Incentive Based Control Via Game Theory

Bridging game theory and control systems, the work tackles decentralized decision-making in a sharing economy. It models and optimizes multiagent interactions in production, logistics, and energy, using feedback, learning, and decision-making under uncertainty to steer self-interested agents toward socially beneficial outcomes. Engineering systems in the new economy are increasingly shaped by sharing – shared logistics, joint investments, virtual power plants, and more – a shift that calls for models that capture decentralized decision-making, resource pooling, and cooperative infrastructure. Across production, transportation, logistics, and energy, a central challenge is modeling and optimizing collective actions. This book aims to bridge game theory and systems and control by developing tools for incentive-based control in dynamic, uncertain, and networked multiagent systems; it extends both cooperative and noncooperative game models into a control-oriented framework, emphasizing feedback, learning, and decision-making under uncertainty. Incentive Based Control via Game Theory stands apart from existing literature by offering a design-oriented approach that focuses on using game theory to design mechanisms and control strategies that guide self-interested agents toward stable and socially desirable outcomes in dynamic, uncertain systems; novel engineering-focused content that introduces advanced topics rarely covered in game theory and engineering texts, including dynamic coalitional games, reverse Stackelberg games, and rigorously analysed best-response dynamics; and an application-driven perspective that combines mathematical rigor with real-world engineering applications, showing how theory translates into practical implementation.