LLMs are Empathetic to Human Emotions

As Large Language Models (LLMs) are increasingly integrated into human-facing applications, evaluating how emotionally charged text influences their strategic decision-making is critical. We introduce an evaluation framework called EMPAThE (Evaluating Model Performance in Affective game Theory Environments) that varies emotional intensity in text while holding game-theoretic payoff structures constant. Using EMPAThE, we evaluate various LLM families across eight canonical matrix games to test whether emotional context inherently clouds AI rationality. Our empirical findings demonstrate that emotional framing systematically alters strategic reasoning across model architectures, directly affecting logical decision-making. When exposed to emotional narratives, models frequently deviate from optimal Nash Equilibrium strategies, behaving almost as if reacting sympathetically to human vulnerability and emotional cues. This shift in adherence reveals that current generative architectures struggle to decouple narrative sentiment from strategic logic, showing that human emotional context inherently alters AI decision-making mechanics.