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Implications of LLM integration for validation and calibration

Determine the implications of integrating Large Language Models (LLMs) into agent-based models for achieving rigorous validation and calibration, including whether and how generative agent-based models can attain operational validity across their intended domains.

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Background

A central historical critique of agent-based models (ABMs) concerns difficulties in rigorous calibration and validation against empirical data. The paper argues that while LLMs may improve behavioral realism, it is uncertain whether they help resolve the longstanding validation and calibration challenges. Clarifying this is essential for assessing the scientific utility of generative ABMs.

This open problem is positioned as pivotal for the field’s future: if LLMs do not facilitate robust validation and calibration procedures, generative ABMs may fail to contribute meaningfully to social scientific theory or policy-relevant modeling.

References

However, while LLMs promise to address the first key challenge of ABMs by making agents more realistic, their implications for the second -- rigorous validation and calibration -- remain an open question that is central to the future potential of generative ABMs.

— Do Large Language Models Solve the Problems of Agent-Based Modeling? A Critical Review of Generative Social Simulations (2504.03274 - Larooij et al., 4 Apr 2025) in Introduction (Section 1)