Motivation and Bias in Group Decision Making: A Computational Approach

Abstract

In the group decision-making research, the development of a synthetic framework integrating information processing biases and motivated information processing will benefit researchers to systematically understand the group information processing mechanism and its performance. We built an agent-based computational model to simulate a group deliberation and decision-making process by identifying and integrating human motivations and biases in group information processing. From the computational model, theoretical propositions of motivated information processing regarding the relationship between different types of information processing biases and motivation, the effect of motivated information processing on the quality and quantity of learning, and group performance were built. We propose that while high epistemic motivation will improve the actual performance of group decision-making process, prosocial motivation will improve the perceived performance of group decision-making process. The gap between the ...