Kevin Leyton-Brown is a professor of Computer Science at the University of British Columbia and an associate member of the Vancouver School of Economics. He holds a PhD and M.Sc. from Stanford University (2003; 2001) and a B.Sc. from McMaster University (1998). He studies the intersection of computer science and microeconomics, addressing computational problems in economic contexts and incentive issues in multiagent systems. He also applies machine learning to the automated design and analysis of algorithms for solving hard computational problems.

• Games in Normal Form.- Analyzing Games: From Optimality to Equilibrium.- Further Solution Concepts for Normal-Form Games.- Games with Sequential Actions: The Perfect-information Extensive Form.- Generalizing the Extensive Form: Imperfect-Information Games.- Repeated and Stochastic Games.- Uncertainty about Payoffs: Bayesian Games.- Coalitional Game Theory.- History and References.- Index.

Kevin Leyton-Brown is a professor of Computer Science at the University of British Columbia and an associate member of the Vancouver School of Economics. He holds a PhD and M.Sc. from Stanford University (2003; 2001) and a B.Sc. from McMaster University (1998). He studies the intersection of computer science and microeconomics, addressing computational problems in economic contexts and incentive issues in multiagent systems. He also applies machine learning to the automated design and analysis of algorithms for solving hard computational problems.

• Games in Normal Form.- Analyzing Games: From Optimality to Equilibrium.- Further Solution Concepts for Normal-Form Games.- Games with Sequential Actions: The Perfect-information Extensive Form.- Generalizing the Extensive Form: Imperfect-Information Games.- Repeated and Stochastic Games.- Uncertainty about Payoffs: Bayesian Games.- Coalitional Game Theory.- History and References.- Index.

Kevin Leyton-Brown is a professor of Computer Science at the University of British Columbia and an associate member of the Vancouver School of Economics. He holds a PhD and M.Sc. from Stanford University (2003; 2001) and a B.Sc. from McMaster University (1998). He studies the intersection of computer science and microeconomics, addressing computational problems in economic contexts and incentive issues in multiagent systems. He also applies machine learning to the automated design and analysis of algorithms for solving hard computational problems.

• Games in Normal Form.- Analyzing Games: From Optimality to Equilibrium.- Further Solution Concepts for Normal-Form Games.- Games with Sequential Actions: The Perfect-information Extensive Form.- Generalizing the Extensive Form: Imperfect-Information Games.- Repeated and Stochastic Games.- Uncertainty about Payoffs: Bayesian Games.- Coalitional Game Theory.- History and References.- Index.