Divergent Minds, Convergent Baselines: A Bounded-Rationality Account of LLM-Human Strategic Behaviour

Abstract

Researchers have started using LLM agents in place of human subjects in behavioural and political-science experiments, often as a cheaper substitute for laboratory pools. The substitution does not hold up in strategic settings: humans and LLMs reliably make different choices, and neither fine-tuning on human response data nor persona conditioning has closed the gap. The behavioural-economics literature has, since Simon's introduction of bounded rationality, modelled human strategic behaviour as a classical baseline plus an additive correction term $\delta$. The framework proposed here reads $\delta$ as the mathematical signature of bounded computation: the gap between what an unboundedly-rational agent would compute and what a computationally bounded agent actually produces. For canonical games whose solutions are present in standard training corpora, LLMs retrieve and recombine corpus material, bypassing the bound that produces $\delta$ in humans. The framing extends to reasoning-distilled models through cognitive-hierarchy theory: their accessible level-$k$ strategic reasoning is bounded by compute budget and context length rather than by the cognitive constraints that bound humans, and the $\delta$ they produce, if any, carries different structural signatures. Four operational tests (conditional dependence, distributional asymmetry, path-dependence under repetition, and paraphrase-robustness) are proposed to discriminate human-shaped $\delta$ from LLM-shaped $\delta$. A moderator prediction is that $|\delta|$ scales with peer-signal individuation in the decision environment, with a quantitative bound of Cohen's $d \geq 0.5$ between named-opponent and aggregate-opponent settings.