04027nam a22006135i 4500 978-981-97-9286-3 DE-He213 20251030114602.0 cr nn 008mamaa 241203s2024 si | s |||| 0|eng d 9789819792863 978-981-97-9286-3 10.1007/978-981-97-9286-3 doi Q342 UYQ bicssc COM004000 bisacsh UYQ thema Shen, Weiran. author. aut http://id.loc.gov/vocabulary/relators/aut 88538 AI-Driven Mechanism Design [electronic resource] / by Weiran Shen, Pingzhong Tang, Song Zuo. 1st ed. 2024. Singapore : Springer Nature Singapore : Imprint: Springer, 2024. VIII, 130 p. 31 illus., 17 illus. in color. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda Artificial Intelligence: Foundations, Theory, and Algorithms, 2365-306X Chapter 1. Introduction -- Chapter 2. Multi-Dimensional Mechanism Design via AI-Driven Approaches -- Chapter 3. Dynamic Mechanism Design via AI-Driven Approaches -- Chapter 4. Multi-Objective Mechanism Design via AI-Driven Approaches -- Chapter 5. Summary and Future Directions. Due to its huge success in industry, mechanism design has been one of the central research topics at the interface of economics and computer science. However, despite decades of effort, there are still numerous challenges, in terms of both theory and applications. These include the problem of how to design mechanisms for selling multiple items, dynamic auctions, and balancing multiple objectives, given the huge design space and buyer strategy space; and the fact that in practice, the most widely applied auction format (the generalized second price auction) is neither truthful nor optimal. Furthermore, many theoretical results are based upon unrealistic assumptions that do not hold in real applications. This book presents the AI-driven mechanism design framework, which aims to provide an alternative way of dealing with these problems. The framework features two abstract models that interact with each other: the agent model and the mechanism model. By combining AI techniques with mechanism design theory, it solves problems that cannot be solved using tools from either domain alone. For example, it can reduce the mechanism space significantly, build more realistic buyer models, and better balance different objectives. The book focuses on several aspects of mechanism design and demonstrates that the framework is useful in both theoretical analysis and practical applications. Computational intelligence. 64052 Electronic commerce. Multiagent systems. Machine learning. 55059 Game theory. Computational Intelligence. 64052 e-Commerce and e-Business. Multiagent Systems. Machine Learning. 55059 Game Theory. Tang, Pingzhong. author. aut http://id.loc.gov/vocabulary/relators/aut 88539 Zuo, Song. author. aut http://id.loc.gov/vocabulary/relators/aut 88540 SpringerLink (Online service) Springer Nature eBook Printed edition: 9789819792856 Printed edition: 9789819792870 Printed edition: 9789819792887 Artificial Intelligence: Foundations, Theory, and Algorithms, 2365-306X 85110 https://doi.org/10.1007/978-981-97-9286-3 ZDB-2-INR ZDB-2-SXIT 65478 65478