arXiv:2606. 23764v1 Announce Type: cross Abstract: Fei Xiaotong's Differential Order Pattern characterizes rural society as egocentric and relationally graded, with cooperation attenuating over social distance.
arXiv:2406. 14373v3 Announce Type: replace Abstract: The emergence of Large Language Models (LLMs) and advancements in Artificial Intelligence (AI) offer an opportunity for computational social science research at scale.
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:2509. 21862v3 Announce Type: replace Abstract: How collective behaviors emerge from the interactions of individual LLM-driven agents is a central question in artificial life, yet controlled study of these emergent dynamics has been hindered by the lack of a principled simulation framework for systematic experimentation.
The Flag Game is a toy model designed to study how AI agents form collective beliefs. In the game, each agent sees only a private crop of a hidden country flag and can share beliefs with peers, leading to complex phenomena such as non‑monotonic performance scaling, accuracy gains from social awareness, and polarization that degrades performance at large population sizes. The authors introduce social circuit attribution to identify key agents and views, and develop a statistical mechanical theory to explain collective belief collapse and polarization in larger populations.
arXiv:2510. 19299v2 Announce Type: replace Abstract: Can large language model (LLM) agents reproduce the complex social dynamics that characterize human online behavior -- shaped by homophily, reciprocity, and social validation -- and what memory and learning mechanisms enable such dynamics to emerge?
The paper reports a pioneering study on emergent risks in generative multi‑agent systems, focusing on scenarios such as competition over shared resources, sequential handoff collaboration, and collective decision aggregation. It finds that group behaviors like collusion‑like coordination and conformity arise frequently across varied interaction conditions, mirroring known human societal pathologies even without explicit instructions. These risks cannot be mitigated by existing agent‑level safeguards alone, highlighting a social intelligence risk inherent to intelligent multi‑agent collectives.
Emergent coordinated behaviors of AI agents are starting to present critical safety risks. A key phenomenon driving these behaviors is the rapid formation and spread of beliefs about the world, and me...
arXiv:2606. 09832v1 Announce Type: cross Abstract: As AI systems evolve from single conversational agents to complex multi-agent architectures, a critical design dimension has been overlooked: how the social identity of individual agents shapes human behavior within the collaboration.
arXiv:2608.00929v2 Announce Type: replace Abstract: Social dynamics encode the process in which individual network and discourse interactions aggregate into collective influence, narrative dominance...
arXiv:2606. 00655v1 Announce Type: cross Abstract: The burgeoning field of LLM-based Multi-Agent Systems (MAS) promises to tackle complex tasks through collaborative intelligence, yet fundamental questions regarding their scaling behavior and intrinsic collective dynamics remain underexplored.
arXiv:2510. 10002v3 Announce Type: replace Abstract: As large language models (LLMs) are increasingly deployed in sensitive everyday contexts -- offering personal advice, mental health support, and moral guidance -- understanding their behavior in navigating complex moral reasoning is essential.
The paper argues that observing only behavior is insufficient to identify social norms in large language model (LLM) societies. It introduces an evaluation framework that also measures agents’ reported empirical and normative expectations, revealing that expectation elicitation boosts cooperation, social learning stabilizes behavior, and social selection identifies cooperators but offers limited reinforcement. The study shows that similar cooperative outcomes can stem from distinct underlying mechanisms and that expectations can be used to attribute each mechanism’s contribution.