On Surprise, Change, and the Effect of Recent Outcomes

The leading models of human and animal learning rest on the assumption that individuals tend to select the alternatives that led to the best recent outcomes. The current research highlights three boundaries of this “recency” assumption. Analysis of the stock market and simple laboratory experiments suggests that positively surprising obtained payoffs, and negatively surprising forgone payoffs reduce the rate of repeating the previous choice. In addition, all previous trails outcomes, except the latest outcome (most recent), have similar effect on future choices. We show that these results, and other robust properties of decisions from experience, can be captured with a simple addition to the leading models: the assumption that surprise triggers change

UNDER-DIVERSIFICATION AND THE ROLE OF BEST REPLY TO PATTERN

Three experiments are presented that compare alternative explanations to the coexistence of risk aversion and under-diversification in investment decisions. The participants were asked to select one of several assets under two feedback conditions. In each case, one asset was a weighted combination of the other assets, allowing for lower volatility. The frequency of choice of the composite asset was highly sensitive to feedback condition. The composite asset was the least popular asset when the feedback included information concerning forgone payoffs, and increased in frequency when the feedback was limited to the obtained payoff. These results support the assertion that under-diversification can be a product of learning from feedback and in particular best reply to pattern.Risk; Diversification; Learning

On the descriptive value of loss aversion in decisions under risk: Six clarifications

Previous studies of loss aversion in decisions under risk have led to mixed results. Losses appear to loom larger than gains in some settings, but not in others. The current paper clarifies these results by highlighting six experimental manipulations that tend to increase the likelihood of the behavior predicted by loss aversion. These manipulations include: (1) framing of the safe alternative as the status quo; (2) ensuring that the choice pattern predicted by loss aversion maximizes the probability of positive (rather than zero or negative) outcomes; (3) the use of high nominal (numerical) payoffs; (4) the use of high stakes; (5) the inclusion of highly attractive risky prospects that creates a contrast effect; and (6) the use of long experiments in which no feedback is provided and in which the computation of the expected values is difficult. In addition, the results suggest the possibility of learning in the absence of feedback: The tendency to select simple strategies, like “maximize the worst outcome” which implies “loss aversion”, increases when this behavior is not costly. Theoretical and practical implications are discussed

Correction: Erev, I. et al. a choice prediction competition for market entry games : an introduction

Ion Juvina found an error in our manuscript published in Games

Stationary concepts for experimental 2x2-games

Five stationary concepts for completely mixed 2 x 2-games are experimentally compared: Nash equilibrium, quantal response equilibrium, action-sampling equilibrium, payoff-sampling equilibrium (Martin J. Osborne and Ariel Rubinstein 1998), and impulse balance equilibrium. Experiments on 12 games, 6 constant sum games, and 6 nonconstant sum games were run with 12 independent subject groups for each constant sum game and 6 independent subject groups for each nonconstant sum game. Each independent subject group consisted of four players 1 and four players 2, interacting anonymously over 200 periods with random matching. The comparison of the five theories shows that the order of performance from best to worst is as follows: impulse balance equilibrium, payoff-sampling equilibrium, action-sampling equilibrium, quantal response equilibrium, Nash equilibrium

Predicting human decisions with behavioral theories and machine learning

Behavioral decision theories aim to explain human behavior. Can they help predict it? An open tournament for prediction of human choices in fundamental economic decision tasks is presented. The results suggest that integration of certain behavioral theories as features in machine learning systems provides the best predictions. Surprisingly, the most useful theories for prediction build on basic properties of human and animal learning and are very different from mainstream decision theories that focus on deviations from rational choice. Moreover, we find that theoretical features should be based not only on qualitative behavioral insights (e.g. loss aversion), but also on quantitative behavioral foresights generated by functional descriptive models (e.g. Prospect Theory). Our analysis prescribes a recipe for derivation of explainable, useful predictions of human decisions

A Choice Prediction Competition: Choices From Experience and From Description

Erev, Ert, and Roth organized three choice prediction competitions focused on three related choice tasks: one shot decisions from description (decisions under risk), one shot decisions from experience, and repeated decisions from experience. Each competition was based on two experimental datasets: An estimation dataset, and a competition dataset. The studies that generated the two datasets used the same methods and subject pool, and examined decision problems randomly selected from the same distribution. After collecting the experimental data to be used for estimation, the organizers posted them on the Web, together with their fit with several baseline models, and challenged other researchers to compete to predict the results of the second (competition) set of experimental sessions. Fourteen teams responded to the challenge: the last seven authors of this paper are members of the winning teams. The results highlight the robustness of the difference between decisions from description and decisions from experience. The best predictions of decisions from descriptions were obtained with a stochastic variant of prospect theory assuming that the sensitivity to the weighted values decreases with the distance between the cumulative payoff functions. The best predictions of decisions from experience were obtained with models that assume reliance on small samples. Merits and limitations of the competition method are discussed.Economic