title: A Mechanism for Fair Distribution of Resources without Payments.
abstract: We design a mechanism for Fair and Efficient Distribution of Resources (FEDoR) in the presence of strategic agents. We consider a multiple-instances, Bayesian setting, where in each round the preference of an agent over the set of resources is a private information. We assume that in each of r rounds n agents are competing for k non-identical indivisible goods, (n > k). In each round the strategic agents declare how much they value receiving any of the goods in the specific round. The agent declaring the highest valuation receives the good with the highest value, the agent with the second highest valuation receives the second highest valued good, etc. Hence we assume a decision function that assigns goods to agents based on their valuations. The novelty of the mechanism is that no payment scheme is required to achieve truthfulness in a setting with rational/strategic agents. The FEDoR mechanism takes advantage of the repeated nature of the framework, and through a statistical test is able to punish the misreporting agents and be fair, truthful, and socially efficient. FEDoR is fair in the sense that, in expectation over the course of the rounds, all agents will receive the same good the same amount of times. FEDoR is an eligible candidate for applications that require fair distribution of resources over time. For example, equal share of bandwidth for nodes through the same point of access. But further on, FEDoR can be applied in less trivial settings like sponsored search, where payment is necessary and can be given in the form of a flat participation fee. FEDoR can be a good candidate in a setting like that to solve the problem of starvation of publicity slots for some advertisers that have a difficult time determining their true valuations. To this extent we perform a comparison with traditional mechanisms applied to sponsored search, presenting the advantage of FEDoR.
authors.affiliation: IMDEA Networks Institute, Madrid, Spain.|Universidad Carlos III, Madrid, Spain.
authors.forename: Evgenia
authors.initials: E
authors.lastname: Christoforou
authors.affiliation: IMDEA Networks Institute, Madrid, Spain.
authors.forename: Antonio Fernández
authors.initials: AF
authors.lastname: Anta
authors.affiliation: IMDEA Networks Institute, Madrid, Spain.|Universidad Rey Juan Carlos, Madrid, Spain.
authors.forename: Agustín
authors.initials: A
authors.lastname: Santos
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D006295:Health Resources; D006801:Humans; D040541:Marketing / Q000379:methods*; D012919:Social Behavior