Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/339375 
Erscheinungsjahr: 
2026
Schriftenreihe/Nr.: 
ECONtribute Discussion Paper No. 391
Verlag: 
University of Bonn and University of Cologne, Reinhard Selten Institute (RSI), Bonn and Cologne
Zusammenfassung: 
AI-driven systems are rapidly moving from decision support to directing human behavior through rules, recommendations, and compliance requests. This shift expands everyday human-AI interaction and raises the possibility that AI may function as an authority figure. However, the behavioral consequences of AI as an authority figure remain poorly understood. We investigate whether individuals differ in their willingness to comply with arbitrary rules depending on whether these rules are attributed to an AI agent (ChatGPT) or to a fellow human. In a between-subject design, 977 US Prolific users completed the coins task: they could earn a monetary payoff by stopping the disappearance of coins at any time, but a rule instructed them to wait for a signal before doing so. There are no conventional reasons to follow this rule: complying is costly and nobody is harmed by non-compliance. Despite this, we find high rule-following rates: 64.3% followed the rule set by ChatGPT and 63.9% complied with the human-set rule. Descriptive and normative beliefs about rule following, as well as compliance conditional on these beliefs, are also largely unaffected by the rule's origin. However, subjective social closeness to the rule setter significantly predicts how participants condition their behavior on social expectations: when participants perceive the rule setter as subjectively closer, conditional compliance is higher and associated beliefs are stronger, irrespective of whether the rule setter is human or AI.
Schlagwörter: 
Artificial intelligence
AI-human interaction
ChatGPT
rule-following
coins task
CRISP framework
social expectations
conditional rule conformity
social closeness
IOS11
online experiments
JEL: 
C91
D91
Z13
Dokumentart: 
Working Paper

Datei(en):
Datei
Größe
2.07 MB





Publikationen in EconStor sind urheberrechtlich geschützt.