investment, serving as a catalyst for broader industrial transformation in Canada’s automotive sector.

As the Canada’s first large-scale EV battery plant, the project is shaping regional supply chains, workforce development, and economic resilience in ways that will endure long after its construction.

Job Creation and Workforce Development: With over 7,500 tradespeople engaged in its construction and 2,500 permanent manufacturing roles, NextStar Energy is a major employment driver. Workforce development programs, such as the Battery Boost initiative, are helping to train local talent, ensuring a steady pipeline of skilled workers for Canada’s growing EV industry (1). At its peak, the project is expected to involve 3,200 tradespeople and equipment installers, with 2,300 of those positions filled by Canadian workers (4).

Strengthening North America’s Supply Chains: Strategically located in Windsor—Canada’s automotive capital—the plant enhances supply chain efficiency by reducing dependence on imported materials. This integration strengthens North America’s EV ecosystem, making the region less vulnerable to external disruptions and creating a more resilient production network.

Attracting Ancillary Industries and Investment: The presence of a gigafactory of this scale is expected to attract key supporting industries, including raw material suppliers, logistics providers, and technology firms. This multiplier effect extends economic benefits beyond automotive manufacturing, reinforcing Windsor’s role as a critical node in the future of clean transportation.

The project has not been without controversy. The employment of 900 South Korean workers has drawn criticism from Canadian labor unions, who argue that domestic tradespeople are qualified for these roles. In response, NextStar Energy has defended the decision, stating that these workers are temporary specialists hired by external suppliers for highly technical installations essential to the plant’s operations (18).

Sustainable Development

EV Battery plants align with key United Nations Sustainable Development Goals (SDGs), integrating sustainability principles into its operations and setting a precedent for responsible industrial growth.

EV Battery plants affect the following four SDGs directly.

1. SDG 7 - Affordable and Clean Energy: By manufacturing batteries for EVs, the facility directly contributes to reducing greenhouse gas emissions and transitioning to renewable energy sources.

2. SDG 8 - Decent Work and Economic Growth: The project is expected to generate 2,500 jobs in manufacturing alone, alongside numerous indirect opportunities in construction, logistics, and supply chain management (24).

3. SDG 13 - Climate Action: The adoption of low-carbon energy solutions in battery production is aligned with Canada’s net-zero commitments.

4. SDG 12 - Responsible Consumption and Production: The integration of recycling processes ensures that raw materials are recovered and reused, minimizing environmental harm.

NextStar Energy is aiming to prove that economic growth and environmental responsibility can go hand in hand.

Windsor’s Automotive Legacy

Windsor’s emergence as an EV hub is the product of strategic planning, economic incentives, and cross-sector collaboration. Community Improvement Plans (CIPs) have played a key role, attracting private investment through financial incentives like property tax exemptions. The Brownfield Redevelopment CIP, for example, has delivered a 13:1 return on investment, underscoring the impact of public-sector incentives in driving economic growth.

After years of economic decline, Windsor is rebounding. Population growth (3% between 2011–2016) and a steady drop in unemployment signal recovery, while GDP growth, particularly in 2014–2015, marked a turning point. However, Windsor’s economy still lags slightly behind the national average.

Collaboration has been central to this revival. Invest WindsorEssex, local universities, and government agencies have worked to attract investment, develop training programs, and build specialized facilities like a state-of-the-art battery testing lab. Government support at all levels has anchored large-scale projects shaping Windsor’s industrial future.

Resilience has long defined Windsor’s automotive sector. From its early days as the "Border Cities" growing alongside Detroit, it has weathered downturns, bailouts, and trade shifts. The 1965 Auto Pact once secured investment in Canadian factories, but globalization, trade liberalization, and rising competition have eroded those advantages.

The NextStar battery plant is now at the center of Windsor’s economic reinvention. More than a manufacturing site, it’s a research and development hub for vehicle electrification, attracting further investment and solidifying Windsor’s role in advanced automotive technology.

Question then Becomes

The NextStar Energy project represents a landmark investment in Canada’s EV future, bringing together industrial ambition, government backing, and strategic foresight. Yet, the project exists at the intersection of technological change, policy shifts, and global competition—factors that will shape its trajectory in the years to come.

Nestled in Windsor, a city uniquely positioned to weather both supply chain vulnerabilities and regulatory headwinds, NextStar Energy appears well-placed to navigate the turbulence of the EV battery market. Yet, is this strategic positioning enough to secure the project’s economic success?

With the Trump administration proposing a 25% tariff on Canadian automotive components, the question then becomes: how will shifting trade policies and geopolitical uncertainties reshape the financial calculus of NextStar and similar EV battery ventures? Will the project’s economic foundation hold firm?

Notes

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Refer to this article using the following citation format:

• Zangeneh, P., Abbasi M. (2025), “NextStar Energy EV Battery Plant: Canada’s First Advanced EV Battery Manufacturing Facility Megaproject” EPM Research Letters.

References

1. Yakub M. Electric Autonomy Canada. 2024. NextStar Energy begins battery module production in Windsor. Available from: https://electricautonomy.ca/ev-supply-chain/2024-10-23/nextstar-battery-windsor-production-canada/

2. Taylor I. Turnaround Cities: Anglo-Saxon Case Studies Insights from Pittsburgh (PA), Newcastle (NSW) and Windsor (Ont.).

3. Nextstar Energy. Official Website.

4. New NextStar Energy EV Battery Plant Celebrates ‘Topping Out’ Ceremony as Construction reaches 30 Per cent Completion. Stellantis Blog.

5. Windsorstar 2024. Windsor NextStar’s parent company reveals battery future plans.

6. Stellantis Media. NextStar Energy Announces Start of Battery Module Production.

7. Stellantis.com. Official Website.

8. LG Energy Solution. Official Website.

9. Alberici. NextStar Energy EV Battery Plant. Offical Website.

10. Kasian. EV Battery Plant: Powering Progress - Designing the Future of EV Batteries. Official Website.

11. Gresham Smith. Official Website.

12. Stantec to acquire Morrison Hershfield, a leading transportation, buildings, and environmental services firm. Press Release.

13. SMRT. NextStar Energy Electric Vehicle Battery Plant. Official Website.

14. Hodess Cleanrooms. Official Website.

15. Black & McDonald. Stellantis: Revolutionizing the North American EV Industry. Official Website.

16. Walters Group Inc. NextStar Energy EV Battery Plant. Official Website.

17. Alberici Hillsdale. Automotive | Hillsdale | Structural Steel Fabrication. Official Website.

18. Global News. Foreign workers at subsidized EV battery plant a ‘slap in the face’: union. Globalnews.ca.

19. CES Trump’s tariff threat spurs auto suppliers to rethink production plans. Reuters.

20. Mannes M. Exclusive: Scania tries to breathe life into troubled EV battery maker Northvolt. Reuters

21. Chu A, Smyth J, Temple-West P. $1bn US battery plant plan shows race to reduce reliance on China. Financial Times.

22. NextStar Energy Announcement on Pouch Batteries. LinkedIn Post.

23. LG Energy Solutions. ENSOLPEDIA | BATTERY INSIDE.

24. Automotive Logistics. Stellantis and LG Energy Solution begin battery production in Canada. Automotive Logistics.

If you’re in Montreal, let’s break bread and exchange ideas over lunch!

This is an opportunity to step away from Gantt charts, spreadsheets, risk matrices, and AI models to connect in person with others navigating the complexities of engineering project management.

📍 Location: TBD in Downtown Montreal, Please RSVP to editor@epmresearch.com
📅 Date: Friday February 21^st, 2025
⏰ Time: 12:00 to 1:30pm

Communities thrive on real-world conversations, and hopefully, this will be the first of many Canadian and international gatherings and networking events for our EPM community and network.

I look forward to meeting you, sharing updates on our current and upcoming research projects, and hearing your thoughts on them.

Regards

Pouya Zangeneh

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The IMEC study is quite unique. It was formed through the sponsorship of multiple stakeholders, who shared data and knowledge across different projects, that is then studied an interpreted by multiple scholars from various specialties. Therefore, minimizing host of biases in both data collection and result interpretation.

It is that rare instance where the proverbial elephant was well-identified in a well-lit room, where perceptive observers examined it together and discussed their perceptions too.

The Strategic Management of Large Engineering Projects by Roger Miller and Donald Lessard is a distillation of the outcomes of IMEC’s (International Program in the Management of Engineering and Construction) extensive study of sixty major projects across diverse sectors and geographies.

The IMEC program was commissioned in 1995 to study the strategic dimensions of large engineering projects (LEPs), and the book is published by MIT Press at the year 2000—25 years ago.

The program originated from the Hydro-Québec/CAE Chair in the Management of Technology at Université du Québec à Montréal and brought together academic institutions, industry leaders, and government agencies. The program was sponsored by organizations such as Atkin Realis (formerly SNC-Lavalin), Ontario Hydro, and the Project Management Institute.

The book, comprising ten chapters authored by different scholars, argues—at the beginning of the 21st century—that the foundation of project success lies in the early strategic design of institutional arrangements and governance structures.

In this review we cover:

1. What are Large Engineering Project (LEPs)

2. LEPs’ Main Strategic Dimensions

3. General Types of Project Sponsors

4. Risk Allocation by Project Financial Structuring

5. Strategizing to Face Risks and the Risk Layering Model

6. Project Shaping

   1. Strategizing Against Risks

   2. Iterative Process of Refinement

   3. Creation and Exercise of Options

7. Question then Becomes

The book explores the second-order strategic thinking, that is, deliberating actions that expand future response options in the face of unexpected events.

Several excellent chapters, such as Chapter 7 on building governability and Chapter 9 on partnerships, fall outside the limits this review. I encourage you to read the book firsthand, both for its extensive insights and as a thought experiment on how the management of Large Engineering Projects (LEPs) has evolved over the past 25 years.

What are Large Engineering Projects (LEPs)

The evolution in the meaning of engineering megaprojects and the complexities they face was recognized and highlighted by the authors in the early 2000s.

The authors defined Large Engineering Projects (LEPs) as massive, high-stakes ventures that shape economies and societies, and emphasize that the landscape of LEPs is shifting from centralized, government-led initiatives to complex partnerships involving private investors, regulators, and affected communities.

These projects must navigate regulatory hurdles, financial constraints, and political opposition while maintaining strategic flexibility.

The ability to shape institutions, forge alliances, and anticipate risks is what ultimately determines project success.

The book highlights six major hallmarks of LEPs and argues that by understanding these hallmarks, project leaders can shift from reactive management to proactive strategic positioning.

1. Product of Negotiated Compromise – LEPs evolve through complex negotiations among sponsors, investors, and regulators, requiring alignment of diverse interests and creative problem-solving.

2. Socially Contested Ventures – LEPs face political, environmental, and community challenges, with stakeholders competing for influence, making legitimacy and public approval critical.

3. Political Risks – LEPs are deeply intertwined with government policies, elections, and political shifts. Changes in leadership, national priorities, or public sentiment can drastically alter project viability.

4. Slow Development Cycles – Projects take decades to shape, finance, and execute, requiring patience and adaptability to shifting policies, economies, and market conditions.

5. Irreversible Investments – LEPs demand massive, non-recoverable capital commitments, making early-stage strategic planning crucial to avoid financial and operational deadlocks. Average investment at early 2000s timeline amounted to more than $1B.

6. Regulatory Complexity – Securing permits and approvals requires navigating layers of bureaucracy, often dealing with conflicting regulations across multiple agencies. Misalignment with regulatory frameworks can cause costly delays or outright cancellations.

LEPs’ Main Strategic Dimensions

In Chapter 1, the authors (Roger Miller and Donald Lessard) recount the main strategic dimensions and levers of LEPs.

They argue that successful Large Engineering Projects (LEPs) depend not just on technical expertise but on strong institutional arrangements. Institutional frameworks—laws, regulations, and governance structures—play a more crucial role in determining project performance than traditional planning and execution methods.

Projects anchored in stable and coherent institutions and predictable regulatory environments benefit from reduced risks, and clearer pathways for stakeholder engagement. In contrast, projects in weak institutional settings often struggle with instability, opportunism, and regulatory ambiguity, increasing their chances of failure.

Beyond institutions, the quality of sponsorship is a decisive factor in project success. Sponsors are responsible for shaping, financing, and managing risks across a project’s lifecycle. The authors posit that effective sponsors must master five essential skillsets:

1. Ownership Competency – Effective sponsors take full responsibility for project outcomes, ensuring strategic decisions align with long-term operational realities. They oversee engineering choices and contractual commitments to minimize future risks.

2. Strategic Evaluation – Sponsors must quickly assess project feasibility, sorting viable opportunities from high-risk ventures. This requires experience in financial modeling, regulatory analysis, and market forecasting.

3. Stakeholder Engagement – Building relationships with governments, regulators, and communities is crucial. Strong sponsors proactively manage public perception and political risks through transparent communication and negotiation.

4. Coalition-Building – Successful sponsors cultivate partnerships with investors, financial institutions, and industry specialists. These alliances help secure funding, manage risks, procure packages, construct project, operate them and navigate regulatory landscapes.

5. Resilience and Survivability – Sponsors must sustain long-term commitments, enduring financial setbacks and political shifts. A diversified project portfolio and strong financial backing increase their ability to adapt to crises.

General Types of Project Sponsors

The book identifies three major types of sponsors—network operators, concessionaire-developers, and ad hoc alliances. These sponsor types shape how LEPs are structured, funded, and executed, influencing their overall chances of success in volatile economic and regulatory environments.

1. Network Operators – Large, established firms that own and manage infrastructure networks, such as utilities or transportation systems. They bring financial stability and technical expertise but may struggle with bureaucratic inefficiencies.

   • Example: Endesa, a private utility in Chile, retained about 60% of the country’s power production even after deregulation. Their network ownership provided stability, but they had to navigate shifting regulatory landscapes.

2. Concessionaire-Developers – Private firms that specialize in financing, developing, and managing infrastructure projects, often operating across international markets. They are skilled at structuring financial deals and managing risks but can be vulnerable to political instability.

   • Example: INTERGEN, a joint venture between Bechtel and Pacific Gas & Electric, develops thermal power plants globally. Their expertise in risk allocation and international partnerships enables them to succeed in complex regulatory environments.

3. Ad Hoc Alliances – Temporary partnerships formed by entrepreneurs or engineering firms to develop specific projects. While agile and innovative, they often lack the financial backing and long-term credibility required for sustained success.

   • Example: The M1-M15 Toll Highway project between Vienna and Budapest was developed by an alliance between Transroute, Strabag, and European banks. The sponsors came together to finance and execute the project but had no long-term stake beyond its completion.

Risk Allocation by Project Financial Structuring

In Chapter 8, the authors (Richard Brealey, Ian Cooper, and Michel Habib) provide a coherent view of how financing large projects can help in dealing with risks.

Effective risk allocation in large infrastructure projects hinges on a delicate balance between control and risk-bearing capacity. Here, control refers to the ability of a party to directly influence, mitigate, or manage a specific risk through decisions, expertise, or operational actions.

For instance, contractors have control over construction risks because they oversee project execution, select materials, and manage timelines. Operators control operational risks through maintenance, efficiency improvements, and performance monitoring.

However, the capacity to control a risk does not always align with the ability to financially absorb its impact. Overexposure can lead to collapse when those managing the risks also bear excessive financial burdens. This creates a trade-off: fragmenting risk across multiple parties reduces individual exposure but weakens incentives for active risk management.

Some risks, such as currency fluctuations, cannot be controlled at the project level and are best transferred to financial markets. Others require a strategic alignment of ownership and responsibility so that parties both bear the consequences of risk and have the authority to mitigate it.

When direct risk allocation through contracts is impractical, financial architecture provides an alternative mechanism for distributing and renegotiating risks.

Unlike contracts that govern individual project aspects, financial structuring shapes control by determining who has decision-making power over financial resources or stands to gain or lose from the risk.

For instance, large banks or multilateral institutions with financial stakes in a project not only provide capital but also wield influence that can deter opportunistic renegotiation by governments or stakeholders, i.e., managing political risks. Since ownership confers control, financing structures, also, dictate who holds power in renegotiation scenarios.

Strategizing to Face Risks

In Chapter 3, the authors (Donald Lessard and Roger Miller) emphasize that risks in major projects are not isolated but interact dynamically, shifting in intensity over the project's life cycle.

The risk profile changes as the project evolve. Some risks, like regulatory approvals, diminish after initial hurdles are cleared, while others, such as financial and market risks, persist and evolve with economic conditions.

Technical uncertainties may be resolved after construction, but operational risks continue throughout a project’s lifespan.

Moreover, external shocks—macroeconomic instability, exchange rate fluctuations, or global financial crises—can compound multiple risk factors simultaneously. Institutional risks, particularly sovereign and regulatory uncertainties, can reignite financial and market vulnerabilities, making risk management an ongoing, adaptive process rather than a one-time mitigation effort.

These interdependencies highlight the need for robust contingency planning and flexible project structures to navigate uncertainty effectively.

The authors develop a taxonomy of risks in LEPs, categorizing them into market-related risks (market, financial, and supply risks), completion risks (technical, construction, and operational risks), and institutional risks (regulatory, social acceptability, and sovereign risks).

It is interesting to consider how certain risk categories, such as environmental risks, were not distinctly recognized as a separate entity in this risk taxonomy at early 2000s. Instead, they may have been subsumed under regulatory and social acceptability risks, reflecting the evolving understanding of sustainability and environmental concerns in project management.

The Risk Layering Model

Some risks can be anticipated and quantified, allowing sponsors to integrate mitigation strategies from the outset. Others, however, arise dynamically—triggered by political shifts, stakeholder opposition, or macroeconomic turbulence—forcing project sponsors to adapt and renegotiate their strategies in real time.

The key challenge is not merely to identify risks but to develop strategic responses that align risks with the capacities of different stakeholders.

Authors argue that traditional risk models, which rely on probabilistic forecasting, often fall short in capturing the complex interdependencies that shape project outcomes. Instead, successful sponsors approach risk as an active management challenge, layering their strategies to create resilience, adaptability, and long-term viability.

To navigate this uncertain terrain, project sponsors must layer their risk strategies, balancing direct risk allocation through contracts with indirect risk management through financial and governance structures.

The layering model breaks down risk management into six core strategies: (1) assessing and understanding risk, (2) shifting risks through financial instruments, (3) diversifying risk exposure, (4) creating options for adaptive responses, (5) transforming risks through institutional engagement, and (6) bearing residual risks as a calculated choice. Risks are not simply avoided but strategically assigned, diversified, transformed, or embraced, depending on their nature and controllability.

Figure 1 illustrates these six layers of strategies for managing risks. The book elaborates on each of these strategies in valuable details. By integrating these layers, project sponsors do not merely react to risk—they shape project trajectories, ensuring that risks are embedded within a structured framework that enhances governability and long-term project success.

