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GenSim: A General Social Simulation Platform with Large Language Model based Agents

Jiakai Tang, Heyang Gao, Xuchen Pan, Lei Wang, Haoran Tan, Dawei Gao, Yushuo Chen, Xu Chen, Yankai Lin, Yaliang Li, Bolin Ding, Jingren Zhou, Jun Wang, Ji-Rong Wen

arXiv:2410.04360v3cs.MAcs.AI

TL;DR

Prior LLM-agent simulations have often been scenario-specific and small-scale, and existing platforms may lack effective ways to correct errors. GenSim offers a customizable platform designed for large-scale simulations and error correction. In a multi-round evaluation, PPO and SFT improved simulation performance, with improvements increasing as rounds grew.

  • Problem

    Prior LLM-agent simulations have often been limited to specific scenarios and small populations, and many platforms lack error-correction mechanisms.

    Source: §1 · p. 1

  • Method

    GenSim combines a general programming framework and distributed parallel technology with LLM-based or human feedback strategies for correcting simulation errors.

    Source: §1 · pp. 1–2, §2.3 · p. 4

  • Results

    With PPO or SFT, simulation performance improved significantly, and the improvements increased as the number of simulation rounds grew.

    Source: §2.3 · p. 5

  • Takeaways & Limitations

    GenSim is presented as an initial general, large-scale, and correctable LLM-agent platform with usage examples and experiments evaluating its effectiveness and efficiency.

    Source: §1 · p. 2, §5 · p. 6

    Generalization across sociocultural contexts remains unverified, and LLM-as-a-judge scores were not systematically checked against expert human ratings.

    Source: Limitation · p. 7

Abstract

from arXiv · show

With the rapid advancement of large language models (LLMs), recent years have witnessed many promising studies on leveraging LLM-based agents to simulate human social behavior. While prior work has demonstrated significant potential across various domains, much of it has focused on specific scenarios involving a limited number of agents and has lacked the ability to adapt when errors occur during simulation. To overcome these limitations, we propose a novel LLM-agent-based simulation platform called \textit{GenSim}, which: (1) \textbf{Abstracts a set of general functions} to simplify the simulation of customized social scenarios; (2) \textbf{Supports one hundred thousand agents} to better simulate large-scale populations in real-world contexts; (3) \textbf{Incorporates error-correction mechanisms} to ensure more reliable and long-term simulations. To evaluate our platform, we assess both the efficiency of large-scale agent simulations and the effectiveness of the error-correction mechanisms. To our knowledge, GenSim represents an initial step toward a general, large-scale, and correctable social simulation platform based on LLM agents, promising to further advance the field of social science.

1 Introduction

Social science studies using real human data face high costs, poor controllability, and reproducibility challenges, while LLM-agent simulations have largely remained scenario-specific and small-scale. GenSim proposes a general platform for customized, large-scale social simulations with mechanisms to correct errors.

1 Introduction

  • Social science research using real human data can be costly, poorly controllable, and difficult to reproduce.The introduction gives recruiting more than 6,000 participants for a controlled experiment as an example of collecting human data.Source: §1 · p. 1
  • GenSim is an LLM-agent platform designed to address scenario-specific, small-scale simulations and the lack of effective error correction in prior methods.The authors describe a general programming framework for single-agent construction, multi-agent scheduling, and environment setup.Source: §1 · p. 1
  • Distributed parallel technology supports simulations with one hundred thousand agents, and error-correction mechanisms use self-evaluation or human feedback before fine-tuning.The platform also provides three default scenarios as references for implementing customized simulations.Source: §1 · p. 2
  • The paper presents GenSim as a general, large-scale, and correctable platform, with usage examples and experiments evaluating its effectiveness and efficiency.Figure 1 is identified as depicting the general framework for social simulation.Source: §1 · p. 2, Figure 1 · p. 2

2 Features of GenSim

GenSim combines a configurable social-simulation framework, support for large populations, and mechanisms for correcting simulated behavior. Its framework spans agents, interactions, and environments, while its evaluations examine scaling and error correction.

