An evidential Markov decision making model

TL;DR

This paper introduces an Evidential Markov decision making model based on Dempster-Shafer theory to effectively predict the disjunction effect in human decision-making, addressing limitations of classical models.

Contribution

The novel EM model incorporates uncertainty states and Deng entropy to better simulate human decision processes and predict disjunction effects with fewer parameters.

Findings

Successfully predicts the disjunction effect in experiments

Reduces the number of free parameters compared to existing models

Addresses limitations of classical Markov models in decision making

Abstract

The sure thing principle and the law of total probability are basic laws in classic probability theory. A disjunction fallacy leads to the violation of these two classical laws. In this paper, an Evidential Markov (EM) decision making model based on Dempster-Shafer (D-S) evidence theory and Markov modelling is proposed to address this issue and model the real human decision-making process. In an evidential framework, the states are extended by introducing an uncertain state which represents the hesitance of a decision maker. The classical Markov model can not produce the disjunction effect, which assumes that a decision has to be certain at one time. However, the state is allowed to be uncertain in the EM model before the final decision is made. An extra uncertainty degree parameter is defined by a belief entropy, named Deng entropy, to assignment the basic probability assignment of the uncertain state, which is the key to predict the disjunction effect. A classical categorization decision-making experiment is used to illustrate the effectiveness and validity of EM model. The disjunction effect can be well predicted and the free parameters are less compared with the existing models.