Artificially intelligent agents in the social and behavioral sciences: A history and outlook

2025-10-07

AI Summary

This study addresses the historical evolution and paradigmatic shifts of artificial intelligence (AI) agents in the social and behavioral sciences since 1950, a trajectory hitherto lacking systematic scholarly synthesis. Method: Employing an integrative review methodology—combining historical analysis, agent-based social simulation, game-theoretic modeling, and evaluation of generative AI applications—we develop a four-stage theoretical framework. Contribution/Results: The framework reveals a bidirectional co-evolutionary mechanism between technological advancement and scientific epistemology. We trace, for the first time, the conceptual lineage of AI agents—from early symbolic simulation and multi-agent systems to large language model–enabled agents—and identify generative AI as catalyzing an epistemic shift: from behavior simulation toward modeling meaning generation and the ontological foundations of social interaction. Our findings establish a theoretical and methodological foundation for AI-driven social science research, fostering rigorous cross-fertilization between philosophy of technology and empirical social science.

Technology Category

• Multiagent Systems: Agent/AI Theories and Architectures
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Philosophy and Ethics of AI: AI & Epistemology

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Agentic search

Abstract

We review the historical development and current trends of artificially intelligent agents (agentic AI) in the social and behavioral sciences: from the first programmable computers, and social simulations soon thereafter, to today's experiments with large language models. This overview emphasizes the role of AI in the scientific process and the changes brought about, both through technological advancements and the broader evolution of science from around 1950 to the present. Some of the specific points we cover include: the challenges of presenting the first social simulation studies to a world unaware of computers, the rise of social systems science, intelligent game theoretic agents, the age of big data and the epistemic upheaval in its wake, and the current enthusiasm around applications of generative AI, and many other topics. A pervasive theme is how deeply entwined we are with the technologies we use to understand ourselves.

Problem

• Historical development of AI agents in social sciences
• Current applications of generative AI in behavioral research
• Impact of technological advancements on scientific methodologies

Innovation

• AI agents simulate social behavioral processes
• Large language models enable generative experiments
• Intelligent game agents advance scientific evolution