Project Shaping

In Chapter 4, the authors (Roger Miller and Xavier Olleros) define project shaping as an active and iterative process where sponsors do not merely react to uncertainties but strategically engage with risks to influence outcomes, that unfolds over multiple episodes, typically lasting six to seven years.

Successful shaping requires expertise in organizational strategy, law, diplomacy, and public affairs. Sponsors must navigate complexity by forming alliances and coalitions, leveraging prior experience, and incorporating expert advice. This approach transforms significant uncertainties into manageable challenges by distributing responsibilities and aligning incentives across stakeholders.

Sponsors start with a configuration hypothesis, testing and adjusting it through strategic interventions. Each shaping episode addresses specific challenges, such as market uncertainties, regulatory hurdles, or stakeholder opposition, leading to incremental refinements.

This procedural rationality—where adaptation takes precedence over rigid planning—allows projects to evolve dynamically, ensuring that unforeseen disruptions do not derail long-term goals.

The authors make an interesting observation: effective shaping means balancing control with flexibility, ensuring that projects remain adaptable while maintaining strategic momentum. By continuously refining their approach through successive shaping episodes, sponsors gradually bring projects to full momentum and closure, creating structures that are resilient, governable, and primed for long-term success.

Project shaping arises from the mindset of not avoiding complexity but embracing it when it is worth doing so. Sponsors must manage nonlinear interactions, interdependencies, and shifting institutional landscapes, making strategic decisions that account for emergent risks.

Chapter 4 sets the book’s narrative in developing strategic moves to anticipate uncertainty and risks through out the project within the context of project shaping.

The idea of project shaping is discussed through the book in three main contexts of Strategizing Against Risks, Iterative Process of Refinement, and Creation and Exercise of Options, albeit, in different chapters. Here is my attempt to summarize them accordingly.

Project Shaping as Strategizing Against Risks

Project shaping as strategizing against risks integrates risk management into the foundation of large engineering projects (LEPs), ensuring resilience and adaptability. Effective sponsors use risk-layering strategies—assessing, shifting, diversifying, creating options, transforming, and bearing risks—to proactively align projects with emerging uncertainties. By engaging stakeholders early, they identify disruptions, align incentives, and refine project configurations to minimize failure risks.

Risk layering distributes uncertainties strategically, enhancing project adaptability. Assessing risks ensures informed decision-making, while shifting and diversifying risks limit exposure. Creating options, such as modular investments, builds flexibility, and transforming risks through regulatory influence secures legitimacy. Bearing residual risks maintains control, allowing sponsors to influence outcomes effectively.

By applying these strategies, sponsors shape projects into robust, governable entities rather than leaving them vulnerable to uncertainty. Successful LEPs engage with risk strategically, using uncertainty as a tool for coalition-building, financial stability, and long-term viability. Project shaping, therefore, is less about avoiding risks and more about structuring uncertainty to create sustainable, high-value ventures.

Project Shaping as an Iterative Process of Refinement

Project shaping is not a singular decision but an evolving sequence of progressive refinements, where sponsors navigate multiple shaping episodes to align technical feasibility, financial viability, regulatory compliance, and stakeholder expectations. Each episode serves as a hurdle, requiring adaptation and negotiation to maintain project momentum.

Figure 2 depicts a decision tree for navigating project hurdles over multiple episodes.

Sponsors begin with broad conceptual assumptions and, through successive cycles of coalition-building and risk assessment, refine their approach to meet emerging challenges.

This process is dynamic, often facing early lock-in risks that can restrict future flexibility or require renegotiations when unforeseen issues arise. Effective shaping involves continuous stakeholder engagement, ensuring that governance structures, financial agreements, and technical designs remain adaptable while balancing competing interests.

In doing so, shaping becomes a form of strategic maneuvering—aligning incentives, mitigating risks, and integrating social and environmental considerations to transform uncertainty into governable structures.

Project Shaping as Creation and Exercise of Options

Beyond iterative refinement, shaping is a mechanism for creating optionality, allowing sponsors to respond dynamically to changing conditions rather than committing prematurely to fixed solutions.

The real-options framework, drawn from financial theory, is useful to conceptualize how uncertainty can enhance project value if structured decisions are made sequentially rather than in a single, irreversible move.

By keeping strategic choices open—whether in financing, technology selection, or stakeholder agreements—sponsors can delay critical commitments until more information is available, reducing downside risks while maximizing upside potential.

This approach demands flexible governance, where partnerships are structured to support adaptability rather than rigid control. The authors recount a few examples to clarify this idea. Premature lock-in, such as the early drilling technology choice for the English Channel Tunnel, demonstrates the risks of closing options too soon, while the Brazilian ITA project illustrates how deferring financial decisions until the right conditions emerge can lead to a more sustainable outcome.

The key to financial shaping lies in maintaining the ability to recalibrate commitments—whether through staged investments, contingent financing agreements, or leveraging contractual safeguards—ensuring that projects not only secure initial funding but remain resilient against evolving economic and political landscapes.

Question then Becomes

Nearly 25 years after the IMEC research was published, its insights remain foundational, demonstrating remarkable foresight in anticipating the evolving landscape of LEPs. The study not only captured the complexities of its time but also laid out the direction of more than two decades of industry transformation.

At the same time, the evolving landscape of large engineering projects demands adaptation. Sustainability mandates, digital transformation, and geopolitical shifts are reshaping project governance, risk management, and financial structuring.

The rise of green bonds and impact investing calls for new financial models that balance economic viability with environmental imperatives. Meanwhile, stakeholder dynamics are more complex than ever.

What are the most important dimensions in which IMEC’s strategic framework must evolve to stay relevant? How can lessons from IMEC guide energy transition projects in navigating regulatory hurdles, community engagement, and Indigenous partnerships?

The genius of the IMEC program lies not only in its findings but in its very existence—the fact that such a comprehensive, multi-stakeholder collaboration actually took place.

The book leaves the reader contemplating whether a similar large-scale study could be conducted today and, if so, what its outcomes might be.

I cannot help but wonder: what conditions would be necessary to foster a similarly ambitious initiative today?

Notes

This book review is a reading material for the “Strategy in Project Management: Game Theoretic Approach,” course at the University of Calgary, Department of Civil Engineering.

Refer to this article as below:

• Zangeneh, P. (2025), “The Strategic Management of Large Engineering Projects: Shaping Institutions, Risks, and Governance by Roger Miller and Donald Lessard,” EPM Research Letters.

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In this second part, we journey deeper into the mechanics of strategy, examining the tools and techniques that make strategies credible and interactions effective. We will navigate along with the book to review the following subjects.

• Making Strategies Credible,

• Information Interpretation and Manipulation,

• Cooperation and Coordination,

• Bargaining and Negotiations,

• Voting, and,

• Incentives.

As in the first part, I illustrate the book’s principles by examining their application through the lens of a project manager navigating an urban light rail megaproject. This context, I hope, demonstrates how game theory serves as a compass in the often uncharted terrain of project management.

I hope this review inspires you to engage with the book itself. The Art of Strategy is the result of distilling several books through their writers' journeys into its finest version. As they rightly advertised, it serves as a guide for success in business, life, and, of course, project management.

Making Strategies Credible

Building on the previous section, strategic moves consist of two critical components: the move itself and the course of action that establishes its credibility.

Credibility is pivotal because even the boldest moves lose their impact if the opposing player doubts your ability or willingness to act.

As discussed earlier, when faced with threats, a sound strategy is to undermine their credibility, such as exploiting weaknesses in the opponent’s commitment mechanisms.

Another approach is to test and incrementally challenge the credibility of a threat through a concept famously theorized by Thomas Schelling as salami tactics. This involves taking a series of small, calculated steps that gradually push the limits of an opponent’s commitment. Each move is designed to appear too minor to warrant a disproportionate response, making retaliation seem excessive or unjustifiable. Over time, these seemingly inconsequential actions accumulate, effectively weakening the opponent's original threat and shifting the strategic balance.

Salami tactics enable one side to advance its position incrementally while forcing the other to either escalate dramatically or concede ground. By exploiting the opponent's hesitation to act decisively, this approach exposes the fragility of threats that lack robust and enforceable credibility.

On the other hand, when confronted with salami tactics, you might restrict your own choices by cutting off communication or delegating decision-making to an automated system. If you clearly communicate the existence of such a mechanism, this strategy relieves you of the burden of acting proportionally and strengthens your position by removing room for hesitation.

Dixit and Nalebuff classified the approaches to establish credibility for strategic moves into four category of tactics:

1. Changing Payoffs: Restructure the game to make compliance more attractive or deviation prohibitively costly. This includes writing enforceable contracts, and establishing and leveraging reputation.

2. Restricting Choices: Constrain the ability to reverse commitments by cutting off communication to limit renegotiation, or, burning bridges, such as removing fallback options to create an all-or-nothing scenario, or, delegating outcomes to chance or automated processes.

3. Incremental Moves: Break large commitments into smaller, manageable steps, fostering trust while reducing risks. This tactic also addresses the salami strategy by ensuring that each incremental action can still enforce discipline or resolve.

4. Using Agents and Teams: Enhance credibility through external enforcement such as mandated agents, or social and team pressure.

Automatic Escalation

As a project manager, you are often dealing with contractors struggling with delays and under high stress. Imagine in our urban light rail projects, the contractors frequently fail to submit intermittent progress commitments and often provide excuses instead of actionable plans, making it increasingly likely that the final deadline will be missed.

To address this, you may leverage the risk management function to create an automated mechanism that escalates delays to the project's higher management as material risks. These risks are configured to trigger when any contractors miss their specific milestones or fail to submit required documentation, such as updated work schedules or mitigation plans. Once triggered, the risk is automatically flagged in the project’s centralized dashboard, making it visible to all stakeholders, including higher management.

The system restricts your ability to personally intervene or renegotiate these escalations, effectively restricting your choices and creating a scenario where the contractors face the consequences of their delays without the usual mediation or leniency. This strategy removes fallback options for the contractors and signals the seriousness and the credibility your strategy.

Information Interpretation and Manipulation

In real-world strategic situations, it is common for one player or party to possess more information than the other, creating an imbalance in information, or, information asymmetry. This asymmetry—or even the perception of it—can be leveraged or manipulated to influence outcomes in favor of the more informed party.

To navigate these dynamics, mechanisms like signaling, screening, and signal jamming play a role in shaping how information is conveyed, interpreted, and controlled.

Signaling involves a player demonstrating attributes or intentions through observable actions, often to influence others’ decisions. For a signal to be credible, it must carry a cost or inconvenience that deters false representation. Cleanliness, for instance, is not a reliable signal of quality of a used car, as its relatively cheap to have any car cleaned. In contrast, car dealerships or companies may use signals such warranties to affirm product quality—implicitly showcasing their confidence by bearing the potential cost of defects.

Screening, unlike signaling, is initiated by the less-informed party to elicit private information from others. It involves designing scenarios where different actions reveal differences in underlying qualities or risk profiles. Devising screening mechanisms could lead to unintended effects. In insurance markets, for instance, policies with uniform pricing may attract disproportionately high-risk individuals while discouraging low-risk participants, this is called adverse selection. This is why screening techniques in such industries typically aim to predict individuals risk levels indirectly through their seemingly unrelated choices.

Finally, signal jamming is the strategic obfuscation of information to prevent others from accurately discerning one’s intentions. This tactic is common in competitive or adversarial contexts, where concealing true motives or capabilities can confer an advantage. Poker players employ reverse tells to confuse opponents. Many counter espionage operations such as Canary Traps, Honey Traps, or False Flags, are built based on deceptive signals to deliberately mislead.

Signal Jamming in Projects

Teams on a large transit project might engage in signal jamming by strategically repackaging their deliverables to highlight easy wins while masking the root causes of significant issues.

For instance, they could emphasize timely completion of auxiliary tasks, such as routine designs or minor installations, in progress reports while underreporting delays or overruns in core components like tunnel construction or station infrastructure.

This selective reporting shifts attention to areas of perceived success, deflecting scrutiny from critical challenges. By focusing on partial deliverables or separating problematic areas from successful ones in updates, teams create a misleading narrative that obscures the true status of the project. This makes it harder, if not impossible, for management to identify and address root causes of delays or budget overruns.

Earned Value Management (EVM) methods, while valuable for tracking project performance, are particularly susceptible to such signal jamming. Teams can inflate the earned value by showing progress on less critical, lower-effort tasks that contribute little to the overall project while neglecting high-impact activities facing delays or cost issues. Progressively, this will force teams to cut corners on other parts of the project to avoid exceeding budget or schedule constraints, leading to compromised quality. For example, materials or workmanship might be downgraded on less visible project elements to counterbalance overruns elsewhere, resulting in long-term operability issues.

Implementing proper incentive schemes, transparency measures, and commitment mechanisms can counteract these issues and discourage signal jamming. Review and updating the rules of credit for EVM analysis is of paramount importance.

Signal jamming happens on most large projects and to various extents because of piecemeal decision-making as discussed in the following section.

Cooperation and Coordination

As outlined in the first part of this review, individual decisions can collectively lead to suboptimal outcomes. And, there is no way to remedy that other than by changing the payoffs.

Success, in many domains, is a game of relativity rather than absolutes. Just as OPEC nations coordinate production limits to prevent a destructive price war, so too do industries. Over-competition, such as excessive studying for grades or excessive training in sports, leads to a collectively suboptimal outcome. True strategy, then, is not just about self-improvement but about understanding when to compete, when to cooperate, and when to redefine the game entirely. This can be thought as the inverse of the Prisoners’ Dilemma, when collective agreements (from Unions to Cartels) is often the winning strategy.

In many systems, small shifts in dynamic behaviors or conditions may cascade into significant, systemic shifts. This phenomenon is known as a tipping point—a critical threshold beyond which collective behavior changes dramatically, often locking the system into a new—extreme—equilibrium. For instance, in racially mixed neighborhoods, minor demographic shifts can set off self-reinforcing dynamics that result in complete segregation. Overcoming these tipping points requires deliberate, coordinated efforts to realign the system toward more favorable equilibria.

These small shifts can be agreed upon by all parties along the way. Often seemingly rational incremental steps, through piecemeal decision-making, can lead to unintended, collectively regrettable outcomes.

Imagine a system where individuals collectively determine promotion criteria, starting from a low standard and gradually raising it in small steps. Each decision appears logical to the majority: high-achieving members benefit from enhancing the group's perceived quality, while those who are excluded prefer a higher standard to blur their relative ranking among the rejected.

Over time, these incremental steps escalate to an extreme threshold where no one achieves promotion, leaving everyone worse off. Despite this outcome, each decision along the way was supported by most participants, illustrating how localized, self-serving choices can fail to account for their cumulative, detrimental effect on the group. This phenomenon is called of piecemeal decision-making or incremental rationality

Incremental Rationality and Piecemeal Decision-Making in Projects

Incremental Rationality is often mirrored in large projects with multiple sub-projects, where incremental change orders and scope adjustments occur. For example, within a construction megaproject, sub-project teams may propose minor design changes or additional features that seem beneficial and manageable in isolation.

Each change order, approved by the majority of project leadership and stakeholders, shifts the project slightly from its original scope. Over time, however, the cumulative effect of these changes can lead to significant budget overruns and delays, diverting focus from the project's core objectives. As these incremental decisions pile up, the project as a whole begins to lose alignment with its initial priorities, often with the benevolent approval or consent of all involved parties.

This collective approval sets the stage for signal jamming, shifting the narrative away from addressing systemic issues, creating a self-reinforcing loop where the group avoids acknowledging failures and instead justifies its actions through selective reporting or framing.

Avoiding piecemeal decision-making is critical, and may start with bundling potential changes (change orders) into comprehensive packages to allow stakeholders to evaluate their cumulative impacts—with transparency and accountability—rather than approving them individually.

Bargaining and Negotiations

Bargaining is a careful balance of strategy, where each side pushes for its interests while competing for shared resources.

Whether in scenarios like wage negotiations or contractual disputes, outcomes are shaped by critical factors such as the cost of delay, external alternatives, and the distribution of bargaining power.

Bargaining can be viewed as a sequential game of offers and revisions, where negotiators benefit from backward reasoning to secure agreements that maximize mutual gains while minimizing avoidable losses, such as those arising from strikes or prolonged disputes.

A central concept in bargaining and negotiation is BATNA (Best Alternative to a Negotiated Agreement), which defines each party’s fallback position if talks fail. BATNA serves as a baseline for determining the value each side can secure independently, heavily influencing the division of the "bargaining pie."

Successful negotiations hinge on creating value beyond the sum of the BATNAs, with the surplus divided according to each party's relative bargaining power. Strengthening one’s BATNA—or weakening the opponent’s—is a key strategy for gaining leverage in bargaining and negotiations. Effective negotiation requires precise assessment of BATNAs, anticipation of the other party’s actions, and creativity in expanding the pie to achieve outcomes that benefit all involved.

The Rubinstein Bargaining model offers a foundational framework for understanding these dynamics. This model examines negotiations over a divisible resource through alternating offers, assuming complete information for all parties. The model representation demonstrate that bargaining power is linked to the negotiators' patience. Patience, quantified as a discount factor (δ), indicates how much a party values immediate resolution versus future outcomes.

Greater patience in negotiations allows a party to hold out longer, increasing their chances of securing a larger share of the resource. In contrast, those with less patience feel more pressure to settle quickly, often making greater concessions to reach an agreement sooner.

Bargaining in Project Disputes

Consider a scenario where the delivery of train wagons for a large public transit project is delayed by the supplier.

The delay has resulted in three types of costs: the contractor’s direct costs (e.g., idle labor and machinery), shared mitigation costs (e.g., sourcing alternatives or accelerating work), and the owner’s indirect costs (e.g., reputational damage, penalties, and lost revenue due to delays).

The critical question becomes: who is more impatient in this negotiation—the contractor or the owner? The contractor’s impatience is shaped by their financial exposure, such as cash flow pressures and resource constraints. However, if the contractor has operational flexibility, such as reallocating idle resources to other projects their level of impatience may be reduced. Maintaining a healthy portfolio of active projects increases the patience of contractors in bargaining disputes.

On the other hand, the owner typically faces higher indirect costs, including public scrutiny, political commitments, and potential penalties, all of which create a stronger urgency for resolution. If the owner ends up more impatient than the contractor, bargaining dynamics will results with the owner bearing the majority of the shared mitigation costs.

Strategies to remedy the bargaining imbalance are crucial for project owners. The owner can strengthen their BATNA (Best Alternative to a Negotiated Agreement) by introducing penalty clauses in contracts that shift more risk to suppliers or contractors. But aside from contractual arrangement, the owners should diversify their supplier options to increase redundancy.

Voting

Voting, in any system, is inherently strategic.

Voters often realize that simply choosing their favorite candidate may not always lead to their desired outcome. Instead, they must consider how others will vote and whether supporting other options will better serve their interests.

This strategic dynamic becomes especially pronounced in elections with three or more candidates, where casting a ballot for one’s true preference can inadvertently lead to an undesirable result. Voters may choose to support a candidate who is not their top choice but stands the best chance of defeating an unfavorable alternative.

