This comprehensive textbook explores the intersection of game theory and computer science, examining how computational perspectives illuminate strategic behavior while game-theoretic insights inform algorithm design. The editors, leading researchers in the field, assembled contributions covering the full range of algorithmic game theory. The book addresses computational complexity of finding equilibria, showing which game-theoretic solutions are tractable and which are computationally intractable. It examines mechanism design, the engineering of games to achieve desired outcomes, with applications to auctions, markets, and resource allocation. Chapters explore combinatorial auctions where bidders compete for bundles of goods, incentive design in networked systems, and pricing strategies for digital goods. The text addresses both theoretical foundations and practical applications, from internet advertising auctions to spectrum allocation. It examines how self-interested behavior affects system performance and how to design systems robust to strategic manipulation. The book serves as both graduate textbook and reference for researchers, assuming background in algorithms and basic game theory while developing advanced topics systematically. It represents a field that emerged from the recognition that computational systems increasingly must account for strategic behavior by their users.