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Profitable Bayesian implementation in one-shot mechanism settings

Wu, Haoyang (2019): Profitable Bayesian implementation in one-shot mechanism settings.

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Abstract

In the 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. Traditionally, the designer may be in a dilemma in the sense that even if she is not satisfied with some outcome with low profit, she has to announce it because she must obey the mechanism designed by herself. In this paper, we investigate a case where the designer can induce each agent to adjust his type in a one-shot mechanism. We propose that for a profitable Bayesian implementable social choice function, the designer may escape from the above-mentioned dilemma by spending the optimal adjustment cost and obtain a higher profit. Finally, we construct an example to show that the designer can breakthrough the limit of expected profit which she can obtain at most in the traditional optimal auction model.

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