Algorithmic Game theory is about strategic interactions among intelligent individuals, and mechanism design is about creating effective incentives in economic settings. Together, they're fascinating ways to understand human behavior and the challenges of designing and building systems. Algorithmic game theory (AGT) is a way of analyzing social interactions that use mathematical models to predict the strategies that individuals will adopt in any given situation. A simple game theory model can predict human behavior in many situations. But the surprising thing is that this same model can also explain the complex, self-organizing systems that power the World Wide Web.

We then describe three broad areas of current inquiry by AI researchers in algorithmic game theory: game playing, social choice, and mechanism design. Finally, we give short summaries of each of the six articles appearing in this issue. The field took on its modern form in the 1940s and 1950s (von Neumann and Morgenstern 1947; Nash 1950, Kuhn 1953), with even earlier antecedents (such as Zermelo 1913 and von Neumann 1928). Although it has had occasional and significant overlap with computer science over the years, game theory received most of its early study by economists. Indeed, game theory now serves as perhaps the main analytical framework in microeconomic theory, as evidenced by its prominent role in economics textbooks (for example, Mas-Colell, Whinston, and Green 1995) and by the many Nobel prizes in economic sciences awarded to prominent game theorists.

Lecture 1 of Tim Roughgarden's Algorithmic Game Theory class at Stanford (Autumn 2013) Class description: Topics at the interface of computer science and game theory such as: algorithmic mechanism design; combinatorial auctions; computation of Nash equilibria and relevant complexity theory; congestion and potential games; cost sharing; game theory and the Internet; matching markets; network formation; online learning algorithms; price of anarchy; prior-free auctions; selfish routing; sponsored search.

And see also this great piece from Mashable on what manufacturers are up to next. In the near future, sensing algorithms will achieve super-human performance for capabilities required for driving. Automated perception, including vision, is already near or at human-performance level for well-defined tasks such as recognition and tracking. Advances in perception will be followed by algorithmic improvements in higher level reasoning capabilities such as planning. Beyond self-driving cars, we'll have a variety of autonomous vehicles including robots and drones.

And see also this great piece from Mashable on what manufacturers are up to next. In the near future, sensing algorithms will achieve super-human performance for capabilities required for driving. Automated perception, including vision, is already near or at human-performance level for well-defined tasks such as recognition and tracking. Advances in perception will be followed by algorithmic improvements in higher level reasoning capabilities such as planning. Beyond self-driving cars, we'll have a variety of autonomous vehicles including robots and drones.

Cooperative game theory is a branch of (micro-)economics that studies the behavior of self-interested agents in strategic settings where binding agreements among agents are possible. Our aim in this book is to present a survey of work on the computational aspects of cooperative game theory. We begin by formally defining transferable utility games in characteristic function form, and introducing key solution concepts such as the core and the Shapley value. We then discuss two major issues that arise when considering such games from a computational perspective: identifying compact representations for games, and the closely related problem of efficiently computing solution concepts for games. We survey several formalisms for cooperative games that have been proposed in the literature, including, for example, cooperative games defined on networks, as well as general compact representation schemes such as MC-nets and skill games.

We briefly survey the rise of game theory as a topic of study in artificial intelligence, and explain the term algorithmic game theory. Finally, we give short summaries of each of the six articles appearing in this issue.

Indeed, game theory now serves as perhaps the main analytical framework in microeconomic theory, as evidenced by its prominent role in economics textbooks (for example, Mas-Colell, Whinston, and Green 1995) and by the many Nobel prizes in economic sciences awarded to prominent game theorists. Artificial intelligence got its start shortly after game theory (McCarthy et al. 1955), and indeed pioneers such as von Neumann and Simon made early contributions to both fields (see, for example, Findler [1988], Simon [1981]). Both game theory and AI draw (nonexclusively) on decision theory (von Neumann and Morgenstern 1947); for example, one prominent view defines artificial intelligence as "the study and construction of rational agents" (Russell and Norvig 2003), and hence takes a decision-theoretic approach when the world is stochastic. However, artificial intelligence spent most of its first 40 years focused on the design and analysis of agents that act in isolation, and hence had little need for game-theoretic analysis. Starting in the mid to late 1990s, game theory became a major topic of study for computer scientists, for at least two main reasons. First, economists began to be interested in systems whose computational properties posed serious barriers to practical use, and hence reached out to computer scientists; notably, this occurred around the study of combinatorial auctions (see, for example, Cramton, Shoham, and Steinberg 2006). Second, the rise of distributed computing in general and the Internet in particular made it increasingly necessary for computer scientists to study settings in which intelligent agents reason about and interact with other agents.