Mode of access: World Wide Web.

System requirements: Adobe Acrobat Reader.

Part of: Synthesis digital library of engineering and computer science.

Series from website.

Includes bibliographical references (p. 135-142).

Part I. Game theory background --

1. Static games and solution concepts -- Strategic form games -- Solution concepts -- Dominant and dominated strategies -- Iterated elimination of strictly dominated strategies -- Nash equilibrium -- Correlated equilibrium -- Existence of a Nash equilibrium -- Games with finite pure strategy sets -- Games with infinite pure strategy sets -- Continuous games -- Discontinuous games -- Uniqueness of a Nash equilibrium -- Appendix: metric spaces and probability measures -- Appendix: nonlinear optimization --

2. Game theory dynamics -- Extensive form games -- Learning dynamics in games: fictitious play -- Convergence of fictitious play -- Non-convergence of fictitious play -- Convergence proofs -- Games with special structure -- Supermodular games -- Potential games -- Appendix: lattices --

Part II. Network games --

3. Wireline network games -- Selfish routing, Wardrop equilibrium and efficiency -- Routing model -- Wardrop equilibrium -- Inefficiency of the equilibrium -- Multiple origin-destination pairs -- Partially optimal routing -- Background and motivation -- The model -- Efficiency of partially optimal routing -- Extensions -- Congestion and provider price competition -- Pricing and efficiency with congestion externalities -- Model -- Monopoly pricing and equilibrium -- Oligopoly pricing and equilibrium -- Efficiency analysis -- Extensions -- Concluding remarks --

4. Wireless network games -- Noncooperative transmission scheduling in collision channels -- The model and preliminaries -- Equilibrium analysis -- Achievable channel capacity -- Best-response dynamics -- Discussion -- Noncooperative power control in collision channels -- The model -- Equilibrium analysis -- Best-response dynamics and convergence to the power efficient equilibrium -- Equilibrium (in)efficiency and Braess-like paradoxes -- Discussion -- Related work and extensions -- Future directions --

5. Future perspectives -- Bibliography -- Authors' biographies.

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Traditional network optimization focuses on a single control objective in a network populated by obedient users and limited dispersion of information. However, most of today's networks are large-scale with lack of access to centralized information, consist of users with diverse requirements, and are subject to dynamic changes.These factors naturally motivate a new distributed control paradigm, where the network infrastructure is kept simple and the network control functions are delegated to individual agents which make their decisions independently ("selfishly").The interaction of multiple independent decision-makers necessitates the use of game theory, including economic notions related to markets and incentives.

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Title from PDF t.p. (viewed on March 11, 2011).