Prospect Theoretic Utility Based Human Decision Making in Multi-Agent Systems
Prospect Theoretic Utility Based Human Decision Making in Multi-Agent Systems
复制标题
多Agent系统中基于理论效用的人的决策前景
DOI：
10.1109/tsp.2020.2970339
复制
发表时间：
2020-01-01
影响因子：
5.4
通讯作者：
Rangaswamy, Muralidhar
中科院分区：
文献类型：
--
作者：
Geng, Baocheng;Brahma, Swastik;Rangaswamy, Muralidhar
本文在考虑个体行为差异的二元假设检验框架下，通过基于效用的方法来研究人类决策。与理性决策者为了最大化预期效用而做出决策不同，人类倾向于最大化他们的主观效用，而主观效用通常会因认知偏差而被扭曲。我们使用前景理论中的价值函数和概率权重函数来建模人类的认知偏差，并得到它们在决策中的主观效用函数。首先，我们证明了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本质，我们将人类的决策阈值建模为一个高斯随机变量，其均值由他/她的认知偏差决定，其方差代表了主体做出决策时的不确定性。这种行为偏差下的人类决策框架既包含认知偏差，也包含不确定性。我们考虑了几种包括人类在内的决策融合场景。本文提供了大量的数值结果来说明人类行为偏差对决策系统性能的影响。
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.