2,616 research outputs found

    Pareto-Optimal Assignments by Hierarchical Exchange

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    A version of the Second Fundamental Theorem of Welfare Economics that applies to a money-free environment, in which a set of indivisible goods needs to be matched to some set of agents, is established. In such environments, "trade" can be identied with the set of hierarchical exchange mechanisms dened by Papai (2000). Papai (2000)'s result – that any such mechanism yields Pareto-optimal allocations – can be interpreted as a version of the First Fundamental Theorem of Welfare Economics for the given environment. In this note, I show that for any Pareto-optimal allocation and any hierarchical exchange mechanism one can nd an initial allocation of ownership rights, such that the given Pareto-optimal allocation arises as a result of trade.

    Pareto-Optimal Matching Allocation Mechanisms for Boundedly Rational Agents

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    This article is concerned with the welfare properties of trade when the behavior of agents cannot be rationalized by preferences. I investigate this question in an environment of matching allocation problems. There are two reasons for doing so: rstly, the niteness of such problems entails that the domain of the agents' choice behavior does not need to be restricted in any which way to obtain results on the welfare properties of trade. Secondly, some matching allocation mechanisms have been designed for non-market environments in which we would typically expect boundedly rational behavior. I nd qualied support for the statements that all outcomes of trade are Pareto-optimal and all Pareto optima are reachable through trade. Contrary to the standard case, dierent trading mechanisms lead to dierent outcome sets when the agents' behavior is not rationalizable. These results remain valid when restricting attention to \minimally irrational" behavior.Bounded Rationality, House Allocation Problems, Fundamental Theorems of Welfare, Multiple Rationales

    Search based software engineering: Trends, techniques and applications

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    © ACM, 2012. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version is available from the link below.In the past five years there has been a dramatic increase in work on Search-Based Software Engineering (SBSE), an approach to Software Engineering (SE) in which Search-Based Optimization (SBO) algorithms are used to address problems in SE. SBSE has been applied to problems throughout the SE lifecycle, from requirements and project planning to maintenance and reengineering. The approach is attractive because it offers a suite of adaptive automated and semiautomated solutions in situations typified by large complex problem spaces with multiple competing and conflicting objectives. This article provides a review and classification of literature on SBSE. The work identifies research trends and relationships between the techniques applied and the applications to which they have been applied and highlights gaps in the literature and avenues for further research.EPSRC and E

    Learning matters: reappraising object allocation rules when agents strategically investigate

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    Individuals form preferences through search, interviews, discussion, and investigation. In a stylized object allocation model, we characterize the equilibrium learning strategies induced by different allocation rules and trace their welfare consequences. Our analysis reveals that top trading cycles rules dominate serial priority rules under inequality‐averse measures of social welfare

    Network Flexibility for Recourse Considerations in Bi-Criteria Facility Location

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    What is the best set of facility location decisions for the establishment of a logistics network when it is uncertain how a company’s distribution strategy will evolve? What is the best configuration of a distribution network that will most likely have to be altered in the future? Today’s business environment is turbulent, and operating conditions for firms can take a turn for the worse at any moment. This fact can and often does influence companies to occasionally expand or contract their distribution networks. For most companies operating in this chaotic business environment, there is a continuous struggle between staying cost efficient and supplying adequate service. Establishing a distribution network which is flexible or easily adaptable is the key to survival under these conditions. This research begins to address the problem of locating facilities in a logistics network in the face of an evolving strategic focus through the implicit consideration of the uncertainty of parameters. The trade-off of cost and customer service is thoroughly examined in a series of multi-criteria location problems. Modeling techniques for incorporating service restrictions for facility location in strategic network design are investigated. A flexibility metric is derived for the purposes of quantifying the similarity of a set of non-dominated solutions in strategic network design. Finally, a multi-objective greedy random adaptive search (MOG) metaheuristic is applied to solve a series of bi-criteria, multi-level facility location problems

    Computing large market equilibria using abstractions

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    Computing market equilibria is an important practical problem for market design (e.g. fair division, item allocation). However, computing equilibria requires large amounts of information (e.g. all valuations for all buyers for all items) and compute power. We consider ameliorating these issues by applying a method used for solving complex games: constructing a coarsened abstraction of a given market, solving for the equilibrium in the abstraction, and lifting the prices and allocations back to the original market. We show how to bound important quantities such as regret, envy, Nash social welfare, Pareto optimality, and maximin share when the abstracted prices and allocations are used in place of the real equilibrium. We then study two abstraction methods of interest for practitioners: 1) filling in unknown valuations using techniques from matrix completion, 2) reducing the problem size by aggregating groups of buyers/items into smaller numbers of representative buyers/items and solving for equilibrium in this coarsened market. We find that in real data allocations/prices that are relatively close to equilibria can be computed from even very coarse abstractions
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