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基于自动加工的多理由决策

Multiple-reason decision making based on automatic processing.

作者信息

Glöckner Andreas, Betsch Tilmann

机构信息

Max Planck Institute for Research on Collective Goods, Bonn, Germany.

出版信息

J Exp Psychol Learn Mem Cogn. 2008 Sep;34(5):1055-75. doi: 10.1037/0278-7393.34.5.1055.

Abstract

It has been repeatedly shown that in decisions under time constraints, individuals predominantly use noncompensatory strategies rather than complex compensatory ones. The authors argue that these findings might be due not to limitations of cognitive capacity but instead to limitations of information search imposed by the commonly used experimental tool Mouselab (J. W. Payne, J. R. Bettman, & E. J. Johnson, 1988). The authors tested this assumption in 3 experiments. In the 1st experiment, information was openly presented, whereas in the 2nd experiment, the standard Mouselab program was used under different time limits. The results indicate that individuals are able to compute weighted additive decision strategies extremely quickly if information search is not restricted by the experimental procedure. In a 3rd experiment, these results were replicated using more complex decision tasks, and the major alternative explanations that individuals use more complex heuristics or that they merely encode the constellation of cues were ruled out. In sum, the findings challenge the fundaments of bounded rationality and highlight the importance of automatic processes in decision making.

摘要

研究已反复表明,在时间限制下进行决策时,个体主要使用非补偿性策略,而非复杂的补偿性策略。作者认为,这些发现可能并非源于认知能力的限制,而是由于常用实验工具Mouselab(J. W. 佩恩、J. R. 贝特曼和E. J. 约翰逊,1988年)所施加的信息搜索限制。作者在3个实验中对这一假设进行了检验。在第一个实验中,信息是公开呈现的,而在第二个实验中,标准的Mouselab程序在不同的时间限制下使用。结果表明,如果信息搜索不受实验程序的限制,个体能够极其迅速地计算加权加法决策策略。在第三个实验中,使用更复杂的决策任务重复了这些结果,并排除了个体使用更复杂启发式方法或仅仅对线索组合进行编码的主要替代解释。总之,这些发现挑战了有限理性的基础,并突出了自动过程在决策中的重要性。

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