Reprinted from the Britten Coyne Partners Strategic Risk Blog
Once the immediate challenges posed by the global COVID-19 pandemic have been met, as they surely shall, a great deal of research will focus on this question: Why did it seem to take so many people, companies, and governments by surprise?
In our work with clients, we stress that strategic disasters results from some combination of three failures: to anticipate a threat, to accurately assess its potential impact, and to adapt to it in time.
The root causes of each of these failures lie in a complex mix of interacting individual, group, network, and organizational factors. In this post, we’ll take a closer look at recent research into one of these: the ways human beings react to surprise.
Some researchers distinguish between two types of surprise, which they found to activate different areas of the brain (see “Information Theoretic Characterization of Uncertainty” by Loued-Khenissi and Preuschoff).
“Stimulus Surprise” is triggered by something that (a) is not consistent with our expectations; and (b) whose potential impact on us or on people we care about is substantially negative. This type of surprise is an emotional alarm signal that automatically triggers our “System 1” “fight or flight” response.
In contrast, “Bayesian Surprise” is, at first glance, a conscious, “System 2” cognitive response that triggers reflection and learning to improve the accuracy of our mental models and the expectations they produce.
However, in “Neural Mechanisms of Updating Under Reducible and Irreducible Uncertainty”, Kobayashi and Hsu find that brain regions associated with learning automatically increase their activity only in the presence of reducible uncertainty. So here too there is an automatic aspect to our response to surprise.
In “Evidence for Surprise Minimization and Value Maximization in Choice Behavior”, Schwartenbeck et al note that, “classical economic models are predicated on the idea that the ultimate aim of choice is to maximize utility or reward. In contrast, an alternative perspective highlights the fact that adaptive behavior requires agents’ to model their environment and minimize surprise about the states they frequent.” The authors present evidence that “choice behavior can be more accurately accounted for by surprise minimization compared to reward or utility maximization alone.”
A considerable amount of research has identified numerous obstacles to our ability to accurately update our mental models of the world (e.g., our natural human tendencies toward over-optimism, overconfidence, hindsight bias, and, especially when uncertainty is high, conformity to the views of our group or a dominant leader). New research has added to this list.
In “All Thinking is Wishful Thinking”, Kruglanski begin by succinctly describing the ideal updating process: “Basically, we construct new beliefs from prior beliefs by assimilating new evidence. We do so through an inference process probabilistically modeled by Bayesian principles. According to that portrayal, relevant evidence (to which we are exposed) occasions an updating of our beliefs on the topic.”
“In Bayesian belief updating, two components are crucial: (i) the strength of the prior belief; namely, the subjective probability of it being true; and (ii) the cogency of the new evidence; namely, the degree to which it strengthens or weakens prior beliefs. In other words, people update their prior beliefs given new evidence, depending on whether the new evidence is perceived as precise, strong, and relevant (versus imprecise, weak, and irrelevant) and whether their prior belief was held with high (versus low) confidence. The change in prior beliefs, in light of the new evidence, is quantified by the degree of “informational gain” or Bayesian surprise.”
However, the authors go on to present evidence that “the belief updating process is suffused by motivation; people actively seek to obtain, avoid, or create new information about the world to increase the consistency between their [existing] models and the evidence at hand.”
In “Valuation of Knowledge and Ignorance in Mesolimbic Reward Circuitry”, Charpentier et al study the activation of various parts of the brain after positive and negative surprises. They find that humans “pursue opportunities to gain knowledge about favorable outcomes but not unfavorable ones…We choose ignorance about future undesirable outcomes more often than desirable ones.”
In “Evidence Accumulation is Biased by Motivation”, Gesiarz et al reach a similar conclusion: People tend to gather information before making judgments. As information is often unlimited a decision has to be made as to when the data is sufficient to reach a conclusion. Here, we show that the decision to stop gathering data is influenced by whether the data points towards a desirable or undesirable conclusion…The motivation to hold a certain belief decreases the need for supporting evidence.”
At this point, many people reading this will be nodding their head in painful recognition of the authors’ conclusion.
Who has not at some point found themselves in a meeting where a surprising result or new piece of information was either dismissed as an anomaly not worth exploring, or when the potential implications of the surprise created so much cognitive dissonance (and/or political risk for some people in the room) that they were dismissed as implausible (which is not the same as impossible).
These situations always bring to mind the conclusion reached by a 1983 CIA study of failed forecasts: “Each involved historical discontinuity, and, in the early stages…unlikely outcomes. The basic problem was…situations in which trend continuity and precedent were of marginal, if not counterproductive value” (“Report on the Study of Intelligence Judgments Preceding Significant Historical Failures”).
That is why being alert to surprises, and committed to discovering the meaning of the high value information they contain, is one of the hallmarks of high reliability organizations.
These new research findings provide different lenses we can use to better understand the initial reactions we observed to the increasing flow of news about the appearance of a new coronavirus in Wuhan, and then its exponential spread around the globe.
For many (perhaps most) people, their initial assessment of early news items about a novel coronavirus was strongly affected by motivated cognition, and a desire to avoid collecting information about the potential negative consequences of the new virus. This would have particularly been the case if they held strong prior views based on memories that the successful containment of both the 2003 SARS coronavirus outbreak and the 2012 MERS coronavirus outbreak.
But between January 23rd (when Wuhan was locked down) and March 8th (when the quarantine of Lombardy was announced), people around the world experienced a “Stimulus Surprise” which initially produced a sharp spike in uncertainty and anxiety, leading to increased social copying like the panic buying of toilet paper, masks, hand sanitizer, and other supplies.
This was followed by the still ongoing “Bayesian Surprise” phase, in which people have struggled to sort through an exponentially increasing flood of information (of varying value and credibility), to gain some measure of situation awareness as the first step in formulating expectations about possible future scenarios and designing a “go forward” plan.
For some (perhaps most) people, this process was undoubtedly complicated by the cognitive bias Daniel Kahneman has called “What You See Is All There Is” or WYSIATI. This refers to our tendency to reach quick intuitive judgments using a narrative constructed solely on the basis of information that is in front of us, and rather than a more deliberate approach that takes into account a wider range of unresolved uncertainties and the different future scenarios they imply.
For other people, this sensemaking process has been a more systematic struggle to develop a better understanding of the complex trends and uncertainties driving the evolving COVID-19 situation, their relationships to each other, and the potential future outcomes they could create.
Regardless of the approach used, reducing the unprecedented level of uncertainty created by COVID-19 continues to be a slow process, with progress only made at a high mental and emotional cost as we work through the surprises we have experienced.