Comparable costly punishment toward AI and human free-riders among Japanese participants: Evidence from a public goods game

Abstract

Costly punishment, in which individuals incur a personal cost to sanction norm violators, is a key mechanism for maintaining cooperation in social dilemmas. Although such behavior has been extensively studied in human–human interaction, it remains unclear whether people direct comparable costly punishment toward artificial intelligence (AI), particularly within culturally specific contexts. The present study examined this question among Japanese participants.

We conducted an online public goods game involving 588 Japanese participants who interacted with three pre-programmed agents presented as either humans or AI. Participants could incur a personal cost to report a free-rider, and this reporting behavior was used as an observable measure of costly punishment. A three-way ANOVA, equivalence testing (TOST), regression analyses, and supplementary time-series analyses were performed.

The results showed that costly punishment toward AI free-riders was statistically and practically equivalent to that directed toward human free-riders. Participants’ behavior was primarily influenced by whether another agent had already punished the free-rider, regardless of whether that agent was presented as human or AI. Supplementary analyses further showed that this behavioral equivalence was already evident during the early rounds, while time-series analyses indicated that participants' behavioral patterns stabilized after the initial phase of repeated interaction, suggesting that the equivalence was not limited to either initial impressions or later repeated interactions. In contrast, questionnaire responses revealed differences in some subjective evaluations despite comparable behavioral responses.

These findings suggest that, among Japanese participants under tightly controlled label-based conditions, simply presenting an interaction partner as AI rather than human did not substantially alter observable costly punishment. The study extends research on negative human–AI interaction by showing that AI can function as a socially accountable target of costly punishment in a Japanese sample, while emphasizing that the generalizability of this equivalence to other cultural contexts requires cross-cultural validation.