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The online exchange of social recognition including, for instance, theFacebook "like" appears to produce a scarce allocation without a clear utilityfunction defined for anyone involved. Given the importance attached to suchdigital commodities by both users and advertisers, it is of interest to studythe forces governing their economics. Here we propose a centrality measure akinto eigenvector centrality to describe an individual's perceived importance inan online social network. It is shown in silico that strategically maximizingthis prestige metric results in finite nontrivial rates of "like" endowment.Furthermore, it is found that systems of reputation-seeking agents aresupported most robustly by networks with the features of real human societiesincluding preferential attachment and the small-world property. We concludethat the incentive system studied here can produce realistic behavior and maytherefore provide a framework for a more general model of decision-making inonline communities.

A Reputation-Based Model for Decision-Making in Online Social Networks