经验性决策:不完备信息条件下的搜索与学习

Experience-based Decisions:Exploring and Learning under Incomplete Information Conditions

经验性决策是个体基于经验做出的决策,其特点表现为信息不完备、经验依赖和重复动态。研究发现,经验性决策情境下个体的行为规律与描述性决策情境下存在显著差异,其根源是内在认知过程的不同,主要体现在信息搜索(内外部)和经验学习两方面。在经验性决策过程的外部信息搜索中,个体通常依赖于小样本进行决策;而工作记忆的有限性又导致了内部信息搜索的"近因效应";同时,搜索规则也会受到信息本身及信息搜索者特点的影响。基于反馈的经验学习在经验性决策过程中扮演着重要角色,据此发展出的学习模型具备一定的解释力。未来研究应重点考察"描述-经验差异"的深层原因,扩展现有的实验范式,探讨经验性决策的核心心理机制,以及构建经验性决策模型等。

Experience-based decisions refer to decisions made by individuals＇experience,with incomplete information,reliance on experience and repetition.Research evidences have shown that people behave differently in description-based decision and experience-based decision,resulting in a systematic and robust＂description of experience gap＂.The difference of internal cognitive processes between these two kinds of decisions could explain the gap.This paper aims to clarify how information exploring and experience learning as two important cognitive processes in experience-based decision.In experience-based decision,people search information both externally and internally.In external information exporing,people tend to rely on small samples.The internal information search is highly restricted by individual＇s working memory capacity.The adoption of exploring rule is influenced by several factors.Learning on feedbacks plays an important role in experience-based decision.A few learning models have been developed to better understand this process.Possible directions for future research may include：The expansion of experiment paradigms and decision scenarios,especially more realistic,more universal and more dynamic designs;The study of key cognitive processes and mental mechanisms during the decision-making process,such as role of emotion,attention and mental representation;The construction,testing and constant adjustment of modeling.