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LLM infers negotiation state from dialogue while theory-grounded algorithms optimize offers, showing how psychological assumptions can be embedded in an agent\u2019s decision process."},{"key":"10.1016\/j.copsyc.2026.102470_bib66","unstructured":"This paper illustrates a hybrid approach using LLM-enhanced reinforcement learning to steer negotiation behavior through theory-informed strategy rewards and Brown-and-Levinson politeness modeling."},{"key":"10.1016\/j.copsyc.2026.102470_bib67","unstructured":"The authors use an embodied AI counterpart to test how embodiment shapes negotiation behavior, exemplifying confederate designs built around a specific psychological question rather than generic human substitution."},{"key":"10.1016\/j.copsyc.2026.102470_bib68","unstructured":"This article shows that populations of interacting LLM agents spontaneously develop shared conventions and collective biases, illustrating why regularities in AI-v-AI simulation cannot be read as human social 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