Incentive Compatible Queues Without Money
Isaac Grosof, Michael Mitzenmacher

TL;DR
This paper proposes incentive-compatible scheduling policies without monetary charges, encouraging truthful job time estimates in queueing systems by probabilistically punishing overestimations, and analyzes their effectiveness.
Contribution
It introduces two scheduling policies, BlindTrust and MeasuredTrust, and provides an efficient method to determine incentive-compatible punishment probabilities.
Findings
Both policies can be tuned for incentive compatibility.
MeasuredTrust's incentive compatibility range expands to [0,1] as estimates improve.
Framework enables future study of queue-based incentive mechanisms.
Abstract
For job scheduling systems, where jobs require some amount of processing and then leave the system, it is natural for each user to provide an estimate of their job's time requirement in order to aid the scheduler. However, if there is no incentive mechanism for truthfulness, each user will be motivated to provide estimates that give their job precedence in the schedule, so that the job completes as early as possible. We examine how to make such scheduling systems incentive compatible, without using monetary charges, under a natural queueing theory framework. In our setup, each user has an estimate of their job's running time, but it is possible for this estimate to be incorrect. We examine scheduling policies where if a job exceeds its estimate, it is with some probability "punished" and re-scheduled after other jobs, to disincentivize underestimates of job times. However, because…
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Taxonomy
TopicsAdvanced Queuing Theory Analysis · Healthcare Operations and Scheduling Optimization · Smart Grid Security and Resilience
