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Evaluation of Payoff Matrices for Non-Cooperative Games via Processing Binary Expert Estimations

Abstract

A problem of evaluating the non-cooperative game model is considered in the paper. The evaluation is understood in the sense of obtaining the game payoff matrices whose entries are single-point values. Experts participating in the estimation procedure make their judgments on all the game situations for every player. A form of expert estimations is suggested. The form is of binary type, wherein the expert’s judgment is either 1 or 0. This type is the easiest to be implemented in social networks. For most social networks, 1 can be a “like” (the currently evaluated situation is advantageous), and 0 is a “dislike” (disadvantageous). A method of processing expert estimations is substantiated. Two requirements are provided for obtaining disambiguous payoff averages along with the clustered payoff matrices.

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Modern approaches to modeling user requirements on resource and task allocation in hierarchical computational grids

Olsder, G. (1995). Dynamic Non-cooperative Game Theory , 2nd Edn., Academic Press, London. Brandic, I., Pllana, S. and Benkner, S. (2006). An approach for the high-level specification of qos-aware grid workflows considering location affinity, Scientific Programming 14 (3-4): 231-250. Braun, T., Siegel, H.J., Beck, N., Boloni, L.L., Maheswaran, M., Reuther, A.I., Robertson, J.P., Theys, M.D., Yao, B., Hensgen, D. and Freund, R.F. (2001). A comparison of eleven static heuristics for mapping a class of independent

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Sensitivity of computer support game algorithms of safe ship control

References Baba, N. and Jain, L. (2001). Computational Intelligence in Games , Physica-Verlag, New York, NY. Basar, T. and Olsder, G. (1982). Dynamic Non-Cooperative Game Theory , Academic Press, New York, NY. Bist, D. (2000). Safety and Security at Sea , Butter Heinemann, Oxford/New Delhi. Błaszczyk, J., Karbowski, A. and Malinowski, K. (2007). Object library of algorithms for dynamic optimization problems: Benchmarking SQP and nonlinear interior point methods, International Journal

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Together or Alone: The Price of Privacy in Collaborative Learning

Modeling and User-Adapted Interaction , 2016. [4] Jihun Hamm, Yingjun Cao, and Mikhail Belkin. Learning privately from multiparty data. In International Conference on Machine Learning , 2016. [5] J. Han, M. Kamber, and J. Pei. Data Mining: Concepts and Techniques . Morgan Kaufmann Publishers, 2012. [6] John C Harsanyi, Reinhard Selten, et al. A general theory of equilibrium selection in games. MIT Press Books , 1988. [7] Stratis Ioannidis and Patrick Loiseau. Linear regression as a non-cooperative game. In International Conference on Web and

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Sensitivity of the Game Control of Ship in Collision Situations

References 1. Astrom K.J.: Model uncertainty and robust control. Lecture notes on iterative identification and control design. Lund Institut of Technology, Sweden 2000, pp. 63-100. 2. Baba N., Jain L.C.: Computational intelligence in games. Physica-Verlag, New York 2001. 3. Basar T., Olsder G.J.: Dynamic non-cooperative game theory. Siam, Philadelphia 2013. 4. Bist D.S.: Safety and security at sea. Butterworth Heinemann, Oxford-New Delhi 2000. 5. Bole A., Dineley B., Wall A

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