Prediction markets are virtual stock markets used to gain insight and forecast events by leveraging the wisdom of crowds. Popularly applied in the public to cultural questions (election results, box-office returns), they have recently been applied by corporations to leverage employee knowledge and forecast answers to business questions (sales volumes, products and features, release timing). Determining whether to run a prediction market requires practical experience that is rarely described.

It is theorized that the process of coordination is a distributed search through a hierarchical space of agent behaviors. By specifying agent activities along multiple dimensions and at different levels of abstraction, the hierarchical behavior space provides a single, rich representation that agents can use to organize, plan, and schedule their collective actions. A computational instance of the evolving theory, which implements a particular choice of distributed protocol, local algorithm, metrics, and heuristics, as applied to resolving resource conflicts in an unstructured delivery domain, is described. In this domain, agents that initially do not know with whom they might interact exploit the hierarchical behavior representation to selectively exchange more details about themselves until they can resolve conflicting behaviors. It was experimentally demonstrated how the hierarchical protocol and multidimensional representation provide powerful and practical mechanisms for coordinating these agents, and important research issues to be addressed are highlighted.>