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arXiv · 2609.22225

Do LLMs Choose Like Humans? Using Cognitive Theory to Evaluate LLM Decision-Making

Johnathan Sun·Andrei Shleifer·Yonatan Belinkov
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Abstract

Large language models (LLMs) exhibit a range of human-like decision-making behaviors, but whether these reflect similar underlying mechanisms or surface-level mimicry remains unclear. We evaluate whether LLM context sensitivity aligns with a cognitive economic theory that explains human behavior through problem categorization and attention allocation. Across 12 open-source and commercial LLMs on a novel 140,000-trial product choice benchmark, context induces human-like shifts in choice and problem categorization, but does not reliably reweight attention between features like price and quality. Neither scale nor chain-of-thought reasoning reliably attenuates context sensitivity or generates human-like behavior. These results suggest that LLM decision mechanisms are distinct from human ones.

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Johnathan Sun, Andrei Shleifer, Yonatan Belinkov. 2026-09-02. Do LLMs Choose Like Humans? Using Cognitive Theory to Evaluate LLM Decision-Making. https://arxiv.org/abs/2609.22225

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