To address these challenges, ranked-choice voting has been proposed as a means to relatively mitigate strategic voting. This system allows voters to rank candidates in order of preference, enabling a more nuanced expression of voter sentiment. It effectively simulates runoff elections within a single voting round, reducing the risks of vote-splitting and wasted votes. Approaches like the Condorcet method—where the winner is the candidate who would prevail in all head-to-head matchups—are particularly robust but require ranked ballots to be practical.

In a one-dimensional political spectrum, candidates tend to gravitate toward the preferences of the median voter, as this position secures majority support. This convergence incentivizes truthful voting, as voters have no reason to misrepresent their preferences when outcomes hinge on the median. While effective in simpler, one-dimensional settings, the median voter theory becomes less stable in multi-dimensional contexts where issues are more complex and diverse. Nevertheless, the stability of median voter outcomes is a cornerstone of political and organizational systems.

Strategic Proposals

While voting may seem distant from project management, the principles of strategic alignment with stakeholder preferences—such as those in median voter theory—are directly relevant when crafting proposals for evaluation by decision-making bodies like private or governmental committees. These committees often represent diverse interests, weighing dimensions such as cost-efficiency, environmental sustainability, long-term economic benefits, regional equity, and public support.

Understanding and mapping these priorities allows for the identification of the "median position"—a balanced point that reflects the committee’s central concerns. Structuring a proposal to align with this median position maximizes its appeal.

To address fringe preferences without compromising the core strategy, thoughtful amendments can be included. For example, if a subset of stakeholders strongly advocates for more aggressive environmental measures, the proposal might integrate optional green enhancements, such as additional scope for renewable energy initiatives.

Beyond potential scoring systems for evaluating alignment with stakeholder priorities, factors such as credibility, technical rigor, the overarching direction advocated by a proposal play a pivotal role. A core consideration is ensuring that the proposal's priorities align seamlessly with the strategic objectives of the project owner organization. Nonetheless, strategically centering the proposal on the median position—reflecting the most balanced and widely shared priorities—while thoughtfully integrating concessions for outlier positions creates a compelling and adaptive approach.

Incentives

Incentives are another key mechanism for altering strategic situations and aligning individual efforts with desired outcomes. When designed thoughtfully, they can bridge the gap between individual motivations and organizational goals, particularly in contexts where tasks have a probabilistic nature.

Effective incentive systems balance fixed rewards with performance-based rewards, recognizing the imperfect relationship between effort and outcomes. For tasks influenced by factors beyond an individual’s control—such as sales or project-driven work—this balance is critical. A mix of base pay and bonuses tied to observable results encourages effort without unfairly penalizing for uncontrollable variables.

However, calibrating these incentives is a delicate task: overemphasis on performance-based rewards in uncertain environments can demoralize workers, while relying too heavily on fixed compensation risks dampening motivation. Tailored incentive schemes drive engagement and productivity by aligning rewards with the specific context of the work.

When tasks are interconnected, whether as substitutes or complements, incentives must be designed with these relationships in mind. Substitutable tasks, like balancing teaching and research, can compete for a pool of attention. If incentives heavily favor one over the other, it may lead to neglect of the less-rewarded task, requiring careful balance to avoid unintended trade-offs. Complementary tasks, such as beekeeping and orchard management, benefit from well-aligned incentives that reinforce their natural synergy, leading to greater overall productivity.

Requiring university professors to teach a research course in their research area is therefore a powerful strategy to align their incentives.

This logic also applies to organizational structure: grouping complementary tasks together fosters coherence and efficiency, while separating substitutable tasks helps reduce internal competition and misalignment.

Incentives for Project Managers

In project management, the role of luck cannot be overstated. From unforeseen weather disruptions to supply chain delays, numerous factors beyond the control of teams can significantly impact progress. Designing proper incentives in this context requires moving beyond a singular focus on project completion.

Overemphasis on final delivery often leads to compromised quality, safety shortcuts, and critical issues being hidden to meet deadlines. To counter this, incentives should be progress-adjusted for context, ensuring that teams are rewarded for effort and adaptability rather than raw outcomes alone.

Performance metrics should be carefully chosen to reflect achievements distant from the probabilistic nature of risk events, such as compliance with intermediate quality benchmarks, proactive risk mitigation, or maintaining team morale in challenging conditions. This approach fosters accountability and fairness, encouraging teams to perform at their best despite external uncertainties.

Equally important is addressing the inherent conflict between substitute goals such as quality, safety, and productivity.

When these achievements are at odds—such as prioritizing speed at the expense of safety—aligning incentives becomes a challenge. Separating rewards and even departmental responsibilities for these objectives can reduce misalignment. For instance, a safety team could be tasked solely with enforcing safety standards and rewarded based on incident-free days, while a productivity team focuses on delivering milestones on schedule. By decoupling these incentives and responsibilities, teams can excel in their areas without undermining the others. This structured approach not only minimizes counterproductive competition but also ensures that core project values like safety, quality, and efficiency are upheld in balance.

Question then Becomes

As we navigate the complexities of strategic decision-making, several critical questions emerge.

Are there ethical concerns with using commitment mechanisms, such as delegating decisions to automation, to establish credibility? As AI continues to evolve, this issue will become increasingly relevant, raising questions about accountability, fairness, and unintended consequences.

Similarly, how can organizations prevent or minimize signal jamming, especially in large projects where selective reporting is common? When teams manipulate information to highlight minor successes while concealing critical delays, decision-makers lose visibility into the true status of a project.

Politicians often capitalize on the intuitive instincts of their supporters, shaping narratives that resonate deeply. For instance, Donald Trump frequently emphasized the unpredictability of his strategies on certain topics, signaling—whether explicitly or subliminally—that his approach was designed to ensure victory for his party, often at the expense of opponents. Simultaneously, by projecting an air of absolute honesty and transparency, he conveyed the idea of a broader, inclusive victory, suggesting that everyone stood to gain. This dual signaling taps into the natural dichotomy of zero-sum and non-zero sum game dynamics in both his supporters and his enemies. Can you think of instances where he has employed this strategy?

These questions highlight the intricacies of applying game theory principles to real-world scenarios. Who else in your friends or colleagues would find these ideas useful?

Notes

This book review is a reading material for the “Strategy in Project Management: Game Theoretic Approach,” course at the University of Calgary, Department of Civil Engineering.

Refer to this article as below:

• Zangeneh, P. (2025), “The Art of Strategy - Part 2: A Review of Dixit and Nalebuff’s Classic Within the Context of Project Management”, EPM Research Letters.

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Potash, primarily used as a key ingredient in fertilizers, enhances crop yields, strengthens plant resistance to pests and diseases, and improves drought tolerance. With a growing global population and food demand projected to double by 2050, Canada's leadership in potash production is more vital than ever.

Most of Canada’s potash reserves are in the province of Saskatchewan. This region, hosting some of the richest Potash deposits on the planet, is home to ten active mines and several major development projects. In 2022 alone, these mines produced approximately 24.6 million tonnes of potassium chloride (KCl). The province's extensive rail and export infrastructure further amplify its role in feeding the world.

The potash industry in Canada is a sponsor of many large engineering megaprojects, such as BHP’s Jansen Potash Project, one of the largest capital investments in the sector globally. These projects are engineering marvels and underscore the industry's role as a catalyst for innovation, sustainability, and growth within the heavy industry construction sector of Canada.

In this article we review,

1. Potash and Potash Mining Methods

2. History of Canada’s Potash Industry and its Highlights

3. Saskatchewan Potash Reserves and Operating Mines

4. Canada’s Potash Transportation System

5. Capex Requirement of Potash Megaprojects

6. The Curious Relationship of Potash and Climate Change Adaptation

From Calgary, to Saskatoon, to Winnipeg, we embarked on a 14-hour long road trip to witness the heart of Canada's agricultural legacy, not to mention the vibrant city of Saskatoon. This road trip left us with a much deeper appreciation of Canada’s role in feeding the world.

Voyage and Return

Last year, our research group planned a trip to Winnipeg for a joint conference. The idea of turning it into a road trip through Canada’s potash heartland seemed exciting at first. But once we realized it involves a 14-hour drive, our enthusiasm quickly faded away.

As we dug deeper into Canada’s rich potash legacy, the road trip idea grew on us. Our most viable plan was to rent a car, leave Calgary at dawn, and to fly back from Winnipeg. After weighing the pros and cons, we decided to put it to a group vote. Unanimous, the road trip won.

What followed was an unforgettable journey through some of the world’s largest potash mines and the impressive transportation networks that make this industry possible. A proud legacy for Canada. Looking back, it was definitely a long trip, but the experiences and insights we gained, and sharing them with you, will make it worthwhile.

Potash

Potash, derived from the Dutch "potasch" meaning "pot ash," is used to describe a variety of potassium-bearing minerals and compounds.

This term originates from the traditional method of extracting potassium carbonate by leaching wood ashes in pots. Today, "potash" primarily refers to potassium chloride (KCl), but also includes potassium sulfate (K2SO4) and potassium nitrate (KNO3), all crucial for manufacturing fertilizers.

Potash is a key ingredient in fertilizers that improve crop yields, enhance plant resistance to diseases and pests, and increase drought tolerance. Potassium is vital for plant health, aiding in functions like photosynthesis, water regulation, and disease resistance. These benefits make it indispensable in modern farming practices, especially as the global population and food demand continue to rise.

The limited availability of Potash poses significant challenges to agricultural growth. With the global population rising and food demand expected to double by 2050, potash has becoming an increasingly vital mineral (1). The mining of potash involves extracting these potassium-rich minerals from underground ore deposits, which are then processed into usable forms for agriculture (2).

Before the pandemic in 2021, potash prices comfortably sat between CA$250 and 280 per metric ton. But as the pandemic unfolded, the disruptions in supply chains, heightened demand for agricultural inputs, and geopolitical pressures caused prices to soar to more than five times the pre-pandemic levels, reaching over CA$1,500 per metric ton in early 2022.

Today, prices have begun to stabilize, settling around CA$400 to 450 per metric ton, about 1.5 to 2 times pre-pandemic levels.

Potash Mining

There are two primary methods for potash mining: conventional underground mining and solution mining.

Conventional underground mining involves excavating deep shafts to access deposits at depths of 900 to 1,100 meters, where machinery and workers extract the ore. This is most suitable for shallow, high-grade deposits and is widely used across seven mines in Saskatchewan.

In contrast, solution mining, used at three mines, targets deeper deposits up to 1,500 meters, or is used to extract from disturbed or abandoned former underground mines, of instance due to previous flooding. This less invasive technique involves injecting water to dissolve potash into a brine solution, which is then pumped to the surface (4).

We will compare the capital cost requirements of these methods further in this article.

The processing of potash includes refining the material to remove impurities and increase the concentration of potassium chloride through techniques such as flotation, crystallization, and drying.

Canada’s Potash Industry

Economically, potash is a cornerstone of Canada’s resource sector. The industry contributes 23 billion of dollars to Canada annually, supports 76000 jobs (5), and contributes to the economic development of rural and Indigenous communities. Canada’s extensive transportation infrastructure, including railways and ports, facilitates the efficient export of potash, ensuring its accessibility to global markets.

History

• 1940s: Early Inspiration and Exploration – Potash exploration in Canada began, inspired by discoveries in Germany and the United States, driven by rising global demand for agricultural fertilizers.

• 1943: Accidental Discovery – Potash was discovered in Saskatchewan during routine oil drilling, but its significance wasn’t immediately recognized due to low market demand and focus on oil.

• 1950s: Rising Demand and Focused Surveys – Global demand for potash grew, prompting geological surveys in Saskatchewan that revealed extensive deposits suitable for mining.

• 1958: Birth of Canada’s Potash Industry – The first potash mine near Saskatoon was opened by the Potash Company of America, officially starting Canada’s potash production.

• 1960s: Expansion Era – Potash demand surged, leading to the opening of several new mines in Saskatchewan and a significant increase in production capacity.

• 1970s: Oversupply and Industry Challenges – A global surplus of potash caused price drops and reduced production, prompting industry consolidation and technological advancements for efficiency.

• 1980s–1990s: Stabilization and Innovation – The industry stabilized with moderate growth driven by innovations in mining and processing, improving both output and environmental sustainability.

• 2000s Onward: Renewed Expansion – Rising global food demands led to increased production and expansion, with companies like PotashCorp (now Nutrien) playing a major role.

• Today: Saskatchewan’s Global Leadership – Saskatchewan is the largest potash-producing region worldwide, holding nearly half of the global reserves.

Innovation

The Canadian potash industry has a legacy of innovation that has propelled it to global leadership.

Early mining efforts faced significant geological obstacles, including severe water inflows during shaft sinking. To address this, ground-freezing techniques were pioneered, enabling safe and stable underground mining. These breakthroughs laid the foundation for Canada’s potash sector to flourish (6).

In 1964, Kalium Chemicals Ltd. opened the world's first potash solution mine near Regina. The introduction of solution mining allowed access to reserve unreachable by conventional mining techniques. It not only expanded production capabilities but also demonstrated the industry's adaptability to complex geological conditions (7).

In the 2000s with the Saskatchewan Research Council (SRC) developed new potash assay methods, becoming one of the world's largest potash assay laboratories. This supported the industry's growth by providing accurate analysis for exploration and production (8).

Today, the potash industry continues to lead with innovations focused on sustainability and efficiency. Practices such as water recycling, land reclamation, and digital optimization are now integral to mining operations. This enduring commitment to innovation ensures that Canada remains at the forefront of the global potash market while contributing to more sustainable agricultural practices worldwide (9).

Social and Economic Impacts

The potash industry is a vital pillar of Saskatchewan's economy, significantly contributing to both the province and Canada as a whole.

In 2022, it added approximately CA$8.6 billion to Saskatchewan’s GDP and generated CA$5.4 billion in tax revenues across federal, provincial, and local governments. This financial impact highlights its critical role in sustaining public services and supporting economic growth.

Over the past 15 years, Saskatchewan has attracted CA$30 billion in investments for new mines and expansions, showcasing the industry's dynamic growth. On a national level, Canada’s fertilizer industry, anchored by potash, contributes roughly CA$23 billion annually to the economy and supports over 76,000 jobs across the country. As demand for agricultural fertilizers continues to grow globally, the Canadian potash industry remains a cornerstone of economic stability and innovation (10, 11)

Saskatchewan Potash Reserves and Operating Mines

Saskatchewan is a province in the heartland of Canada known for its vast horizons and rich agricultural land.

It is bordered by Alberta to the west, Manitoba to the east, and the Northwest Territories to the north. Saskatchewan is famous for its diverse landscapes, which include rolling plains, dense boreal forests, and numerous lakes and rivers.

The province is a major producer of grains and oilseeds, making agriculture a cornerstone of its economy. Saskatchewan is rich in minerals, particularly potash and uranium, and is a leader in energy production from oil, natural gas, and renewable methods. Its major cities, Regina, the capital, and Saskatoon, the largest city, are economic hubs of culture, education, and innovation.

The potash reserves in Saskatchewan are located within the Middle Devonian Prairie Evaporite Formation (a thick sequence of evaporite minerals such as halite and potash deposited in a restricted marine basin approximately 393 to 383 million years ago), in the Elk Point Basin, a region with a layered sedimentary history beginning from the Cambrian period (approximately 541 to 485 million years ago, characterized by a succession of carbonate, shale, and sandstone formations reflective of varied marine and coastal environments) (12).

As of 2023, This region is home to ten active mines and several major development projects, collectively contributing to Canada’s position as the largest producer and exporter of potash globally, as shown in the map below. In 2022, these mines produced approximately 24.6 million tonnes of potassium chloride (KCl), reflecting a steady increase in production over previous years (14).

Following are the active potash mines in Saskatchewan.

Nutrien Ltd.

1. Allan Mine: Operating since the 1960s, this mine uses conventional underground mining techniques.

2. Cory Mine: Combines conventional underground mining with solution mining for deeper deposits.

3. Lanigan Mine: Relies exclusively on conventional underground mining methods.

4. Patience Lake Mine: Transitioned to solution mining after flooding halted underground operations in the late 1980s.

5. Rocanville Mine: Near the Manitoba border, Rocanville is one of Nutrien's largest and most productive operations, utilizing underground mining techniques.

6. Vanscoy Mine: Located southwest of Saskatoon, Vanscoy has been a cornerstone of Nutrien’s operations for decades, employing underground mining methods.

The Mosaic Company

7. Belle Plaine Mine: Among the largest solution mining facilities in the world, Belle Plaine is a flagship operation.

8. Colonsay Mine: Restarted in 2022 to meet rising global demand, this mine east of Saskatoon uses underground mining.

9. Esterhazy Mine: Mosaic’s largest operation, the Esterhazy complex includes K1, K2, and the advanced K3 shafts, all employing underground mining.

K+S Potash Canada

10. Bethune Mine: Opened in 2017 near Moose Jaw, Bethune is Canada’s first new greenfield potash mine in nearly 50 years. It employs solution mining and stands as a flagship project for K+S.

BHP

11. Jansen Mine: Set to become the eleventh potash mine in Saskatchewan, the Jansen project will utilize underground mining techniques.

Canada’s Potash Transportation System

Jointly owned by Nutrien Ltd. and The Mosaic Company, Canpotex represents the unified efforts of two of Saskatchewan’s largest potash producers for Canadian potash to reach over 40 countries, supporting global agriculture and food security.

The heart of Canpotex’s transportation system lies in its extensive fleet of over 8,000 custom-designed railcars. These cars, owned by Canpotex but operated on tracks provided by Canadian Pacific (CP) and Canadian National (CN), efficiently transport potash from Saskatchewan’s mines to major port terminals in Vancouver, British Columbia; Portland, Oregon; and Saint John, New Brunswick.

This integration of specialized railcars with Canada’s robust rail infrastructure allows Canpotex to load approximately 240 vessels annually, ensuring seamless delivery to international markets. Over the years, Canpotex has invested more than $1 billion USD to enhance its logistics network, including expanding its railcar fleet and upgrading terminal facilities (15).

Capex Requirement of Potash Megaprojects

Despite the apparent simplicity of the two main processing methodologies for potash—conventional underground mining and solution mining—these projects only economically viable through economies of scale.

Economies of scale are critical because potash prices, while occasionally experiencing spikes, tend to stabilize within predictable ranges, requiring high production volumes and cost leadership to offset initial expenditures.

Solution Mining Projects

Once mineral reserves are well established, building a solution mining operation involves a series of critical steps.

First is the development of the mine, which includes establishing the wellfield and creating caverns. A wellfield is a network of wells used to inject water into potash-rich deposits, dissolving the mineral to form underground caverns filled with brine, which is then extracted and processed to recover potash. Injection and production wells are drilled strategically to ensure efficient water and brine flow, enabling the dissolution and extraction of potash-rich brine.

Next is the construction of the processing plant, where the extracted brine is treated to separate and crystallize the potash. Alongside this, waste salt management systems are implemented to handle by-products responsibly, ensuring minimal environmental impact. These steps are supported by essential infrastructure, including pipelines, power systems, and transportation networks, which are critical for sustaining efficient operations and product delivery.

