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Generalizing mechanism design theory to a case where agents' types are adjustable

Wu, Haoyang (2018): Generalizing mechanism design theory to a case where agents' types are adjustable.

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In mechanism design theory, a designer would like to implement a desired social choice function which specifies her favorite outcome for each possible profile of all agents' types. Since agents' types are modelled as their private information, what the designer can do is to construct a mechanism and choose an outcome after observing a specific profile of agents' strategies. Traditionally, the designer has no way to adjust agents' types and hence may be in a dilemma in the sense that even if she is not satisfied with some outcome, she has to announce it because she must obey the mechanism designed by herself. In this paper, we consider a generalized case where agents' types are adjustable. After defining a series of notions such as adjusted types, optimal adjustment cost and profitably Bayesian implementability, we propose that the notion of Bayesian incentive compatibility does not hold in this generalized case. Finally, we construct an auction example to show that the designer can obtain an expected profit greater than the maximum profit that she can obtain in the traditional optimal auction.

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