Generative Agent Simulations of 1,000 People

• URL: http://arxiv.org/abs/2411.10109v1
• Date: Fri, 15 Nov 2024 11:14:34 GMT
• Title: Generative Agent Simulations of 1,000 People
• Authors: Joon Sung Park, Carolyn Q. Zou, Aaron Shaw, Benjamin Mako Hill, Carrie Cai, Meredith Ringel Morris, Robb Willer, Percy Liang, Michael S. Bernstein,
• Abstract summary: We present a novel agent architecture that simulates the attitudes and behaviors of 1,052 real individuals. The generative agents replicate participants' responses on the General Social Survey 85% as accurately as participants replicate their own answers. Our architecture reduces accuracy biases across racial and ideological groups compared to agents given demographic descriptions.
• Score: 56.82159813294894
• License:
• Abstract: The promise of human behavioral simulation--general-purpose computational agents that replicate human behavior across domains--could enable broad applications in policymaking and social science. We present a novel agent architecture that simulates the attitudes and behaviors of 1,052 real individuals--applying large language models to qualitative interviews about their lives, then measuring how well these agents replicate the attitudes and behaviors of the individuals that they represent. The generative agents replicate participants' responses on the General Social Survey 85% as accurately as participants replicate their own answers two weeks later, and perform comparably in predicting personality traits and outcomes in experimental replications. Our architecture reduces accuracy biases across racial and ideological groups compared to agents given demographic descriptions. This work provides a foundation for new tools that can help investigate individual and collective behavior.

Related papers

• Personality Structured Interview for Large Language Model Simulation in Personality Research [8.208325358490807]
  We explore the potential of the theory-informed Personality Structured Interview as a tool for simulating human responses in personality research. We have provided a growing set of 357 structured interview transcripts from a representative sample, each containing an individual's response to 32 open-ended questions. Results from three experiments demonstrate that well-designed structured interviews could improve human-like heterogeneity in LLM-simulated personality data.
  arXiv  Detail & Related papers  (2025-02-17T18:31:57Z)
• AgentSociety: Large-Scale Simulation of LLM-Driven Generative Agents Advances Understanding of Human Behaviors and Society [32.849311155921264]
  We propose AgentSociety, a large-scale social simulator that integrates a realistic societal environment. Based on the proposed simulator, we generate social lives for over 10k agents, simulating their 5 million interactions. We focus on four key social issues: polarization, the spread of inflammatory messages, the effects of universal basic income policies, and the impact of external shocks such as hurricanes.
  arXiv  Detail & Related papers  (2025-02-12T15:27:07Z)
• From Individual to Society: A Survey on Social Simulation Driven by Large Language Model-based Agents [47.935533238820334]
  Traditional sociological research often relies on human participation, which, though effective, is expensive, challenging to scale, and with ethical concerns. Recent advancements in large language models (LLMs) highlight their potential to simulate human behavior, enabling the replication of individual responses and facilitating studies on many interdisciplinary studies. We categorize the simulations into three types: (1) Individual Simulation, which mimics specific individuals or demographic groups; (2) Scenario Simulation, where multiple agents collaborate to achieve goals within specific contexts; and (3) Simulation Society, which models interactions within agent societies to reflect the complexity and variety of real-world dynamics.
  arXiv  Detail & Related papers  (2024-12-04T18:56:37Z)
• Designing LLM-Agents with Personalities: A Psychometric Approach [0.47498241053872914]
  This research introduces a novel methodology for assigning quantifiable, controllable and psychometrically validated personalities to Agents. It seeks to overcome the constraints of human subject studies, proposing Agents as an accessible tool for social science inquiry.
  arXiv  Detail & Related papers  (2024-10-25T01:05:04Z)
• PersLLM: A Personified Training Approach for Large Language Models [66.16513246245401]
  We propose PersLLM, integrating psychology-grounded principles of personality: social practice, consistency, and dynamic development. We incorporate personality traits directly into the model parameters, enhancing the model's resistance to induction, promoting consistency, and supporting the dynamic evolution of personality.
  arXiv  Detail & Related papers  (2024-07-17T08:13:22Z)
• User Behavior Simulation with Large Language Model based Agents [116.74368915420065]
  We propose an LLM-based agent framework and design a sandbox environment to simulate real user behaviors. Based on extensive experiments, we find that the simulated behaviors of our method are very close to the ones of real humans.
  arXiv  Detail & Related papers  (2023-06-05T02:58:35Z)
• Measuring the Effect of Influential Messages on Varying Personas [67.1149173905004]
  We present a new task, Response Forecasting on Personas for News Media, to estimate the response a persona might have upon seeing a news message. The proposed task not only introduces personalization in the modeling but also predicts the sentiment polarity and intensity of each response. This enables more accurate and comprehensive inference on the mental state of the persona.
  arXiv  Detail & Related papers  (2023-05-25T21:01:00Z)
• Generative Agents: Interactive Simulacra of Human Behavior [86.1026716646289]
  We introduce generative agents--computational software agents that simulate believable human behavior. We describe an architecture that extends a large language model to store a complete record of the agent's experiences. We instantiate generative agents to populate an interactive sandbox environment inspired by The Sims.
  arXiv  Detail & Related papers  (2023-04-07T01:55:19Z)

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.