The study audits 576 LLM-based social simulations from 350 papers using the PIMMUR framework, which evaluates agent profile, interaction, memory, minimal control, unawareness, and realism. Results show that PIMMUR principles are met more often than minimal control, unawareness, and realism, with frontier LLMs correctly identifying the underlying experiment in 65.2% of cases and half of prompts pre‑determining outcomes. Reproducing five experiments revealed that many reported collective phenomena disappear or reverse when PIMMUR principles are enforced, suggesting that apparent emergent behaviors may be methodological artifacts rather than genuine social dynamics.
arXiv:2507.19364v3 Announce Type: replace Abstract: The integration of Large Language Models (LLMs) into social simulation has generated considerable enthusiasm, but also raises substantial methodolo...
arXiv:2509.03736v3 Announce Type: replace Abstract: LLM agents are increasingly proposed as substitutes for human participants in social research, usually on the strength of survey responses that mat...
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.
The paper introduces SILICA, an open instrument designed to evaluate whether large language model (LLM) agent societies replicate human behavioural distributions. Using five environments with human‑anchored data and perturbations, the study finds that most LLMs only match human behaviour at initial stages, failing to reproduce end‑state cooperation or correct acceptance thresholds. The results suggest that current LLM societies can support exploratory claims but do not yet reliably emulate human social dynamics.
arXiv:2606. 30454v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used as agents in simulations of social systems, yet it remains unclear when their behavior can be interpreted as a faithful proxy for human decision-making.
arXiv:2608.29803v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed as proxies for human participants in social simulations, yet whether they update their beliefs...
arXiv:2509.24877v3 Announce Type: replace Abstract: The social science of large language models (LLMs) examines how these systems evoke mind attributions, interact with one another, and transform hum...
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...
The paper investigates whether compact surrogate models can reduce the cost of simulating large language model (LLM) societies while still reproducing their collective behavior. Using 9,455 published trajectories and new opinion‑dynamics experiments, it finds that incorporating neighbor information improves individual predictions across all 16 public‑data settings and enhances pooled collective forecasts on held‑out questions, though the collective gains vary with transfer conditions. Additional tests on 24 new statements do not confirm earlier contrasting history effects, and Qwen shows benefit from history only after three observed rounds, underscoring the need for direct collective validation, explicit limits on available observations, and comparisons with simple baselines.
Computational Multi-Agents Society Experiments (CMASE) is a generative agent‑based framework that enables researchers to participate in‑world and trace interventions at runtime, treating simulation as a simulated ethnographic field. It combines LLM‑driven perception, memory, and emotion with a composable environment and researcher roles that allow real‑time interaction, controlled experiments, and forward exploration of intervention trajectories. Using CMASE, the authors replicated two published field studies—urban greenery and social fragmentation, and a public goods game—demonstrating that embedded social interventions produce observable, auditable processes that parametric manipulation alone does not.
arXiv:2606. 03137v1 Announce Type: new Abstract: LLM-based multi-agent simulation offers a promising way to study social interaction, deliberation, and collective opinion dynamics.
arXiv:2608. 11215v1 Announce Type: new Abstract: Simulating societies of many large language model (LLM) agents is expensive, yet the questions asked of such simulations are usually macroscopic: phase behaviour, stylised facts, and scaling with the number of agents $N$, not the cognition of any single agent.