Economics Faculty Database
Arts & Sciences
Duke University

 HOME > Arts & Sciences > Economics > Faculty    Search Help Login pdf version printable version 

Publications [#323531] of Giuseppe Lopomo

Chapters in Books

  1. Albert, M; Conitzer, V; Lopomo, G, Maximizing revenue with limited correlation: The cost of ex-post incentive compatibility, 30th Aaai Conference on Artificial Intelligence, Aaai 2016 (January, 2016), pp. 376-382, ISBN 9781577357605
    (last updated on 2019/07/15)

    © Copyright 2016, Association for the Advancement of Artificial Intelligence ( All rights reserved. In a landmark paper in the mechanism design literature, Cremer and McLean (1985) (CM for short) show that when a bidder's valuation is correlated with an external signal, a monopolistic seller is able to extract the full social surplus as revenue. In the original paper and subsequent literature, the focus has been on ex-post incentive compatible (or IC) mechanisms, where truth telling is an ex-post Nash equilibrium. In this paper, we explore the implications of Bayesian versus ex-post IC in a correlated valuation setting. We generalize the full extraction result to settings that do not satisfy the assumptions of CM. In particular, we give necessary and sufficient conditions for full extraction that strictly relax the original conditions given in CM. These more general conditions characterize the situations under which requiring expost IC leads to a decrease in expected revenue relative to Bayesian IC. We also demonstrate that the expected revenue from the optimal ex-post IC mechanism guarantees at most a (|Θ| + 1)/4 approximation to that of a Bayesian IC mechanism, where |Θ| is the number of bidder types. Finally, using techniques from automated mechanism design, we show that, for randomly generated distributions, the average expected revenue achieved by Bayesian IC mechanisms is significantly larger than that for ex-post IC mechanisms.

Duke University * Arts & Sciences * Economics * Faculty * Research * Staff * Master's * Ph.D. * Reload * Login