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决策的规范模型与描述模型:时间贴现与风险敏感性

Normative and descriptive models of decision making: time discounting and risk sensitivity.

作者信息

Kacelnik A

机构信息

Department of Zoology, Oxford University, UK.

出版信息

Ciba Found Symp. 1997;208:51-67; discussion 67-70. doi: 10.1002/9780470515372.ch5.

Abstract

The task of evolutionary psychologists is to produce precise predictions about psychological mechanisms using adaptationist thinking. This can be done combining normative models derived from evolutionary hypotheses with descriptive regularities across species found by experimental psychologists and behavioural ecologists. I discuss two examples. In temporal discounting, a normative model (exponential) fails while a descriptive one (hyperbolic) fits both human and non-human data. In non-humans hyperbolic discounting coincides with rate of gain maximization in repetitive choices. Humans may discount hyperbolically in non-repetitive choices because they treat them as a repetitive rate-maximizing problem. In risk sensitivity, a theory derived from fitness considerations produces inconclusive results in non-humans, but succeeds in predicting human risk proneness and risk aversion for both the amount and delay of reward in a computer game. Strikingly, and in contrast with the existing literature, risk aversion for delay occurs as predicted. The predictions of risk aversion for delay may fail in many animal experiments because the manipulations of the utility function are not appropriate. In temporal discounting animal experiments help the interpretation of human results, while in risk sensitivity studies human results help the analysis of non-human data.

摘要

进化心理学家的任务是运用适应主义思维对心理机制做出精确预测。这可以通过将源自进化假设的规范模型与实验心理学家和行为生态学家发现的跨物种描述性规律相结合来实现。我讨论两个例子。在时间折扣方面,一个规范模型(指数模型)不适用,而一个描述性模型(双曲线模型)则适合人类和非人类数据。在非人类中,双曲线折扣与重复选择中的收益最大化率相吻合。人类在非重复选择中可能会双曲线式地折扣,因为他们将其视为一个重复的率最大化问题。在风险敏感性方面,一个从适应性考虑得出的理论在非人类中产生了不确定的结果,但成功地预测了人类在电脑游戏中对奖励数量和延迟的风险倾向和风险厌恶。引人注目的是,与现有文献相反,对延迟的风险厌恶正如预测的那样出现。对延迟的风险厌恶预测在许多动物实验中可能失败,因为效用函数的操作不合适。在时间折扣方面,动物实验有助于解释人类结果,而在风险敏感性研究中,人类结果有助于分析非人类数据。

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