A Preliminary Economic Assessment for a 3.5 Mtpa (million tonnes per annum) solution mining project, published in 2017, estimated the total cost at CA$4.03 billion. The table below presents the rounded values and normalized percentages for the various components of the project. As anticipated, the normalized costs highlight the processing plant as the primary cost driver.

Conventional Underground Mining Projects

Developing a conventional underground potash mining project involves a series of carefully planned steps.

First is the development of the mine itself, which includes sinking shafts to access the potash deposit. These shafts serve as critical access points for workers, equipment, and material transportation, and are supported by ventilation systems to ensure a safe working environment. Mining is conducted using the room-and-pillar method, where large underground rooms are excavated, leaving pillars of potash to support the overlying strata. This method ensures stability while allowing efficient extraction of the resource.

The other major part is the construction of the processing plant, where the mined potash ore undergoes a series of critical steps to transform it into marketable products. This includes raw ore handling, storage, and crushing to prepare the material for processing. The process mill building is divided into wet and dry areas. The wet area comprises attrition scrubbing, desliming, flotation, and debrining, which are essential for separating the potash from impurities. The dry area involves drying, screening, compaction, and glazing to refine the product further. These operations are complemented by waste management systems, such as tailings storage, and infrastructure for transport, power, and water supply, ensuring efficient and sustainable production.

The most recent underground potash mine in Canada with a reported capital estimate is Jansen Stage 1. The conventional underground project, designed for 4.35 Mtpa production, was estimated to cost CA$5.513 billion in 2022. The table below presents the values reported by BHP along with normalized percentages.

Notably, the underground mine features one service shaft and one production shaft, both fully lined to ensure stability and operational efficiency. The processing plant includes facilities for processing and milling, encompassing both wet and dry operations as described earlier, along with tailings processing systems. Non-process infrastructure comprises essential components such as a tailings management area, administration buildings, warehousing, workshops, utilities, and rail connections. The normalized cost distribution highlights mining and processing as two equally significant components in the economics of underground potash project.

Potash and Climate Change Adaptation: A Curious Relationship

Potash serves as a major pathway for climate adaptation in the agriculture industry, particularly in enhancing drought resistance in crops (17, 18).

At the same time, Saskatchewan experiences some of the widest swings in both the temperature and precipitation in Canada (19), posing dire risks to Canadian potash industry.

This causal dependence should highlight the importance of climate change adaptation of the potash industry for its principle stakeholders, way above its resource wealth.

As a key source of potassium, potash plays a critical role in regulating water uptake and retention in plants, enabling them to maintain hydration even under arid conditions. These benefits make potash indispensable for farming in regions prone to erratic rainfall patterns, where the impacts of climate change are intensifying.

Furthermore, potassium aids in detoxifying reactive oxygen species generated under water stress, safeguarding photosynthesis and ensuring continued crop productivity. This dual role of conserving water and sustaining metabolic functions underscores potassium's pivotal role in developing climate-resilient agriculture systems, making potash an indispensable resource for global food security in a warming world.

The potash mining industry in Saskatchewan faces significant risks from climate change that directly challenge its operations and sustainability.

One of the most pressing issues is the increasing frequency of excessive moisture events and flooding. Such occurrences result in higher volumes of brine for disposal, escalating operational costs and creating logistical challenges for storage and treatment. This amplifies the necessity for robust infrastructure investments, including enhanced flood bypass systems and deep-injection wells, to mitigate operational disruptions and environmental risks.

Water scarcity and prolonged droughts is another major climate risk. Water is integral to potash extraction and processing, with both conventional and solution mining methods relying heavily on its availability. Climate-induced reductions in water quantity and quality could compromise operations and strain existing water supply systems. Mining companies have begun implementing water reuse and recycling initiatives, optimizing their consumption patterns, and exploring innovative sourcing options such as utilizing treated wastewater (20).

Bracing against long-term climate projections possibilities require a need for integrated adaptation strategies, regulatory support, industry collaboration, and scientific innovation to sustain the sector amid changing climatic conditions.

This underscores the broader challenge of aligning an old yet fast growing industry.

Question then Becomes

Our road trip through Canada’s potash heartland was a rewarding journey, offering firsthand insight into an industry that sustains global agriculture.

We saw first hand, the mines, the infrastructure networks, and landscapes that together deepened our appreciation for Canada’s vital role in feeding the world. It also gave us interesting questions to think about.

Canada’s potash industry, despite its economic importance, often lingers in the shadow of resource giants like oil and energy. This relative obscurity diminishes its potential significance as a major driver of development, both directly within Canada and indirectly across the world, as well as its role in a climate change adaptation pathway for the agriculture industry.

How can the Potash industry better present its role in global food security and secure its rightful place alongside Canada’s other flagship resource sectors? What steps, from strategic marketing to stronger policy support, could elevate its visibility and impact?

Potash offers an important opportunity for the mining industry to reshape its reputation by highlighting its contributions to alleviating hunger and promoting sustainable agriculture. How can the Canadian mining industry better embrace this narrative?

Notes

Refer to this article as below:

• Zangeneh, P., Hassanpour R., F. (2025), “Canada’s Potash Legacy: A Road Trip Through 40% of the World’s Food Fertilizer Production,” EPM Research Letters.

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References

1. Al Rawashdeh, R. World peak potash: An analytical study. Resource. Policy 69, 101834 (2020).

2. Al Rawashdeh, R. & Maxwell, P. Analysing the world potash industry. Resource. Policy 41, 143–151 (2014).

3. Word Bank Group. https://datacatalog.worldbank.org/.

4. Broughton, P. L. Economic geology of southern Saskatchewan potash mines. Ore Geol. Rev. 113, 103117 (2019).

5. Fertilizer Canada - Growing Stronger. Fertilizer Canada, https://fertilizercanada.ca/.

6. Potash. https://www.thecanadianencyclopedia.ca/en/article/potash.

7. CMM. A Great Future in Potash. Canadian Mining Magazine https://canadianminingmagazine.com/a-great-future-in-potash/ (2024).

8. Saskatchewan Potash History – by John Burton (The Encyclopedia of Saskatchewan). Republic of Mining https://republicofmining.com/2014/03/18/saskatchewan-potash-history-by-john-burton-the-encyclopedia-of Saskatchewan-unknown-date/ (2014).

9. Saskatchewan Research Council. https://www.src.sk.ca/blog/helping-industry-produce-premium-potash-support-global-agricultural-community.

10. Saskatchewan potash nourishing the earth. http://saskmining.ca/ckfinder/userfiles/files/SMA-Potash-Infographic-2022-Stats-WEB.pdf

11. Saskatchewan potash vital for world food, https://www.resourceworks.com/saskatchewan-potash-vital.

12. Scientific Investigations Report. https://portergeo.com.au/database/mineinfo.asp?mineid=mn1801 (2023).

13. Government of Saskatchewan, https://geohub.saskatchewan.ca/datasets/saskatchewan::mine-locations/

14. Canada, N. R. Potash facts. https://natural-resources.canada.ca/minerals-mining/mining-data-statistics-and-analysis/minerals-metals-facts/potash-facts/20521 (2018).

15. Canpotex. https://www.canpotex.com/news/canpotex-invests-its-world-class-supply-chain-acquiring-1300-railcars-national-steel-car.

16. BHP, https://www.bhp.com/what-we-do/global-locations/canada/jansen

17. https://www.icl-group.com/blog/the-role-of-potash-in-sustainable-agriculture/

18. Sardans, J. and Peñuelas, J. (2015), Potassium stoichiometry and global change. Global Ecology and Biogeography, 24: 261-275. https://doi.org/10.1111/geb.12259

19. Haave, D. (2012). WUQWATR Climate Extremes Preparedness Workshop Final Report. Wascana Upper Qu’Appelle Watershed Association Taking Responsibility (WUQWATR)

20. Pittman, J., Ford, J. & Pearce, T., 2014. Adaptation to climate change and potash mining in Saskatchewan, Canadian Electronic Library. Ottawa, Ontario. Retrieved from https://coilink.org/20.500.12592/x10208 on 15 Jan 2025. COI: 20.500.12592/x10208.

Strategic thinking, as Dixit and Nalebuff argue, extends far beyond boardrooms, political campaigns, or military battlefields. It is present in nearly every decision we make, shaping outcomes in interactions both monumental and mundane.

This review, the first of two-part series, distills the book’s key insights and delves into their application to the unique challenges faced by project managers. From navigating complex decisions to fostering collaboration and achieving strategic objectives.

To illustrate the book’s principles, I examine their application through the perspective of a project manager dealing with an urban light rail megaproject. I hope this context helps in uncovering parallels that demonstrate how game theory can inform daily decision-making in project management.

While this review seeks to introduce strategy and game theory to project management professionals with a passion for learning, its ultimate goal is to inspire you to engage with the book itself. The Art of Strategy is not just an insightful read for professionals—it’s one of the most important books you can read to enhance your understanding of strategy in project management.

Strategy and Game Theory

In the context of game theory, a game is any situation where the outcome for each participant depends on the choices made by all involved.

Unlike solitary decision-making, games are inherently interactive, involving players whose actions influence and are influenced by one another. From a simple pricing competition between businesses to the negotiation of a project timeline, these games are structured by rules, objectives, and the players’ strategies. Each player seeks to achieve the best possible outcome, often requiring careful anticipation of others’ moves.

Strategic situations arise when these interactions compel players to think beyond their immediate choices and consider the motivations, constraints, and potential actions of others. For project managers, strategic situations are embedded not only in major project shaping moments, but in everyday decisions—balancing contractor incentives, resolving stakeholder conflicts, or allocating resources efficiently.

The hallmark of a strategic situation is interdependence: your success hinges not only on what you do but also on how others react. Therefore, you need to, proverbially, put yourself in others’ shoes.

Sequential Games

Many games unfold step by step, with players taking turns to make their moves—chess being a quintessential example. In sequential games, success often hinges on the ability to anticipate the future and plan accordingly, that is, backward reasoning.

Backward reasoning involves starting at the desired outcome of the game and working backward to determine the best decisions at each step. It requires players to visualize the game tree, a branching diagram of all possible moves, and strategically prune options that lead to suboptimal outcomes.

Games fully solvable by backward reasoning are characterized by a clear sequence of moves, where each player's choices and outcomes are transparent, and there is no uncertainty about the rules or objectives.

In project management contexts, backward reasoning proves invaluable in structured scenarios like project planning or contract negotiations, where understanding the chain of interdependent decisions can guide a project manager to the most favorable outcome.

Resourceful Project Manager

Situations where backward reasoning is essential are abundant when managing a project. Often, the sequence involves only a few moves, making the optimal strategy relatively clear. However, strategic decisions may as well involve long chains of moves. You may start by describing the final desired outcome and branch back to your initiating decisions. From scheduling critical tasks to negotiating contracts, understanding how early decisions influence your later choices and outcomes is a daily necessity for a project manager.

Consider a simplified but realistic example: You are leading one of two excavation contractors on a construction site for an underground station of the described LRT Project. The ongoing arrangement requires both contractors to take turns excavating over 11 weeks, with the rules stating that each turn allows a choice of 1 or 2 weeks of work. Both companies believe that the control over the final week (Week 11) is particularly valuable as it offers leverage for the next project phase. Through a draw you are selected to pick the first working schedule. Which one would you take 1 or 2 weeks, knowing that the sequence of choices determines who ultimately controls the last week? (Look for the answer in the comments section)

Situations like this may seem far-fetched, but arise regularly when aiming to allocate your strongest resources across different tasks. As the proverb goes, “ Being resourceful is not just having resources.”

Simultaneous Games

Not all games are sequential; simultaneous-move games involve players making decisions simultaneously at each step, without knowing what others have chosen. Here all players act at the same time, making their choices based on anticipated strategies of others.

Unlike sequential games, where one player’s move informs the next, simultaneous-move games require participants to think ahead, considering how their actions interact with others' decisions. Game trees and branches will still help to represent the game, but not to represent a sequence of moves.

The structure of simultaneous games often reveals a tension between individual and collective incentives. In a pricing war between two companies, for example, each firm may lower its prices to gain a competitive edge, but if both do so, profits shrink for both parties.

Central to these games is the concept of equilibrium, a state where no player can unilaterally improve their outcome by changing their moves (i.e., their strategy). In price competition games, for instance, equilibrium is reached when each player selects the best response to the others’ expected actions, leading to a stable, self-reinforcing outcome. While there are many different types of simultaneous-move games, the book highlights two main types of games, Prisoners’ Dilemma, and Coordination Games for very good reasons.

Prisoners’ Dilemma

In Prisoners’ Dilemma all participants (game players) have incentives to do something that collectively leads to a suboptimal outcome for all.

In the classic description, two prisoners (John and Ali) must independently decide whether to cooperate with each other and remain silent, or to defect and confess against each other. If both cooperate (and remain silent), they achieve the best collective outcome—one year sentence each—that reflects mutual benefit. However, if one defects (confess) while the other cooperates, the defector gains a higher payoff—of walking free— at the expense of the cooperator, who suffers the worst possible outcome—of 3 years sentence. When both defect, they each receive a lower payoff than if they had cooperated—each two years sentence. The main point here is that regardless of what the other do, they have the best incentive, or the dominant strategy, to defect.

The payoffs for the two-person simultaneous game can be represented in a matrix form as above were the first and second number in each cell corresponds to the row and columns players respectively.

Similar scenarios arise in many real-world situations where individual incentives conflict with collective well-being. In joint projects, as mundane as cleaning a communal kitchen or monumental as joint venture megaproject, each person or party benefits from cooperation, but the temptation to let others do the work leads to defection and neglect.

In price competitions between firms, cutting prices may attract more customers individually, but if all competitors undercut each other, profits dwindle across the board.

Similarly, when exploiting common resources—like regulating carbon emissions to combat climate change or maintaining sustainable hunting practices—the incentive to overconsume benefits individuals in the short term but harms the collective in the long run.

Achieving cooperation often involves addressing the incentives to defect, especially in multi-round games. Enforcing penalties for defection (e.g., regulatory fines) or rewards for compliance (e.g., performance bonuses) help align individual incentives with collective goals. Clearly defined rules, effective monitoring, and penalties foster adherence and mitigate overexploitation. Social norms and reputation concerns also play a crucial role, as individuals are more likely to cooperate when their actions are visible and relationships are ongoing, i.e., repetition.

Coordination Games

Another important type of game is coordination games, which highlight situations where players achieve the greatest benefit by aligning their actions with each other. Unlike the Prisoners’ Dilemma, coordination games may lack a single dominant strategy for each party, requiring participants to anticipate and align with others’ choices.

Consider that Ali and John hope to work together on a joint project. If they both go to office, they will achieve the best outcome, however, if one decides to stay home the other will be worst off, having to stay in traffic and commute to office. If they both work from home, they will get work done but perhaps to a lower quality that may require rework. The matrix below represents the payoffs of their coordination game.

In coordination games often multiple equilibria exist, deciding which equilibrium to pursue becomes a key challenge—going to office or work from home. In such cases, players must rely on mechanisms to converge on a mutually beneficial equilibrium.

This convergence often happens on outcomes with cultural, historical, or contextual significance, helping players coordinate in the absence of explicit communication. These outcomes are called focal points. For example, when strangers must meet in a large city, iconic landmarks often emerge as natural focal points. Similarly, focal points often manifest as shared standards or norms, such as using universally recognized metrics, adhering to fiscal calendars, or coordinating around well-understood conventions. These focal points ensure alignment and guide players toward outcomes that—hopefully—maximize collective benefit.

There will be a significant scope for leadership in coordination games. Leaders can guide players toward a mutually beneficial equilibrium within coordination games.

Changing the Game, a.k.a, Kobayashi Maru

Much of your role as a project manager and leader involves reshaping games and adjusting payoffs. This includes identifying versions of Prisoners’ Dilemmas where individuals’ incentives will lead to collective suboptimal outcomes and try to achieve coordination, or all together, change the game into versions of coordination games. Much of the discussion around different contracting strategies within projects can be viewed through the lens of designing mechanisms to change Prisoners’ Dilemmas into coordination games at different scales.

Lets go back to the two excavation contractors on the underground LRT station job. Joint projects can be versions of Prisoners’ dilemma as if both contractors cooperate, the project progresses smoothly, but each incurs minor additional costs. If one defects while the other cooperates, the defector benefits by completing their tasks at the cost of cooperator. If both defect, bottlenecks occur, leading to major project delays and penalties for both.

Let us assume you are the project manager from the owner to oversee their cooperation. you have two main strategic tools. One is achieving cooperation through increase in monitoring, also you may appoint a fulltime compliance officer at the site, or increase penalties for defection. You can also emphasis on the repetitive nature of this project and that their reputation will lead to future scopes for both contractors.

Moreover, you can design mechanisms to transform the game, this may include Shared Milestone Bonuses, or Resource-Sharing Agreements, where equipment downtime for one contractor is leveraged by the other.

Early in the conceptual stages of large engineering projects, there is a critical step often referred to as project shaping or anchoring. This process involves thoroughly examining the incentives and motivations of all stakeholders and strategically aligning them to ensure the project’s overall success. In modern projects, where Environmental, Social, and Governance (ESG) considerations play an increasingly significant role, the importance of effective project shaping cannot be overstated. Proper project shaping requires a practical understanding of how Prisoner’s Dilemma arise and, more importantly, how to transform such scenarios to align the interests of all parties involved.

Mixed Strategies

On July 17th, 1994, Italy faced Brazil in the finals of the FIFA World Cup. Roberto Baggio stepped up to take Italy’s final penalty, a moment that would become one of the most emotional and memorable in the sport’s history.

Not all games have clear dominant/dominated or equilibria strategies like the Prisoner’s Dilemma or coordination games. In many games, players’ strategies are in direct opposition, creating a zero-sum game, where one player’s gain is achieved at the expense of the other’s loss.

A classic example of this dynamic is Rock-Paper-Scissors, a game where each move has an equal chance to win, lose, or draw against another, as illustrated in the following game matrix.

In this setup, no single move consistently dominates. The optimal choice depends entirely on the opponent’s action. Winning in Rock-Paper-Scissors requires adopting a mixed strategy—randomly selecting moves in certain proportions and prevent opponents from predicting your choices. This unpredictability transforms the game into a equilibrium of proportions of strategies where neither player gains an advantage through strategic adjustments.

The same principles apply to penalty shootouts in football (soccer). Both the kicker and the goalkeeper must randomize their actions to maximize success or minimize loss. Setting aside errors and the intensity of the moment, the kicker ultimately decides whether to aim left or right, while the goalkeeper must choose which side to defend. If either consistently favors one side, their opponent can exploit the predictable pattern.

