2017/02/18 by Diodato Ferraioli, Ferraioli, Diodato, Carmine Ventre +1
Computer Science · Decision Sciences · Economics, Econometrics and Finance · #Auction Theory and Applications #Computer Science and Game Theory (cs.GT) #FOS: Computer and information sciences #Game Theory and Voting Systems #Optimization and Search Problems
paper · pdf · doi:10.48550/arxiv.1702.05640
openalex publication_date 2017/02/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Obvious strategyproofness (OSP) is an appealing concept as it allows to maintain incentive compatibility even in the presence of agents that are not fully rational, e.g., those who struggle with contingent reasoning [Li, 2015]. However, it has been shown to impose some limitations, e.g., no OSP mechanism can return a stable matching [Ashlagi and Gonczarowski, 2015]. We here deepen the study of the limitations of OSP mechanisms by looking at their approximation guarantees for basic optimization problems paradigmatic of the area, i.e., machine scheduling and facility location. We prove a number of bounds on the approximation guarantee of OSP mechanisms, which show that OSP can come at a significant cost. However, rather surprisingly, we prove that OSP mechanisms can return optimal solutions when they use monitoring -- a novel mechanism design paradigm that introduces a mild level of scrutiny on agents' declarations [Kovacs et al., 2015].