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The theory of indirect reciprocity holds that people who earn a good reputation by helping others are more likely to be rewarded by third parties, but widespread cooperation depends on agreement about reputations.

In most theoretical models examining how reputations impact people's desire to cooperate with one another, reputations are binary—good or bad—and based on limited information. But there is a lot of information available about people's behavior in today's world, especially with social media.

Biology professors Joshua B. Plotkin of the University of Pennsylvania and Corina Tarnita of Princeton University lead teams that have been collaborating on theoretical research about cooperation. Sebastián Michel-Mata, a doctoral student in Tarnita's lab, came up with the idea of addressing how to judge someone in an information-rich environment.

"The current theory of indirect reciprocity suggests that reputations can only work in a few societies, those with complex norms of judgment and public institutions that can enforce agreement," Michel-Mata says. But, as an anthropologist, he sees that such societies are the exception and not the rule, and he wondered about the simple idea that reputations are summaries of multiple actions.

"Prior models have typically assumed that a single action determines someone's reputation, but I think there's more nuance to how we assign reputations to people. We often look at multiple actions someone has taken and see if they are mostly good actions or bad actions," says Mari Kawakatsu, a postdoctoral researcher in Plotkin's lab.

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