Strategic Reasoning and Sensitivity to Stakes in the Dictator and Ultimatum Games: LLMs vs. Human Proposers

Solomon Polachek (), Kenneth Romano () and Ozlem Tonguc ()
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Solomon Polachek: Binghamton University, New York
Kenneth Romano: State University of New York at Binghamton (Binghamton University)
Ozlem Tonguc: State University of New York at Binghamton (Binghamton University)

No 18545, IZA Discussion Papers from IZA Network @ LISER

Abstract: This study examines how large language models (LLMs) respond to varying stake sizes in the Dictator and Ultimatum games using the high-stakes design introduced by Andersen et al. (2011). We test ten leading LLMs chosen for their accessibility, prominence, and differences in reasoning capabilities. Results reveal substantial variation across models: Only 5 of 10 models exhibit strategic behavior by offering more in the Ultimatum Game (UG) than in the Dictator Game (DG). Relative to humans, 4 models are consistently more generous, 2 consistently less, and 4 vary with stake size. Only 1 model shows a monotonic decline in UG offers as stakes increase; the remaining 9 are non-monotonic or stable. Unlike humans, most models reduce UG offers when endowed with wealth. Prompting for "human-like" decisions generally increases generosity in the UG. These findings are important for evaluating whether LLMs can serve as realistic proxies for human subjects in behavioral experiments and highlight key limitations and future directions for model development.

Keywords: ultimatum game; dictator game; fairness; payoff stakes; artificial intelligence (search for similar items in EconPapers)
JEL-codes: C72 C90 D01 (search for similar items in EconPapers)
Date: 2026-04
New Economics Papers: this item is included in nep-ain, nep-exp and nep-gth
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