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Monotone and Online Fair Division

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KI 2019: Advances in Artificial Intelligence (KI 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11793))

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Abstract

We study a new but simple model for online fair division in which indivisible items arrive one-by-one and agents have monotone utilities over bundles of the items. We consider axiomatic properties of mechanisms for this model such as strategy-proofness, envy-freeness and Pareto efficiency. We prove a number of impossibility results that justify why we consider relaxations of the properties, as well as why we consider restricted preference domains on which good axiomatic properties can be achieved. We propose two mechanisms that have good axiomatic fairness properties on restricted but common preference domains.

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References

  1. Aleksandrov, M., Aziz, H., Gaspers, S., Walsh, T.: Online fair division: analysing a food bank problem. In: Proceedings of the Twenty-Fourth IJCAI 2015, Buenos Aires, Argentina, 25–31 July 2015, pp. 2540–2546 (2015)

    Google Scholar 

  2. Aleksandrov, M., Walsh, T.: Expected outcomes and manipulations in online fair division. In: Kern-Isberner, G., Fürnkranz, J., Thimm, M. (eds.) KI 2017. LNCS (LNAI), vol. 10505, pp. 29–43. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-67190-1_3

    Chapter  Google Scholar 

  3. Aleksandrov, M., Walsh, T.: Most competitive mechanisms in online fair division. In: Kern-Isberner, G., Fürnkranz, J., Thimm, M. (eds.) KI 2017. LNCS (LNAI), vol. 10505, pp. 44–57. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-67190-1_4

    Chapter  Google Scholar 

  4. Aleksandrov, M., Walsh, T.: Pure Nash equilibria in online fair division. In: Sierra, C. (ed.) Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI 2017), pp. 42–48 (2017)

    Google Scholar 

  5. Amanatidis, G., Birmpas, G., Markakis, V.: Comparing approximate relaxations of envy-freeness. In: Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, IJCAI 2018, Stockholm, Sweden, 13–19 July 2018, pp. 42–48 (2018)

    Google Scholar 

  6. Aziz, H., Bouveret, S., Caragiannis, I., Giagkousi, I., Lang, J.: Knowledge, fairness, and social constraints. In: AAAI, pp. 4638–4645. AAAI Press (2018)

    Google Scholar 

  7. Barman, S., Biswas, A., Murthy, S.K.K., Narahari, Y.: Groupwise maximin fair allocation of indivisible goods. In: AAAI, pp. 917–924. AAAI Press (2018)

    Google Scholar 

  8. Barman, S., Krishnamurthy, S.K., Vaish, R.: Greedy algorithms for maximizing nash social welfare. In: Proceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems, AAMAS 2018, Stockholm, Sweden, 10–15 July 2018, pp. 7–13 (2018)

    Google Scholar 

  9. Benade, G., Kazachkov, A.M., Procaccia, A.D., Psomas, C.A.: How to make envy vanish over time. In: Proceedings of the 2018 ACM Conference on Economics and Computation, EC 2018, pp. 593–610. ACM, New York (2018)

    Google Scholar 

  10. Brams, S.J., King, D.L.: Efficient fair division: help the worst off or avoid envy? Ration. Soc. 17(4), 387–421 (2005)

    Article  Google Scholar 

  11. Budish, E.: The combinatorial assignment problem: approximate competitive equilibrium from equal incomes. J. Polit. Econ. 119(6), 1061–1103 (2011)

    Article  Google Scholar 

  12. Budish, E., Cantillon, E.: The multi-unit assignment problem: theory and evidence from course allocation at Harvard. Am. Econ. Rev. 102(5), 2237–2271 (2012)

    Article  Google Scholar 

  13. Caragiannis, I., Kurokawa, D., Moulin, H., Procaccia, A.D., Shah, N., Wang, J.: The unreasonable fairness of maximum nash welfare. In: Proceedings of the 2016 ACM Conference on EC 2016, Maastricht, The Netherlands, 24–28 July 2016, pp. 305–322 (2016)

    Google Scholar 

  14. Chevaleyre, Y., Endriss, U., Estivie, S., Maudet, N.: Multiagent resource allocation in k-additive domains: preference representation and complexity. Ann. Oper. Res. 163(1), 49–62 (2008)

