SocaSim uses LLM-based multi-agent simulations to model Putnam's Social Capital Theory, reproducing macro-level patterns and aligning with human group-level decisions, then applies the framework to smart elderly care adoption.
arXiv:2502.20432 [cs.AI] https://arxiv.org/abs/2502.20432
3 Pith papers cite this work. Polarity classification is still indexing.
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LLMs act as static level-k players in behavioral games with k set by model scale, showing no within-game updating or backward induction unlike humans.
Persona-conditioned LLM agents favor Green outcomes even against explicit Tragedy-dominant payoffs, but the headline 65–90% 'Tragedy equilibrium' recovery is contradicted by the paper's own appendix (0 Tragedy profiles in those cells).
citing papers explorer
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From Blueprint to Reality: Modeling and Applying Putnam's Social Capital Theory with LLM-based Multi-agent Simulations
SocaSim uses LLM-based multi-agent simulations to model Putnam's Social Capital Theory, reproducing macro-level patterns and aligning with human group-level decisions, then applies the framework to smart elderly care adoption.
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LLM Agents as Static Level-k Players in Behavioural Games
LLMs act as static level-k players in behavioral games with k set by model scale, showing no within-game updating or backward induction unlike humans.
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When Identity Overrides Incentives: Representational Choices as Governance Decisions in Multi-Agent LLM Systems
Persona-conditioned LLM agents favor Green outcomes even against explicit Tragedy-dominant payoffs, but the headline 65–90% 'Tragedy equilibrium' recovery is contradicted by the paper's own appendix (0 Tragedy profiles in those cells).