Curated Content from 25 Years of Writing

Decision Making, Risk, and Uncertainty

Whether as a consultant working on turnaround and growth issues, CFO, CEO, parent, coach, or writer about investment management, I have spent a great deal of time over the past 25 years thinking and writing about how to make good decisions in the face of time pressure, uncertainty, and information overload.

On the decision making side, if I had to summarize the key lessons I've learned, I would point to the following. First, there is a critical difference between situations in which we know the full range of possible outcomes, as well as their associated probabilities and impacts, and situations in which this is not the case. Frank Knight called the firs situation "risk" and the second, "uncertainty." Unfortunately, when schools teach decision making, they focus on those rare situations that are risky (e.g., rolling dice, or drawing cards), instead of the far greater challenge of making decisions in the face of uncertainty, which is what we usually face in our daily lives. Second, Gary Klein, Gerd Gigerenzer and others have focused on the differences between the "naturalistic" way we make most decisions in our lives, and the relatively rare occasions when we employ a "classical" or "rational" multi-attribute decision process (i.e., formulating options and criteria for evaluating them, performing the evaluation, etc.).

Third, Mica Endsley and her colleagues (and John Boyd before her) have shown us the critical importance of situation awareness in decision situations, while writers on the subject of surprise attack (like Richard Betts, Cynthia Grabo, and Ephraim Kam) help us better understand its inherent limitations. Fourth, the work of Peter Pirolli, to say nothing of the enduring applicability of Thomas Bayes, help us to better manage our thinking and research time in the face of information overload. Pirolli's work on the "
Sensemaking Loop for Analysts" is, for me, a classic that every student should be taught (but few if any are).

My thinking has also been deeply influenced by writers who have highlighted the limitations of quantitative modeling. This includes not only Emanuel Derman's excellent book on
Models.Behaving.Badly, but, also Hemez and Haim's paper on "The Good, the Bad, and The Ugly of Predictive Science." The authors of this excellent paper make the critical point, which too many model builders and users fail to understand, that there are inescapable tradeoffs between a model's fidelity to historical data (i.e., "fit"), its robustness to uncertainty, and the confidence one can have in its predictions. You can have two of these, but only at the expense of the third.

Four other writers whose excellent research has had a substantial impact on my views are Jerker Denrell, Leonard Mlodinow, Nassim Taleb, and Russ Wermers. Their work forces us to confront an uncomfortable truth -- that luck/randomness plays a much greater role in life's outcomes than we can comfortably acknowledge. This is related to another point I've frequently made over the years -- that given challenge of uncertainty and the role of randomness, our decisions are best judged on the basis of the quality of the process that was used to make them, rather than on their eventual outcomes.

Finally, the work of Kahneman and Tversky, Ariely, Heuer, Coates and others have forced us to acknowledge that assessment and decision making is inevitably both an emotional and rational process, that emotional inputs are equally valid, and that in using our cognitive faculties we will always be subject to errors and biases, not to mention the under appreciated impact of randomness. For all these reasons, I have learned over time to judge decisions not on their outcomes, but rather on the quality of the process that was used to make them, often in the face of time pressure and uncertainty. And I have also learned that when intuition and analytical results don't agree, the right response is to dig deeper to understand why, and not to reject intuition out of hand.

Another lesson I've learned over the years is that we pay too little attention to failure, and too much to success. This seems to not only reinforce our natural tendencies towards over-optimism and overconfidence, but also weaken our ability to learn (indeed, studies have repeatedly shown that we learn much more from failure than we do from success). Yet it is the success stories that usually make it into the media, history, and business books. To be sure, there are exceptions; in fact, I'd recommend that any student spend some time reading all of the following: (1)
Choke, by Sian Beilock; (2) Inviting Disaster, by James Chiles; (3) How the Mighty Fall, by Jim Collins; (4) The Logic of Failure, by Dietrich Dorner; (5) Why Smart Executives Fail, by Sydney Finkelstein; (6) Why Decisions Fail, by Paul Nutt; and (7) Why Most Things Fail, by Paul Ormerod.

All of these lessons have contributed to the papers I've written on decision making in the face of uncertainty, including a guide to this issue for
younger children; an overview of causal reasoning, a note on "Practical Hypothesis Testing to Guide Decision Making in Complex Adaptive Systems", a Letter to New Graduates on decision making issues, a short guide for adults, and longer papers on "The Powerful Impact of Regret", "Understanding Uncertainty Shocks", "Why Managers and Investors Get Surprised", and "Understanding and Predicting Uncertainty Shocks".

The logical complement to my work on decision making in the face of uncertainty has been my work on risk management. In one interesting paper, I took a stab at
Grounding Risk Management in Neuroscience, to summarize the rapidly growing and very important research in this area. In a more recent paper, I also stepped back to summarize my views on A Practical Look at Enterprise Risk Management, and what organizations should expect from it. The most recent version of this paper is available on the Social Science Research Network. The key point I make is that avoiding failure and achieving success are not simply two sides of the same coin; rather, they are very different phenomenon. In the face of uncertainty, the key role of enterprise risk management is to avoid failure and ensure survival, and thus the time needed for a combination of skill and luck to produce significant success. I have also written a more detailed articles on "A Practical Approach to the Vexing Issue of 'Risk Appetite'", and on assessing and managing model risk. Last but not least, I can also vouch for the successful application of these risk management lessons in a variety of contexts, not the least of which was instituting some simple rules to quickly turn around the safety culture at an oilfield services company.