    Article  MathSciNet  Google Scholar 

  15. Chevaleyre, Y., Lang, J., Maudet, N., Monnot, J., Xia, L.: New candidates welcome! Possible winners with respect to the addition of new candidates. Math. Soc. Sci. 64(1), 74–88 (2012)

    Article  MathSciNet  Google Scholar 

  16. Dickerson, J.P., Procaccia, A.D., Sandholm, T.: Dynamic matching via weighted myopia with application to kidney exchange. In: Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence (2012)

    Google Scholar 

  17. Dickerson, J.P., Procaccia, A.D., Sandholm, T.: Failure-aware kidney exchange. In: ACM Conference on Electronic Commerce, EC 2013, pp. 323–340 (2013)

    Google Scholar 

  18. Freeman, R., Zahedi, S.M., Conitzer, V., Lee, B.C.: Dynamic proportional sharing: a game-theoretic approach. Proc. ACM Meas. Anal. Comput. Syst. 2(1), 3:1–3:36 (2018)

    Article  Google Scholar 

  19. Gibbard, A.: Manipulation of voting schemes: a general result. Econometrica 41(4), 587–601 (1973)

    Article  MathSciNet  Google Scholar 

  20. Greenwald, A., Boyan, J.: Bidding under uncertainty: theory and experiments. In: Proceedings of the 20th Conference on Uncertainty in Artificial Intelligence, UAI 2004, pp. 209–216. AUAI Press, Arlington (2004)

    Google Scholar 

  21. Hosseini, H., Larson, K., Cohen, R.: Matching with dynamic ordinal preferences. In: Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, Austin, Texas, USA, 25–30 January, AAAI 2015, pp. 936–943. AAAI Press (2015)

    Google Scholar 

  22. Kash, I.A., Procaccia, A.D., Shah, N.: No agent left behind: Dynamic fair division of multiple resources. JAIR 51, 579–603 (2014). https://doi.org/10.1613/jair.4405

    Article  MathSciNet  MATH  Google Scholar 

  23. Lipton, R.J., Markakis, E., Mossel, E., Saberi, A.: On approximately fair allocations of indivisible goods. In: Proceedings of the 5th ACM Conference on Electronic Commerce (EC-2004), New York, NY, USA, 17–20 May 2004, pp. 125–131 (2004)

    Google Scholar 

  24. Manea, M.: Serial dictatorship and Pareto optimality. Games Econ. Behav. 61(2), 316–330 (2007). https://doi.org/10.1016/j.geb.2007.01.003

    Article  MathSciNet  MATH  Google Scholar 

  25. Nguyen, N., Nguyen, T.T., Roos, M., Rothe, J.: Computational complexity and approximability of social welfare optimization in multiagent resource allocation. Auton. Agent. Multi-Agent Syst. 28(2), 256–289 (2014)

    Article  Google Scholar 

  26. Plaut, B., Roughgarden, T.: Almost envy-freeness with general valuations. In: Proceedings of the Twenty-Ninth Annual ACM-SIAM Symposium on Discrete Algorithms, SODA 2018, New Orleans, LA, USA, 7–10 January 2018, pp. 2584–2603 (2018)

    Google Scholar 

  27. Walsh, T.: Online cake cutting. In: Brafman, R.I., Roberts, F.S., Tsoukiàs, A. (eds.) ADT 2011. LNCS (LNAI), vol. 6992, pp. 292–305. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-24873-3_22

    Chapter  Google Scholar 

  28. Walsh, T.: Allocation in practice. In: Lutz, C., Thielscher, M. (eds.) KI 2014. LNCS (LNAI), vol. 8736, pp. 13–24. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-11206-0_2

    Chapter  Google Scholar 

  29. Xia, L., Conitzer, V.: Strategy-proof voting rules over multi-issue domains with restricted preferences. In: Saberi, A. (ed.) WINE 2010. LNCS, vol. 6484, pp. 402–414. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-17572-5_33

    Chapter  Google Scholar 

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Correspondence to Martin Aleksandrov .

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Aleksandrov, M., Walsh, T. (2019). Monotone and Online Fair Division. In: Benzmüller, C., Stuckenschmidt, H. (eds) KI 2019: Advances in Artificial Intelligence. KI 2019. Lecture Notes in Computer Science(), vol 11793. Springer, Cham. https://doi.org/10.1007/978-3-030-30179-8_5

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  • DOI: https://doi.org/10.1007/978-3-030-30179-8_5

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