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Reputation-based reciprocity in human-bot and human-human networks.
Ashley Harrell1, Margaret L Traeger2
1Department of Sociology, Duke University, Durham, NC 27705, USA.
PNAS Nexus
|May 19, 2025
Summary
Reputation-based reciprocity (RBR) is less effective at promoting generosity in human-bot hybrid systems. People are less generous when aware of bots, altering social norms around helping.
Area of Science:
- Social Psychology
- Human-Computer Interaction
- Behavioral Economics
Background:
- Reputation-based reciprocity (RBR) incentivizes helping behavior in human networks.
- Hybrid systems integrate humans and artificial intelligence (AI), creating new social dynamics.
- Understanding RBR's efficacy in human-bot interactions is crucial for designing these systems.
Purpose of the Study:
- To investigate whether RBR promotes generosity in human-bot networks compared to human-only networks.
- To examine how the presence of bots influences human prosocial behavior and norm perceptions within RBR systems.
Main Methods:
- Two experiments were conducted: one with indirect reciprocity networks and known/unknown bot presence, and another with one-shot dyadic human-bot interactions.
- Behavioral observations and evaluations of generosity were recorded in both network and dyadic settings.
- Participants' willingness to help and their punishment/reward behaviors were analyzed.
Main Results:
- RBR was significantly less effective in fostering generosity in hybrid human-bot systems.
- Participants exhibited reduced generosity when aware of bots in the network, attributed to altered helping norms.
- In dyadic interactions, people were less willing to help bots, even with reputational incentives, and judged those who helped bots more harshly.
Conclusions:
- The presence of bots diminishes the effectiveness of RBR in promoting prosocial behavior.
- Humans do not extend the same norms of helping to bots as they do to other humans.
- Hybrid systems require careful consideration of social norm adaptation to maintain prosociality.
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