About: Description-experience gap is a research topic. Over the lifetime, 60 publications have been published within this topic receiving 30344 citations.
TL;DR: This work found that in the case of decisions from experience, people make choices as if they underweight the probability of rare events, and explored the impact of two possible causes of this underweighting—reliance on relatively small samples of information and overweighting of recently sampled information.
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.
TL;DR: 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.
TL;DR: The authors explored situations in which the information available to decision makers is limited to feedback concerning the outcomes of their previous decisions and found 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.
Abstract: 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.
TL;DR: In this article, a belief-based account of decision under uncertainty is proposed. But it is not consistent with the partition inequality implied by the classical theory of decision-under-uncertainty.
Abstract: We develop a belief-based account of decision under uncertainty. This model predicts decisions under uncertainty from (i) judgments of probability, which are assumed to satisfy support theory; and (ii) decisions under risk, which are assumed to satisfy prospect theory. In two experiments, subjects evaluated uncertain prospects and assessed the probability of the respective events. Study 1 involved the 1995 professional basketball playoffs; Study 2 involved the movement of economic indicators in a simulated economy. The results of both studies are consistent with the belief-based account, but violate the partition inequality implied by the classical theory of decision under uncertainty.