The effects of reputational and social knowledge on cooperation
GAICIL Insights
Earlier experiments on cooperation in dynamic networks contained a hidden assumption, and this paper pulls it into daylight.1 The effects of reputational and social knowledge on cooperation, by Edoardo Gallo and Chang Yan, notes that previous studies showing cooperation emerging in networks where people choose their connections fixed the reputation information available and did not display the network to subjects. This study varies both kinds of knowledge systematically.
The verdict is clean: common knowledge about everyone's reputation is the main driver of cooperation, producing dense and clustered networks. Treatments in which subjects see the previous five actions of everyone in the group reach significantly higher cooperation than treatments showing only neighbors' previous five actions. The authors conclude that the main driver of earlier results was the implicit assumption that global reputational knowledge was available. Global reputation also produces cooperative hubs, individuals with many connections because they are highly cooperative.
The second finding is subtler. Global social knowledge, knowledge of the network itself, has no effect on its own on overall cooperation, payoffs or aggregate network metrics. It does change how networks form: subjects with it tend to propose connections to people already linked to their neighbors. Combined with reputational knowledge, social knowledge reshapes the distribution of cooperation: cooperators form a separate community and earn higher payoffs from within-community interactions than members of the less cooperative community.
So the decisive variable is breadth of reputation. Seeing everyone's record beats seeing only the neighbors'. That turns a lab result into a design rule for the digital economy.
Every platform must answer the question of what participants see. Reputation systems are the backbone of online marketplaces, gig platforms and collaborative communities. The experiment shows that broad, common knowledge of reputation drives cooperation far more effectively than local knowledge. Platforms that make reputational information widely visible, accurate and comparable support high cooperation among strangers.
Bring the result forward to the present. Artificial intelligence can widen the window. AI tools can aggregate behavior across many interactions, detect rating manipulation and present reputational information in usable forms. In the paper's terms, AI can push participants from local toward global reputational knowledge. The evidence suggests that would raise cooperation. A fraud-detection model is, on this reading, a cooperation-production technology.
Network visibility raises a harder design choice. When people see the network as well as reputations, cooperators cluster and separate from less cooperative participants. Sorting benefits cooperators, who earn more within their community. It may also isolate the rest. Designers and policymakers should ask whether visibility promotes efficient sorting or creates divisions. The answer depends on context, and experiments like this offer a way to find it.
The cooperative hubs deserve attention. With global reputation, highly cooperative people attract many connections. Online, such hubs might be trusted sellers, reliable contributors or reputable service providers. Reputation-driven hubs channel activity toward trustworthy participants and raise the network's efficiency. AI-enhanced reputation systems that identify reliable participants accurately could amplify that effect.
Then comes a policy lever few people discuss: reputation portability. Reputation locked inside one platform provides local knowledge. Reputation that travels across platforms expands knowledge and may strengthen cooperation across the digital economy. Privacy must be balanced against that benefit, yet the experimental evidence underscores the social value of widely available reputational information. A seller's track record that follows them to a new marketplace is a public good.
The broader message is that cooperation depends on what people know about one another. Systems that make reputations visible to all foster cooperation and dense, productive networks. As AI increasingly shapes the information environment of exchange, designing it to expand reputational knowledge offers a powerful route to more cooperative and prosperous digital communities.
The challenge for platform builders reduces to one question: is the reputation shown to each user global or local? On this evidence, that choice may be decisive.
Introduction
Cooperation is essential for societies to prosper. Recent experiments show that cooperation emerges in dynamic networks in which subjects can select their connections. However, these studies fixed the amount of reputation information available and did not display the network to subjects. Here, we systematically vary the knowledge available to subjects about reputation and the network to investigate experimentally their roles in determining cooperation in dynamic networks. Common knowledge about everyone’s reputation is the main driver of cooperation leading to dense and clustered networks. The addition of common knowledge about the network affects the distribution of cooperative activity: cooperators form a separate community and achieve a higher payoff from within-community interactions than members of the less cooperative community.
