arXiv:2602.10324v2 Announce Type: replace Abstract: As Large Language Models (LLMs) are increasingly deployed in social and strategic scenarios, it becomes critical to understand where and why their behavior diverges from that of humans. While behavioral game theory (BGT) provides a framework for analyzing behavior, existing models do not fully capture the idiosyncratic behavior of humans or black-box, non-human agents like LLMs. We employ AlphaEvolve, a cutting-edge program discovery tool, to directly discover interpretable models of human and LLM behavior from data, thereby enabling open-end
The increasing deployment of LLMs in social and strategic applications necessitates a deeper understanding of their behavioral divergence from humans to ensure safe and effective integration.
Understanding how LLMs strategize differently from humans is crucial for designing robust AI systems, avoiding unexpected outcomes in human-AI interaction, and anticipating future AI capabilities.
New methodologies for directly discovering interpretable models of AI behavior from data will refine how we evaluate and predict the actions of sophisticated AI systems.
- · AI Safety Researchers
- · Game Theory Experts
- · AI Development Companies
- · Regulatory Bodies
- · Developers neglecting alignment research
- · Companies with opaque AI systems
Improved understanding of LLM decision-making mechanisms will lead to more predictable and controllable AI.
This understanding will inform the development of regulatory frameworks and ethical guidelines tailored to AI's strategic behavior.
The insights gained could lead to novel AI architectures that explicitly model and manage behavioral differences for enhanced human-AI collaboration or competition.
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Read at arXiv cs.AI