Do people expect different behavior from large language models acting on their behalf? Evidence from norm elicitations in two canonical economic games

Pawe{\l} Niszczota and Elia Antoniou

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Abstract: While delegating tasks to large language models (LLMs) can save people time, there is growing evidence that offloading tasks to such models produces social costs. We use behavior in two canonical economic games to study whether people have different expectations when decisions are made by LLMs acting on their behalf instead of themselves. More specifically, we study the social appropriateness of a spectrum of possible behaviors: when LLMs divide resources on our behalf (Dictator Game and Ultimatum Game) and when they monitor the fairness of splits of resources (Ultimatum Game). We use the Krupka-Weber norm elicitation task to detect shifts in social appropriateness ratings. Results of two pre-registered and incentivized experimental studies using representative samples from the UK and US (N = 2,658) show three key findings. First, people find that offers from machines - when no acceptance is necessary - are judged to be less appropriate than when they come from humans, although there is no shift in the modal response. Second - when acceptance is necessary - it is more appropriate for a person to reject offers from machines than from humans. Third, receiving a rejection of an offer from a machine is no less socially appropriate than receiving the same rejection from a human. Overall, these results suggest that people apply different norms for machines deciding on how to split resources but are not opposed to machines enforcing the norms. The findings are consistent with offers made by machines now being viewed as having both a cognitive and emotional component.

Date: 2026-01
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