Takeaway
Our contributions are to show that the extent of reputational knowledge and the extent of social knowledge play crucial and different roles for the emergence and distribution of cooperation, as well as the network features associated with cooperative activity. Our first contribution is to show that the presence of global reputational knowledge is crucial for the emergence and sustenance of a high level of cooperation. This highlights that the main driver of the results in previous experiments (7, 9, 10) was the implicit assumption of the availability of global reputational knowledge. The treatments in which subjects have access to the previous five actions of everyone in the group achieve a significantly higher level of cooperation than the treatments in which they only have access to the neighbors’ previous five actions. These findings are in agreement with a recent, similar experiment (29) that shows that reputation fosters cooperation by varying the number of past actions available to subjects. The results show the crucial role of global reputational knowledge in determining the emergence of cooperative hubs: individuals who have a high number of connections because they are highly cooperative. Our second contribution is to show that the availability of global social knowledge on its own affects the process of net-work formation but has no effect on the overall level of co-operation, payoff, and aggregate network metrics.1
Specifically, we show that the availability of global social knowledge is associated with a subject’s tendency to propose connections to other subjects who are already connected to her neighbors, which leads to a positive correlation between the individual’s clustering coefficient and her cooperativeness. Previous theoretical work (18, 19) has highlighted the importance of social knowledge in the process of network formation, and our study shows the first evidence to our knowledge of its influence in the network formation process. Moreover, the role of social knowledge in games on networks has so far been almost completely unexplored experimentally, with the exception of a re-cent experiment showing that social knowledge matters in public good games on networks (21). The systematic experimental study of the role of social knowledge in network formation and in game play across different types of games is a promising area of future studies. Our third contribution is to show that the availability of both global reputational and global social knowledge has an effect on the distribution of cooperative activity in the group. The presence of both types of knowledge allows cooperators to remove links from defectors and reject their link proposals in future rounds. In this way, cooperators are able to form a community that is more cooperative than the community of defectors, and it generates a larger social surplus from within community interactions.
An open question for future research is the scalability and robustness of these findings to the overall size of the group. It is reasonable to imagine that the complexity of processing information about the network grows exponentially with the size of the network, making it more challenging to use it in the process of network formation. However, at the same time, the benefits from belonging to the cooperative community grow with the size of the community, making the establishment of a large cooperative community more attractive in a larger group. The finding that global reputational information is crucial for the emergence of a high level of cooperation is consistent with previous “theoretical and empirical studies of indirect reciprocity[that] stress the importance of monitoring not only partners in continuing interactions but also all individuals within the social network”(30) and with the finding that cooperation is higher inc onnected networks within which everyone is monitored (31). As predicted by theory (32, 33), experimental studies on indirect reciprocity with well-mixed populations show that reputation is important for cooperation (14, 34) and that the level of cooperation increases with the richness of the reputation in-formation (35), as well as the punishment/reward technology(36) available. In this study, we find that, in the context of dynamic networks, cooperation almost doubles if reputation is available about everyone, rather than just one’s connections.
The experimental result that the presence of global reputational knowledge is essential for the emergence of a high level of cooperation contributes to a growing and fruitful two-way dialogue between the theoretical and experimental literatures that study cooperation in networked contexts. For instance, a recent experimental contribution has shown that the structural features of a fixed network have an effect on the level of cooperation if the benefits from cooperation are large enough (37): this is in agreement with theoretical studies (38–42) and reconciles the fact that the theoretical predictions had not received support from previous experimental studies on fixed networks (24, 43–46).
Notes
- Edoardo Gallo & Chang Yan, The effects of reputational and social knowledge on cooperation, 112 Proc. Nat'l Acad. Sci. 3647 (2015), https://doi.org/10.1073/pnas.1415883112. Takeaway source: Discussion, reproduced from a free author or repository copy. ↩
Cite as: Edoardo Gallo & Chang Yan, The effects of reputational and social knowledge on cooperation, 112 Proc. Nat'l Acad. Sci. 3647 (2015), https://doi.org/10.1073/pnas.1415883112
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