Lower Bound for Envy-Free and Truthful Makespan Approximation on Related Machines
Lisa Fleischer, Zhenghui Wang

TL;DR
This paper establishes fundamental lower bounds on the approximation ratio for envy-free, truthful scheduling mechanisms on related machines, showing that better than a 2-approximation is impossible in certain settings.
Contribution
It proves that no deterministic envy-free, truthful mechanism can achieve an approximation ratio better than 2-1/m, and characterizes the unique allocation for two machines, highlighting inherent limitations.
Findings
Deterministic envy-free, truthful mechanisms cannot approximate better than 2-1/m.
For two machines, the optimal envy-free, truthful allocation is to assign all jobs to the fastest machine.
Payments cannot improve approximation ratios of existing monotone, locally efficient allocations beyond m-approximation.
Abstract
We study problems of scheduling jobs on related machines so as to minimize the makespan in the setting where machines are strategic agents. In this problem, each job has a length and each machine has a private speed . The running time of job on machine is . We seek a mechanism that obtains speed bids of machines and then assign jobs and payments to machines so that the machines have incentive to report true speeds and the allocation and payments are also envy-free. We show that 1. A deterministic envy-free, truthful, individually rational, and anonymous mechanism cannot approximate the makespan strictly better than , where is the number of machines. This result contrasts with prior work giving a deterministic PTAS for envy-free anonymous assignment and a distinct deterministic PTAS for truthful anonymous mechanism. 2. For two…
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Taxonomy
TopicsAuction Theory and Applications · Optimization and Search Problems · Blockchain Technology Applications and Security
