This paper studies human decision making via a utility based approach in a binary hypothesis testing framework that includes the consideration of individual behavioral disparity. Unlike rational decision makers who make decisions so as to maximize their expected utility, humans tend to maximize their subjective utilities, which are usually distorted due to cognitive biases. We use the value function and the probability weighting function from prospect theory to model human cognitive biases and obtain their subjective utility function in decision making. First, we show that the decision rule which maximizes the subjective utility function reduces to a likelihood ratio test (LRT). Second, to capture the unreliable nature of human decision making behavior, we model the decision threshold of a human as a Gaussian random variable, whose mean is determined by his/her cognitive bias, and the variance represents the uncertainty of the agent while making a decision. This human decision making framework under behavioral biases incorporates both cognitive biases and uncertainties. We consider several decision fusion scenarios that include humans. Extensive numerical results are provided throughout the paper to illustrate the impact of human behavioral biases on the performance of the decision making systems.

中文翻译：

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基于前景理论效用的多智能体系统中的人类决策

本文通过基于效用的方法在二元假设检验框架中研究人类决策，该框架包括对个体行为差异的考虑。与做出决策以最大化其预期效用的理性决策者不同，人类倾向于最大化其主观效用，而这通常由于认知偏差而被扭曲。我们使用前景理论中的价值函数和概率加权函数来模拟人类的认知偏差，并获得他们在决策中的主观效用函数。首先，我们表明最大化主观效用函数的决策规则简化为似然比检验（LRT）。其次，为了捕捉人类决策行为的不可靠性质，我们将人类的决策阈值建模为高斯随机变量，其均值由他/她的认知偏差决定，方差代表智能体在做决定时的不确定性。这种在行为偏见下的人类决策框架结合了认知偏见和不确定性。我们考虑了包括人类在内的几种决策融合场景。整篇论文提供了广泛的数值结果，以说明人类行为偏差对决策系统性能的影响。