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α: calibrated so average coauthorship-adjusted count equals average raw count
In many markets, clients engage repeatedly but infrequently in mutually beneficial, trust-intensive interactions with an agent (e.g., markets with investors and entrepreneurs, borrowers and lenders, experience goods, and short-term apartment rentals). To study the role of intermediaries in such markets, we develop a new model of partially observable trust networks, and characterize networks that are robust to variations in market participants' beliefs with respect to the network structure. We show that in all robust networks, intermediaries who have exclusivity over a large enough number of interaction opportunities are essential to overcome incentive problems that would otherwise shut down the market. We argue our methodology could be applied more generally to the study of network games in which individuals can eliminate links, and thus offers an alternative to the often-made assumption that all individuals perfectly observe the network.