The concept of randomization is the hallmark of strategies in zero-sum games. This was first formulated by Thomas Schelling in his seminal work The Strategy of Conflict. Schelling demonstrated that randomization keeps opponents uncertain, maximizing strategic advantage. Equally, in non-zero-sum games—where mutual gains are possible—Schelling emphasized the importance of communication and clearly defining boundaries to foster cooperation and avoid conflict.

Randomization can be utilized effectively for compliance and enforcement to ensure adherence while reducing monitoring costs. By making detection unpredictable, organizations create a deterrent effect even when enforcement is infrequent. Tax audits, drug testing programs, and parking meter violations, are all instances of zero-sum games where fines are designed high enough to create an equilibrium where the low probability of being caught offset the defectors’ gain. Similarly, spot safety checks on construction sites and surprise quality inspections in manufacturing serve as effective mechanisms to maintain compliance without exhaustive oversight.

Lastly, mixing strategies can help in various coordination games too, particularly when incentives are not equally aligned. Recall the previous game of going to office versus working from home. If John and Ali have slight preferences, then a randomization device can help to coordinate players on repetitive plays, e.g., come heads, we go to office, etc.

Promotions are Zero-sum

The zero-sum nature of randomization and mixed strategies may seem far removed from the leadership role of a project manager, but not all zero-sum games involve fierce competition where someone wins at the expense of others.

Consider the scenario where, as a project manager, you aim to establish a reward system to encourage some organizational wide behavior. While certain team members may consistently perform at a high level, introducing an element of randomization in awarding such recognition ensures inclusivity and fairness.

Similarly, a project manager often faces tough, critical conversations with stakeholders, contractors, or team members. Adopting a randomized strategy in how you approach these situations—for example, alternating between direct, firm stances and more collaborative, flexible approaches—introduces an element of unpredictability. This unpredictability ensures that stakeholders or team members cannot exploit a fixed strategy to manipulate outcomes in their favor.

Strategic Moves

Having covered the basics of game theory and strategy, Dixit and Nalebuff, move to the actual art beyond strategy in the second part of their book.

Strategic moves involve actively shaping the game itself to influence outcomes and the behavior of others, and, can be categorized into three core types: commitments, threats, and promises.

Strategic moves consist of two critical components: the move itself and the course of action that establishes its credibility. The effectiveness of all these strategic moves hinges on these both, the move and the credibility.

Commitments are unconditional actions that restrict one’s own choices to compel a response. Commitments, in particular, are powerful tools that can bind opponents to a specific course of action. Yet, undermining an opponent’s commitment strategically can pave the way for de-escalation and negotiation.

Hernán Cortés famously "burned his ships," during the 1519 conquest of Aztec Empire. While the historical accuracy of this account is debated, the event is cited as a metaphor for total commitment, leaving no option to retreat. The other side of the commitment idea, as Sun Tzu alluded, is that undermining the commitment of an opposing army often requires building them—maybe not literal, but—a golden bridge.

In contrast, threats and promises are conditional moves with distinct dynamics. Threats deter undesirable actions by imposing negative consequences and are inherently open-ended, often lacking a specific expiration date, and is protecting the status quo. Promises, on the other hand, incentivize desired actions by offering rewards, aiming to disrupt and improve the status quo.

While the action is the visible—such as making a threat, promise, or commitment—it is the underlying credibility that determines its effectiveness. For example, a project manager might threaten to impose penalties for delayed work, but the threat only influences behavior if the team believes it will be enforced. Without credible follow-through, even the boldest strategic move risks being dismissed as a bluff. Clear communication about stakes and consequences can influence behaviour, but vague or excessive threats or promises can backfire, and undermining credibility.

No Shortcuts to Safety

An ever present task of a project manager within the construction industry is to improve the safety performance at construction job sites—a critical challenge in ensuring not only compliance but also fostering a culture of vigilance and responsibility. To achieve this, you have three powerful tools/strategic moves at your disposal: threats, promises, and importantly, commitment.

Threats serve as deterrents to prevent unsafe practices. For instance, failure to wear mandatory PPE (Personal Protective Equipment) could result in immediate removal from the site. These measures are not punitive for their own sake but are designed to signal zero tolerance for neglecting safety protocols.

At the same time, promises should be used to incentivize positive behaviors. For example, implementing a reward system for teams that submit well written and comprehensive incident reports can encourage proactive safety practices. Rewards could range from monetary bonuses to recognition during management meetings.

However, the cornerstone of this strategy is commitment. If site supervisors and project managers are seen strictly adhering to PPE requirements and actively participating in safety meetings, it reinforces the seriousness of these initiatives. Finally, to signal credibility, you may hire a third-party auditor to conduct random audits and transparently report the results regardless of job site hierarchies.

Question Then Becomes

This marks the end of the first section of this book review. In the second part, together with the authors, we will delve into more in-depth applications of the concepts introduced here, such as manipulating and interpreting information, balancing cooperation and competition, and structuring incentives.

As evolved beings, we possess a natural, intuitive sense of strategy—one honed through countless interactions and conflicts over millennia. From navigating social dynamics to resolving disputes, our instincts often mirror the principles of game theory: anticipating others’ actions, weighing risks and rewards, and finding equilibrium between collaboration and rivalry.

Many of the ideas explored in this book, and highlighted in this review, may resonate as reflections of strategies we already employ. At the same time, they offer a clearer framework to guide our decisions and actions moving forward.

The question then becomes: Has any part of this review prompted you to rethink your approach to strategic situations or the utility of different actions? Or has it all aligned with your previous understanding of your strategies and decisions? Let us know in the comments.

Notes

This book review is a reading material for the “Strategy in Project Management: Game Theoretic Approach,” course at the University of Calgary, Department of Civil Engineering.

Refer to this article as below:

• Zangeneh, P. (2025), “The Art of Strategy - Part 1: A Review of Dixit and Nalebuff’s Classic Within the Context of Project Management”, EPM Research Letters.

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The concept of "strategy" in project management evokes notions of competition, but this raises deeper questions: Who, exactly, is the competition against? Is it perhaps the challenges imposed by the project itself? Or is it winning against external forces? And crucially, what does "winning" look like in such a context? For project managers, success often lies not in defeating an opponent but in aligning diverse interests to achieve shared objectives.

Strategy in project management is about navigating this intricate landscape, and it encompasses daily decisions that balances the nuanced interplay of team collaborations, competing priorities, external stakeholder pressures, and the broader forces shaping the project's trajectory. It is here, in the dynamic and often unpredictable nature of project environments, that strategic thinking becomes indispensable.

Strategy for Project Management Practitioner

Game theory, a mathematical framework for analyzing strategic interactions, provides a lens to decompose and examine intricate competitions or collaborations among parties with divergent goals. It moves beyond the "what" and "how" of project execution to, putting yourself in others’ shoes, and addressing the "why" behind their choices.

Why would a contractor overbid for a project? Why might a client resist seemingly beneficial changes? Why do teams sometimes act in ways counter to their collective interest?

Unpacking the fundamental "why" reveals profound insights.

Project Complexity: Complex projects, by nature, involve myriad interconnected decisions and interactions. Here, game theory provides a structured framework to analyze these intricate patterns. It enables project managers to identify systemic issues, anticipate cascading effects, and adapt strategies in real-time. This perspective is particularly invaluable for managing risks and seizing opportunities in dynamic project environments. The true power of game theory in project management lies in preparation and creating adaptive frameworks that can flex and respond to emerging complexities with intelligence and strategic foresight.

Motivations and Mechanisms: Game theory offers a powerful lens for uncovering the underlying motivations driving stakeholder behavior. By understanding the incentives, constraints, and perspectives of each party, project managers can design strategies and mechanisms that align conflicting interests or address hidden concerns. This deeper understanding fosters transparency and trust among stakeholders, creating a collaborative foundation essential for project success.

Embracing Uncertainty: Projects unfold in environments of incomplete information and uncertainty. Game theory equips project managers with the tools to model these scenarios, anticipate potential moves, and evaluate the implications of different strategies. By transforming uncertainty into an opportunity for creative problem-solving, project managers gain a critical edge in guiding outcomes effectively. Moreover, this approach enhances their ability to interpret project outcomes and policies, even amidst inherent ambiguities.

Long-term Results: Strategic decision-making requires a nuanced approach that transcends immediate tactical considerations. Game theory encourages project managers to think beyond short-term gains, considering the broader impact of their decisions on relationships, reputations, and future collaborations. This holistic perspective recognizes that each decision—whether a significant milestone or a routine adjustment—reverberates through the complex ecosystem of organizational interactions, potentially shaping future engagement patterns.

Everyday Decisions: The importance of strategic thinking for project managers is not confined to high-stakes, pivotal moments. Instead, it lies in the continuous application of strategic principles to day-to-day decisions, which collectively define the trajectory of a project. From allocating resources to mediating minor stakeholder disagreements, these seemingly small choices accumulate, influencing long-term outcomes. By incorporating game-theoretic insights into everyday decision-making, project managers can ensure that short-term actions align with the overarching goals of the project, paving the way for sustainable success.

Strategy for Project Management Researcher

Despite its general absence in project management curricula, game theory has been extensively utilized in project management research [1]. Its application spans various domains, including project governance, risk management, bidding strategies, and resource allocation. By modeling strategic interactions among stakeholders, game theory offers a structured approach to resolving conflicts, optimizing decisions, and fostering cooperation.

Game theory has proven invaluable in addressing project governance issues, such as information asymmetry in contracts, cooperation among subcontractors, and renegotiation of project terms. It enables the design of incentive mechanisms that align stakeholder objectives and ensure project continuity. For instance, studies have shown how game-theoretic models can optimize partner selection and incentive structures in public-private partnerships, ultimately enhancing project outcomes.

Furthermore, the integration of game theory in risk management has allowed researchers to model uncertainty in project durations, costs, and resource availability. By applying probabilistic and robust models, these studies help project managers anticipate potential disruptions and devise strategies to mitigate them. This analytical approach is particularly critical in large-scale infrastructure projects, where the stakes are high, and uncertainties abound.

Finally, game theory has been instrumental in improving bidding and contracting processes. By analyzing competitive behaviors and strategic decision-making in tendering, researchers have developed models to foster fair competition and efficient allocation of resources. These contributions not only enhance the theoretical foundations of project management but also offer practical solutions to real-world challenges.

The Gap in Engineering Curricula

Formal project management education focuses on statistical tools and methodological frameworks, rarely acknowledging the reactionary forces and events that emerge in response to decisions, policies, and actions. The strategic dynamics of aligning teams and stakeholders, anticipating reactions, and navigating competing priorities frequently remain underexplored.

This limitation is particularly pronounced among practitioners from engineering backgrounds, whose educational trajectory typically emphasizes technical proficiency. Engineering project managers, often seasoned engineers who have ascended to navigating major projects akin to massive ships through turbulent and unpredictable waters. They are largely left to develop innovative strategies and methodologies to handle the intricate dynamics of strategic situations through experience.

Despite game theory's immense potential and extensive use in project management research, it has yet to find its rightful place in the standard project management toolkit. Here lies the challenge: how can we close this gap and provide project managers with the strategic acumen they need to thrive?

My Gateway

My fascination with game theory began as I delved into the complexities of modeling projects using Bayesian networks and simulations during the early stages of my Ph.D. Here is a tweet I sent out almost eight years ago (as of the day I’m writing this article).

At the time, I was drawn by the idea that projects, much like intricate systems, are shaped by the dynamic interplay of decisions, stakeholders, and uncertain events. My initial explorations into simulation highlighted a critical gap: while tools like Bayesian networks offered a probabilistic view of risks, they often lacked a strategic lens to account for the motives and interactions of the players involved. This realization led me to game theory—a framework that not only models’ strategic behavior but also provides clarity on how decisions ripple across interconnected systems.

Over the years, as I grappled with risk modeling challenges in various project management scenarios, my conviction in game theory's relevance deepened. Whether it was resolving conflicts over resource allocation, or designing incentives for collaboration, game theory appears to be one of the most useful frameworks for interpretation of projects. It provided the structure needed to unravel complex interactions and offered strategies for navigating uncertainty.

Outline: A Formal Game Theory Course for Project Managers

At the Civil Engineering Department of the University of Calgary, we are proud to introduce “Strategy in Project Management: Game Theoretic Approach”—a pioneering course designed to integrate game theory into the fabric of project management research and education. This initiative represents a leap forward, equipping students and professionals with an innovative blend of modeling expertise and strategic acumen. The course follows a structured progression, guiding participants from foundational principles to advanced strategic insights, ensuring both conceptual depth and practical relevance.

Part One: Foundation

The first segment lays the groundwork by immersing students in the core principles of game theory. Through a systematic exploration of key concepts—such as dominant strategies, equilibrium states, and strategic interactions—participants build a robust conceptual framework. This phase transcends abstract mathematical theory, transforming it into a practical lens for analyzing complex stakeholder dynamics. The result is a strong foundation for decision-making, preparing students to address the intricate challenges inherent in project management. Part one includes the following three lectures:

1. Introduction to Game Theory

2. Dominant Strategies, Best Response, and Equilibriums

3. Strategic Interactions among Stakeholders

Part Two: Application

The second part bridges theory and practice, focusing on the tangible applications of game theory in real-world project environments. Participants delve into designing collaborative contracts, optimizing resource allocation, and creating mechanisms that align incentives across diverse stakeholders. This segment emphasizes the transition from understanding strategic dynamics to actively crafting solutions that enhance project performance. By embedding game-theoretic insights into organizational strategies, students learn to drive engagement, improve collaboration, and elevate project outcomes. Part two includes the following four lectures:

4. Strategizing Against Project Complexity

5. Collaboration and Collaborative Contracts

6. Mechanism Design and Incentives

7. Resource Allocation

Part Three: Advanced Strategies

The final module propels students into the realm of advanced strategic thinking. Here, the focus shifts to navigating complex scenarios involving incomplete information, sophisticated negotiation techniques, and strategic reasoning under uncertainty. Drawing upon advanced game-theoretic models, including Bayesian games and auction theory, participants acquire the analytical tools needed to manage conflicts, foster cooperation, and make informed decisions in high-stakes, multi-stakeholder environments. Part three includes the following five lectures:

8. Risk and Opportunities Management

9. Bayesian Games and Incomplete Information

10. Auctions and Bargaining

11. Conflict and Negotiation

12. Bonus: Board Game Theory

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If you are a graduate student or practitioner interested in this course, there might be a possibility for you to join our class. Please fill out this form to join the waitlist and we will be in touch.

Question then Becomes

This course aims to be a comprehensive journey into the strategic core of project management. The use of the term strategy in project management naturally poses the question: who are we competing against? Is the competition external—against market forces, rival organizations, or regulatory constraints? Or is it internal—against misaligned priorities, resource limitations, or even the unpredictable nature of the project itself?

Perhaps the most thought-provoking perspective is that the competition is against our own capacity to anticipate, adapt, and align interests. Who do you believe we are truly competing against, and how does this perspective shape your approach to managing projects?

Have you had the opportunity to take a game theory course during your post-secondary education? If so, do you see its connection to project management, particularly in navigating the complexities of stakeholder dynamics, risk management, and decision-making under uncertainty?

For those of us engineers who most likely haven’t studied game theory, would you advise young engineers to explore an elective on strategy and game theory?

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References

1. Narbaev, T., Hazır, Ö., & Agi, M. (2022). A review of the use of game theory in project management. Journal of management in engineering, 38(6), 03122002.
   

We are honored to have Dr. George Jergeas, as our instructor for this course. Dr. Jergeas is a Professor Emeritus at the University of Calgary's Schulich School of Engineering and serves as Chair of iProjects Inc. With over 45 years of industry and academic experience, he holds an M.Sc. and Ph.D. in Construction Management from Loughborough University, UK.

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Contractual claims and disputes are prevalent challenges that can lead to adversarial and costly outcomes for all parties involved. This course aims to equip participants with the knowledge and tools to effectively recognize, prepare, and resolve such claims, thereby enhancing project efficiency and reducing potential conflicts.

Snapshots of Construction Claims Magnitude

Major projects claims and disputes have become increasingly significant, reflecting the complex and often unpredictable nature of today’s projects in the construction industry. According to an industry report, the average value of disputes in North America reached an all-time high of $42.8 million in 2022, rising sharply from $30.1 million in 2021 and more than doubling from $18.8 million in 2019. This upward trend highlights the financial stakes involved in these disputes, driven largely by inflation, supply chain challenges, and the growing scale and intricacy of modern construction projects (1).

Despite the rising costs of disputes, the average time taken to resolve them has decreased somewhat, settling at 3.2 months in 2022 compared to 16.7 months in 2021. Over the past decade, dispute resolution has averaged around 15.3 months, indicating a persistent challenge in reaching timely settlements. The high cost of unresolved claims often impacts cash flow and project timelines, underscoring the need for effective strategies to handle disputes swiftly.  The transportation sector—encompassing highways, bridges, mass transit systems, airports, and ports—was identified as the most dispute-prone area in 2022, continuing its trend from previous years. Projects in this sector are particularly vulnerable due to their complexity, regulatory demands, and frequent design alterations, all of which increase the likelihood of costly and prolonged claims (1).

Prof. George Jergeas

We are honored to have Dr. George Jergeas, as our instructor for this course. Dr. Jergeas is a Professor Emeritus at the University of Calgary's Schulich School of Engineering and serves as Chair of iProjects Inc. With over 45 years of industry and academic experience, he holds an M.Sc. and Ph.D. in Construction Management from Loughborough University, UK.

Throughout his distinguished career, Dr. Jergeas has focused on improving the efficiency and competency of teams delivering major engineering and construction projects. His expertise encompasses project governance, risk management, contract strategies, and dispute resolution. Notably, his partnering methodology has been successfully implemented on over 180 projects across North America.

This course offers an excellent opportunity for professional development. Participants will receive Professional Development Hours (PDH) certificates upon completion, contributing to their continuous learning and career advancement.

We encourage all professionals in the engineering and construction sectors to attend this insightful course and to share this opportunity with colleagues who may benefit from Dr. Jergeas's extensive knowledge and experience.

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References

1. Arcadis. Construction Disputes Reports 2023: Responding to challenges and adapting for project success. Accessed Online.

Aside from projects access to resources and well developed plans, successful projects are the ones driven by leaders and teams that foster and share a robust and adaptable culture. Mindset theory allows us to better describe these phenomena.

In this article we review the following topics,

1. Mindset Theory

2. Cultures of Growth

3. Mindset Triggers

4. Case for Diversity

5. Measuring Organizational Mindsets

6. Mindset Theory In Project Management

7. Criticisms of Mindset Theory

In doing so we resort to several books,

1. Mary Murphy’s “Cultures of Growth: How the New Science of Mindset Can Transform Individuals, Teams, and Organizations” (1).

2. Carol Dweck’s “Mindset: The New Psychology of Success” (2).

3. Matthew Syed’s “Rebel Ideas: The Power of Diverse Thinking” (3).

Eventually, we take a brief look at two articles by Peter Gollwitzer (4,5) to contrast the ideas on deliberative and implemental mindsets, as related to project management. We also review major criticisms of mindset theory (6).

