I am particularly interested in understanding the extent to which social dilemmas result from the intrinsic complexity of n individuals agreeing upon one or more goals and cooperating (or competing) to reach those goals, when both the outcome of any action and the means to any particular subgoal may be unknown. This is sometimes called coordination complexity. Can learning strategies be used to overcome coordination complexity and the inevitable mistakes that arise from uncertainty and limited rationality (i.e. limited computational resources)? What means are available to reduce the complexity of problems requiring large-scale cooperation?
A number of areas in artificial intelligence are relevant to this question, including machine learning and computational learning theory, distributed AI and multiagent systems, and knowledge representation and ontologies. Relevant threads in other subject domains include Axelrod's theory of the evolution of cooperation, other limited-rationality approaches to strategic games, complexity theory, agent-based modeling of social systems, and work on collective action and social dialogs.