Bent Flyvbjerg is a pioneering scholar and leading authority in the field of megaproject management and urban planning. As a professor at Oxford, Copenhagen, Delft, and Aalborg, Bent has dedicated his career to improving the delivery and success of large-scale projects worldwide. In this interview, Bent shares his expertise on managing megaprojects, addressing common misconceptions, and exploring the future of project management, offering valuable lessons in the challenges and opportunities of large-scale urban and infrastructure development.

Q: Your work has extensively addressed issues like strategic misrepresentation and optimism bias in megaprojects. How can policymakers and practitioners better implement your reference class forecasting methods to improve project outcomes?
A: Strategic misrepresentation is a behavior, while reference class forecasting is a forecasting method. They are related because reference class forecasting can help identify potential strategic misrepresentation. Once identified, you can consider ways to prevent it. However, the forecasting method alone does not prevent strategic misrepresentation; incentives are needed to discourage such behavior. Since we’re dealing with behavior, it’s important to promote actions that disincentivize deliberate misrepresentation, which involves intentionally providing false information.

Q: Given your pioneering research in behavioural science and phronetic social science, what do you believe are the biggest challenges in translating social science insights into practical management strategies?
A: The biggest challenge is that human behavior is very difficult to change. Good behaviours are hard to alter, which is beneficial, but bad behaviours, such as those that  make large projects, policies, and plans wasteful are particularly problematic because people tend to be very set in their ways.

To address this, we need mechanisms that can influence and change behavior. One key factor, supported by research, is “skin in the game,” meaning individuals have something at stakes, such as rewards if things go well and losses if they don’t. When people have their own assets at risk, they are more likely to act responsibly.

Therefore, it’s essential to create incentive structures that encourage desired behaviours and discourage misconduct. Simply using forecasting methods like reference class forecasting isn’t enough; you must also consider the incentives that will lead to the outcomes you want. Understanding the most likely future and aligning incentives accordingly is crucial to achieving the desired results

Q: How do you add personal accountability to a megaproject?
A: By establishing incentive structures that promote responsible behavior, encouraging desirable outcomes and discouraging non-desirable ones. This is achievable, but it requires strong support from top management.

The entire organization must view this as a strategic priority aiming to foster behaviours that prevent project failures. It involves careful planning at all levels: individual, group, and organizational. It’s essential to consider how to incentivize people to act in ways that secure desired outcomes.

Every organization should address this, and successful ones do so effectively. We have seen organizations that get it right and understand the importance of aligning incentives with strategic goals.

Q: You’ve advised numerous governments and organizations on megaproject delivery. Can you share a particularly challenging project you worked on and what lessons it taught you about managing complexity and risk?
A:The most challenging but ultimately also most rewarding example I mention in my book, How Big Things Get Done,was a project to build 20,000 schools and classrooms in Nepal. This was particularly difficult because it was in remote areas of the country, where the terrain is rugged, to say the least, with no roads but only footpaths and very limited infrastructure.

Nepal’s government, along with development organizations including the Danish International Development Agency (for whom I worked), aimed to build many of these schools in areas with no existing educational facilities. Despite the challenges, the project succeeded, and this success has been independently studied and evaluated, including by the Bill and Melinda Gates Foundation. They recognized it as a model for creating local schools that increased general, and especially girls’ enrolment, which is crucial in developing nations.

The key to success was a focus on two main principles: technical and social. Technically, we designed simple, modular schools built from standardized units of one classroom, or multiple classrooms for larger schools using local materials and labour. This standardization allowed local teams to improve their skills through repetition, as they built the same type of school many times, rather than doing bespoke projects each time, which often leads to problems.

On the social side, we learned that ownership and responsibility mattered. Inspections revealed that buildings built and handed over without community involvement often deteriorated quickly. Conversely, buildings where the local community was involved in their construction were better maintained. This taught us to involve local people in building their schools, fostering a sense of ownership and responsibility.

