arXiv AI
From the Fluency Fallacy to the Micro-to-Macro Validity Gap: Opportunities and Pitfalls of LLMs in Social Simulation
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
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...
Hugging Face Trending Papers
Sep 21
SocioVerse2: A Longitudinal Dynamic Social Simulation Framework under a Human-AI Co-evolutionary Paradigm
Social simulation offers the social sciences an experimental instrument that the real world cannot supply, and generative agents have transformed it by acting as silicon samples that unite agent-based...
arXiv Computation and Language
Sep 22
SocioVerse2: A Longitudinal Dynamic Social Simulation Framework under a Human-AI Co-evolutionary Paradigm
arXiv:2609.24911v1 Announce Type: new Abstract: Social simulation offers the social sciences an experimental instrument that the real world cannot supply, and generative agents have transformed it by...
arXiv AI
Oct 19
Computational Multi-Agents Society Experiments: Social Modeling Framework Based on Generative Agents
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 Computation and Language
Sep 11
The PIMMUR Principles: Ensuring Validity in Collective Behavior of LLM Societies
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 AI
Jun 30
LLM Agents Grounded in Self-Reports Enable General-Purpose Simulation of Individuals
arXiv:2411. 10109v3 Announce Type: replace Abstract: Machine learning can predict human behavior well when substantial structured data are available for well-defined outcomes.