Truthful Mechanisms with Implicit Payment Computation
Moshe Babaioff, Robert D. Kleinberg, Aleksandrs Slivkins

TL;DR
This paper introduces a simple, general reduction method to create randomized truthful mechanisms from monotone allocation rules, simplifying payment computation and enabling new applications in various mechanism design problems.
Contribution
It provides a black-box reduction transforming monotone allocation rules into truthful, individually rational randomized mechanisms with minimal re-evaluation, applicable to single- and multi-parameter domains.
Findings
Mechanisms achieve near-1 probability of implementing the original outcome.
Application to multi-armed bandits yields near-optimal regret bounds.
Randomization circumvents communication complexity and welfare approximation lower bounds.
Abstract
It is widely believed that computing payments needed to induce truthful bidding is somehow harder than simply computing the allocation. We show that the opposite is true: creating a randomized truthful mechanism is essentially as easy as a single call to a monotone allocation rule. Our main result is a general procedure to take a monotone allocation rule for a single-parameter domain and transform it (via a black-box reduction) into a randomized mechanism that is truthful in expectation and individually rational for every realization. The mechanism implements the same outcome as the original allocation rule with probability arbitrarily close to 1, and requires evaluating that allocation rule only once. We also provide an extension of this result to multi-parameter domains and cycle-monotone allocation rules, under mild star-convexity and non-negativity hypotheses on the type space and…
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Taxonomy
TopicsAuction Theory and Applications · Advanced Bandit Algorithms Research · Optimization and Search Problems
