Towards the design of prospect-theory based human decision rules for hypothesis testing
Towards the design of prospect-theory based human decision rules for hypothesis testing
复制标题
设计基于前景理论的人类决策规则以进行假设检验
DOI：
10.1109/allerton.2016.7852310
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发表时间：
2016
期刊：
影响因子：
--
通讯作者：
P. Varshney
中科院分区：
文献类型：
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作者：
V. S. S. Nadendla;Swastik Brahma;P. Varshney
传统上，检测规则是为理性代理设计的，它使贝叶斯风险（平均决策成本）最小化。随着人群感知系统的出现，有必要重新设计行为代理的二元假设检验规则，其认知行为不能被传统的效用函数（如贝叶斯风险）捕获。在本文中，我们采用基于前景理论的决策模型。本文考虑了一类特殊的智能体模型，即乐观者和悲观者，推导了不同场景下的最优检测规则。使用一个说明性的例子，我们还展示了人类代理的决策规则如何偏离贝叶斯决策规则下的各种行为模型，在本文中考虑。
Detection rules have traditionally been designed for rational agents that minimize the Bayes risk (average decision cost). With the advent of crowd-sensing systems, there is a need to redesign binary hypothesis testing rules for behavioral agents, whose cognitive behavior is not captured by traditional utility functions such as Bayes risk. In this paper, we adopt prospect theory based models for decision makers. We consider special agent models namely optimists and pessimists in this paper, and derive optimal detection rules under different scenarios. Using an illustrative example, we also show how the decision rule of a human agent deviates from the Bayesian decision rule under various behavioral models, considered in this paper.