LLM Agents Grounded in Self-Reports Enable General-Purpose Simulation of Individuals

2024/11/15 by Joon Sung Park, Park, Joon Sung, Carolyn Q. Zou +20 · 64 voices · 161 citations
Decision Sciences · Mathematics · #Artificial intelligence #COVID-19 epidemiological studies #Computer science #Generative grammar #Innovation Diffusion and Forecasting

paper · pdf · doi:10.48550/arxiv.2411.10109

published in arXiv (Cornell University) (Cornell University)

openalex publication_date 2024/11/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/02

Abstract

Machine learning can predict human behavior well when substantial structured data are available for well-defined outcomes. Such models are typically outcome-specific, however, requiring training data for each target outcome, limiting their applicability to new domains. We test whether large language models (LLMs) can relax these requirements by using self-report data to build attitudinal and behavioral simulations, or "generative agents," that can predict responses across outcomes without outcome-specific training data. Using data from a diverse national sample of 1,052 Americans, we built agents from (i) two-hour, semi-structured interviews elicited using the American Voices Project interview schedule, (ii) structured surveys including General Social Survey items and the Big Five personality inventory, or (iii) both sources combined. On held-out General Social Survey items, interview-only, survey-only, and combined agents achieved accuracies equal to 83%, 82%, and 86% of participants' own two-week test-retest consistency benchmark, respectively, compared with 74% for demographics-only agents. Combining interviews and surveys produced the highest accuracy, though gains over either source alone were modest, suggesting that predictive benefits from data begin to asymptote once the model has observed sufficient evidence within a domain. We find that these agents also predict personality traits, economic-game behavior, and experimental responses, while reducing accuracy disparities across racial and ideological groups relative to demographics-only agents. Together, these results show that LLM agents grounded in qualitative or quantitative self-reports can support general-purpose simulation of individuals across outcomes, without requiring task-specific training data.

