Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Computational Multi-Agents Society Experiments: Social Modeling Framework Based on Generative Agents

Hanzhong Zhang, Muhua Huang, Jindong Wang

TL;DR

Experimental results show that CMASE can not only simulate complex phenomena, but also generate behavior trajectories consistent with both statistical patterns and mechanistic explanations, demonstrating CMASE's methodological value for intervention modeling, highlighting its potential to advance interdisciplinary integration in the social sciences.

Abstract

This paper introduces CMASE, a framework for Computational Multi-Agent Society Experiments that integrates generative agent-based modeling with virtual ethnographic methods to support researcher embedding, interactive participation, and mechanism-oriented intervention in virtual social environments. By transforming the simulation into a simulated ethnographic field, CMASE shifts the researcher from an external operator to an embedded participant. Specifically, the framework is designed to achieve three core capabilities: (1) enabling real-time human-computer interaction that allows researchers to dynamically embed themselves into the system to characterize complex social intervention processes; (2) reconstructing the generative logic of social phenomena by combining the rigor of computational experiments with the interpretative depth of traditional ethnography; and (3) providing a predictive foundation with causal explanatory power to make forward-looking judgments without sacrificing empirical accuracy. Experimental results show that CMASE can not only simulate complex phenomena, but also generate behavior trajectories consistent with both statistical patterns and mechanistic explanations. These findings demonstrate CMASE's methodological value for intervention modeling, highlighting its potential to advance interdisciplinary integration in the social sciences. The official code is available at: https://github.com/armihia/CMASE .

Computational Multi-Agents Society Experiments: Social Modeling Framework Based on Generative Agents

TL;DR

Experimental results show that CMASE can not only simulate complex phenomena, but also generate behavior trajectories consistent with both statistical patterns and mechanistic explanations, demonstrating CMASE's methodological value for intervention modeling, highlighting its potential to advance interdisciplinary integration in the social sciences.

Abstract

This paper introduces CMASE, a framework for Computational Multi-Agent Society Experiments that integrates generative agent-based modeling with virtual ethnographic methods to support researcher embedding, interactive participation, and mechanism-oriented intervention in virtual social environments. By transforming the simulation into a simulated ethnographic field, CMASE shifts the researcher from an external operator to an embedded participant. Specifically, the framework is designed to achieve three core capabilities: (1) enabling real-time human-computer interaction that allows researchers to dynamically embed themselves into the system to characterize complex social intervention processes; (2) reconstructing the generative logic of social phenomena by combining the rigor of computational experiments with the interpretative depth of traditional ethnography; and (3) providing a predictive foundation with causal explanatory power to make forward-looking judgments without sacrificing empirical accuracy. Experimental results show that CMASE can not only simulate complex phenomena, but also generate behavior trajectories consistent with both statistical patterns and mechanistic explanations. These findings demonstrate CMASE's methodological value for intervention modeling, highlighting its potential to advance interdisciplinary integration in the social sciences. The official code is available at: https://github.com/armihia/CMASE .

Paper Structure

This paper contains 23 sections, 3 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Simulating Social Behavior with Large Language Models
  4. Human-AI Collaborative Modeling in Social Systems
  5. CMASE Framework
  6. Environment Maker
  7. Environment
  8. Agent
  9. Affective Variables
  10. Cognitive Variables
  11. Memory Variables
  12. Action Space
  13. Event
  14. Platform Demonstration: Simulating Context-Dependent Social Dynamics
  15. Setup
  16. ...and 8 more sections

Figures (3)

  • Figure 1: Overall Structure of CMASE.
  • Figure 2: Comparison of CMASE simulation with the original study results. (a) Simulation results generated by the CMASE framework. (b) Predictions using the EVI index from the original study. (c) Predictions using the NDVI index from the original study. (d) Movement trajectories of agents during CMASE simulation. (e) Regional variation in agents’ emotional states.
  • Figure 3: CMASE performance under different agent counts. (a) Duration per time step. (b) Human evaluator responsiveness ratings.