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Risk Aversion as a Perceptual Bias

机译:风险厌恶感

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The theory of expected utility maximization (EUM) explains risk aversion as due to diminishing marginal utility of wealth. However, observed choices between risky lotteries are difficult to reconcile with EUM: for example, in the laboratory, subjects' responses on individual trials involve a random element, and cannot be predicted purely from the terms offered; and subjects often appear to be too risk averse with regard to small gambles (while still accepting sufficiently favorable large gambles) to be consistent with any utility-of-wealth function. We propose a unified explanation for both anomalies, similar to the explanation given for related phenomena in the case of perceptual judgments: they result from judgments based on imprecise (and noisy) mental representation of the decision situation. In this model, risk aversion is predicted without any need for a nonlinear utility-of-wealth function, and instead results from a sort of perceptual bias - but one that represents an optimal Bayesian decision, given the limitations of the mental representation of the situation. We propose a specific quantitative model of the mental representation of a simple lottery choice problem, based on other evidence regarding numerical cognition, and test its ability to explain the choice frequencies that we observe in a laboratory experiment.
机译:预期效用最大化(EUM)理论解释了风险厌恶是由于财富的边际效用减少所致。但是,观察到的危险彩票之间的选择很难与EUM调和:例如,在实验室中,受试者对个别试验的反应涉及随机因素,不能完全根据所提供的术语进行预测;并且受试者似乎对小赌注过于厌恶风险(尽管仍然接受足够有利的大赌注),以致于与任何财富效用函数都不相符。我们对这两种异常提出统一的解释,类似于在感知判断中对相关现象的解释:它们是基于对决策情况的不精确(且嘈杂)心理表示的判断得出的。在此模型中,不需要任何非线性的财富效用函数就可以预测风险规避,而是由一种感知偏差引起的,但由于情境的心理表征存在局限性,因此可以代表最佳贝叶斯决策。 。我们基于有关数字认知的其他证据,提出了一个简单的彩票选择问题的心理表征的特定定量模型,并测试了其解释我们在实验室实验中观察到的选择频率的能力。

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  • 来源
    《Working Paper Series》 |2017年第23294期|qt0-qt11-45|共47页
  • 作者单位

    Department of Economics Columbia University 420 W. 118th Street New York, NY 10027;

    Department of Economics Columbia University 420 W. 118th Street New York, NY 10027;

    Department of Economics Columbia University 420 W. 118th Street New York, NY 10027 and NBER;

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