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Multivariate Multi-Objective Allocation in Stratified Random Sampling: A Game Theoretic Approach

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  • Yousaf Shad Muhammad
  • Ijaz Hussain
  • Alaa Mohamd Shoukry

Abstract

We consider the problem of multivariate multi-objective allocation where no or limited information is available within the stratum variance. Results show that a game theoretic approach (based on weighted goal programming) can be applied to sample size allocation problems. We use simulation technique to determine payoff matrix and to solve a minimax game.

Suggested Citation

  • Yousaf Shad Muhammad & Ijaz Hussain & Alaa Mohamd Shoukry, 2016. "Multivariate Multi-Objective Allocation in Stratified Random Sampling: A Game Theoretic Approach," PLOS ONE, Public Library of Science, vol. 11(12), pages 1-11, December.
    • Handle: RePEc:plo:pone00:0167705
    DOI: 10.1371/journal.pone.0167705
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    References listed on IDEAS

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    3. Romero, Carlos, 2001. "Extended lexicographic goal programming: a unifying approach," Omega, Elsevier, vol. 29(1), pages 63-71, February.
    4. Cheng-Yi Xia & Sandro Meloni & Yamir Moreno, 2012. "Effects Of Environment Knowledge On Agglomeration And Cooperation In Spatial Public Goods Games," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 15(supp0), pages 1-17.
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