ABSTRACT-When people have access to information sources such as newspaper weather forecasts, drug-package inserts, and mutual-fund brochures, all of which provide convenient descriptions of risky prospects, they can make decisions from description. When people must decide whether to back up their computer's hard drive, cross a busy street, or go out on a date, however, they typically do not have any summary description of the possible outcomes or their likelihoods. For such decisions, people can call only on their own encounters with such prospects, making decisions from experience. Decisions from experience and decisions from description can lead to dramatically different choice behavior. In the case of decisions from description, people make choices as if they overweight the probability of rare events, as described by prospect theory. We found that in the case of decisions from experience, in contrast, people make choices as if they underweight the probability of rare events, and we explored the impact of two possible causes of this underweighting-reliance on relatively small samples of information and overweighting of recently sampled information. We conclude with a call for two different theories of risky choice.

According to a common conception in behavioral decision research, two cognitive processes-overestimation and overweighting-operate to increase the impact of rare events on people's choices. Supportive findings stem primarily from investigations in which people learn about options via descriptions thereof. Recently, a number of researchers have begun to investigate risky choice in settings in which people learn about options by experiential sampling over time. This article reviews work across three experiential paradigms. Converging findings show that when people make decisions based on experience, rare events tend to have less impact than they deserve according to their objective probabilities. Striking similarities in human and animal experience-based choices, ways of modeling these choices, and their implications for risk and precautionary behavior are discussed.

We use simple learning models to track the behavior observed in experiments concerning three extensive form games with similar perfect equilibria. In only two of the games does observed behavior approach the perfect equilibrium as players gain experience. We examine a family of learning models which possess some of the robust properties of learning noted in the psychology literature. The intermediate term predictions of these models track well the observed behavior in all three games, even though the models considered differ in their very long term predictions. We argue that for predicting observed behavior the intermediate term predictions of dynamic learning models may be even more important than their asymptotic properties. Journal of Economic Literature Classification Numbers: C7, C92.

The present paper explores situations in which the information available to decision makers is limited to feedback concerning the outcomes of their previous decisions. The results reveal that experience in these situations can lead to deviations from maximization in the opposite direction of the deviations observed when the decisions are made based on a description of the choice problem. Experience was found to lead to a reversed common ratio/certainty effect, more risk seeking in the gain than in the loss domain, and to an underweighting of small probabilities. Only one of the examined properties of description-based decisions, loss aversion, seems to emerge robustly in these 'feedback-based' decisions. These results are summarized with a simple model that illustrates that all the unique properties of feedback-based decisions can be a product of a tendency to rely on recent outcomes. Copyright # 2003 John Wiley & Sons, Ltd. key words Probability learning; feedback-based decisions; reinforcement learning; prospect theory; Allais paradox Many common activities involve 'small' decision problems. Driving, for example, requires repeated selection among routes, speeds, and various other options. Although little time and effort is typically invested in these and similar small decisions, they can be consequential. The estimated cost of traffic accidents in the USA is more than 100 billion dollars a year (see e.g. Blincoe, 1994 1 ), and many of the accidents are at least partially products of ex-post unwise decisions.The current paper focuses on an important subset of the small decision problems exemplified above that can be referred to as 'small feedback-based' decisions. These problems are defined by three main properties. First, they are repeated; decision makers face the same problem many times in similar situations. Second,

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Analysis of binary choice behavior in iterated tasks with immediate feedback reveals robust deviations from maximization that can be described as indications of 3 effects: (a) a payoff variability effect, in which high payoff variability seems to move choice behavior toward random choice; (b) underweighting of rare events, in which alternatives that yield the best payoffs most of the time are attractive even when they are associated with a lower expected return; and (c) loss aversion, in which alternatives that minimize the probability of losses can be more attractive than those that maximize expected payoffs. The results are closer to probability matching than to maximization. Best approximation is provided with a model of reinforcement learning among cognitive strategies (RELACS). This model captures the 3 deviations, the learning curves, and the effect of information on uncertainty avoidance. It outperforms other models in fitting the data and in predicting behavior in other experiments.

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. Copyright © 2009 John Wiley & Sons, Ltd.

ABSTRACT-When people have access to information sources such as newspaper weather forecasts, drug-package inserts, and mutual-fund brochures, all of which provide convenient descriptions of risky prospects, they can make decisions from description. When people must decide whether to back up their computer's hard drive, cross a busy street, or go out on a date, however, they typically do not have any summary description of the possible outcomes or their likelihoods. For such decisions, people can call only on their own encounters with such prospects, making decisions from experience. Decisions from experience and decisions from description can lead to dramatically different choice behavior. In the case of decisions from description, people make choices as if they overweight the probability of rare events, as described by prospect theory. We found that in the case of decisions from experience, in contrast, people make choices as if they underweight the probability of rare events, and we explored the impact of two possible causes of this underweighting-reliance on relatively small samples of information and overweighting of recently sampled information. We conclude with a call for two different theories of risky choice.