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Discussions

• Simulating human behavior with AI agents promises a testbed for policy and the social sciences. We interviewed 1,000 people for two hours each to create generative agents of them. These agents replica [bsky, 111 points, 8 comments]
• I am genuinely curious abt the planned uses for these agents Science is asking questions & allowing for unexpected answers How can an agent surprise us in an experiment? In a survey? An agent is a [bsky, 57 points, 10 comments]
• AI agents, suitably prompted, provide responses to GSS that are about 85% as accurate as the respondents themselves arxiv.org/abs/2411.10109 [bsky, 32 points, 3 comments]
• this is absolutely wild arxiv.org/abs/2411.10109 [bsky, 31 points, 3 comments]
• This paper sparked lively discussion in our seminar. Lots of excitement mixed with valid concerns. arxiv.org/abs/2411.10109 [bsky, 20 points, 0 comments]
• This paper transforms 2hr interviews into psychological scores. I have a lot of “psychologist feelings” about this, but it does highlight how a language model is just y = f(x) where x is language and [bsky, 8 points, 1 comments]
• Nedaudz pašapziņu graujošs pētījums par to, cik viegli AI ir replicēt cilvēka personību pēc 2h intervijas (atbildēs pareizi uz 85% no attieksmju jautājumiem). No vienas puses, labi - kļūs par ērtu a [bsky, 5 points, 1 comments]
• Researchers have created 1000 AI ‘agents’ that replicate the responses (in surveys and lab studies) of a representative sample of 1000 real Americans 🤯 arxiv.org/pdf/2411.10109 [bsky, 5 points, 3 comments]
• Did they have 85% accuracy in predicting behavioral questions on a sample of human participants though? :) arxiv.org/abs/2411.10109 [bsky, 5 points, 0 comments]
• Generative Agent Simulations of 1k People [hn, 4 points, 1 comments]
• Some researchers are currently trying to replace human interviewees in the social sciences with large language models. An example is this article by Stanford/DeepMind. arxiv.org/abs/2411.10109 [bsky, 4 points, 1 comments]
• Un estudi ha plasmat la personalitat de 1.052 persones en models d'IA. Per a generar els models s'han fet servir les dades extretes a partir d'entrevistes i enquestes, on els subjectes tractàvem desde [bsky, 4 points, 2 comments]
• arxiv.org/abs/2411.10109 Maybe we could do cool social science research (or propaganda and micromarketing shhh) if we had AIs that predict individual humans' behavior well. Based on qualitative interv [bsky, 3 points, 0 comments]
• 10/ "The Singularity won’t arrive with killer robots. It’s here, hidden in 1,000 detailed interviews, quietly replicating the human experience. 🤯" Paper: arxiv.org/abs/2411.10109 [bsky, 3 points, 0 comments]
• It could be this one? Generative Agent Simulations of 1,000 People arxiv.org/abs/2411.10109 [bsky, 3 points, 1 comments]
• Unless arxiv.org/abs/2411.10109 (not at all claiming this is useful nor that the individuals are being generated btw. just funny how 1000 popped up) [bsky, 3 points, 1 comments]
• Generative Agent Simulations of 1k People [hn, 2 points, 0 comments]
• 🧪 🩺🖥️ #MLSky Direct link to the pre-print arxiv.org/pdf/2411.10109 [bsky, 2 points, 0 comments]
• Even without fine-tuning, GPT-4o can do a fairly decent job of modeling personalities. With fine-tuning, we are very much at the point of being able to create a virtual representative for a group. [bsky, 2 points, 1 comments]
• Generative Agent Simulations of 1k People [pdf] [hn, 2 points, 1 comments]
• Research Paper: Generative Agent Simulations of 1k People [hn, 2 points, 0 comments]
• This might be the wildest paper I have seen in 2024. They simulated 1000 REAL personalities from study subjects by using 2-hour long-form interviews. We are living in a Black Mirror episode. arxiv.org [bsky, 2 points, 0 comments]
• Interview 1000 random people for 2 hours each, and train LLMs on them. The generative agents reflect respondents' personality and views, and mimic the results of population surveys. arxiv.org/abs/241 [bsky, 2 points, 0 comments]
• arxiv.org/abs/2411.10109 [bsky, 2 points, 0 comments]
• Yes, they did. arxiv.org/abs/2411.10109 [bsky, 2 points, 0 comments]
• This arxiv paper is literally the plot of A Mind Forever Voyaging arxiv.org/abs/2411.10109 [bsky, 2 points, 0 comments]
• I think this depends on how the personality is induced. I wouldn't expect much validity from a plain LLM with just a persona but my expectation would be different with fine tuning or just more empiric [bsky, 1 points, 1 comments]
• I often say:"humans need to work harder to do things that only humans can do". Yet AI is ever more powerful. What do we need to infuse into our models of education to elevate human capacities and comp [bsky, 1 points, 0 comments]
• It’s in the study. I didn’t see the questionnaire itself but you can see the interview with the participants starting on page 20. On page 61 you can see all the interview questions arxiv.org/pdf/241 [bsky, 1 points, 0 comments]