These two principles, standardized technical design and community involvement were crucial in overcoming the project’s difficulties and ensuring its success.

Q: Your recent papers explore the fat-tailed distribution of project risks, especially in IT projects. How do these findings influence your approach to project risk management and forecasting?
A:  Data shows that most projects outcomes, not just for IT projects, are fat-tailed, meaning their risk distributions have long, heavy tails. Only a few project types – about three or four out of twenty-three – are not fat-tailed. The majority are fat-tailed, making risk management challenging. In some cases, risks are so extreme that they are effectively infinite, meaning they cannot be predicted.

Fat tails with infinite variance are particularly problematic because traditional project risk management methods assume normal (Gaussian) or near-normal distributions with thin tails. These methods become useless when risks are infinite or extremely high, as they underestimate the true risk.

Even less extreme fat tails classified as high or extreme risk are still problematic because conventional methodologies assume near-normal distributions. This leads to a dangerous disconnect: project managers often believe risks are small and manageable when, in reality, they are extreme and unmanageable.

This misperception is a major issue in project management. Many practitioners rely on risk assessment methods based on normal or near-normal distribution assumptions, which ignore fat-tailed risks. This results in underestimating the true risk, sometimes worse than having no risk assessment at all, because it provides a false sense of security.

In summary, the standard practices in project management are based on flawed assumptions, and this disconnect between reality and methodology is a significant and widespread problem, highlighted by our data

Q: How do you think the concepts of phronesis, and practical wisdom can be integrated into the education and training of future project managers and city planners?
A: Practical wisdom, or common sense, should be emphasized more in education. While courses in analysis, statistics, and econometrics are valuable, they are most effective when used by people without practical wisdom. As the saying goes, “A fool with a tool is still a fool.” Educating only on tools without teaching wisdom results in ineffective use.

We need to cultivate practical wisdom, which is often overlooked because it’s seen as non-quantitative, yet, good management and leadership depend on it. Practical wisdom involves applying heuristic rules of thumb that guide decision-making. For example, “Think from right to left” – start with the goal and work backward – and “Your biggest risk is you.”

In How Big Things Get Done, we present 11 rules of thumb based on common sense and practical wisdom. These are designed to complement data and forecasting models, including reference class forecasting. The most successful project leaders are those who combine practical wisdom with data and models.

Q: Your research emphasizes the importance of accountability and transparency in reducing misinformation. What practical steps can organizations take to foster a culture of honesty and accuracy in project planning?
A:First, there has to be a genuine desire within the organization to promote integrity, ideally from everyone, and certainly from top management. Without top management’s commitment, success is unlikely. This desire should be embedded in the organization’s policies and strategy.

Second, it’s essential to make it difficult for bad behavior to occur by establishing disincentives for misconduct and positive incentives for good behavior. This system must be continuously monitored and improved, as loopholes can always emerge.

Fortunately, many honest organizations and individuals worldwide demonstrate that it’s possible to succeed. However, human nature means there will always be some bad apples, and sometimes many, in this line of work.

Q: In your opinion, what is the most significant misconception about megaprojects that still persists in both academia and practice?
A: We published an article earlier this year in Harvard Business Review titled The Uniqueness Trap. The core idea of the article is that the common conception that projects are inherently unique is a misconception. The belief is very common, especially in megaprojects, but it also applies to smaller projects. People tend to think that what they are doing is one-of-a-kind.

This, we argue, is a human tendency – a behavioural bias. We all tend to see ourselves as more unique than we are, and this extends to our children, our projects, and our products. The article documents that the more people believe their projects are unique, the worse the projects perform. This is a significant finding: for instance, the perception of uniqueness correlates with much higher cost overruns for projects.

When project leaders think their project is so unique that they cannot learn from others, they often fail to seek previous examples or lessons learned from similar projects. This lack of learning from past experiences leads to repeated mistakes. The most critical issue with this bias is that it prevents project teams from looking at previous projects to avoid making the same errors.

