Book contents
- Frontmatter
- Contents
- Credits and Acknowledgments
- Introduction
- 1 Distributed Constraint Satisfaction
- 2 Distributed Optimization
- 3 Introduction to Noncooperative Game Theory: Games in Normal Form
- 4 Computing Solution Concepts of Normal-Form Games
- 5 Games with Sequential Actions: Reasoning and Computing with the Extensive Form
- 6 Richer Representations: Beyond the Normal and Extensive Forms
- 7 Learning and Teaching
- 8 Communication
- 9 Aggregating Preferences: Social Choice
- 10 Protocols for Strategic Agents: Mechanism Design
- 11 Protocols for Multiagent Resource Allocation: Auctions
- 12 Teams of Selfish Agents: An Introduction to Coalitional Game Theory
- 13 Logics of Knowledge and Belief
- 14 Beyond Belief: Probability, Dynamics, and Intention
- Appendices: Technical Background
- Bibliography
- Index
12 - Teams of Selfish Agents: An Introduction to Coalitional Game Theory
Published online by Cambridge University Press: 05 June 2012
- Frontmatter
- Contents
- Credits and Acknowledgments
- Introduction
- 1 Distributed Constraint Satisfaction
- 2 Distributed Optimization
- 3 Introduction to Noncooperative Game Theory: Games in Normal Form
- 4 Computing Solution Concepts of Normal-Form Games
- 5 Games with Sequential Actions: Reasoning and Computing with the Extensive Form
- 6 Richer Representations: Beyond the Normal and Extensive Forms
- 7 Learning and Teaching
- 8 Communication
- 9 Aggregating Preferences: Social Choice
- 10 Protocols for Strategic Agents: Mechanism Design
- 11 Protocols for Multiagent Resource Allocation: Auctions
- 12 Teams of Selfish Agents: An Introduction to Coalitional Game Theory
- 13 Logics of Knowledge and Belief
- 14 Beyond Belief: Probability, Dynamics, and Intention
- Appendices: Technical Background
- Bibliography
- Index
Summary
In Chapters 1 and 2 we looked at how teams of cooperative agents can accomplish more together than they can achieve in isolation. Then, in Chapter 3 and many of the chapters that followed, we looked at how self-interested agents make individual choices. In this chapter we interpolate between these two extremes, asking how self-interested agents can combine to form effective teams. As the title of the chapter suggests, this chapter is essentially a crash course in coalitional game theory, also known as cooperative game theory. As was mentioned at the beginning of Chapter 3, when we introduced noncooperative game theory, the term “cooperative” can be misleading. It does not mean that, as in Chapters 1 and 2, each agent is agreeable and will follow arbitrary instructions. Rather, it means that the basic modeling unit is the group rather than the individual agent. More precisely, in coalitional game theory we still model the individual preference of agents, but not their possible actions. Instead, we have a coarser model of the capabilities of different groups.
We proceed as follows. First, we define the most widely studied model of coalitional games, give examples of situations that can be modeled in this way, and discuss a series of refinements to the model. Then we consider how such games can be analyzed. The main solution concepts we discuss here are the Shapley value, the core, and the nucleolus.
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- Multiagent SystemsAlgorithmic, Game-Theoretic, and Logical Foundations, pp. 367 - 392Publisher: Cambridge University PressPrint publication year: 2008