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

• URL: http://arxiv.org/abs/2502.12450v1
• Date: Tue, 18 Feb 2025 02:30:46 GMT
• Title: Investigating and Extending Homans' Social Exchange Theory with Large Language Model based Agents
• Authors: Lei Wang, Zheqing Zhang, Xu Chen,
• Abstract summary: Homans' Social Exchange Theory (SET) is widely recognized as a basic framework for understanding the formation and emergence of human civilizations and social structures. Recent advances in large language models (LLMs) have shown promising capabilities in simulating human behaviors. We construct a virtual society composed of three LLM agents and have them engage in a social exchange game to observe their behaviors.
• Score: 9.430661117447782
• License:
• 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.

Related papers

• Are Human Interactions Replicable by Generative Agents? A Case Study on Pronoun Usage in Hierarchical Interactions [29.139828718538418]
  We investigate whether interactions among Large Language Models (LLMs) agents resemble those of humans. Our evaluation reveals the significant discrepancies between LLM-based simulations and human pronoun usage. We urge caution in using such social simulation in practitioners' decision-making process.
  arXiv  Detail & Related papers  (2025-01-25T17:42:47Z)
• Cultural Evolution of Cooperation among LLM Agents [1.4261864659766343]
  We study whether a "society" of AI agents can learn mutually beneficial social norms in the face of incentives to defect. We find that the evolution of cooperation differs markedly across base models.
  arXiv  Detail & Related papers  (2024-12-13T16:45:49Z)
• AgentSense: Benchmarking Social Intelligence of Language Agents through Interactive Scenarios [38.878966229688054]
  We introduce AgentSense: Benchmarking Social Intelligence of Language Agents through Interactive Scenarios. Drawing on Dramaturgical Theory, AgentSense employs a bottom-up approach to create 1,225 diverse social scenarios constructed from extensive scripts. We analyze goals using ERG theory and conduct comprehensive experiments. Our findings highlight that LLMs struggle with goals in complex social scenarios, especially high-level growth needs, and even GPT-4o requires improvement in private information reasoning.
  arXiv  Detail & Related papers  (2024-10-25T07:04:16Z)
• Artificial Leviathan: Exploring Social Evolution of LLM Agents Through the Lens of Hobbesian Social Contract Theory [8.80864059602965]
  Large Language Models (LLMs) and advancements in Artificial Intelligence (AI) offer an opportunity for computational social science research at scale. Our work introduces a simulated agent society where complex social relationships dynamically form and evolve over time. We analyze whether, as the theory postulates, agents seek to escape a brutish "state of nature" by surrendering rights to an absolute sovereign in exchange for order and security.
  arXiv  Detail & Related papers  (2024-06-20T14:42:58Z)
• Cooperate or Collapse: Emergence of Sustainable Cooperation in a Society of LLM Agents [101.17919953243107]
  GovSim is a generative simulation platform designed to study strategic interactions and cooperative decision-making in large language models (LLMs) We find that all but the most powerful LLM agents fail to achieve a sustainable equilibrium in GovSim, with the highest survival rate below 54%. We show that agents that leverage "Universalization"-based reasoning, a theory of moral thinking, are able to achieve significantly better sustainability.
  arXiv  Detail & Related papers  (2024-04-25T15:59:16Z)
• Can Large Language Model Agents Simulate Human Trust Behavior? [81.45930976132203]
  We investigate whether Large Language Model (LLM) agents can simulate human trust behavior. GPT-4 agents manifest high behavioral alignment with humans in terms of trust behavior. We also probe the biases of agent trust and differences in agent trust towards other LLM agents and humans.
  arXiv  Detail & Related papers  (2024-02-07T03:37:19Z)
• LLM-Based Agent Society Investigation: Collaboration and Confrontation in Avalon Gameplay [55.12945794835791]
  Using Avalon as a testbed, we employ system prompts to guide LLM agents in gameplay. We propose a novel framework, tailored for Avalon, features a multi-agent system facilitating efficient communication and interaction. Results affirm the framework's effectiveness in creating adaptive agents and suggest LLM-based agents' potential in navigating dynamic social interactions.
  arXiv  Detail & Related papers  (2023-10-23T14:35:26Z)
• Exploring Collaboration Mechanisms for LLM Agents: A Social Psychology View [60.80731090755224]
  This paper probes the collaboration mechanisms among contemporary NLP systems by practical experiments with theoretical insights. We fabricate four unique societies' comprised of LLM agents, where each agent is characterized by a specific trait' (easy-going or overconfident) and engages in collaboration with a distinct thinking pattern' (debate or reflection) Our results further illustrate that LLM agents manifest human-like social behaviors, such as conformity and consensus reaching, mirroring social psychology theories.
  arXiv  Detail & Related papers  (2023-10-03T15:05:52Z)
• The Rise and Potential of Large Language Model Based Agents: A Survey [91.71061158000953]
  Large language models (LLMs) are regarded as potential sparks for Artificial General Intelligence (AGI) We start by tracing the concept of agents from its philosophical origins to its development in AI, and explain why LLMs are suitable foundations for agents. We explore the extensive applications of LLM-based agents in three aspects: single-agent scenarios, multi-agent scenarios, and human-agent cooperation.
  arXiv  Detail & Related papers  (2023-09-14T17:12:03Z)
• A Survey on Large Language Model based Autonomous Agents [105.2509166861984]
  Large language models (LLMs) have demonstrated remarkable potential in achieving human-level intelligence. This paper delivers a systematic review of the field of LLM-based autonomous agents from a holistic perspective. We present a comprehensive overview of the diverse applications of LLM-based autonomous agents in the fields of social science, natural science, and engineering.
  arXiv  Detail & Related papers  (2023-08-22T13:30:37Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.