From anomalies to forecasts: Toward a descriptive model of decisions under risk, under ambiguity, and from experience.

Abstract

Experimental studies of choice behavior document distinct, and sometimes contradictory, deviations from maximization. For example, people tend to overweight rare events in oneshot decisions under risk, and to exhibit the opposite bias when they rely on past experience.The common explanations of these results assume that the contradicting anomalies reflect situation-specific processes that involve the weighting of subjective values and the use of simple heuristics. The current paper analyzes 14 choice anomalies that have been described by different models, including the Allais, St. Petersburg, and Ellsberg paradoxes, and the reflection effect. Next, it uses a choice prediction competition methodology to clarify the interaction between the different anomalies. It focuses on decisions under risk (known payoff distributions) and under ambiguity (unknown probabilities), with and without feedback concerning the outcomes of past choices. The results demonstrate that it is not necessary to assume situation-specific processes. The distinct anomalies can be captured by assuming high sensitivity to the expected return and four additional tendencies: pessimism, bias toward equal weighting, sensitivity to payoff sign, and an effort to minimize the probability of immediate regret. Importantly, feedback increases sensitivity to probability of regret. Simple abstractions of these assumptions, variants of the model Best Estimate And Sampling Tools (BEAST), allow surprisingly accurate ex ante predictions of behavior. Unlike the popular models, BEAST does not assume subjective weighting functions or cognitive shortcuts.Rather, it assumes the use of sampling tools and reliance on small samples, in addition to the estimation of the expected values.

The impact of rewarding medium effort and the role of sample size

“…Previous studies of the decisions based on past experience focus on binary choice tasks. These studies document a tendency to underweight rare events and highlight the descriptive value of models that assume reliance on small samples (Barron & Erev, 2003;Hertwig, Barron, Weber, & Erev, 2004;Erev et al, 2017;Erev & Haruvy, 2016;Rakow & Newell, 2010; and see related discussion in Osborne & Rubinstein, 1998).…”

Magnitude and incentives: revisiting the overweighting of extreme events in risky decisions from experience

“…One possibility is that rather than an "either/or", the notion of a bias toward extreme outcomes and some form of diminishing sensitivity are required to explain the results. Assuming that choices in risky decisions from experience follow a (evidently) biased (for example, due to underweighting of rare events, see Hertwig & Erev, 2009) multiplicative integration (i.e., expected value calculation) of observed outcomes and learned probabilities (see Erev, Ert, Plonsky, Cohen, & Cohen, 2017;Gonzalez & Dutt, 2011), then a choice bias for the extreme events can be manifested either on the numerical value of the outcome (i.e., utility function), or the objective probabilities (i.e., probability weighting function), or it can affect both.…”

The impact of variability and prechoice experience on taking safety measures: The case of security updates

“…Regret theory (Loomes & Sudgen, ) demonstrates that when choosing between two alternatives, individuals are sensitive to the possibility of regretting forgoing one of the options. Erev, Ert, Plonsky, Cohen, and Cohen () extended this research and showed that experienced regret slows learning. In the current context, variability in the cost of a safety measure implies that people who pay a high cost for taking a safety measure are likely to regret this choice later, when the cost is reduced (even when paying high cost is the optimal choice).…”

“…In order to evaluate this “prechoice experience” explanation, we chose to use data collected by Erev et al (). The participants in Erev et al's studies faced each problem for 25 trials and had to choose between a safe option (getting an outcome with certainty) and a risky option (a gamble).…”

“…In order to evaluate this "prechoice experience" explanation, we chose to use data collected by Erev et al (2017). The participants in Under an alternative explanation of the low initial updating rate, it reflects a reaction to the "updating" cover story used here, as opposed to the typical abstract choice between gambles used in Erev et al (2017) and the studies summarized by Tversky and Kahneman (1992).…”