arXiv:2607. 24649v1 Announce Type: new Abstract: Large language models are increasingly used as social simulators, including as synthetic survey respondents.
arXiv:2609.07943v1 Announce Type: new Abstract: There is significant uncertainty about whether abstractions like beliefs or desires usefully describe the behavior of large language models (LLMs). In...
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:2608.29464v1 Announce Type: cross Abstract: Chain-of-thought (CoT) monitoring assumes that reasoning traces faithfully record the information that shapes a model's answer. Existing faithfulness...
arXiv:2608. 13250v1 Announce Type: cross Abstract: Normative datasets are often used to train and align AI systems, but the norms they contain can function as action-guiding patterns rather than neutral moral knowledge.
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: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: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...
arXiv:2607. 25726v1 Announce Type: new Abstract: Recommender systems mediate everyday consumption, offering a promising channel for encouraging sustainable choices.
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...
The paper introduces a novel framework for assessing second‑order bias in large language models (LLMs), defined as bias in how an LLM judges the acceptability of biased content. Using principles from entitlement epistemology, the authors design a reasoning task that asks LLMs to determine whether a biased text is acceptable for specific demographic groups, and propose two metrics to quantify biased judgments. Experiments on both open‑source and closed‑source models reveal that the task bypasses safety guardrails, uncovers systematic variations across target groups, and demonstrates that models still rely on demographic labels when evaluating bias.
Researchers use synthetic survey respondents generated by large language models as substitutes for human samples, but current validation methods often compare them to human surveys in ways that may not reflect real-world consequential behaviour. The authors propose a new validation framework that requires explicit statements of how well synthetic data correspond to human behaviour, specifies which diagnostics are addressed, and demands subgroup-level validity claims to avoid misrepresentation. The framework operationalises distributional, procedural, and recognition justice dimensions and introduces within-persona counterfactual experiments, illustrated with a case study on electric vehicle charging tariffs and concluded with a reporting checklist for researchers.
arXiv:2606. 02798v1 Announce Type: new Abstract: Many decision-support settings require systems that adapt to individual users, but evaluation data for this problem remain limited.