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Rational Probabilistic Deciders-Part Ⅰ: Individual Behavior

机译:理性概率决策者-第一部分:个人行为

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This paper is intended to model a decision maker as a rational probabilistic decider (RPD) and to investigate its behavior in stationary and symmetric Markov switch environments. RPDs take their decisions based on penalty functions denned by the environment. The quality of decision making depends on a parameter referred to as level of rationality. The dynamic behavior of RPDs is described by an ergodic Markov chain. Two classes of RPDs are considered—local and global. The former take their decisions based on the penalty in the current state while the latter consider all states. It is shown that asymptotically (in time and in the level of rationality) both classes behave quite similarly. However, the second largest eigenvalue of Markov transition matrices for global RPDs is smaller than that for local ones, indicating faster convergence to the optimal state. As an illustration, the behavior of a chief executive officer, modeled as a global RPD, is considered, and it is shown that the company performance may or may not be optimized-depending on the pay structure employed. While the current paper investigates individual RPDs, a companion paper will address collective behavior.
机译:本文旨在将决策者建模为理性概率决策者(RPD),并研究其在平稳对称Markov切换环境中的行为。 RPD根据环境确定的惩罚功能做出决策。决策的质量取决于称为合理性水平的参数。 RPD的动态行为由遍历马尔可夫链描述。考虑了两类RPD:本地和全局。前者根据当前状态下的惩罚做出决定,而后者考虑所有状态。结果表明,这两个类的渐近性(在时间和合理性上)都非常相似。但是,全局RPD的马氏转移矩阵的第二大特征值小于局部RPD的特征值,表明收敛到最佳状态的速度更快。作为示例,考虑了以全球RPD为模型的首席执行官的行为,并且表明根据所采用的薪酬结构,公司绩效可能会优化也可能不会优化。在本文研究个别RPD的同时,另一篇论文将探讨集体行为。

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