Passive Verification of the Strategyproofness of Mechanisms in Open Environments

Consider an open infrastructure in which anyone can deploy mechanisms to support automated decision making and coordination amongst self-interested computational agents. Strategyproofness is a central property in the design of such mechanisms, allowing participants to maximize their individual benefit by reporting truthful private information about preferences and capabilities and without modeling or reasoning about the behavior of other agents. But, why should participants trust that a mechanism is strategyproof? We address this problem, proposing and describing a passive verifier, able to monitor the inputs and outputs of mechanisms and verify the strategyproofness, or not, of a mechanism. Useful guarantees are available to participants before the behavior of the mechanism is completely known, and metrics are introduced to provide a measure of partial verification. Experimental results demonstrate the effectiveness of our method.Engineering and Applied Science

Passive verification of the strategyproofness of mechanisms in open environments

(Article begins on next page) The Harvard community has made this article openly available. Please share how this access benefits you. Your story matters. Citation Kang, Laura, and David C Parkes. 2006. Passive verification of the strategyproofness of mechanisms in open environments. I

Verifiably Truthful Mechanisms

It is typically expected that if a mechanism is truthful, then the agents would, indeed, truthfully report their private information. But why would an agent believe that the mechanism is truthful? We wish to design truthful mechanisms, whose truthfulness can be verified efficiently (in the computational sense). Our approach involves three steps: (i) specifying the structure of mechanisms, (ii) constructing a verification algorithm, and (iii) measuring the quality of verifiably truthful mechanisms. We demonstrate this approach using a case study: approximate mechanism design without money for facility location

LIPIcs, Volume 251, ITCS 2023, Complete Volume

LIPIcs, Volume 251, ITCS 2023, Complete Volum

Multikonferenz Wirtschaftsinformatik (MKWI) 2016: Technische Universität Ilmenau, 09. - 11. März 2016; Band I

Übersicht der Teilkonferenzen Band I: • 11. Konferenz Mobilität und Digitalisierung (MMS 2016) • Automated Process und Service Management • Business Intelligence, Analytics und Big Data • Computational Mobility, Transportation and Logistics • CSCW & Social Computing • Cyber-Physische Systeme und digitale Wertschöpfungsnetzwerke • Digitalisierung und Privacy • e-Commerce und e-Business • E-Government – Informations- und Kommunikationstechnologien im öffentlichen Sektor • E-Learning und Lern-Service-Engineering – Entwicklung, Einsatz und Evaluation technikgestützter Lehr-/Lernprozess

Mechanism Design and Analysis Using Simulation-Based Game Models.

As agent technology matures, it becomes easier to envision electronic marketplaces teeming with autonomous agents. Since agents are explicitly programmed to (nearly) optimally compete in these marketplaces, and markets themselves are designed with specific objectives in mind, tools are necessary for systematic analyses of strategic interactions among autonomous agents. While traditional game-theoretic approaches to the analysis of multi-agent systems can provide much insight, they are often inadequate, as they rely heavily on analytic tractability of the problem at hand; however, even mildly realistic models of electronic marketplaces contain enough complexity to render a fully analytic approach hopeless. To address questions not amenable to traditional theoretical approaches, I develop methods that allow systematic computational analysis of game-theoretic models in which the players' payoff functions are represented using simulations (i.e., simulation-based games). I develop a globally convergent algorithm for Nash equilibrium approximation in infinite simulation-based games, which I instantiate in the context of infinite games of incomplete information. Additionally, I use statistical learning techniques to improve the quality of Nash equilibrium approximation based on data collected from a game simulator. I also derive probabilistic confidence bounds and present convergence results about solutions of finite games modeled using simulations. The former allow an analyst to make statistically-founded statements about results based on game-theoretic simulations, while the latter provide formal justification for approximating game-theoretic solutions using simulation experiments. To address the broader mechanism design problem, I introduce an iterative algorithm for search in the design space, which requires a game solver as a subroutine. As a result, I enable computational mechanism design using simulation-based models of games by availing the designer of a set of solution tools geared specifically towards games modeled using simulations. I apply the developed computational techniques to analyze strategic procurement and answer design questions in a supply-chain simulation, as well as to analyze dynamic bidding strategies in sponsored search auctions. Indeed, the techniques I develop have broad potential applicability beyond electronic marketplaces: they are geared towards any system that features competing strategic players who respond to incentives in a way that can be reasonably predicted via a game-theoretic analysis.Ph.D.Computer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/60786/1/yvorobey_1.pd

ABSTRACT Passive Verification of the Strategyproofness of Mechanisms in Open Environments

Consider an open infrastructure in which anyone can deploy mechanisms to support automated decision making and coordination amongst self-interested computational agents. Strategyproofness is a central property in the design of such mechanisms, allowing participants to maximize their individual benefit by reporting truthful private information about preferences and capabilities and without modeling or reasoning about the behavior of other agents. But, why should participants trust that a mechanism is strategyproof? We address this problem, proposing and describing a passive verifier, able to monitor the inputs and outputs of mechanisms and verify the strategyproofness, or not, of a mechanism. Useful guarantees are available to participants before the behavior of the mechanism is completely known, and metrics are introduced to provide a measure of partial verification. Experimental results demonstrate the effectiveness of our method