Confidence, Part II

Now the confidence heuristic is not the only thing Trump takes advantage of, but we will leave those for another time. I will also avoid the question of whether or not Trump is actually confident. So what is the relationship of confidence and decision making? Daniel Kahneman in Thinking, Fast and Slow on page 13  describes:

a puzzling limitation of our mind:  our excessive confidence in what we believe we know, and our apparent inability to acknowledge the full extent of our ignorance and the uncertainty of the world we live in. We are prone to overestimate how much we understand about the world and to underestimate the role of chance in events. Overconfidence is fed by the illusory certainty of hindsight.

Kahneman and Hogarth on Confidence

Kahneman concludes that the effects  of high optimism on decision making are, at best, a mixed blessing, but the contributions of optimism to good implementation is certainly positive. The main benefit of optimism is resilience or persistence in the face of setbacks.

Robin Hogarth in Educating Intuition (pages 150-3) notes that humans typically assess uncertainty intuitively (Kahneman’s System 1). Uncertainty, being the other side of confidence, is an important decision making input best expressed in probabilistic terms. Hogarth notes that we tend to be overconfident because we underestimate uncertainty. However, we do better when we look at a series of cases and not just one and when we think about frequency and not probability.

Kahneman agrees (page 340).

Decision makers who are prone to narrow framing construct a preference every time they face a risky choice. They would do better by having a risk policy that they routinely apply whenever a relevant problem arises. Familiar examples of risk policies are ” always take the highest possible deductible when purchasing insurance” and ” never buy extended warranties.” A risk policy is a broad frame. In the insurance examples, you expect the occasional loss of the entire deductible, or the occasional failure of an uninsured product. The relevant issue is your ability to reduce or eliminate the pain of the occasional loss by the thought that the policy that left you exposed to it will almost certainly be financially advantageous over the long run.

A risk policy that aggregates decisions is analgous to Hogarth’s probabilistic view. They are remedies against exaggerated optimism and exaggerated caution induced by loss aversion.  Exaggerated optimism protects individuals from the the paralyzing effects of loss aversion; loss aversion protects them from the follies of overconfident optimism. Kahneman notes that:

The upshot is rather comfortable for the decision maker. Optimists believe that the decisions they make are more prudent than they really are, and loss-averse decision makers correctly reject marginal propositions that they might otherwise accept.

On-line Confidence Monitoring

So how do we assess our own confidence in particular decisions and become overconfident or not so overconfident people? Let me start with the paper:  “On-line confidence monitoring during decision making,” that appeared in Cognition (171), 112-121 (2018) authored by Dror  Dotan, Florent Meyniel, and Stanislas Dehaene. Dehaene is good at translating for the layman how the brain and nervous system work and is the author of several understandable books.

There is not general agreement of how confidence is computed. Dotan et. al. assert that two models of confidence computation have been proposed: post-hoc computation using post-decision variables and heuristics, versus online computation using continuous assessment of evidence throughout the decision-making process. The authors conducted experiments to arbitrate between these  theories by continuously monitoring finger movements during a manual sequential decision-making task. Analysis of finger movements indicated that subjects kept separate online records of evidence and confidence: finger deviation continuously reflected the ongoing accumulation of evidence, whereas finger speed continuously reflected the momentary degree of confidence. According to Dotal et. al end-of-trial finger speed predicted the post-decisional subjective confidence rating. These data indicate that confidence is computed on-line, throughout the decision process. Speed-confidence correlations were previously interpreted as a post-decision heuristics, whereby slow decisions decrease subjective confidence, but the authors suggest an adaptive mechanism that involves the opposite causality: by slowing down when not confident, participants gain time to improve their decisions.

Dotal et. al. note that the online and post-decisional accounts of confidence are not mutually exclusive but complementary since even if confidence is computed online, it can still be submitted to various post-decisional transformations and biases before one reaches a conscious, reportable level of subjective confidence in a decision.

The authors conclude that confidence is computed continuously, online, throughout the decision making process, thus lending support to models of the mind as a device that computes with probabilistic estimates and probability distributions.


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