UEC - The University of Electro-Communications

Analysis and Control in Bayesian Game

In this research, we apply a control engineering approach to the game theoretical framework that models human and organizational decision making. This research aims to develop a control theoretical mechanism design method that achieves the desired Nash equilibrium. For example, if the game model can describe a situation where multiple players make competitive decisions (gut-searching), we can find the necessary incentives to change to the desired equilibrium state and present rewards such as institutional design and negotiation. We have realized equilibrium control and belief estimation methods using algorithms based on feedback control systems for standard games, which are often used in prisoner's dilemmas, and Bayesian games, which can handle uncertainty in decision making.

Furthermore, to make human behavioral predictions more human, we need to focus on the irrational aspects of humans. We are also working on a decisional model with irrationality based on an emotional model and design problems based on that model. These results will lead to the development of robots and AI (game player) algorithms for human-like decision-making in the future.

Thus, we are discussing the development of theoretical tools to analyze and design the decision-making field of human beings and organizations, and the sustainable cyber-physical human system from the viewpoint of control engineering (especially, design theory).


Papers Selected:

  1. M. Kanawaga and K. Kogiso: Estimation of belief in Bayesian game by feedback control, Transactions of the Society of Instrument and Control Engineers, Vol. 51, No. 2, pp. 128-135, 2015. (in Japanese)

  2. K. Kogiso: Transition models of equilibrium assessment in Bayesian game, IEEE Conference on Decision and Control, pp. 5996-6003, 2015/12/15-12/18. [ieeexplore]

  3. K. Kitagawa and K. Kogiso: Control of proclivity toward selling electricity using persuasive dialog system, SICE International Symposium on Control Systems, 2A2-2, 2016/3/7-3/10. [jstage]

  4. T. Suzuki and K. Kogiso: Steady-state analysis of autonomous system in equilibrium assessment of Bayesian game, SICE International Symposium on Control Systems, 4A1-4, 2016/3/7-3/10. [ieeexplore]

  5. N. Kitagawa, H. Hata, A. Ihara, K. Kogiso, and K. Matsumoto: Code review participation: Game theoretical modeling of reviewers in gerrit datasets, 9th International Workshop on Cooperative and Human Aspects of Software Engineering, pp. 64-67, 2016/5/16. [ieeexplore]

  6. K. Kitagawa and K. Kogiso: Filling demand-supply gap by adjusting electricity selling prices under stochastic acceptance, IEEE International Conference on Cyber-Physical Systems, Networks, and Applications, pp. 25-28, 2016/10/6-10/7. [ieeexplore]

  7. K. Kitagawa and K. Kogiso: Dynamic control of proclivity toward selling electricity using persuasive dialogue system, SICE Journal of Control, Measurement, and System Integration, Vol. 9, No. 6, pp. 264-270, 2016. DOI: 10.9746/jcmsi.9.264 

  8. K. Kitagawa, M. Guo, K. Kogiso, and H. Hata: Utility design for two-player normal-form games, Asian Control Conference, pp. 2077-2082, 2017/12/17-12/20. [ieeexplore]

  9. K. Iinuma and K. Kogiso: Emotion-involved human decision-making model, Mathematical and Computer Modelling of Dynamical Systems, volume 27, Issue 1, pp. 543-561, 2021. [MCMDS]


This work was supported by JSPS Grants-in-Aid for Challenging Exploratory Research 26630199 (2014-2016), and JSPS Grant-in-Aid for Challenging Research (Exploratory) #JP21K19762 (2021-2022).