Traditional economic theory presumes that decision makers have no limits on their ability to absorb and process information, to carry out calculations, or to explore the implications of alternative courses of action. In reality, however, economic agents often face problems that are too complex for any individual to treat in this way. Therefore, it is important to examine whether the results of traditional models are robust when the assumption of unlimited informational capacity is relaxed. The focus of this project is to analyze a new way of describing limits on this capacity. Specifically, each decision maker is represented by a network of simple computers. The aggregate computational power of a network often exceeds the sum of the capabilities of individual machines. Small but well designed computer networks can store and process substantial amounts of information. Notion of complexity will be formalized, and the implications of limited capacity to handle complexity will be investigated. In addition, such networks will be used to study adaptive learning processes. Some of the initial results obtained in this framework are strikingly different from those found in the existing literature on bounded rationality. It is then essential to further this research by critically reviewing the existing tools for modeling the limited informational capability of economic agents.
