Recommender and reputation systems process ratings from past users to guide current users' behavior. Such systems provide a natural laboratory for the study of large-scale social phenomena, including the provision of public goods (the ratings themselves), and the role of reputation as a regulator of trust and trustworthiness.
This project will focus on two widely used systems, eBay and Slashdot. Usage logs provided by the two sites will be assessed in depth. This analysis should reveal the social practices that have emerged surrounding the provision of ratings, and the ways ratings actually guide users' decisions about what to buy and from whom, and what messages to read. Game-theoretic analyses of incentives will combine with the empirical analysis to guide the design of potential system improvements.
This research should improve the functioning and public understanding of web sites where millions of people already interact with each other. In addition, it will provide fundamental insights into the workings of impersonal social capital, i.e., productive social relations that emerge among people who remain strangers rather than forming networks of long-term relationships.
