Optimal Mechanisms for Single Machine Scheduling
We study the design of optimal mechanisms in a setting where job-agents compete for being processed by a service provider that can handle one job at a time. Each job has a processing time and incurs a waiting cost. Jobs need to be compensated for waiting. We consider two models, one where only the waiting costs of jobs are private information (1-d), and another where both waiting costs and processing times are private (2-d). Probability distributions represent the public common belief about private information. We consider discrete and continuous distributions. In this setting, an optimal mechanism minimizes the total expected expenses to compensate all jobs, while it has to be Bayes-Nash incentive compatible. We derive closed formulae for the optimal mechanism in the 1-d case and show that it is efficient for symmetric jobs. For non-symmetric jobs, we show that efficient mechanisms perform arbitrarily bad. For the 2-d discrete case, we prove that the optimal mechanism in general does not even satisfy IIA, the `independent of irrelevant alternatives'' condition. Hence any attempt along the lines of the classical auction setting is doomed to fail. In the 2-d discrete case, we also show that the optimal mechanism is not even efficient for symmetric agents.
|Date of creation:||2008|
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