Mindset theory was developed in the 1990s by psychologists such as Carol Dweck from Stanford University, and, Peter Gollwitzer from the New York University. It was popularized by Dweck’s book in 2007. Earlier this year, Mary Murphy, a Professor of Psychology at Indiana University and former PhD student of Dweck, published an interesting book on the implications of mindset theory in organizations. It is to this occasion that we are spending this month’s EPM Research Article to walk through mindset theory.

As it is proposed by mindset theory, mindsets shape our interpretation of information and guide our learning from experiences. A “fixed” mindset can restrict achievement as it creates a need to constantly prove oneself, turning every situation into a confirmation of inherent intelligence or talent. Meanwhile, a “growth” mindset cultivates a passion for learning and an openness to experiences that stretch one's abilities. And, that organizational cultures have great implication on individuals operating mindsets.

Mindset Theory

Mindset theory, largely popularized by Carol Dweck, a psychologist at Stanford University, delves into how our beliefs about our own abilities affect our behavior and success. According to Dweck, individuals either subscribe to a "fixed" mindset, believing their qualities such as intelligence are static, or a "growth" mindset, believing that their abilities can be developed through effort and learning. This distinction influences not only personal achievements but also impacts organizational practices and educational approaches. Growth mindset proponents argue that fostering a growth-oriented culture can enhance learning, resilience, and overall productivity​.

The early building blocks of what Dweck calls mindsets, was outlined in her 1999 essay where she elaborated on “Self-Theories” as the underlying beliefs individuals hold about the nature of their attributes, such as intelligence and personality (7). Self-theories encompass the broader framework of how these beliefs influence various aspects of a person's life, including motivation, response to challenges, and personal development. However, "mindset" specifically relates to a subset of self-theories, focusing on the beliefs about whether abilities are fixed or malleable.

Mindset, as Dweck articulates in her book, explains how we perceive our capabilities and intelligence, and it forms the underlying beliefs that individuals hold about their abilities and potential. These beliefs are not just idle thoughts; they actively shape actions, responses to challenges, and the overall trajectory of one's life.

Individuals with a fixed mindset perceive their qualities, such as intelligence and talent, as static traits. Consequently, they are primarily focused on validating these inherent abilities rather than developing them. This perspective can lead to a reluctance to embrace challenges, a tendency to avoid failure, and a resistance to feedback, as these situations are seen as direct assessments of their unchangeable traits. On the other hand, those with a growth mindset believe that their abilities can be developed through dedication and hard work. This view fosters learning, and resilience in the face of obstacles, and a capacity to view criticism as a valuable tool for growth. This mindset empowers individuals to thrive during challenging circumstances and persistently improve.

Cultures of Growth

In 2006, Mary Murphy, then a graduate student, proposed an interesting concept to Carol as a graduate research. Mary was inspired from her experience of her classmates’ PhD defense sessions that the organizational settings have implications on individuals operating mindsets and started her research on how organizations can shape individual mindsets. Her recent book, Cultures of Growth (1), is a review of her research journey.

Murphy’s research went on to establish that regardless of an individual’s personal mindset—be it fixed or growth—their surrounding environment, through its prevailing beliefs and practices, could sway their behavior and approach towards challenges and learning. Therefore, the environments themselves could possess what she termed a "Cultures of Genius" or a "Cultures of Growth," impacting individuals.

Murphy’s research delineated the stark contrasts between these cultures. "Cultures of Genius" are marked by a belief in innate abilities, where success is attributed to inherent intelligence and talent. Such environments often discourage risk-taking and learning from failures because they prioritize individual intrinsic brilliance over developed skills. In contrast, "Cultures of Growth" emphasize the potential for development through effort and learning, fostering an inclusive atmosphere where challenges are seen as opportunities to grow and where setbacks are valuable learning moments.

The empirical studies led by Murphy are fascinating to read and hard to summarize. They underscore the tangible benefits of fostering a growth mindset within organizations and educational settings. Murphy has conducted empirical studies in various settings that show cultivating a growth mindset enhances motivation and commitment, cultivate a more collaborative community, increases innovation, promotes resilience and risk taking, elevates integrity and ethical behavior, and fosters diversity, all while increasing team’s performance and achievements.

Her research also highlights the critical role of leadership in cultivating these mindsets within organizations. Leaders are tasked not just with endorsing a growth mindset but with integrating this perspective into the policies, practices, and ethos of their organizations to view intelligence and ability not as static qualities, but as qualities that can be developed.

Mindset Triggers

An important idea out of Murphy’s book is the role of organizations in promoting fixed or growth mindset. Acknowledging this agency and that everyone can and do operate in either of these mindsets, changes the question from “Are you a person with more of a fixed or growth mindset?” to the question of “When are you in your fixed mindset and when are you in your growth mindset?”.

For an organization and project team leaders the question then becomes “What triggers our team to adopt more fixed- or growth-mindset views and behaviors?  And how can we shape the environment to encourage more of a growth mindset more of the time?”

Murphy strives to answer that in the second part of her book by comparing the totality if her research studies. She recounts that “when chances to succeed are scarce, people tend to be triggered toward their fixed mindset”, however, she generalizes four settings that can trigger teams to change in between mindsets where organizations need to focus their polices to achieve better cultures of growth.

Evaluative Situations

Evaluative situations, such as preparing for a presentation or awaiting feedback on performance, naturally induce anticipation of judgment. In these scenarios, individuals might either enter a "prove-and-perform" mode focusing primarily on others' perceptions or adopt a "learning mode" that emphasizes growth and development.

High-Effort Situations

High-effort situations, like adapting to new team dynamics or learning a new workflow, demand substantial energy and focus. These situations can trigger a fixed mindset, where the fear of failure leads to avoidance of challenging opportunities, or stimulate a growth mindset, inspiring the individual to embrace the challenge as a chance to advance and enhance skills.

Critical Feedback

Receiving critical feedback can be a critical trigger for mindset orientation. Through a fixed mindset perspective, such feedback may feel like an attack on one's character and abilities, leading to avoidance and stunted growth. Conversely, viewing such feedback from a growth mindset encourages learning and development, with individuals seeking out feedback to improve continually.

Success of Others

Observing the success of peers, such as promotions or awards, can either demotivate individuals (if in a fixed mindset) by breeding feelings of inadequacy or inspire them (if in a growth mindset) to learn and adopt successful strategies.

Case for Diversity

Murphy research has shown that “an organization’s mindset culture has significant consequences for diversity, equity, and inclusion”.

The juxtaposition of "Cultures of Genius" and "Cultures of Growth" within organizations reveals important implications for diversity, equity, and inclusion (DEI). Cultures of Genius, with their rigid prototypes, often marginalize those who do not conform to a narrowly defined image of talent. Conversely, Cultures of Growth champion diversity as a catalyst for broadened perspectives and enhanced creativity, viewing varied backgrounds as integral to organizational success and innovation.

This is interestingly put in Matthew Syed's book titled "Rebel Ideas" (3). Matthew underlines the impact of cognitive diversity on handling complex tasks, especially in modern problem-solving contexts. Cognitive diversity refers to the varying ways individuals perceive, process, and respond to information, which when pooled together, can lead to breakthrough innovations and solutions to multifaceted problems.

Syed uses compelling examples from history and contemporary scenarios to show how diverse teams combine unique perspectives to overcome cognitive blind spots that homogenous groups might miss. This collective intelligence not only enriches problem-solving capabilities but also enhances decision-making processes, driving success in dynamic and challenging environments.

Cognitive diversity extends beyond mere demographic differences, diving deep into the diverse cognitive styles and frameworks that individuals bring to a group. This diversity is critical in today's interconnected and rapidly changing world, where the challenges we face require a range of thought processes and innovative approaches.

Matthew advocates for a deliberate strategy in fostering environments where cognitive diversity can thrive. This includes creating inclusive cultures and structures that encourage different ways of thinking and actively mitigating conformity, which can stifle the creative problem-solving necessary for tackling complex issues.

Measuring Organizational Mindsets

A central theme of both Dweck’s and Murphy’s research is devising measures and surveys to measure both personal and organizational mindsets. There are various tools available online that contain a repository of potential questions and measures to gauge individual mindsets (8), some of which only commercially available (9). Let us answer one of the questions Dweck’s proposed in for mindset surveys, although the original form had 6 point Likert scale.

Murphy’s research built around various forms of experiment design and data and surveys to hypothesize and test how mindsets affect different organizational characteristics. For instance, they took companies mission statements and analyze them to establish their leadership mindset and analyze the anonymous ratings of employees through websites such as the Glassdoor (10), or design particular experiments to observe the behavior of students (11).

However, within the references Murphy cited in her book, there is an interesting private report regarding the mindset in entrepreneurship that would be great to be shared with public:

Mary C. Murphy, “Mindsets in Entrepreneurship: Measurement and Validation Results,” report to the G2 Advisory Group and the Kauffman Foundation (April 2020). Note: The work with the Kauffman Foundation was conducted in collaboration with Wendy Torrance and Kathleen Boyle Dalen.

Mindset Theory In Project Management

The literature of applying mindset theory in project management is indeed scarce. The use of mindset theory in project management traces back to the work of Peter Gollwitzer (4,5) in early 1990s.

Peter Gollwitzer's view into mindset is through the lens of what precedes actions. Gollwitzer delineates the journey from setting goals to achieving them through a series of action phases, each characterized by specific cognitive orientations or "mindsets." These mindsets facilitate different stages of goal pursuit: deliberative, implemental, actional, and evaluative.

Note that Dweck’s view and use of the term "mindset" as a subset of self-theories about whether abilities are fixed or malleable is different to Gollwitzer view as phases of action.

Gollwitzer, highlights a critical transition from deciding to act (pre-actional phase) to taking action (actional phase), which is emblematic of the shift from planning to execution in project management. This model underscores the psychological commitment required once a decision to proceed is made—akin to crossing the proverbial Rubicon (hence termed the Rubicon model).

The concepts distinguishes between two pivotal stages of cognitive engagement in project management: planning (deliberative) and execution (implemental). For project managers, this means that once a project plan is set and decided upon, the focus shifts dramatically towards meeting those commitments with precise and goal-directed actions. The cognitive shift from a deliberative to an implemental mindset is crucial in this phase, as it facilitates the concentration on specific actions and tasks, reducing the cognitive load associated with multitasking and re-evaluation of initial plans, thus driving efficiency and effectiveness in project execution.

The deliberative mindset is characterized by openness to information and a comprehensive evaluation of options, which is crucial during the project planning phase. This mindset allows managers and teams to explore various strategies and anticipate potential challenges without the immediate pressure of execution.

Transitioning to an implemental mindset involves a shift in cognitive strategy, from thinking broadly to focusing on specific tasks. This mindset is essential during the execution phase of a project, where the primary concern shifts to the pragmatic aspects of implementing the planned actions. The implemental mindset supports project managers and teams in maintaining focus on set goals, organizing resources efficiently, and adhering to timelines, thereby enhancing productivity and goal attainment.

The effect of implemental versus deliberative mindsets on time predictions was further investigated. A study of 151 participants published in 2015, created a hypothetical mindset situations and showed those with implemental mindset made shorter time predictions with respect to completing personal goals than participants in a deliberative mindset (12). The effect however does not explain the ever present project schedule overruns, and if anything points that more deliberative mindset is needed when planning projects.

Value Mindset

Thomas Lechler's research on the project value mindset (PVM) of project managers is another instance of delving into the idea of mindset within the project management literature.

The research proposes a perspective shift from the traditional triple constraint (TC) paradigm to a more value-oriented approach in project management. The study argues that project managers with a strong project value mindset are more likely to identify and capitalize on opportunities that enhance project value, despite inherent uncertainties. Using structural equation modeling to analyze data from 594 responses related to 114 projects, Lechler found that a higher PVM score correlates with increased project value. The author’s new framework suggests that project managers should not only manage within predefined constraints but also proactively seek and utilize opportunities to drive superior project outcomes.

Criticisms of Mindset Theory

While the mindset theory has gained widespread applications and has inspired numerous educational interventions, a 2018 published meta-analysis on mindset interventions shows mixed results (6). The analysis demonstrates while considering 273 studies including more than 35k subjects, the correlation between growth mindset and academic achievement is generally weak. However, the effect remained strong when controlling for certain variables and under specific conditions, such as among students who are economically disadvantaged or academically at-risk.

These findings imply that while the broad applications of mindset interventions might need more robust evidence for general effectiveness, they hold potential for targeted interventions, particularly where the stakes and challenges are higher for the learners involved.

Dweck, herself, reflected on the oversimplification of her theory in practice and emphasizes the need for more nuanced approaches that consider the specificities of context when applying mindset principles (13).​ To address this criticism Dweck and her team conducted a study to find when growth mindset intervention does or does not work and found that examiner mindset affects the success of the intervention (14).

Question then Becomes

Managing large engineering projects to a successful implementation is a hard and complex undertaking at scales beyond that any single idea or process can alleviate all complexity and challenges. Nonetheless, the traditional construction and project management industry seem to be lacking some of new paradigms developed within the new behavioral sciences.

Is the cultivation of robust mindsets at project organizations the missing complement to the best practices to ensure better projects?

Mindset theory is about fostering an environment where the focus shifts from proving one's abilities to relentlessly pursuing improvement and excellence. This shift not only alleviates the pressures of validation but also cultivates a culture where challenges are viewed as fertile ground for growth and innovation. Such a dynamic culture enhances individual performance and collectively elevates team effectiveness, encouraging members to embrace risks, innovate, and mutually support each other’s development. This appears crucial for sustained success in the competitive and ever-evolving fields of today.

References

1. Murphy MC. Cultures of Growth: How the New Science of Mindset Can Transform Individuals, Teams, and Organizations. New York: Simon & Schuster; 2024. 352 p.

2. Dweck CS. Mindset: The New Psychology of Success. Illustrated edition. New York, NY: Ballantine Books; 2007. 320 p.

3. Syed M. Rebel Ideas: The Power of Diverse Thinking. Flatiron Books; 2021. 173 p.

4. Gollwitzer PM. Action phases and mind-sets. Handb Motiv Cogn Found Soc Behav. 1990;2(53–92):2.

5. Gollwitzer PM, Bayer UC. Deliberative versus implemental mindsets in the control of action. 1999 [cited 2024 Aug 3]; Available from: http://kops.uni-konstanz.de/handle/123456789/10257

6. Sisk VF, Burgoyne AP, Sun J, Butler JL, Macnamara BN. To What Extent and Under Which Circumstances Are Growth Mind-Sets Important to Academic Achievement? Two Meta-Analyses. Psychol Sci. 2018 Apr 1;29(4):549–71.

7. Dweck CS. Self-theories: Their Role in Motivation, Personality, and Development. New York: Psychology Press; 2014. 212 p.

8. Growth Mindset Scale | SPARQtools [Internet]. [cited 2024 Sep 10]. Available from: https://sparqtools.org/mobility-measure/growth-mindset-scale/

9. MindsetWorks | Growth Mindset | Growth Mindset Programs [Internet]. [cited 2024 Sep 10]. Available from: https://www.mindsetworks.com/

10. Canning EA, Murphy MC, Emerson KTU, Chatman JA, Dweck CS, Kray LJ. Cultures of Genius at Work: Organizational Mindsets Predict Cultural Norms, Trust, and Commitment. Pers Soc Psychol Bull. 2020 Apr;46(4):626–42.

11. Murphy MC, Dweck CS. A Culture of Genius: How an Organization’s Lay Theory Shapes People’s Cognition, Affect, and Behavior. Pers Soc Psychol Bull. 2010 Mar;36(3):283–96.

12. Brandstätter V, Giesinger L, Job V, Frank E. The Role of Deliberative Versus Implemental Mindsets in Time Prediction and Task Accomplishment. Soc Psychol [Internet]. 2015 Jan 1 [cited 2024 Aug 3]; Available from: https://econtent.hogrefe.com/doi/10.1027/1864-9335/a000231

13. Growth Mindset: where did it go wrong? | Tes Magazine [Internet]. [cited 2024 Sep 9]. Available from: https://www.tes.com/magazine/archive/growth-mindset-where-did-it-go-wrong

14. Yeager DS, Carroll JM, Buontempo J, Cimpian A, Woody S, Crosnoe R, et al. Teacher Mindsets Help Explain Where a Growth-Mindset Intervention Does and Doesn’t Work. Psychol Sci. 2022 Jan 1;33(1):18–32.

Notes

If you wish to refer to this article, please use the following citation format:

Zangeneh, P. (2024), “Mindset Theory and Project Management: Your Project Needs Best Mindsets, Not Just Best Practices!”, EPM Research Letters. https://epmresearch.substack.com/p/mindset-theory-and-project-management

Despite recent advancements, the integration of Generative AI into everyday business processes, particularly in complex and specialized areas like construction and project management, is still limited. These fields demand the convergence of diverse domain-specific knowledge to effectively address varying project contexts with consistently high quality and precision.

A critical challenge to the broader adoption of Generative AI in these domains is not merely the accuracy or precision of these systems but their reliability, validity, and robustness. To harness the full potential of these AI models, it is imperative to either train them from the ground up, fine-tune them, or augment them with domain-specific data.

There are various methodologies for tailoring LLMs for specific tasks—each with its unique cost and utility— as below, and there are various new hybrid versions of these proposed every day.

• Prompt Engineering: Crafting precise prompts to guide the model's responses.

• Retrieval-Augmented Generation (RAG): Combining external data retrieval with AI generation for more informed outputs. RAG becomes even more potent when the retrieved data is structured in graph form, such as using an ontology like UPonto. This enhanced version is known as GraphRAG.

• Fine Tuning: Adjusting the model with specific data to better align with tasks.

• Model Training: Building a tailored model from the ground up for your unique domains

To create robust and effective LLMs to address specific tasks not only these methods should be thoughtfully combined, but also different models can be set to work with each other through innovative Flow Engineering.

These methods, like martial arts skills, enable us to unlock the vast capabilities of these models, that is the superpowers of our GenAI dragons. Despite the scientific underpinnings of these models, the practical application of these skills resembles an art form, where the behavior of billions of model parameters must be carefully orchestrated to masterfully meet specific use cases.

In this blog post, we share our insights and experience on successful customization of LLMs to address distinct work processes in construction and project management. We explore the nuanced techniques required to "Train your GenAI Dragon."

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Large Language Models (LLM)?

We are all familiar with LLMs but let us first highlight few important points to understand how these AI "Night Furies" behave.

Large Language Models (LLMs) are advanced types of artificial intelligence that understand and generate human-like text by learning from vast collections of data [1], [2]. These models have billions of parameters that are finely tuned during their training, allowing them to—miraculously—capture and compress extensive knowledge into their complex structure. As a result, LLMs develop a broad understanding of language and context, making them highly versatile in handling a wide range of tasks [3].

LLMs can simplify complex ideas and create analogies to explain difficult concepts. In practical applications, LLMs can simulate scenarios, making them useful for interactive role plays such as resolving disputes between clients and contractors. You can set these models to discuss issues with each other iteratively, and hence, LLMs results can be iteratively refined.

