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The goal of this paper is to develop a generic framework for converting modern optimization algorithms into mechanisms where inputs come from self-interested agents. We focus on aggregating preferences from n players in a context without money. Special cases of this setting include voting, allocation of items by lottery, and matching. Our key technical contribution is a new meta-algorithm we call (Adaptive Pricing Equalizing Externalities). The framework is sufficiently general to be combinedarXiv:2106.07752v1 fatcat:cit3kuf6jzhidphr7b5iepszwa