ゲーム理論に基づくインセンティブメカニズム設計

個々のユーザの合理的な意思決定に基づく結果は必ずしもシステム全体の最適化とはならない場合があります． これは，個々のユーザの目的関数とシステムが目指す目的関数にギャップが存在するからです． 本研究では，個々のユーザが知覚する情報を制御することで，このギャップを抑制する利己的最適誘導制御方式の確立を目指します1． 利己的最適制御以外にも，クラウドにタスクをオフロードする際のユーザの意思決定に着目したインセンティブメカニズム設計を検討しています2．

Incentive Mechanism Design Based on Game Theory

The result based on the rational decision-making of individual users may not necessarily lead to the optimization of the entire system. This is because there is a gap between the objective function of individual users and the objective function that the system aims for. In this research, we aim to establish a selfish yet optimal control method that suppresses this gap by controlling the information perceived by individual users1. In addition to selfish optimal control, we are also considering incentive mechanism design focusing on users’ decision-making when offloading tasks to the cloud2.

  1. T. Hara, M. Sasabe, and S. Kasahara, “Selfish Yet Optimal Routing by Adjusting Perceived Traffic Information of Road Networks,” IEEE Open J. Intell. Transp. Syst., vol. 1, pp. 120–133, 2020, doi: 10.1109/OJITS.2020.3019935. ↩︎ ↩︎

  2. K. Yamada, T. Hara, and S. Kasahara, “Repeated Stochastic Game and Lyapunov Optimization for Mining Task Offloading in Decentralized Applications,” IEICE Transactions on Communications, pp. 1–10, 2024, doi: 10.23919/transcom.2024CEP0001. ↩︎ ↩︎