Generosity and forgiveness: The importance of nuanced reputation

Abstract Indirect reciprocity, supported by simple reputation assessment and social norms, has been demonstrated as an effective mechanism for enabling cooperation in populations of self-interested individuals. However, it has been shown that where there is noise in the performance of actions, or in observers’ perceptions, cooperation may not emerge. Higher-order social norms and generosity have been investigated as potential mechanisms to support cooperation in such environments, but are ineffective without additional, limiting, assumptions. In particular, higher-order norms have typically been investigated in cases where reputation is binary (‘good’ or ‘bad’) and where all agents ascribe the same reputation to an individual, implying full and perfect observation of actions. Generosity with an ‘aligned’ strategy, where all agents have the same likelihood of being generous, has been shown to be ineffective where reputation is binary. In this work we consider reciprocity emergence mechanisms in noisy domains and where agent observations may be incomplete or inaccurate. Our hypothesis is that nuanced reputation scores will enable generosity to be more effective, since an individual act of generosity will have a less extreme impact on reputation. We also investigate whether replacing the ‘aligned’ setting for generosity with a ‘non-aligned’ alternative, which we refer to as forgiveness, will support cooperation in noisy partially observable environments, without the level of ‘unjustified benevolence’ exhibited by generosity. We show both analytically and empirically that generosity when combined with fine grained reputation can help cooperation emerge, and that forgiveness can support cooperation in certain settings.

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Publication Details

Journal
Autonomous Agents and Multi-Agent Systems
Published
2026-06-25
DOI
https://doi.org/10.1007/s10458-026-09761-9
Primary Topic
Evolutionary Game Theory and Cooperation
Type
article
Field-Weighted Citation Impact
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article

Generosity and forgiveness: The importance of nuanced reputation

Nir Oren, Nathan Griffiths
Autonomous Agents and Multi-Agent Systems
Evolutionary Game Theory and Cooperation
article

Generosity and forgiveness: The importance of nuanced reputation

Nir Oren, Nathan Griffiths
article en

Abstract

Abstract Indirect reciprocity, supported by simple reputation assessment and social norms, has been demonstrated as an effective mechanism for enabling cooperation in populations of self-interested individuals. However, it has been shown that where there is noise in the performance of actions, or in observers’ perceptions, cooperation may not emerge. Higher-order social norms and generosity have been investigated as potential mechanisms to support cooperation in such environments, but are ineffective without additional, limiting, assumptions. In particular, higher-order norms have typically been investigated in cases where reputation is binary (‘good’ or ‘bad’) and where all agents ascribe the same reputation to an individual, implying full and perfect observation of actions. Generosity with an ‘aligned’ strategy, where all agents have the same likelihood of being generous, has been shown to be ineffective where reputation is binary. In this work we consider reciprocity emergence mechanisms in noisy domains and where agent observations may be incomplete or inaccurate. Our hypothesis is that nuanced reputation scores will enable generosity to be more effective, since an individual act of generosity will have a less extreme impact on reputation. We also investigate whether replacing the ‘aligned’ setting for generosity with a ‘non-aligned’ alternative, which we refer to as forgiveness, will support cooperation in noisy partially observable environments, without the level of ‘unjustified benevolence’ exhibited by generosity. We show both analytically and empirically that generosity when combined with fine grained reputation can help cooperation emerge, and that forgiveness can support cooperation in certain settings.

Autonomous Agents and Multi-Agent SystemsVol. 40(2)
University of Aberdeen (GB), University of Warwick (GB)
Openalex Percentile: Top 4%
Evolutionary Game Theory and Cooperation
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