How do we allocate scarcere sources? How do we fairly allocate costs? These are two pressing challenges facing society today. I discuss two recent projects at NICTA concerning resource and cost allocation. In the first, we have been working with FoodBank Local, a social startup working in collaboration with food bank charities around the world to optimise the logistics of collecting and distributing donated food. Before we can distribute this food, we must decide how to allocate it to different charities and food kitchens. This gives rise to a fair division problem with several new dimensions, rarely considered in the literature. In the second, we have been looking at cost allocation within the distribution network of a large multinational company. This also has several new dimensions rarely considered in the literature.
There have been a number of complex markets developed to allocate resources which use money. For example, in a combinatorial auction, agents express prices over bundles of items @cite_11 . Our two projects, however, only consider allocation problems where money is not transfered. Nevertheless, there are ideas from domains like combinatorial auctions which we may be able to borrow. For example, we expect the bidding languages proposed for combinatorial auction may be useful for compactly specifying complex, real world preferences even when money is not being transferred. As a second example, as occurs in some course allocation mechanisms used in practice, we can give agents virtual money'' with which to bid and thus apply an auction based mechanism @cite_16 @cite_12 .
@cite_16 @cite_12 @cite_11
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Mechanisms that rely on course bidding are widely used at business schools in order to allocate seats at oversubscribed courses. Bids play two key roles under these mechanisms: to infer student preferences and to determine who have bigger claims on course seats. We show that these two roles may easily conflict, and preferences induced from bids may significantly differ from the true preferences. Therefore, these mechanisms, which are promoted as market mechanisms, do not necessarily yield market outcomes. We introduce a Pareto-dominant market mechanism that can be implemented by asking students for their preferences in addition to their bids over courses. We use theory and field data to study the draft mechanism used to allocate courses at Harvard Business School. We show that the draft is manipulable in theory, manipulated in practice, and that these manipulations cause significant welfare loss. Nevertheless, we find that welfare is higher than under its widely studied strategyproof alternative. We identify a new link between fairness and welfare that explains why the draft performs well despite the costs of strategic behavior, and then design a new draft that reduces these costs. We draw several broader lessons for market design, regarding Pareto efficiency, fairness, and strategyproofness. (JEL D63, D82, I23)