AI Agents Alone Are Not (Yet) Sufficient for Social Simulation
A new position paper published on arXiv challenges the prevailing assumption that Large Language Model (LLM)-integrated agents are currently sufficient for accurate social simulation. Authors Yiming Li and Dacheng Tao argue that there is a systematic mismatch between what current agent pipelines produce and the rigorous requirements of simulation-as-science. The study highlights that role-playing plausibility does not equate to faithful human behavioral validity. Furthermore, collective outcomes in simulations are often driven by agent-environment co-dynamics, interaction protocols, scheduling, and initial information priors, rather than just agent-to-agent messaging. To address these limitations, the authors propose a unified formulation of AI agent-based social simulation as an environment-involved Markov game. This framework includes explicit exposure and scheduling mechanisms to make underlying processes auditable. The paper provides concrete recommendations for the design, evaluation, and interpretation of such simulations, urging researchers to move beyond over-optimistic views of LLM capabilities in modeling complex population dynamics.
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AI Agents Alone Are Not (Yet) Sufficient for Social Simulation
A new position paper published on arXiv challenges the prevailing assumption that Large Language Model (LLM)-integrated agents are currently sufficient for accurate social simulation. Authors Yiming Li and Dacheng Tao argue that there is a systematic mismatch between what current agent pipelines produce and the rigorous requirements of simulation-as-science. The study highlights that role-playing plausibility does not equate to faithful human behavioral validity. Furthermore, collective outcomes in simulations are often driven by agent-environment co-dynamics, interaction protocols, scheduling, and initial information priors, rather than just agent-to-agent messaging. To address these limitations, the authors propose a unified formulation of AI agent-based social simulation as an environment-involved Markov game. This framework includes explicit exposure and scheduling mechanisms to make underlying processes auditable. The paper provides concrete recommendations for the design, evaluation, and interpretation of such simulations, urging researchers to move beyond over-optimistic views of LLM capabilities in modeling complex population dynamics.
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