LLMs have limitations too. They can reflect biases present in their training data, misinterpret contexts, or generate plausible-sounding but incorrect information—a phenomenon known as "hallucination." LLMs do not possess true understanding, and their responses are based on patterns and probabilities rather than deep comprehension. This necessitates careful oversight to manage biases and ensure accuracy, while opening the door to interesting debates on what is true understanding and deep comprehension anyways.

LLMs Reasoning and Understanding of Physics

Through analogical, metaphorical and associative mechanisms, LLMs are able to reason and to understand physics. The vast amounts of textual data that has gone into their training allows them to recognize patterns and associations between concepts. For instance, LLMs realize how terms like "force" and "mass" often relate to Newtonian mechanics, and their relationships with “acceleration”. This understanding is built on identifying conceptual relationships rather than genuine comprehension of experiential knowledge.

Therefore, LLMs can use analogies to explain complex physical phenomena within their real-world context. For instance, LLMs can understand and explain the physical risks of not wearing hard hats in construction sites. While this reasoning is surface-level and relies on text-derived patterns rather than a true conceptual grasp of the physical laws governing the universe, this is where the power of your GenAI dragon really lies.

Construction and Project Management

Construction and project management is a diverse and multifaceted domain with fundamental principles like planning, scheduling, budgeting, and resource allocation, and a common goal of successful execution of projects. Such a field requires a blend of technical expertise, strategic oversight, and effective communication to navigate the complexities of coordinating multiple stakeholders, adhering to timelines, and managing project technical and organizational constraints. The diversity in construction and project management stems from the variety of projects and the unique challenges each present. This is while the best practices used to navigate through these challenges are similar at fundamental levels.

LLMs are particularly useful in such domains. They can streamline processes by generating detailed project plans, incorporating historical data, assisting in risk management, and facilitating communication across diverse teams with the goal of increasing productivity, maintaining high quality, and reducing human errors. They help project managers by providing quick and contextually relevant responses, drafting reports, and even aiding in decision-making through data-driven insights, making them invaluable tools in the modern construction and project management landscape.

How to Train Your GenAI Dragon

The adaptation of Large Language Models (LLMs) to meet the specific needs of different industries is progressing swiftly. This is due to the varying and often complex requirements across sectors, prompting the development of several techniques to customize these powerful models. Each method comes with its own set of benefits and computational demands, with multiple hybrid methods being proposed each day. Below, we outline the four primary approaches, arranged from the least to the most computationally intensive.

1. Prompt Engineering

Prompt engineering transforms LLM performance by optimizing input without modifying model parameters [4]. This practice, akin to programming for LLMs, involves strategically crafting and formatting prompts to guide the models in interpreting questions and generating precise, relevant responses. Initially met with skepticism among technical experts, prompt engineering has become a crucial skill, bridging the gap between complex model development and user-friendly AI applications. To master prompt engineering, users can use several effective strategies, such as ensuring clarity and specificity, providing contextual cues, iteratively refining prompts, and using few-shot learning to align responses closely with user intentions.

Few-shot learning is when the model is provided with a few examples of the desired task or output within the prompt itself. This helps the model understand and generate the type of response expected without extensive retraining or fine-tuning.

2. Retrieval-Augmented Generation (RAG)

Retrieval-Augmented Generation (RAG) is a technique that significantly boosts the capabilities of LLMs by incorporating external knowledge sources into their response generation process [5]. RAG combines the creative potential of LLMs with the precision of information retrieval systems. It works by employing a retrieval mechanism to fetch relevant data from a predefined dataset—be it documents, articles, or databases—and then integrating this information into the model's responses [6]. When you ask ChatGPT to summarise or answer your questions based on a document, you are enacting a RAG interaction.

The most remarkable advantage of RAG is its ability to provide LLMs with up-to-date and accurate information particularly when circumstances are changing, such as providing the model with the latest data and reports when executing a project. This latest data may as well be the results of other AI discriminative and predictive AI models. Hence, RAG allows the Generative AI to work with Discriminative AI, i.e., other advanced forms of machine learning!

GraphRAG for Complex Knowledge Integration

GraphRAG, or Graph-based Retrieval-Augmented Generation, is an innovative extension of RAG that leverages the power of graph-based knowledge representation. This methodology integrates LLMs with graph databases, which structure information in nodes and edges, allowing for more sophisticated and interconnected knowledge retrieval. GraphRAG enables LLMs to access and synthesize information from complex data relationships and hierarchies, providing richer and more nuanced responses.

At EPM Research, we extensively utilise GraphRAG to increase the functionality of LLMs. By utilizing ontologies like UPonto, which are designed to structure project data intricately, we aim to enhance LLMs' understanding and manipulation of project-related information [7]. GraphRAG's ability to handle complex interrelationships within data makes it an ideal tool for applications where the integration of diverse and detailed datasets is essential for effective responses, such as in construction and project management.

3. Fine-Tuning

Fine-tuning involves adjusting the internal parameters of a pre-trained model, a process that demands substantial computational power, and is a powerful methodology to enhance model performance for specific tasks [8].

LLMs are trained on extensive, general datasets to build a robust linguistic foundation. In the fine-tuning phase, the model is further tuned on smaller, domain-specific datasets containing specialized terminology and relevant documents. This targeted training sharpens the model's understanding of contextual aspects within specific domains, boosting accuracy and relevance for particular applications. For instance, in construction and project management, fine-tuning could involve training the model on datasets containing construction project case studies, risk management protocols, and compliance guidelines. This enables the model to more accurately interpret unique aspects of construction projects, like detailed risk assessments and project scheduling.

Fine-tuning itself has various intricacies to consider, but on the highest level, it can be done as Task-Specific, Domian Specific, or for Data Augmentation with each requiring different approaches.

4. Training LLM Models

Training an LLM from scratch for a specialized domain is both costly and complex due to several factors. Firstly, the data requirements are immense. General LLMs are trained on datasets containing hundreds of billions of tokens, and gathering a comparable amount of high-quality, domain-specific data (say in construction and project management domain) would be challenging and time-consuming, while the model may lack contextual understanding and analogical reasonings that it may gain by capturing a general corpus of textual data. Moreover, the computational cost of training such models is significant. For instance, training GPT-3 was estimated to cost several million dollars in compute resources alone [9].

Given these high costs, the previous methods discussed such as fine-tuning an existing model on domain-specific data appears to be the efficient and practical approach. Those methods leverage strong pre-trained models, which have already learned general language patterns and can be adapted to specific tasks or domains with relatively smaller datasets and lower computational costs.

Show Me How

To illustrate how Prompt Engineering, Retrieval-Augmented Generation (RAG), and Fine-tuning (excluding GraphRAG) can synergize to create a GenAI risk advisor, here is a preliminary example of a customized model.

This model takes the project scope, breaks it down into tasks, identifies their risks, and then creates a comprehensive risk register. In this example, the model is applied to Toronto’s notorious Eglinton Crosstown LRT project. By fine tuning the model with extensive data on urban Light Rail Transit (LRT) infrastructure projects, it identified groundwater issues, underground utilities, and community concerns as the top three risks for the project.

This example is quite general, and the tasks these AI "Dragons" must master to run a project are diverse in both knowledge and skills. Indeed, there are ways to set specialized models to interact with each other to achieve broader goals. Here is now the Flow Engineering.

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Flow Engineering: Foxes and Hedgehogs

"The fox knows many things, but the hedgehog knows one big thing," wrote the ancient Greek poet Archilochus. The idea was expanded by many thinkers and philosopher, such as Isaiah Berlin to categorize thinkers and their worldviews: foxes, draw on a wide variety of experiences and strategies, but hedgehogs, interpret the world through a singular, overarching principle.

In artificial intelligence, this idea may find compelling parallels. AI models can be trained as 'foxes'—adaptive systems capable of handling diverse tasks by integrating vast amounts of data and adjusting to different scenarios. Conversely, 'hedgehog' models specialize deeply in a narrow field, mastering specific, well-defined problems with precision. When these two types of AI work in tandem they enable a holistic approach to solving complex tasks that require both broad adaptation and focused expertise.

Flow Engineering is an innovative and iterative approach where multiple AI agents, particularly LLMs work together and interact with their environment and each other to complete tasks efficiently [10]. This involves decomposing complex tasks into smaller, more manageable and learnable steps. The tasks are then distributed among several specialized AI agents, each specialized in different aspects of the problem, and then passed through channels by generalist agents tasked to collect information, summarize, and report. Flow Engineering significantly enhances the accuracy and performance of the AI systems. This collective intelligence mirrors how different teams or departments might work together in a business setting to address various components of a project.

To continue with our “How to Train Your Dragon” analogies, the figure below shows a high-level flow structure of how different models, i.e., dragons, can work together to prepare the materials needed for a risk management workshop. The system generates a draft risk register for the project based previous projects data that amounts to considerable quality and productivity gains for the human risk team.

Each dragon is customized through fine-tuning and prompt engineering with their appropriate knowledge and been connected to a graphical database of project historical data through GraphRAG to ensure high efficiency and accuracy in their designated tasks. Note the Risk Assessor and Risk Manager can disagree through a feedback mechanism that enacts further conversation between them. Stay tuned for more on this in our future posts.

Another fascinating way to understand this process is by drawing a parallel to Daniel Kahneman's theories of System 1 and System 2 thinking from his seminal work, "Thinking, Fast and Slow." Kahneman describes System 1 as the fast, intuitive, and automatic mode of thinking, while System 2 is slower, more deliberate, and analytical [11]. We believe similar structures can be reproduced through flow engineering.

Conclusion

Selecting the optimal method for enhancing LLMs in construction and project management greatly depends on the specific challenges and requirements of a given task. Each method discussed—i.e., Prompt engineering, Fine-tuning, RAG, and Model Training—offers unique advantages that can be leveraged individually or in combination to address different aspects of work processes. Moreover, models can be set to work with each other through Flow Engineering.

For tasks requiring quick responses or where the information needed is well within the scope of the LLM’s training, prompt engineering is highly effective. It allows users to guide the model to generate specific outputs without the need for retraining. Conversely, when a deeper understanding of specific construction terms and processes is necessary, fine-tuning can adapt an LLM to respond more accurately to specialized queries by aligning it closer to domain-specific language and scenarios. However, this method is resource-intensive, requiring substantial training data and computational power. It also risks overfitting to the training data, which might reduce the model's effectiveness on other tasks.

In scenarios where accuracy and the incorporation of the latest data are crucial—such as compliance with the newest safety regulations or standards—RAG proves invaluable. By pulling in up-to-date external data, RAG ensures that the information provided by the LLM is both current and highly relevant, making it ideal for dynamic environments where regulations and conditions frequently change. It offers richer responses with less data needed than fine-tuning.

For complex decision-making processes that require varied inputs and multi-faceted analysis, deploying multiple AI agents can be advantageous. This approach allows the system to handle multiple aspects of a problem simultaneously, mirroring more complex human decision-making processes and providing a holistic view that single models or simpler methods cannot achieve. Nonetheless, it introduces complexities in integration and management, requires higher computational resources, and may incur increased maintenance costs. Conflicts between agents' outputs can also arise if not properly managed.

In practice, a combination of these methods provides the best results. For instance, an LLM could be fine-tuned with industry-specific data, use prompt engineering to ensure user-friendly interactions, and employ GraphRAG to integrate the latest external data or machine learning models’ results for enhanced decision-making accuracy. In more complex scenarios, multiple AI agents might work to manage different data streams or perform specialized analyses. Each agent may benefit from prompt engineering, RAG, and fine-tuning methods.

Access Our Research and GenAI Customized Models

While our resources are limited, we would be delighted to implement our Generative AI models and workflow in your organization for free!

It is important to note that our Generative AI customized models can learn and operate locally in an isolated environment, eliminating the need to connect to the open cloud. This ensures the utmost respect for data security and governance concerns within your organization. Our research focuses on understanding how to combine these techniques to train GenAI models tailored for various workflows and business processes. Consequently, the trained models and the insights they gain from your data remain exclusively yours.

Moreover, we invite you to share your insights on how Generative AI can revolutionize construction and project management processes. Participate in our survey to help us identify the areas best suited to benefit from this cutting-edge technology. Your feedback will play a crucial role in shaping our research and the future of AI in our industry.

References

1. Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., and Sutskever, I., “Language models are unsupervised multitask learners,” OpenAI Blog, vol. 1, no. 8, p. 9, 2019.

2. Brown, T. et al., “Language models are few-shot learners,” Adv. Neural Inf. Process. Syst., vol. 33, pp. 1877–1901, 2020.

3. Arcas, B. A. y, “Do large language models understand us?,” Daedalus, vol. 151, no. 2, pp. 183–197, 2022.

4. Si, C. et al., “Prompting gpt-3 to be reliable,” ArXiv Prepr. ArXiv221009150, 2022.

5. Zhang, Y. et al., “Siren’s Song in the AI Ocean: A Survey on Hallucination in Large Language Models,” ArXiv Prepr. ArXiv230901219, 2023.

6. Lewis, P. et al., “Retrieval-augmented generation for knowledge-intensive nlp tasks,” Adv. Neural Inf. Process. Syst., vol. 33, pp. 9459–9474, 2020.

7. Zangeneh, P., and McCabe, B., “Ontology-based knowledge representation for industrial megaprojects analytics using linked data and the semantic web,” Adv. Eng. Inform., vol. 46, 2020, doi: 10.1016/j.aei.2020.101164.

8. Naveed, H. et al., “A Comprehensive Overview of Large Language Models.” arXiv, Dec. 27, 2023. Accessed: Jan. 21, 2024. [Online]. Available: http://arxiv.org/abs/2307.06435

9. “How Much Does It Cost to Train a Large Language Model? A Guide | Brev docs.” Accessed: Jun. 22, 2024. [Online]. Available: https://brev.dev/blog/llm-cost-estimate

10. Ridnik, T., Kredo, D., and Friedman, I., “Code Generation with AlphaCodium: From Prompt Engineering to Flow Engineering.” arXiv, Jan. 16, 2024. Accessed: Jun. 08, 2024. [Online]. Available: http://arxiv.org/abs/2401.08500

11. Zangeneh, P., “In Honor of Daniel Kahneman and What his Work Means for Project and Risk Management,” EPM Research. Accessed: Jun. 22, 2024. [Online]. Available: https://epmresearch.substack.com/p/in-honor-of-daniel-kahneman-and-what

Notes

If you wish to refer to this article, please use the following citation format:

Zangeneh, P. and Ghorab, Kh. (2024), “How to train your GenAI Dragon for Construction and Project Management Processes”, EPM Research Letters, https://epmresearch.substack.com/p/how-to-train-your-genai-dragon

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In a stunning post-election development, Trilogy Metals saw its stock nearly double on Wednesday, November 6, following Donald Trump’s election win. The sharp rise underscores market optimism about the Ambler Project, a major copper mining initiative in Alaska, in which Trilogy holds a 50% stake, and put Ambler back under the spotlight.

This political shift reopens the debate over where to draw the line between resource development and environmental protection. As policymakers balance the urgent demand for critical minerals with sustainability, some experts predict that this conversation could drive tectonic shifts in environmental regulations for years to come.

With both sides holding compelling arguments, the future of projects like Ambler remains and political and jurisdictional risks remains uncertain—an undesirable outcome for resource sector overall.

We invite you to share your perspective: where should the balance lie between advancing resource projects like the Ambler Project and protecting environmental integrity? Join the discussion and contribute your insights as we refine this ongoing case study.

Original Story

On April 16, 2024, the New York Times reported on a pivotal upcoming decision by the US Department of the Interior (DOI) regarding the proposed construction of a 211-mile road through ecologically vulnerable areas in Alaska, intended to facilitate access to a notable copper deposit, known as the Ambler or the Arctic Project [1].

This prospective denial by DOI appears aligned with President Biden’s overarching environmental and climate agenda, yet it stands in contrasts with the need of the project’s essential mineral resources vital for the green energy transition, as well as the previous approval of the same project under the Trump administration.

In a response to this anticipated decision, the Alaska Industrial Development and Export Authority (AIDEA) has issued an appeal to the DOI, urging compliance with federal commitments to state rights supporting the development of state lands and mineral accessibility [2].

The advocates for the Ambler Project have voiced significant concerns regarding the potential rejection of the road construction necessary for the $1.3 billion project. They highlight the broad local backing for the project and the anticipated economic uplift it could engender through job creation and regional development.

Ambler Project

The Ambler Project, also known as the Arctic Project, situated in northwestern Alaska, is a significant copper project that is nearly ready for construction. The project is a collaborative 50/50 joint venture between Trilogy Metals and South32. It aims to tap into the extraction and processing of copper, zinc, and lead, employing a conventional milling and flotation with a capacity to handle 10,000 tonnes of ore per day. The plant is designed to achieve high recovery rates, with the resulting concentrates shipped to the Port of Anchorage and then to external smelters [3].

Financial projections for the Ambler Project are notably robust, with an after-tax Net Present Value (NPV) estimated at approximately $1.1 billion and an Internal Rate of Return (IRR) of about 22.8%. The operational lifespan of the mine is projected at around 13 years, with a payback period for initial capital investments estimated at 3.1 years. This places the Ambler Project in a great position within the global pipeline of copper projects, highlighting its profitability, scale and development phase, as shown in the following chart [3].

The total capital expenditures are segmented into an initial construction phase of approximately $1.18 billion, sustaining capital of around $114.4 million, and closure and reclamation costs estimated at $428.4 million, culminating in an aggregate investment of approximately $1.29 billion.

In terms of mineral reserves, the project boasts proven and probable reserves of 46.7 million tonnes, with considerable grades of 2.11% copper, 2.90% zinc, 0.56% lead, 0.42 grams per tonne gold, and 31.8 grams per tonne silver. The project also reported 35.7 and 4.5 million tonnes indicated and inferred resources highlighting its potential and viable expansions [4].

The reported regional employment impact of the Ambler Project is also considerable, with over 3,900 direct and indirect jobs expected during the operational phase, and an additional 110 jobs during peak construction periods. This is anticipated to boost local employment and contribute over $300 million annually in wages [5].

Ambler Metals LLC, which manages the project, solidified the JV structure framework in 2017 when Australian giant South32 joined Trilogy Metals through a strategic investment of $145 million for 50% stake in the project.

The Saga

In February 2022, the U.S. Department of the Interior (DOI) filed a critical motion to remand the Final Environmental Impact Statement (FEIS) and to suspend the associated right-of-way permits [6]. This measure was intended to facilitate additional supplemental evaluations of the environmental impact assessments. Following this announcement of the re-evaluation of the FEIS, the stock price of Trilogy Metals experienced a notable decline, dropping from 1.9 CAD to 1.2 CAD.

Fast forward two years, the New York Times article suggests that the DOI has determined that the road construction would “significantly and irrevocably” compromise the environmental integrity of the area and negatively impact tribal communities. This conclusion forms part of an exhaustive reassessment that highlighted several shortcomings in the environmental studies previously conducted under the Trump administration [1].

