Narasimhan, HarikrishnaParkes, David2017-04-192016Narasimhan, Harikrishna and David C. Parkes. 2016. A general statistical framework for designing strategy-proof assignment mechanisms. In Proceedings of the Thirty-Second Conference on Uncertainty in Artificial Intelligence (UAI 2016), New York, NY, June 25-29, 2016.http://nrs.harvard.edu/urn-3:HUL.InstRepos:32213741We develop a statistical framework for the design of a strategy-proof assignment mechanism that closely approximates a target outcome rule. The framework can handle settings with and without money, and allows the designer to employ techniques from machine learning to control the space of strategy-proof mechanisms searched over, by providing a rule class with appropriate capacity. We solve a sample-based optimization problem over a space of mechanisms that correspond to agent-independent price functions (virtual prices in the case of settings without money), subject to a feasibility constraint on the sample. A transformation is applied to the obtained mechanism to ensure feasibility on all type profiles, and strategy-proofness. We derive a sample complexity bound for our approach in terms of the capacity of the chosen rule class and provide applications for our results.en-USConference Paper2017-03-1020162017-04-19