arXiv AI
The average-farmer illusion in language-model simulations of agricultural decisions
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arXiv AI
Jul 2
Large language models replicate and predict human cooperation across experiments in game theory
arXiv:2511. 04500v3 Announce Type: replace Abstract: Large language models (LLMs) are increasingly deployed as decision-making agents in high-stakes domains and as imitators of human behavior in the social and behavioral sciences.
arXiv AI
Jul 31
When Synthetic Users Fail: A Cross-Domain Benchmark of LLM-Simulated Human Survey Responses
arXiv:2607. 26348v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used as synthetic users, stand-ins for human respondents whose simulated answers feed product, policy, and market decisions.
arXiv AI
Jul 28
Statistical realism is not evidence that LLMs can estimate treatment effects in social science experiments
arXiv:2604. 02458v3 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly used to simulate human responses and estimate treatment effect of interventions when real-world experiments are costly or infeasible.
arXiv Computation and Language
Sep 15
Readable, Faithful, Used: Three Dissociable Properties of Demographic Identity in a Language Model
arXiv:2608.18768v2 Announce Type: replace Abstract: Large language models are widely used to simulate survey respondents, yet their outputs are homogeneous and unfaithful to real inter-group differen...
arXiv Computation and Language
Sep 25
Artificial Societies Benchmark: A Validation Framework for Synthetic Research
The article introduces the Artificial Societies Benchmark, a validation framework designed to evaluate synthetic populations used in research. It comprises eleven tests covering internal, construct, and external validity, drawing on twenty human data sources and comparing nine language models. The benchmark links specific research uses to the evidence required and assesses how results vary with different respondent information, revealing that strong performance in one domain does not guarantee fidelity in others.
arXiv AI
Sep 16
Interpreting and Steering LLM Agents for Social Simulations
arXiv:2609.16436v1 Announce Type: cross Abstract: Simulations based on large language models (LLMs) have proven to be powerful for understanding human behavior, making them valuable additions to the...