Investigating and Extending Homans' Social Exchange Theory with Large Language Model based Agents

TL;DR

This paper uses large language model agents to simulate and validate Homans' Social Exchange Theory, demonstrating its applicability and extending its scope through virtual experiments that bridge social science and AI.

Contribution

It introduces a novel interdisciplinary approach using LLM-based agents to study and extend Homans' SET, providing a new research paradigm for social science and AI integration.

Findings

Homans' SET is validated in LLM agent society.

Agent behaviors align with human social behaviors.

Extended SET offers a more comprehensive understanding of social exchanges.

Abstract

Homans' Social Exchange Theory (SET) is widely recognized as a basic framework for understanding the formation and emergence of human civilizations and social structures. In social science, this theory is typically studied based on simple simulation experiments or real-world human studies, both of which either lack realism or are too expensive to control. In artificial intelligence, recent advances in large language models (LLMs) have shown promising capabilities in simulating human behaviors. Inspired by these insights, we adopt an interdisciplinary research perspective and propose using LLM-based agents to study Homans' SET. Specifically, we construct a virtual society composed of three LLM agents and have them engage in a social exchange game to observe their behaviors. Through extensive experiments, we found that Homans' SET is well validated in our agent society, demonstrating the consistency between the agent and human behaviors. Building on this foundation, we intentionally alter the settings of the agent society to extend the traditional Homans' SET, making it more comprehensive and detailed. To the best of our knowledge, this paper marks the first step in studying Homans' SET with LLM-based agents. More importantly, it introduces a novel and feasible research paradigm that bridges the fields of social science and computer science through LLM-based agents. Code is available at https://github.com/Paitesanshi/SET.