Motivated Information Processing in Groups: A Design of a Computational Model

Abstract

According to group research, group members often fail to consider critical unique information in a group decision-making setting because of their motivated information-processing behavior. Given the complexity of experimental designs to investigate the problem, agent-based modeling can provide hypotheses that can work as theoretical guidance for future research. The purpose of this paper is to identify human motivations and biases in group information processing relevant to modeling the group decision-making process, and to develop a conceptual framework of a computational model. In this paper, we reviewed the group informationprocessing literature to identify commonly observed human motivations and biases in information processing. Specifically, we provided an integrated framework by combining the hidden profile model, the motivated information-processing model, and the information asymmetries model. Finally, we provided a design of agents for future modeling of group processes, which uses empirical findings from the literature as the fundamental assumptions about agent behavior.