2 Features of GenSim

  • GenSim offers general functions for customized scenarios, supports up to one hundred thousand agents, and uses error-correction mechanisms for more reliable simulations.The paper characterizes generality and scalability as static features and continual correction as a dynamic feature.Source: §2 · p. 2

2.1 General Simulation Framework

  • The framework separates single-agent construction, multi-agent interaction, and environment information; users configure agent profiles, memories, and actions and choose script or agent mode.Script mode generates an interaction in one LLM call; agent mode uses distinct role-playing agents whose outputs depend on the interaction history.Source: §2.1 · pp. 2–3

2.2 Large-scale Simulation

  • As the number of sampled cases increases, simulation-result fluctuation falls; increasing agent counts raises runtime, while more GPUs reduce it.The platform uses actor-based parallelism, a dynamic workflow, and multi-machine simulation to support scale.Source: Figure 2 · p. 3, Figure 3 · p. 3, §2.2 · p. 4

2.3 Simulation Error Correction

  • GenSim collects LLM or human scores or revised actions and uses them to fine-tune the backbone with PPO or SFT.LLM feedback is more efficient and requires no human intervention, while human feedback is more aligned with real humans but can be labor-intensive and less efficient.Source: §2.3 · p. 4
  • In multi-round job-market simulations, PPO and SFT improve performance over no correction, with improvements increasing across rounds.In a single round, both methods also improve results across labeled-sample sizes, and SFT performs better than PPO.Source: §2.3 · pp. 4–5, Figure 4 · p. 4

3 Usages of GenSim

GenSim’s usage section outlines how to configure, launch, observe, and refine a default simulation.

3 Usages of GenSim

  • The section introduces the basic methods for running a default scenario and presents the platform’s complete interfaces in Figure 5.Users start by clicking “Get Started.”Source: §3 · p. 5
  • Users configure scenarios, agent profiles, memory types, agent counts, and LLM parameters before launching a simulation.The interface then displays agents’ behaviors and provides controls for observing and intervening in the simulation.Source: §3 · p. 5
  • The interface provides a search box for tracking an agent, a display of multiple agents’ behaviors, and a functionality window for profiles, interventions, and interaction.Users can stop a simulation after several rounds, label results, and fine-tune the backbone LLM for subsequent simulations.Source: §3 · p. 5

4 Related Work

The related work situates GenSim against the complexity and constraints of social-science experiments and the growing use of LLM-based multi-agent simulations. It identifies limits in prior simulators and presents a comparison of GenSim with existing work.

4.1 Social Science

  • Social science studies diverse phenomena shaped by uncertain, dynamically changing natural and social factors.The paper describes the discipline as using scientific methods and spanning fields such as political science, sociology, economics, and psychology.Source: §4.1 · p. 5
  • Laboratory experiments, field experiments, and randomized controlled trials have been used to study social phenomena, but experiments can be costly, hard to control, irreversible, and ethically challenging.The cited examples include studies with 6,000 individuals and 6,139 participants; long durations and complex environments may also affect validity and timeliness.Source: §4.1 · pp. 5–6

4.2 Multi-Agent Simulation

  • LLM-based agents are used as behavior decision-makers that approximately imitate real individuals in domain-specific simulations, including towns, recommendation systems, social networks, markets, healthcare, and warfare.The related-work examples include Generative Agent, RecAgent, Social Simulacra, EconAgent, Agent Hospital, and WarAgent.Source: §4.2 · p. 6
  • Prior multi-agent simulators are limited in domain range and scale and lack error correction; GenSim is presented as supporting general-domain, large-scale simulations with self-correction.Table 1 compares GenSim with existing multi-agent social-simulation works; its ∞ symbol denotes support for general scenarios.Source: §4.2 · p. 6, Table 1 · p. 6

5 Conclusions

The conclusion presents GenSim as a general, large-scale, and correctable LLM-agent social simulation platform. It identifies faster simulation strategies and more adaptive self-correction as future development aims.

5 Conclusions

  • GenSim is presented as a general, large-scale, and correctable social simulation platform based on LLM agents.The authors describe this as the initial version of the platform.Source: §5 · p. 6
  • The authors plan to add more advanced simulation-acceleration strategies and develop more adaptive self-correction mechanisms to improve simulation performance.These are stated as future plans.Source: §5 · pp. 6–7

Limitation

The paper identifies unverified cross-cultural generalization, potential LLM-judge bias, and unresolved reliability concerns for self-correction as limitations.

Limitation

  • GenSim’s generalization across diverse sociocultural contexts has not been fully verified.The authors call for comparative studies with traditional experiments to validate accuracy across cultural norms, regional socioeconomic dynamics, and institutional settings.Source: Limitation · p. 7
  • LLM-as-a-judge evaluation may introduce assessment bias, and human-annotation costs prevented systematic comparison with expert ratings.The authors particularly note culturally sensitive scenarios and possible latent value misalignments.Source: Limitation · p. 7
  • The reliability of LLM self-correction across varied social scenarios remains unexamined.The authors specifically cite scenarios involving ethical dilemmas or conflicting group interests.Source: Limitation · p. 7

Ethical Considerations

The paper uses public datasets and simulated experiments with LLM-generated synthetic data, and states that it follows public-dataset usage licenses.

Ethical Considerations

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