Strategy Selection and Outcome Evaluation of Change-Based Three-Way Decisions Based on Reinforcement Learning

Abstract

In this chapter, we enhance the trisecting-acting-outcome (TAO) model of three-way decision-making (3WD) with a novel approach for strategy selection and outcome prediction using Q-learning in reinforcement learning. We reinterpret the changes in tripartition and actions in the TAO model as states and actions in reinforcement learning, respectively. The reward is quantified using cumulative prospect theory, and the Q-learning algorithm iteratively determines action sets that achieve target rewards efficiently. This method offers a cost-effective and psychologically attuned action set for predicting the utility in change-based 3WD, demonstrated through a practical example.