OPIM 319, "Agents, Games & Evolution," explores applications and fundamentals of strategic behavior.

Strategic, or game-theoretic, topics arise throughout the social sciences. The topics include---and we shall study---trust, cooperation, market-related phenomena (including price equilibria and distribution of wealth), norms, conventions, commitment, coalition formation, and negotiation. They also include such applied matters as design of logistics systems, auctions, and markets generally (for example, markets for electric power generation).

In addressing these topics we focus on the practical problem of finding effective strategies for agents in strategic situations (or games). Our method of exploration will be experimental: we review and discuss experiments on the behavior of agents in strategic (or game-theoretic) situations.

In focusing on the design and behavior of artificial agents in strategic (or game-theoretic) situations, we will be especially concerned with strategic contexts of commercial import, such as markets, bargaining, and repeated play. We shall dwell on effective agent learning techniques, including evolutionary methods and reinforcement learning. A main theme in the course is the inherent difficulty, even unknowability, of the problem of strategy acquisition.

We will rely mainly on computational experiments (or simulations), in distinction to analytic mathematical methods, for studying strategy formation and strategic behavior (either by individuals or by groups). Much of the class work will be devoted to discussing and interpreting computational experiments that have been reported in the literature, or that can be undertaken with tools provided in class. In doing so, we draw upon the rapidly growing literature in agent-based modeling and agent-based simulation. Agent-Based Computational Economics (for example, http://www.econ.iastate.edu/tesfatsi/ace.htm) and Agent-Based Social Science (for example, http://www.brookings.edu/es/dynamics/papers/csed_wp41.htm) have come to denote active communities of research and application. We shall draw upon them.

Computer programming is neither required nor discouraged for the course. The instructor invites, and will support, projects using NetLogo (as well as other envirnments). Many of the computational demonstrations and experiments we will examine are available as NetLogo programs (http://ccl.northwestern.edu/netlogo/). Students are not, however, at all required to undertake programming exercises, in NetLogo or in any other environment.

Students completing the course can expect to come away with:

1. Solid understanding of what is known and what is not known about the problem of designing procedures for strategic behavior,
2. Familiarity with the principal methods, and results of applying those methods, for the modeling of human agents and design of artificial agents in strategic contexts, and
3. Deepened appreciation for contexts of strategic interaction.

Class meets 3-4:30 p.m., Mondays and Wednesdays. Grading is based on class participation, assigned short essays undertaken during the term, a midterm quiz, and a term project. For further information, contact the principal instructor for the course, Professor Steven O. Kimbrough (kimbrough@wharton.upenn.edu).