The paper examines how collective‑action systems balance decentralization and centralization through delegation. A reduced‑form model shows a scale–accountability tradeoff: larger constituencies lower individual costs but reduce a center’s incentive to retain each member, limiting non‑exploitative delegation to an endogenous size. Simulations with large language model agents reveal that polycentric governance structures naturally arise, cycling through concentration, exploitation, exit, and re‑formation, and that different model capacities vary in their willingness to lead and in their propensity to exploit.
arXiv:2608. 09574v1 Announce Type: new Abstract: LLMs are rapidly embedding themselves into daily life: drafting our emails, managing our schedules, and making decisions on our behalf.
arXiv:2609.35928v1 Announce Type: cross Abstract: Multi-agent LLM systems increasingly mix models from several providers, yet exposing each agent's underlying model identity to its peers significantl...
arXiv:2601. 19082v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly deployed as autonomous agents that negotiate, coordinate, and act on behalf of users.
arXiv:2604. 07821v2 Announce Type: replace-cross Abstract: Large language model (LLM) agents increasingly coordinate in multi-agent systems, yet we lack an understanding of where and why cooperation fails.
arXiv:2608. 03076v1 Announce Type: new Abstract: Multi-agent studies commonly place AI agents in predefined games, markets, or roles, making it difficult to distinguish endogenous economic organization from behavior inherited from the scenario.
The paper introduces Degrees of Freedom (DoF), a continuous framework that measures how much decision-making is delegated to agents in large language model–based multi‑agent systems. By varying DoF, the authors explore a spectrum between fully centralized and fully decentralized coordination, finding that intermediate settings often outperform both extremes across several task domains. The study shows that task characteristics and retained experience influence how coordination granularity affects performance, offering empirical guidance for designing hybrid coordination regimes.
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:2608. 14913v1 Announce Type: cross Abstract: We introduce the Open-Strategy Dictator Game (OSDG), a variant of the classic dictator game in which each player's strategy is a natural-language document visible to all participants.
arXiv:2608.22152v1 Announce Type: new Abstract: Multi-agent systems built from large language models are deployed widely, yet how much performance is lost when two LLMs must coordinate rather than ac...
arXiv:2610.32726v1 Announce Type: cross Abstract: As large language models (LLMs) increasingly operate as interconnected agents, a fundamental question emerges: does individual cooperation translate...
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.
Language-model agents favor their own group because they have watched their members favor each other. The group label alone does little once the decision has a cost; what drives favoritism is observed...