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KPI Science News
ISSN 2617-5509 · e‑ISSN 2663-7472 Open Access · CC BY 4.0
Vol. · Issue 4 · 2021 Jun 1, 2020 article

Finite approximation of zero-sum games played in staircase-function continuous spaces

ВР
Вадим Васильович Романюк Corresponding Polish Naval Academy, Poland Ukraine
PublishedJun 1, 2020
KPISN 4 VOL · 4
VOL · ISS 4 · 2021 View issue

Abstract

Background. There is a known method of approximating continuous zero-sum games, wherein an approximate solution is considered acceptable if it changes minimally by changing the sampling step minimally. However, the method cannot be applied straightforwardly to a zero-sum game played with staircase-function strategies. Besides, the independence of the player’s sampling step selection should be taken into account. Objective. The objective is to develop a method of finite approximation of zero-sum games played in staircase-function continuous spaces by taking into account that the players are likely to independently sample their pure strategy sets. Methods. To achieve the said objective, a zero-sum game, in which the players’ strategies are staircase functions of time, is formalized. In such a game, the set of the player’s pure strategies is a continuum of staircase functions of time, and the time is thought of as it is discrete. The conditions of sampling the set of possible values of the player’s pure strategy are stated so that the game becomes defined on a product of staircase-function finite spaces. In general, the sampling step is different at each player and the distribution of the sampled points (function-strategy values) is non-uniform. Results. A method of finite approximation of zero-sum games played in staircase-function continuous spaces is pre- sented. The method consists in irregularly sampling the player’s pure strategy value set, solving smaller-sized matrix games, each defined on a subinterval where the pure strategy value is constant, and stacking their solutions if they are consistent. The stack of the smaller-sized matrix game solutions is an approximate solution to the initial staircase game. The (weak) consistency of the approximate solution is studied by how much the payoff and optimal situation change as the sampling density minimally increases by the three ways of the sampling increment: only the first player’s increment, only the second player’s increment, both the players’ increment. The consistency is decomposed into the payoff, opti- mal strategy support cardinality, optimal strategy sampling density, and support probability consistency. It is practically reasonable to consider a relaxed payoff consistency. Conclusions. The suggested method of finite approximation of staircase zero-sum games consists in the independent samplings, solving smaller-sized matrix games in a reasonable time span, and stacking their solutions if they are con- sistent. The finite approximation is regarded appropriate if at least the respective approximate (stacked) solution is e-payoff consistent.

Keywords

References

  1. N. N. Vorob’yov, Game theory fundamentals. Noncooperative games, Moscow, Nauka, 1984, 496 p. (in Russian)
  2. N. Nisan et al., Algorithmic Game Theory, Cambridge University Press, 2007, 778 p.
  3. https://doi.org/10.1017/CBO9780511800481
  4. M. J. Osborne, An introduction to game theory, Oxford University Press, 2003, 554 p.
  5. K. Leyton-Brown and Y. Shoham, Essentials of game theory: a concise, multidisciplinary introduction, Morgan & Claypool Publishers, 2008, 104 p.
  6. https://doi.org/10.2200/S00108ED1V01Y200802AIM003
  7. R. B. Myerson, Game theory: Analysis of Conflict, Harvard University Press, 1997, 600 p.
  8. N. N. Vorob’yov, Game theory for economists-cyberneticists, Moscow, Nauka, 1985, 272 p. (in Russian)
  9. V. V. Romanuke, Theory of Antagonistic Games, Lviv, New World — 2000, 2010, 294 p. (in Ukrainian)
  10. E. B. Yanovskaya, “Antagonistic games played in function spaces”, Lithuanian Mathematical Bulletin, no. 3, pp. 547 — 557, 1967.
  11. E. B. Yanovskaya, “Minimax theorems for games on the unit square”, Probability theory and its applications, no. 9 (3), pp. 554 — 555, 1964.
  12. . T. C. Schelling, The Strategy of Conflict, Harvard University, 1980, 328 p.
  13. H. Moulin, “Extension of two-person zero-sum games”, Journal of Mathematical Analysis and Application, no. 55 (2), pp. 490 — 507, 1975.
  14. V. V. Romanuke, “Finite approximation of continuous noncooperative two-person games on a product of linear strategy functional spaces”, Journal of Mathematics and Applications, vol. 43, pp. 123 — 138, 2020.
  15. https://doi.org/10.7862/rf.2020.9
  16. J. Yang et al., “Group formation in the spatial public goods game with continuous strategies”, Physica A: Statistical Mechanics and its Applications, vol. 505, pp. 737 — 743, 2018.
  17. https://doi.org/10.1016/j.physa.2018.03.057
  18. V. V. Romanuke, “Approximation of unit-hypercubic infinite two-sided noncooperative game via dimension-dependent irregular samplings and reshaping the multidimensional payoff matrices into flat matrices for solving the corresponding bimatrix game”, Computer Modelling and New Technologies, vol. 19, no. 3A, pp. 7 — 16, 2015.
  19. V. V. Romanuke and V. G. Kamburg, “Approximation of isomorphic infinite two-person noncooperative games via variously sampling the players’ payoff functions and reshaping payoff matrices into bimatrix game”, Applied Computer Systems, vol. 20, pp. 5 — 14, 2016.
  20. https://doi.org/10.1515/acss-2016-0009
  21. S. P. Coraluppi and S. I. Marcus, “Risk-sensitive and minimax control of discrete-time, finite-state Markov decision processes”, Automatica, vol. 35 (2), pp. 301 — 309, 1999.
  22. https://doi.org/10.1016/S0005-1098(98)00153-8
  23. S. Rahal et al., “Hybrid strategies using linear and piecewise-linear decision rules for multistage adaptive linear optimization”, European Journal of Operational Research, vol. 290, iss. 3, pp. 1014 — 1030, 2021.
  24. https://doi.org/10.1016/j.ejor.2020.08.054
  25. V. V. Romanuke, “Approximation of unit-hypercubic infinite antagonistic game via dimension-dependent irregular samplings and reshaping the payoffs into flat matrix wherewith to solve the matrix game”, Journal of Information and Organizational Sciences, vol. 38, no. 2, pp. 125 — 143, 2014.
  26. R. E. Edwards, Functional Analysis: Theory and Applications, New York, Holt, Rinehart and Winston, 1965, 781 p.
  27. H. Khaloie et al., “Coordinated wind-thermal-energy storage offering strategy in energy and spinning reserve markets using a multi-stage model”, Applied Energy, vol. 259, 114168, 2020.
  28. https://doi.org/10.1016/j.apenergy.2019.114168
  29. V. V. Romanuke, “Adaptive finite approximation of continuous noncooperative games”, Journal of Automation and Information Sciences, vol. 52, iss. 10, pp. 31 — 41, 2020.
  30. https://doi.org/10.1615/JAutomatInfScien.v52.i10.20
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