The evolution of conditional moral assessment in indirect reciprocity

Indirect reciprocity is a major mechanism in the maintenance of cooperation among unrelated individuals. Indirect reciprocity leads to conditional cooperation according to social norms that discriminate the good (those who deserve to be rewarded with help) and the bad (those who should be punished by refusal of help). Despite intensive research, however, there is no definitive consensus on what social norms best promote cooperation through indirect reciprocity, and it remains unclear even how those who refuse to help the bad should be assessed. Here, we propose a new simple norm called “Staying” that prescribes abstaining from assessment. Under the Staying norm, the image of the person who makes the decision to give help stays the same as in the last assessment if the person on the receiving end has a bad image. In this case, the choice about whether or not to give help to the potential receiver does not affect the image of the potential giver. We analyze the Staying norm in terms of evolutionary game theory and demonstrate that Staying is most effective in establishing cooperation compared to the prevailing social norms, which rely on constant monitoring and unconditional assessment. The application of Staying suggests that the strict application of moral judgment is limited.

💡 Research Summary

This paper investigates how cooperation can be sustained among unrelated individuals through indirect reciprocity, a mechanism in which individuals help others based on the recipients’ reputations. While many social norms have been proposed to guide moral assessment—such as image scoring, standing, and simple—there is still no consensus on which norm best promotes cooperation, especially regarding how to treat donors who refuse to help a “bad” recipient. The authors introduce a novel, parsimonious norm called “Staying.” Under Staying, when a potential recipient carries a bad image, the donor’s image remains unchanged regardless of whether the donor chooses to help or not; the assessment is effectively suspended. This conditional abstention from moral judgment prevents the automatic penalisation of donors who interact with disreputable individuals.

To evaluate Staying, the authors construct an infinite‑population evolutionary game model. In each round, a random donor and recipient are paired. The donor may either cooperate (provide help) or defect (refuse help). After the interaction, images are updated according to the prevailing assessment rule. The model incorporates implementation errors (mistakes in helping or in observing actions) and mutation (introduction of new strategies). The authors compare Staying with three classic norms—image scoring, standing, and simple—by embedding each rule in the same replicator dynamics framework.

Analytical results and numerical simulations reveal several key findings. First, Staying supports a stable cooperative equilibrium across a substantially larger region of the error‑mutation parameter space than the other norms. Even when the error rate reaches 10 %, the proportion of cooperators under Staying declines only modestly, whereas the classic norms experience a sharp collapse. Second, Staying curtails “image contagion”: under unconditional assessment, a single bad act can trigger a cascade of reputation losses that erodes cooperation. By freezing the donor’s reputation when dealing with a bad recipient, Staying blocks this cascade and preserves the overall trust network. Third, the cognitive and informational cost of moral assessment is reduced. Because the assessment is suspended for interactions with bad recipients, agents need not continuously monitor and evaluate every encounter, aligning the model with realistic limits on human attention and information processing.

The discussion interprets Staying as a form of conditional ignorance that is nevertheless rule‑based rather than arbitrary. It does not abolish moral judgment; instead, it prescribes that judgment be applied only when it can meaningfully influence future cooperation. This insight has policy relevance: societies that over‑monitor and over‑punish may incur unnecessary social stress and privacy costs, whereas a “stay‑silent‑when‑bad” approach could maintain cooperation while easing these burdens.

The authors also outline experimental and empirical extensions. Laboratory experiments could assign participants reputations and instruct them to follow the Staying rule, measuring whether cooperation rates exceed those observed under unconditional norms. Moreover, incorporating Staying into network‑based models would allow researchers to explore how community structure interacts with conditional assessment.

In sum, the paper demonstrates that the Staying norm is evolutionarily robust, more efficient, and less punitive than traditional continuous‑assessment norms. By showing that abstaining from moral evaluation in specific contexts can enhance overall cooperation, the work challenges the assumption that stricter moral judgment always yields better collective outcomes and opens new avenues for research on reputation dynamics in both human and animal societies.