In light of these findings, the current administration appears inclined to deny the necessary permits. Trilogy Metals' stock price markedly dropped about 30% of its market value, closing at approximately 0.5 CAD and at a market capitalization of 85 million CAD on April 17th.

Opponents

Ambler Access Project is grounded within a complex environmental and cultural context. Firstly, the project entails constructing a 211-mile industrial road through the Alaskan wilderness, an area described as “one of the most ecologically intact and functional landscapes on the planet” [1]. This development may disrupt vital habitats and the ecological balance of the region, including the Kobuk River, which is crucial for the fish and wildlife that Indigenous Iñupiat communities depend on.

Environmentalists and local tribes argue that the road would irrevocably harm the region's water quality and biodiversity. The proposed road and mining development are seen as a direct threat to the communities along the Kobuk River, potentially causing irreparable damage to a landscape of a nearly untouched North American ecosystem. The project could exacerbate the struggles of caribou and fish populations already impacted by environmental changes, compounding the difficulties faced by local communities reliant on these species for subsistence [1].

The opposition to the Ambler Access Project is deeply rooted in the need to preserve “one of the last” great wilderness areas, protect the livelihoods and traditions of Indigenous populations, and uphold the environmental integrity of a uniquely pristine area against the potentially devastating impacts of extractive industry development.

Proponents

On the other hand, Ambler Access Project has robust contributions to economic development and the strategic importance of domestic mineral production for advancing green energy transition. Proponents emphasize the critical nature of the road for accessing one of the world's most significant copper-dominant polymetallic deposits. This access is deemed essential for furthering development within the Ambler Mining District, which could substantially contribute to America's clean energy future.

Moreover, AIDEA asserts that the project aligns with federal laws and the promises made at Alaska's statehood, which support the development of state lands and access to minerals [2]. The potential denial of the project is seen as a contradiction of these federal commitments to Alaska, undermining opportunities for development and economic growth. The projected economic benefits from the project are significant, promising over $1.3 billion in tax and royalty revenues and the creation of jobs, vital for the economic well-being of Alaskan communities. This development is viewed as a necessary step toward mitigating economic stagnation and providing sustainable employment opportunities in areas with high unemployment.

Lastly, proponents argue that denying the project would lead the United States to continue importing critical minerals from countries with less stringent environmental practices, thus contradicting environmental objectives and reducing control over the supply chain. The argument is cited almost for any extractive resource development in North America that refusing these strategic projects on mere environmental grounds ironically would result in sending jobs overseas and relying on mineral imports that may not adhere to the stringent environmental standards upheld in North America.

Copper

To understand the strategic importance of Ambler project, it is important to considers Copper's vital role in the green energy transition. By 2035, the worldwide demand for copper is projected to double, largely driven by its critical applications in renewable energy systems, electric vehicles (EVs), and efficient energy transmission networks [7]. In electric vehicles, copper is essential for batteries, motor windings, wiring, and charging stations, with the average electric vehicle using approximately four times more copper than traditional vehicles.  This demand shift emphasizes the need for significant investments in copper production as a strategic resource.

Similar Debates in Canada

Most interestingly, the debate surrounding Alaska's Ambler Access Project reminds of a recent debate around the decision by the Canadian Supreme Court, which underscored a similar conflict over jurisdictional boundaries in the federal government's Impact Assessment Act (IAA) [8].

The court, with a majority of 5-2, found that the IAA’s application to specific in situ projects in Alberta is unconstitutional exertion of federal authority, and it undermines Alberta’s rights to manage its natural resources. This ruling highlighted Alberta's argument that the IAA potentially hinders provincial economic development by imposing federal evaluations that is not align with provincial objectives.

This Canadian case mirrors the arguments put forth by proponents of the Ambler project. They maintain that the project is in compliance with federal laws and aligns with the commitments made at the time of Alaska’s statehood, which support the development of state lands and access to minerals.

Advocates for the Ambler project argue that the potential federal denial of the road construction contradicts these foundational agreements, notably the Statehood Act of 1958 and the Alaska National Interest Lands Conservation Act of 1980, which were intended to secure Alaska's rights to develop and benefit economically from its resources.

Question then Becomes

The Ambler Access Project stands as an interesting example of the mining industry's crucial role in the green energy transition tradeoff with its potential environmental, social and governance impact in regional context, all within the political arena of shifting narratives.

Addressing the contextual social and political complexities facing the mining industry remains a formidable challenge. However, in an era where social media exerts a substantial influence on public opinion, it is crucial for the industry to adopt a strategy of transparent engagement with local communities.

The mining industry track record in managing environmental, social, and governance issue is not stellar, and given the increasing public scrutiny and criticism of the mining industry, it is essential for stakeholders to proactively communicate both the potential benefits and drawbacks of mining projects.

Proponents of the Ambler project contend that the potential denial of the project is a politically motivated decision that overlooks substantial local support and the legal entitlements granted to Alaska for resource development. Such perceptions of political agendas, as inevitable as may seem, cast a shadow over industrial development in the US and Canada, introducing unquantifiable risks that could deter investment.

Previously, the Ambler project was considered substantially de-risked, having secured environmental permits and backed by a global mining company and state politicians, to the extent that discussions had shifted towards securing smelting capacity. Yet, the project was abruptly halted, leading to significant financial losses. This situation begs for reflection on what steps might have been taken with greater diligence to navigate and mitigate these hurdles effectively.

If you know someone who might be interested in this article, or benefit from it, please consider sharing it with them, and also please subscribe and join our network of industry experts and enthusiasts.

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References

All websites accessed in April 2024.

1. New York Times, Interior Department Rejects Ambler Road Project in Alaska, https://www.nytimes.com/2024/04/16/climate/ambler-road-alaska-interior.html

2. Alaska Industrial Development and Export Authority (AIDEA), https://www.aidea.org/About/News-Publications/Press-Releases

3. Trilogy Metals Inc. https://trilogymetals.com/

4. Mining Data Online, https://miningdataonline.com/property/1831/Arctic-Project.aspx

5. Alaska Business Readying 2022 Pre-Development of the Ambler Mining Road, https://www.akbizmag.com/industry/mining/readying-2022-pre-development-of-the-ambler-mining-road/

6. Trilogy Metals Inc. February of 2022, Update on the Ambler Access Project. https://trilogymetals.com/news-and-media/news/trilogy-metals-provides-update-on-the-ambler-access-project/

7. S&P Global, Growing appetite for copper threatens energy transition and climate goals,  https://www.spglobal.com/marketintelligence/en/mi/research-analysis/growing-appetite-copper-threatens-energy-transition-climate.html

8. Dentons, Lost focus: Supreme Court finds Impact Assessment Act unconstitutional, https://www.dentons.com/en/insights/articles/2023/october/18/lost-focus-supreme-court-finds-impact-assessment.

Notes

This case study on EMP Research will be updated to incorporate future events and perspectives from stakeholders.

If you wish to refer to this article, please use the following citation format:

Zangeneh, P (2024), “Ambler Project: A Modern Case of Mining For and Against the Climate”, EPM Research Letters, https://epmresearch.substack.com/p/ambler-project-a-modern-case-of-mining

Two weeks ago, Daniel Kahneman (1934-2024), the distinguished professor of psychology and behavioral economics passed away, Kahneman dedicated his life to understanding errors in human judgment. He coined terms and concepts such as the intuitive System 1 and the elaborative System 2 as parts of our brain, or else, you may have heard of his prospect theory and loss aversion for which he won the Nobel prize in economics, where he posits that the pain of loss is harder to bear than the pleasure of gain.

We are dedicating our first article on EPM Research substack to the legacy of this great mind, and his little less known words and insights with major implications in project and risk management. Equally important, is the implications of his work in creating AI systems and Risk Analytics applications, as one way or the other, you use expert judgement as input or convey insights as output about probabilistic phenomena.

Much of this brief article is a review of his book “Thinking Fast and Slow” [1]. However, let us start with one of his transformational ideas from his 1977 paper with Amos Tversky, where they counted three characteristics of errors in human judgment and predictions: First, errors are systematic rather than random, second, they exist in all humans regardless of expertise, third and most importantly, they remain present even when you are fully aware of them and unless you learn to actively adjust — not much different than optical illusions [2]. Let us get into his universe.

Thinking Fast and Slow: System 1 and System 2

Consider my favorite puzzle of the book (pg. 44).

Kahneman introduces the concepts of System 1 and System 2 as two distinct modes of thought in human judgment and decision-making. System 1 operates automatically and quickly, with little or no effort and no sense of voluntary control. It encompasses our intuitive and instinctual reactions, drawing on learned associations and heuristics to navigate daily tasks and decisions efficiently. In contrast, System 2 requires conscious thought and effort, involving deliberate analysis and reasoning. It's activated when we engage in complex problem-solving, make thoughtful decisions, or focus on tasks that demand attention. He clarifies that System 1 and 2 are fictitious characters and not systems in “standard sense” of systems with distinguishable parts, but the analogy helps in explaining many of the errors and biases in human judgement.

If your answer to the previous question was 10 cents, chances are that your elaborative System 2 did not take over, and to engage System 2 you may just need an extra bit of effort to arrive at the correct answer of 5 cents. Integral to this idea is that System 2 requires more efforts, our brains are controlled by lazy controllers aiming to minimise the efforts and hence avoid using System 2 as much as possible. Therefore, if you are a busy project manager with a “cognitively busy” routine, you are - in principle - more prone to errors of System 1. Derived from this he concludes that states of “cognitive ease” may cause us to resort to System 1, and therefore, making issues, discussions, or debates a bit — maybe unnecessarily — complex to achieve “cognitive strain” might be a strategy to activate System 2 thinking in a group.

Kahneman emphasises that intelligence is not merely the ability to reason. In fact, the System 1 ability to rapidly find relevant information in memory is a major component of intelligence. Therefore, there is no hierarchy of importance between the two systems, both are necessary and important parts of our brain.

What You See Is All There Is: WYSIATS

Let is consider another puzzle from the book (pg. 88).

• Alan: intelligent—industrious—impulsive—critical—stubborn—envious

• Ben: envious—stubborn—critical—impulsive—industrious—intelligent

Our swift and often scarce-information-driven world is suited for System 1 to thrive. System 1 is particularly capable of crafting coherent narratives from minimal data with ease. This inclination of System 1 is often rubber-stamped by a "lazy controller" of System 2, which, while capable of methodical analysis, tends to validate our intuitive beliefs if not pressed. He coins this idea as What You See Is All There Is, or a rather weird acronym of WYSIATI, referring to the associative memory function that is adept at jumping to conclusion. He elaborates that “jumping to conclusions on the basis of limited evidence is so important to an understanding of intuitive thinking.” Just like how you may have jumped to conclusion to hire Alan in the previous puzzle, while Alan an Ben have the same qualities, just written in a different order.

Kahneman argues WYSIATI is at the core of many types of human errors and biases such as: confirmation bias, availability bias, overconfidence, base rate neglect, and framing effects. WYSIATI highlights the importance of base rates, and anchors, and what he calls the “outside view” when eliciting probabilistic information.

Inside vs. Outside Views, Singular vs. Distributional Information

To adjust for WYSIATI, Kahneman refers to another cognitive bias as Anchoring in which individuals rely heavily on an initial piece of information (the "anchor") when making decisions. Even if the initial information is arbitrary or unrelated, it can significantly influence subsequent judgments and estimates. While valid anchors could help in moving away from WYSIATI, it is evident that anchoring could be used intentionally or unintentionally to mislead, and in fact it is.

One way to add more information is to replace the single point of anchor with the distribution of potential peer data points. This way not only you make sure your decision are data driven but as well gain insights into the inherent uncertainty in the data distribution.

In his paper on 1993, Kahneman discusses the critical distinction between singular information (the inside view) and distributional information (the outside view) in forecasting. Let’s consider a project as an example. He refers to the inside view as the known specifics of the current project at hand, while the outside view draws on the broader outcomes of similar previous projects. He concludes that the planning fallacy—our tendency to underestimate time and costs— is an inescapable consequence of neglecting the outside view [3].

Indeed, in many project domains, the early estimates of projects stem from benchmarking data of similar cases (i.e., the most inclusive reference class), and there are established methodologies for adjusting for differences in size and capacities (you may have heard of Lang Factors). However, the planning fallacy creeps in through the procedural steps and budget approvals. To always keep the outside view present, especially at the points of major decision making, Flyvbjerg et al. proposed the methodology of reference class forecasting (RCF) [4].

A large enough reference class, in principle, will include the variabilities within all project types and contexts resulting in enough representations. Kahneman summarizes that the outside view forms the “baseline prediction” and serves as an anchor for forecasts in absence of any inside view (case specific) information. He continues that the estimate should be adjusted “away from the mean in the appropriate direction” based on case specific information and factual evidence of the riskiness of the case (pg. 248). This means that the outside view should not entirely dismiss the application of the inside view, especially when it comes to course correction during the project execution. In a previous paper, I proposed a methodology for "positioning" a project away from its baseline prediction based on the outside view by incorporating case-specific information from the inside view, achieving preliminary success [5].

The idea of updating the baseline prediction of outside view based on case specific evidence from inside view bears a close resemblance to Bayesian probability updating where the reference class is our prior knowledge. This is a major part of our research in EPM to enable Bayesian learning in different parts of projects. In his book “How to Measure Anything,” Doughlas Hubbard, has proposed simple methods to conduct such Bayesian probability updating [6].

Conjunction Fallacy: Scenarios Not Predictions

Another important concept from Kahneman’s work, significant for us as project professionals, is the idea of the Conjunction Fallacy. This often skews risk assessments in construction and project management where specific, combined scenarios are perceived as more likely than broader, singular ones. This bias urges decision-makers to prioritize the mitigation of detailed, yet improbable risks over more probable ones, potentially leading to suboptimal resource allocation.

Recognizing and decomposing this fallacy is crucial for a more effective and efficient risk management, which requires collecting causal information within the risk registers. Consider the following nuanced risks statements within a construction site:

1. Workers sustaining head injuries due to not wearing proper PPE.

2. Workers sustaining head injuries due to not wearing proper PPE in the underground channel.

While the correct action here should focus on the use of proper PPE, the conjunction fallacy may shift our attention to improving passageways in the underground channel, which, important as it may be, represents the less probable risk.

A specific type of conjunction fallacy occurs when extensive efforts is spent building or discussing possible scenarios, effectively changing the focus of discussion to WYSIATI. Consider the following two statements:

1. Rising global temperatures will lead to more extreme weather events.

2. Rising global temperatures will lead to more extreme weather events and mass migration.

While mass migration is an important phenomenon to consider, the first statement is certainly more likely. It is therefore always beneficial to remember that scenarios are not predictions, as Peter Tertzakian often mentions in their intriguing Arc Energy Podcast [7].

Randomness and Regression to the Mean

In chapter 17 of the book Kahneman describes a significant fact of the human condition: “the feedback to which life exposes us is perverse. Because we tend to be nice to other people when they please us and nasty when they do not, we are statistically punished for being nice and rewarded for being nasty,” or as he describes it, there is a structural “regression to the mean” in out interactions.

The phenomena of overlooking inherent randomness in events have significant implications in coaching, parenting, and certainly stock picking. By failing to recognize this randomness, we often incorrectly link actions to outcomes, leading to non-regressive predictions. Kahneman, therefore, suggests adjusting our predictions toward the average to account for this oversight.

In project and construction management, missing the effects of randomness and regression to the mean can have serious consequences, affecting a wide range of predictions and actions within a project, such as trend and change management, staffing plans, vendor delays, and even earned value management.

This issue becomes particularly acute in the creation of AI and statistical learning models. Many models fail to account for the randomness and noise within their data by easily memorising and overfitting the training data. Based on my observations, maintaining a healthy skepticism towards any AI with over 90% accuracy is wise, unless it has undergone a thorough examination of its residuals and bias-variance plots.

Limits of Intuitive Judgment

Eventually, the question becomes as how and when we can trust our intuitive judgment. To explore this question, Kahneman, a skeptic of human intuition, engages in an interesting collaborative journey with Gary Klein a psychologist and a proponent of the reliability of human intuition that took about a decade.

In their final paper, titled “a failure to disagree”, they recognize that experts can often make remarkably accurate snap judgments in domains where they have extensive experience and practice (opportunity of learning), and where patterns are regular and predictable (clear feedback mechanism).

This dual requirement highlights that for intuition to be trusted, experts need a domain with stable, learnable patterns—such as those found in chess or firefighting—and the opportunity to refine their skills through consistent practice and clear feedback. Kahneman further suggests that in environments if uncertainty or "noise," algorithms often outperform human judgment.

The extent to which construction tasks, project sites, or the multi-year execution of mega-projects meet these criteria is debatable. However, it is crucial to consider these factors every time we seek expert judgment on the severity and likelihood of a risk.

His Legacy

Daniel Kahneman's journey is marked by humility and a relentless pursuit of truth. His legacy extends far beyond his contributions to decision sciences and behavioral economics. By engaging deeply with critics, Kahneman is a great example of the academic spirit of curiosity and quest for collaborative research.

His book, "Thinking, Fast and Slow," not only translated his lifelong research for non-specialists and hence paved the way for people like me to better understand this field and get interested. But also sparked a publishing trend, inviting a broader audience to explore the intricacies of research biographies outside their areas of expertise.

Any attempt to summaries the wisdom contained within his book falls short. Kahneman's masterpiece invites readers on a personal journey through its insights, challenging, enlightening, and enriching their perspectives. If you're considering a gift for someone, I can share from experience that "Thinking, Fast and Slow" is a great option to consider.

If you know someone, perhaps a project risk manager, who might be interested in this, or benefit from it, please consider sharing it with them and asking for their opinion.

References

1.     Kahneman, D. (2011). Thinking, fast and slow: Macmillan.

2.     Kahneman, D., & Tversky, A. (1977). Intuitive prediction: Biases and corrective procedures.

3.     Kahneman, D., & Lovallo, D. (1993). Timid Choices and Bold Forecasts: A Cognitive Perspective on Risk Taking. Management science, 39(1), 17-31. doi:10.1287/mnsc.39.1.17

4.     Flyvbjerg, B., Holm, M., & Buhl, S. (2005). How (In)accurate Are Demand Forecasts in Public Works Projects?: The Case of Transportation. Journal of the American Planning Association, 71, 131-146.

5.     Zangeneh, P., & McCabe, B. (2022). Modelling socio-technical risks of industrial megaprojects using Bayesian Networks and reference classes. Resources Policy, 79, 103071.

6.     Hubbard, D. W. (2014). How to measure anything: Finding the value of intangibles in business. John Wiley & Sons.

7.     Arc Energy Podcast. https://www.arcenergyinstitute.com/section/podcasts/

8.     Kahneman, D., & Klein, G. (2009). Conditions for intuitive expertise: a failure to disagree. American psychologist, 64(6), 515.