How does AI distribute the pie? Large Language Models and the Ultimatum Game

Douglas K.G. Araujo () and Harald Uhlig ()
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Douglas K.G. Araujo: Banco Central do Brasil
Harald Uhlig: University of Chicago, CEPR and NBER

No 2026-29, Working Papers from Becker Friedman Institute for Research In Economics

Abstract: As Large Language Models (LLMs) are increasingly tasked with autonomous decisionmaking, understanding their behavior in strategic settings is crucial. We investigate the choices of various LLMs in the Ultimatum Game, a setting where human behavior notably deviates from theoretical rationality. We conduct experiments varying the stake size and the nature of the opponent (Human vs. AI) across both Proposer and Responder roles. Three key results emerge. First, LLM behavior is heterogeneous but predictable when conditioning on stake size and player types. Second, while some models approximate the rational benchmark and others mimic human social preferences, a distinct “altruistic†mode emerges where LLMs propose hyper-fair distributions (greater than 50%). Third, LLM Proposers forgo a large share of total payoff, and an even larger share when the Responder is human. These findings highlight the need for careful testing before deploying AI agents in economic settings.

Keywords: Ultimatum Game; LLM; AI Agents; Behavioral Economics; Algorithmic Decision Making (search for similar items in EconPapers)
JEL-codes: C70 C90 D91 (search for similar items in EconPapers)
Pages: 22 pages
Date: 2026
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