LEADER 03812nam 22007095 450 001 9910913794103321 005 20241203115306.0 010 $a9789819792863 010 $a981979286X 024 7 $a10.1007/978-981-97-9286-3 035 $a(CKB)36812710000041 035 $a(MiAaPQ)EBC31812223 035 $a(Au-PeEL)EBL31812223 035 $a(DE-He213)978-981-97-9286-3 035 $a(EXLCZ)9936812710000041 100 $a20241203d2024 u| 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aAI-Driven Mechanism Design /$fby Weiran Shen, Pingzhong Tang, Song Zuo 205 $a1st ed. 2024. 210 1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2024. 215 $a1 online resource (135 pages) 225 1 $aArtificial Intelligence: Foundations, Theory, and Algorithms,$x2365-306X 311 08$a9789819792856 311 08$a9819792851 327 $aChapter 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. 330 $aDue to its huge success in industry, mechanism design has been one of the central research topics at the interface of economics and computer science. However, despite decades of effort, there are still numerous challenges, in terms of both theory and applications. These include the problem of how to design mechanisms for selling multiple items, dynamic auctions, and balancing multiple objectives, given the huge design space and buyer strategy space; and the fact that in practice, the most widely applied auction format (the generalized second price auction) is neither truthful nor optimal. Furthermore, many theoretical results are based upon unrealistic assumptions that do not hold in real applications. This book presents the AI-driven mechanism design framework, which aims to provide an alternative way of dealing with these problems. The framework features two abstract models that interact with each other: the agent model and the mechanism model. By combining AI techniques with mechanism design theory, it solves problems that cannot be solved using tools from either domain alone. For example, it can reduce the mechanism space significantly, build more realistic buyer models, and better balance different objectives. The book focuses on several aspects of mechanism design and demonstrates that the framework is useful in both theoretical analysis and practical applications. 410 0$aArtificial Intelligence: Foundations, Theory, and Algorithms,$x2365-306X 606 $aComputational intelligence 606 $aElectronic commerce 606 $aMultiagent systems 606 $aMachine learning 606 $aGame theory 606 $aComputational Intelligence 606 $ae-Commerce and e-Business 606 $aMultiagent Systems 606 $aMachine Learning 606 $aGame Theory 615 0$aComputational intelligence. 615 0$aElectronic commerce. 615 0$aMultiagent systems. 615 0$aMachine learning. 615 0$aGame theory. 615 14$aComputational Intelligence. 615 24$ae-Commerce and e-Business. 615 24$aMultiagent Systems. 615 24$aMachine Learning. 615 24$aGame Theory. 676 $a006.3 700 $aShen$b Weiran$01777604 701 $aTang$b Pingzhong$01777605 701 $aZuo$b Song$01777606 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910913794103321 996 $aAI-Driven Mechanism Design$94299212 997 $aUNINA