Formalizing the Theory of Planned Behavior in Agent-Based Models, Literature review

Abstract

The Theory of Planned Behavior (TPB) offers a valuable framework for understanding humans decision-making. This paper explores how Agent-Based Models (ABMs) utilize TPB to investigate the interplay between social interactions, individual perceptions, and feedback from the environment in shaping behavior. We reviewed a collection of studies that utilize TPB within ABMs, focusing on how they formalize and operationalize this statistical model in a dynamic form. Our analysis reveals a diversity in approaches, which researchers implemented to handle this issue based on their research questions and available data. In most of the reviewed models, the dynamic nature emanates from evolution of SN or ATT. To account for social influence of agents and internal dynamics of the ATT or SN, researchers used Relative Agreement Model of opinion dynamics. Additionally, the review highlights various methods for translating intention into behavior within ABMs, ranging from threshold-based approaches to regression-based model. We conclude by proposing future directions for research, including incorporating dynamic updates for TPB constructs and exploring the Decomposed Theory of Planned Behavior. By addressing these considerations, researchers can develop more powerful ABMs for understanding complex social dynamics and decision-making processes.