Psychologically-Valid Generative Agents: A Novel Approach to Agent-Based Modeling in Social Sciences

Incorporating dynamic realistic human behaviors in population-scale computational models has been challenging. While some efforts have leveraged behavioral theories from social science, validated theories specifically applicable to Agent-based modeling remain limited. Existing approaches lack a comprehensive framework to model the situated, adaptive nature of human cognition and choice. To address these challenges, this paper proposes a novel framework, Psychologically-Valid Generative Agents. These agents consist of a Cognitive Architecture that provides data-driven and cognitively-constrained decision-making functionality, and a Large Language Model that generates human-like linguistic data. In addition, our framework benefits from Stance Detection, a Natural Language Processing technique, that allows highly personalize initialization of the agents, based on real-world data, within Agent-based modeling simulations. This combination provides a flexible yet structured approach to endogenously represent how people perceive, deliberate, and respond to social or other types of complex decision-making dynamics. Previous work has demonstrated promising results by using a subset of the components of our proposed architecture. Our approach has the potential to exhibit highly-realistic human behavior and can be used across a variety of domains (e.g., public health, group dynamics, social and psychological sciences, and financial markets).