Monday, December 14, 2020

Implications of Randomness: Lessons from Nassim Taleb

Most of us know Nassim Nicholas Taleb from his bestseller The Black Swan. However, he wrote an earlier book, Fooled by Randomness*, in which he laid out one of his seminal propositions: a lot of things in life that we believe have identifiable, deterministic causes such as prescient decision making or exceptional skills, are actually the result of more random processes. Taleb focuses on financial markets but we believe his observations can refine our thinking about organizational decision making, mental models, and culture.

We'll begin with an example of how Taleb believes we misperceive reality. Consider a group of stockbrokers with successful 5-year track records. Most of us will assume they must be unusually skilled. However, we fail to consider how many other people started out as stockbrokers 5 years ago and fell by the wayside because of poor performance. Even if all the stockbrokers were less skilled than a simple coin flipper, some would still be successful over a 5 year period. The survivors are the result of an essentially random process and their track records mean very little going forward.

Taleb ascribes our failure to correctly see things (our inadequate mental models) to several biases. First is the hindsight bias where the past is always seen as deterministic and feeds our willingness to backfit theories or models to experience after it occurs. Causality can be very complex but we prefer to simplify it. Second, because of survivorship bias, we see and consider only the current survivors from an initial cohort; the losers do not show up in our assessment of the probability of success going forward. Our attribution bias tells us that successes are due to skills, and failures to randomness.

Taleb describes other factors that prevent us from being the rational thinkers postulated by classical economics or Cartesian philosophy. One set of factors arises from how are brains are hardwired and another set from the way we incorrectly process data presented to us.

The brain wiring issues include the work of Daniel Kahneman who describes how we use and rely on heuristics (mental shortcuts that we invoke automatically) to make day-to-day decisions. Thus, we make many decisions without really thinking or applying reason, and we are subject to other built-in biases, including our overconfidence in small samples and the role of emotions in driving our decisions. We reviewed Kahneman's work at length in our Dec. 18, 2013 post. Taleb notes that we also have a hard time recognizing and dealing with risk. Risk detection and risk avoidance are mediated in the emotional part of the brain, not the thinking part, so rational thinking has little to do with risk avoidance.

We also make errors when handling data in a more formal setting. For example, we ignore the mathematical truth that initial sample sizes matter greatly, much more than the sample size as a percentage of the overall population. We also ignore regression to the mean, which says that absent systemic changes, performance will eventually return to its average value. More perniciously, ignorant or unethical researchers will direct their computers to look for any significant relationship in a data set, a practice that can often produce a spurious relationship because all the individual tests have their own error rates. “Data snoops” will define some rule, then go looking for data that supports it. Why are researchers inclined to fudge their analyses? Because research with no significant result does not get published.

Our Perspective

We'll start with the obvious: Taleb has a large ego and is not shy about calling out people with whom he disagrees or does not respect. That said, his observations have useful implications for how we conceptualize the socio-technical systems in which we operate, i.e., our mental models, and present specific challenges for the culture of our organizations.

In our view, the three driving functions for any system's performance over time are determinism (cause and effect), choice (decision making), and probability. At heart, Taleb's world view is that the world functions more probabilistically than most people realize. A method he employs to illustrate alternative futures is Monte Carlo simulation, which we used to forecast nuclear power plant performance back in the 1990s. We wanted plant operators to see that certain low-probability events, i.e., Black Swans**, could occur in spite of the best efforts to eliminate them via plant design, improved equipment and procedures, and other means. Some unfortunate outcomes could occur because they were baked into the system from the get-go and eventually manifested. This is what Charles Perrow meant by “normal accidents” where normal system performance excursions go beyond system boundaries. For more on Perrow, see our Aug. 29,2013 post.

Of course, the probability distribution of system performance may not be stationary over time. In the most extreme case, when all system attributes change, it's called regime change. In addition, system performance may be nonlinear, where small inputs may lead to a disproportionate response, or poor performance can build slowly and suddenly cascade into failure. For some systems, no matter how specifically they are described, there will inherently be some possibility of errors, e.g., consider healthcare tests and diagnoses where both false positives and false negatives can be non-trivial occurrences.

What does this mean for organizational culture? For starters, the organization must acknowledge that many of its members are inherently somewhat irrational. It can try to force greater rationality on its members through policies, procedures, and practices, instilled by training and enforced by supervision, but there will always be leaks. A better approach would be to develop defense in depth designs, error-tolerant sub-systems with error correction capabilities, and a “just culture” that recognizes that honest mistakes will occur.

Bottom line: You should think awhile about how many aspects of your work environment have probabilistic attributes.


* N.N. Taleb, Fooled by Randomness, 2nd ed. (New York: Random House) 2004.

** Black swans are not always bad. For example, an actor can have one breakthrough role that leads to fame and fortune; far more actors will always be waiting tables and parking cars.