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arXiv · 2603.19167

Evaluating Counterfactual Strategic Reasoning in Large Language Models

Dimitrios Georgousis·Maria Lymperaiou·Angeliki Dimitriou·Giorgos Filandrianos·Giorgos Stamou
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Abstract

We evaluate Large Language Models (LLMs) in repeated game-theoretic settings to assess whether strategic performance reflects genuine reasoning or reliance on memorized patterns. We consider two canonical games, Prisoner's Dilemma (PD) and Rock-Paper-Scissors (RPS), upon which we introduce counterfactual variants that alter payoff structures and action labels, breaking familiar symmetries and dominance relations. Our multi-metric evaluation framework compares default and counterfactual instantiations, showcasing LLM limitations in incentive sensitivity, structural generalization and strategic reasoning within counterfactual environments.

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Dimitrios Georgousis, Maria Lymperaiou, Angeliki Dimitriou, Giorgos Filandrianos, Giorgos Stamou. 2026-03-19. Evaluating Counterfactual Strategic Reasoning in Large Language Models. https://arxiv.org/abs/2603.19167

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