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Task Assignment with Autonomous and Controlled Agents

Author

Listed:
  • Florian Biermann

    () (International School of Economics at TSU, Tbilisi, Republic of Georgia)

  • Victor Naroditskiy

    () (School of Electronics and Computer Science, University of Southampton, UK)

  • Maria Polukarov

    () (School of Electronics and Computer Science, University of Southampton, UK)

  • Alex Rogers

    () (School of Electronics and Computer Science, University of Southampton, UK)

  • Nicholas Jennings

    () (School of Electronics and Computer Science, University of Southampton, UK)

Abstract

We analyse assignment problems in which not all agents are controlled by the central planner. The autonomous agents search for vacant tasks guided by their own preference orders defined over subsets of the available tasks. The goal of the central planner is to maximise the total value of the assignment, taking into account the behaviour of the uncontrolled agents. This setting can be found in numerous real-world situations, ranging from organisational economics to "crowdsourcing" and disaster response. We introduce the Disjunctively Constrained Knapsack Game and show that its unique Nash equilibrium reveals the optimal assignment for the controlled agents. This result allows us to find the solution of the problem using mathematical programming techniques.

Suggested Citation

  • Florian Biermann & Victor Naroditskiy & Maria Polukarov & Alex Rogers & Nicholas Jennings, 2011. "Task Assignment with Autonomous and Controlled Agents," Working Papers 004-11, International School of Economics at TSU, Tbilisi, Republic of Georgia.
  • Handle: RePEc:tbs:wpaper:11-004
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    References listed on IDEAS

    as
    1. Roth,Alvin E. & Sotomayor,Marilda A. Oliveira, 1992. "Two-Sided Matching," Cambridge Books, Cambridge University Press, number 9780521437882, October.
    2. Crawford, Vincent P & Knoer, Elsie Marie, 1981. "Job Matching with Heterogeneous Firms and Workers," Econometrica, Econometric Society, vol. 49(2), pages 437-450, March.
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