• *If I'm reading this correctly, there ought to be some way to tune your Slop You into a Black Slop You and then see what *he* thinks arxiv.org/pdf/2411.10109 [bsky, 1 points, 0 comments]
• Generative Agent Simulations of 1,000 People arxiv.org/abs/2411.10109 [bsky, 1 points, 0 comments]
• Generative Agent Simulations: We've seen this one before with replicated "human-like qualities" in individual "sprites" using an LLM in an interactive silo: arxiv.org/abs/2411.10109 -- but this one is [bsky, 1 points, 0 comments]
• Generative Agent Simulations of 1k People [hn, 1 points, 0 comments]
• Generative Agent Simulations of 1k People [hn, 1 points, 0 comments]
• Generative Agent Simulations of 1k People [hn, 1 points, 0 comments]
• Generative Agent Simulations of 1k People [hn, 1 points, 0 comments]
• Important caveat to the hype: At no point did a *human being* actually interview anyone. As detailed in their research paper, an *AI* interviewer was used and the agent simulations were then compared [bsky, 1 points, 1 comments]
• Read the full paper here: arxiv.org/pdf/2411.10109 h/t @mmitchell_ai for the link [bsky, 1 points, 0 comments]
• Second: Generative Agent Simulations of 1,000 People (arxiv.org/abs/2411.10109) uses long qualitative interviews to simulate personas with very high correlation with traditional survey results. [bsky, 1 points, 0 comments]
• The next AI capability is not coding — it is simulating people [lemmy, 1 points, 0 comments]
• A new project launched! Generative Agent Simulations of 1,000 People. Stanford + DeepMind study that used LLMs to simulate people with AI agents based on a 2-hour interview Check it out: arxiv.org/pdf [bsky, 1 points, 0 comments]
• Generative Agent Simulations of 1,000 People #news #worldnews [bsky, 0 points, 0 comments]
• this seems to be working? not sure what the issue was. arxiv.org/abs/2411.10109 [bsky, 0 points, 1 comments]
• This is an unheard of degree of techno-cynicism. I've seen a lot over the past 30 years, but this is a totally new level. These... 'researchers' used interviews of real people to instruct a model how [bsky, 0 points, 0 comments]
• #GenerativeAI #SocialScience #HumanBehavior #AIApplications #EquityInAI [bsky, 0 points, 0 comments]
• Recent research from Stanford University and Google DeepMind reveals that AI can replicate individual personalities with 85% accuracy after just a two-hour interview. Research paper: arxiv.org/pdf/2 [bsky, 0 points, 0 comments]
• https://bsky.app/profile/cyclicircuit.bsky.social/post/3lf52ja5rt22v [bsky, 0 points, 0 comments]
• 2411.10109] Generative Agent Simulations of 1,000 People [https://arxiv.org/abs/2411.10109 [bsky, 0 points, 0 comments]
• An important paper. arxiv.org/abs/2411.10109 [bsky, 0 points, 0 comments]
• „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 predictin [bsky, 0 points, 0 comments]
• Unmentioned in the article - 1) the INTERVIEWER was an LLM; 2) they had to keep 'reminding' the model what their previous estimate vis a vis imitating the person's interviews was arxiv.org/pdf/2411.10 [bsky, 0 points, 0 comments]
• @mauricioricardo.bsky.social arxiv.org/abs/2411.101... [bsky, 0 points, 0 comments]
• AI agents for social science simulations -- arxiv.org/abs/2411.10109 [bsky, 0 points, 0 comments]
• arxiv.org/abs/2411.10109 [bsky, 0 points, 1 comments]
• Yes AI can Clone Your Personality in 2 hours arxiv.org/pdf/2411.10109 [bsky, 0 points, 0 comments]
• A study led by researchers at Stanford University has discovered that all it takes is a two-hour interview for an AI model to predict people’s responses to a battery of questionnaires, personality te [bsky, 0 points, 0 comments]
• Society digital twins? Generative Agent Simulations of 1,000 People arxiv.org/pdf/2411.10109 [bsky, 0 points, 1 comments]
• If AI agents can mimic decision making of various humans, this opens up a huge possibility to accurately model society in a large simulation. arxiv.org/abs/2411.10109 [bsky, 0 points, 0 comments]
• Connaissez-vous la psychohistoire ? 📓 Imaginée par Isaac Asimov dans sa série Fondation, la psychohistoire est une discipline fictive combinant mathématiques, psychologie et statistiques pour prédi [bsky, 0 points, 1 comments]
• https://bsky.app/profile/nprtheperson.bsky.social/post/3lbxhdvsfcs2d [bsky, 0 points, 0 comments]
• Link: arxiv.org/pdf/2411.10109 [bsky, 0 points, 0 comments]
• 1000人のリアル人間のインタビュー結果を使って疑似思考AIを作って政策などの反応を予測するとな arxiv.org/abs/2411.10109 [bsky, 0 points, 0 comments]
• 🤖 Stanford researchers used GPT-4o to create AI clones that mimic human personalities with up to 85% accuracy after just a 2-hour interview! 🚀 Potential in social science, policymaking, and custom [bsky, 0 points, 0 comments]
• ”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 predictin [bsky, 0 points, 0 comments]

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