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Comparing rank-dependent, subjective weight, and configural weight utility models: Transitivity, monotonicity, coalescing, stochastic dominance, and event-splitting independence.

机译:比较等级相关,主观权重和配置权重实用工具模型:及物性,单调性,合并性,随机支配性和事件拆分独立性。

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摘要

This study compared three models that attempt to predict decision making behavior. One hundred subjects were instructed to choose between gambles that were defined in terms of the probabilities to win different amounts of cash. Three models, Configural Weight Theory (CWT), Cumulative Prospect Theory (CPT), and Subjective Weighted Utility Theory (SWU), were tested for their ability to describe choices between gambles. Three properties that distinguish these models were tested: Coalescing, Stochastic Dominance, and Event Splitting Independence (ESI). Coalescing implies that if two or more outcomes have the same value within a gamble, they can be combined by adding their probabilities, and the combined gamble will be perceived as equivalent to its split form. CPT assumes coalescing whereas SWU and CWT predict violations. The data showed violations of coalescing when the high outcome was split, contrary to CPT. Stochastic dominance implies that coalescing, monotonicity, and transitivity have been satisfied. CPT implies stochastic dominance, whereas SWU and CWT predict violations. The data showed violations of stochastic dominance under conditions predicted by CWT, contrary to CPT. ESI assumes that the effect of splitting any event is independent of the value and relative position of the outcome associated with it. Since CPT does not predict violations of coalescing, the theory does not make a prediction for ESI. Although SWU predicts violations of coalescing, SWU implies ESI. CWT predicts violations. The data, however, were inconclusive for ESI. Further research is called for in order to test ESI.
机译:这项研究比较了三种试图预测决策行为的模型。指示一百名受试者在根据赢得不同金额现金的机率定义的赌博之间进行选择。测试了三个模型,即配置权重理论(CWT),累积预期理论(CPT)和主观加权效用理论(SWU),以描述赌博之间的选择。测试了区分这些模型的三个属性:合并,随机优势和事件分裂独立性(ESI)。合并意味着,如果一个赌博中两个或多个结果具有相同的价值,则可以通过添加其概率来将它们合并,合并后的赌博将被视为等同于其拆分形式。 CPT假定合并,而SWU和CWT预测违规。数据显示,分割高结果时违反了合并,这与CPT相反。随机优势表示已满足合并,单调性和可传递性。 CPT隐含着随机优势,而SWU和CWT则预测违规。数据显示,与CPT相反,在CWT预测的条件下违反了随机优势。 ESI假定拆分任何事件的效果均独立于与其相关的结果的值和相对位置。由于CPT不会预测违反合并的行为,因此该理论无法预测ESI。尽管SWU预测会违反合并,但SWU表示ESI。 CWT会预测违规情况。但是,该数据对于ESI尚无定论。为了测试ESI,需要进一步的研究。

著录项

  • 作者

    Martin, Teresa A.;

  • 作者单位

    California State University, Fullerton.;

  • 授予单位 California State University, Fullerton.;
  • 学科 Psychology Cognitive.
  • 学位 M.S.
  • 年度 1999
  • 页码 77 p.
  • 总页数 77
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 心理学;
  • 关键词

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