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Fair allocation of indivisible goods: improvements and generalizations

Ghodsi, M ; Sharif University of Technology | 2018

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  1. Type of Document: Article
  2. DOI: 10.1145/3219166.3219238
  3. Publisher: Association for Computing Machinery, Inc , 2018
  4. Abstract:
  5. We study the problem of fair allocation for indivisible goods. We use the maxmin share paradigm introduced by Budish [16] as a measure for fairness. Kurokawa, Procaccia, and Wang [36] were the first to investigate this fundamental problem in the additive setting. They show that a maxmin guarantee (1-MMS allocation) is not always possible even when the number of agents is limited to 3. While the existence of an approximation solution (e.g. a 1/2-MMS allocation) is quite straightforward, improving the guarantee becomes subtler for larger constants. Kurokawa et al. [36] provide a proof for the existence of a 2/3-MMS allocation and leave the question open for better guarantees. Our main contribution is an answer to the above question. We improve the result of Kurokawa et al. to a 3/4 factor in the additive setting. The main idea for our 3/4-MMS allocation method is clustering the agents. To this end, we introduce three notions and techniques, namely reducibility, matching allocation, and cycle-envy-freeness, and prove the approximation guarantee of our algorithm via non-trivial applications of these techniques. Our analysis involves coloring and double counting arguments that might be of independent interest. One major shortcoming of the current studies on fair allocation is the additivity assumption on the valuations. We alleviate this by extending our results to the case of submodular, fractionally subadditive, and subadditive settings. More precisely, we give constant approximation guarantees for submodular and XOS agents, and a logarithmic approximation for the case of subadditive agents. Furthermore, we complement our results by providing close upper bounds for each class of valuation functions. Finally, we present algorithms to find such allocations for additive, submodular, and XOS settings in polynomial time. The reader can find a summary of our results in Table 1. 1 © 2018 Association for Computing Machinery
  6. Keywords:
  7. Polynomial approximation ; Allocation methods ; Approximation solution ; Double counting ; Fair allocation ; Indivisible good ; Logarithmic approximation ; Polynomial-time ; Valuation function ; Approximation algorithms
  8. Source: ACM EC 2018 - Proceedings of the 2018 ACM Conference on Economics and Computation11 June 2018 ; 11 June , 2018 , Pages 539-556 ; 9781450358293 (ISBN)
  9. URL: https://dl.acm.org/citation.cfm?doid=3219166.3219238