满足匹配律的策略参数搜索决策模型

匹配律是决策理论的基本定律之一,它建立了对备选目标的偏好与所获奖励之间的对应关系.通过构建获得匹配律的策略模型,研究了该定律成立的可能机制.基于再励学习理论,提出了通过调整策略参数以满足决策目标的策略搜索模型.在该策略模型的基础上,通过设定简单的假设条件推导出满足匹配律的策略算法.理论分析和数值仿真结果均验证了算法的正确性.另一方面利用该算法模拟了经典的心理学与神经生理学的匹配行为实验.研究结果不仅对匹配行为给出了合理的解释,也为建立基于奖励的决策模型提供了一种有效的理论建模方法.

A stochastic policy search model for matching behavior

The matching law is one of the basic empirical laws in decision theory,and it states that a subject’s preference to optional targets depends on which choices are reinforced.In this paper,we study the possible mechanisms that explain why subjects’ decisions often obey this law.On the basis of reinforcement learning theory,we put forward a decision-making model in which the policy is updated by a policy parameter,and the model might be implemented in the brain through the prefrontal cortex and the basal ganglia neural circuit.Based on this model,an algorithm that satisfies the matching law is derived under some simple assumptions.Theoretical analysis and simulation results show that the decision behavior achieved by the algorithm obeys the matching law.In addition,the matching behaviors in two classical experiments are reproduced using the algorithm.Our results provide a reasonable strategy for the matching law and a useful computational tool for rewarded decision-making tasks.