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A Choice Prediction Competition: Choices from Experience and from Description

机译:选择预测竞赛:经验和描述中的选择

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摘要

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.
机译:Erev,Ert和Roth组织了三项选择预测竞赛,重点关注三个相关的选择任务:一是描述中的决策(风险决策),一是经验中的决策,以及经验中的重复决策。每个竞赛都基于两个实验数据集:一个估算数据集和一个竞赛数据集。生成两个数据集的研究使用相同的方法和主题库,并检查了从相同分布中随机选择的决策问题。在收集了用于估计的实验数据后,组织者将它们连同适合的几种基线模型发布到了Web上,并挑战其他研究人员竞争以预测第二组(竞争性)实验会议的结果。 14个团队对此挑战做出了回应:本文的最后7位作者是获胜团队的成员。结果突出了描述决策与经验决策之间差异的鲁棒性。假设对加权值的敏感性随累积支付函数之间的距离而降低,则使用前景理论的随机变体获得了来自描述的最佳决策预测。根据经验得出的最佳预测预测是通过假设依赖小样本的模型获得的。讨论了竞争方法的优缺点。

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