In essence, the uniqueness trap causes people to overlook valuable lessons from history, resulting in consistently poorer performance, especially in large, complex projects. Recognizing and overcoming this bias is crucial for improving project outcomes.

Q: How do you envision the future of megaproject management evolving with the integration of new technologies like AI and big data analytics?
A:AI and big data are already widely used across many fields. I published an article titled AI as Artificial Ignorance on SSRN, which is available for free. The main argument of the article is that current AI, particularly large language models like ChatGPT and Perplexity, do not work very well because they lack any concept of truth. These models are essentially guessing words, they predict the most likely next word or phrase, which is not a reliable basis for decision-making. It often results in strange or incorrect outputs. I illustrate some of these issues in the article, and I emphasize that everyone can test this for themselves.

The key point is that AI should be used in areas where we are highly knowledgeable. When we do so, we can immediately spot its many and significant mistakes. Since these models are just predicting the most probable text, they often produce outputs that are disconnected from reality or factual accuracy. This is the core problem with AI today, in my judgment.

However, I believe the real danger isn’t AI itself, but humans trusting and believing in AI outputs blindly. Our biggest risk is ourselves and how we choose to use AI. The same applies to megaprojects: the challenge isn’t just the technology, but how we rely on it and interpret its results. For the time being, we need to be very cautious and critical in our use of AI, especially in high-stakes contexts.

Q: What are some of the most exciting upcoming projects or research initiatives you are currently involved in or looking forward to?
A: Currently, I’m exploring the fundamental root causes of why some projects succeed while others fail. It turns out that this is a very basic yet powerful explanation that can even account for China’s current success and why they are outperforming the rest of the world both geopolitically and economically in areas like green energy, batteries, EVs, robots, robotaxis, and more.

Discovering fundamental insights like this is incredibly exciting. It’s rare to work on something that offers such a clear explanation for major global phenomena. That’s what I’m deeply engaged with at the moment, and it excites me the most. But there will undoubtedly be many other interesting things ahead even if what I’m doing here and now is what truly motivates me.

Q: What do you think SSRN uniquely contributes to the world of modern research and scholarship, and how do you see it supporting the dissemination of innovative ideas like those in your recent work?
A: I believe SSRN is a tremendous contribution. It’s impossible to overstate how important SSRN and similar platforms are for making knowledge freely and easily accessible worldwide. The platform has a truly global reach, allowing research and ideas to circulate in places where they previously might not have. This broad dissemination can have enormous benefits, both for scholars and for society at large.

For me as a researcher, SSRN is an invaluable tool to share my work and reach readers across the globe. It’s an efficient and highly appreciated way to disseminate knowledge. I’m genuinely grateful that it exists, as it significantly enhances the accessibility and impact of academic work.

Of course, I’m biased as a university person, but the importance of knowledge to human development and progress cannot be overstated. SSRN plays a crucial role in advancing and spreading knowledge, and I see it as a vital part of that broader mechanism.

More About Bent Flyvbjerg

Bent Flyvbjerg’s work has had a profound impact on the fields of project management, infrastructure, and city planning. He is the most cited researcher in the world on megaprojects and has authored or edited ten books and over 200 papers, translated into 22 languages. His research in behavioral science includes the study of strategic misrepresentation, uniqueness bias, optimism bias, the planning fallacy, and the development of reference class forecasting methods that have significantly improved project accuracy and accountability.

Bent’s approach is rooted in his development of phronetic social science, a methodology that emphasizes practical wisdom, power dynamics, and rationality in social science research. His work highlights the importance of transparency, accountability, and incentives in reducing misinformation and improving project outcomes. He has advised governments, organizations, and C-suites worldwide, and is chairman of Oxford Global Projects.

You can see more work by Bent Flyvbjerg on his SSRN Author page here.