Yonatan Belinkov, Johnathan Sun, Andrei Shleifer
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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.
The same record, over MCP at https://syntology.ai/mcp:
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get_code_for_paper("2609.22225")
have("2609.22225")
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curl https://syntology.ai/api/ran/2609.22225.json
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[](https://syntology.ai/paper/2609.22225)
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