Toward a Theory of Knowledge Economics: An Information Systems Approach

A comprehensive theory of knowledge societies and knowledge economics is still missing. This article shows that the analytical tools from computer science, information systems and information theory provide an adequate language to work toward such theory. The presented formalization follows an evolutionary and multilevel approach, and embraces both the concepts of information and knowledge. It is shown that economic evolution can be modeled as a process that selects superior strategies from a set of possibilities through natural (market driven) selection. This reduces uncertainty by the production of negative entropy (´negentropy´). Furthermore, economic ´fitness´ can be modelled in terms of the informational ´fit´ of the economic system with its environment, which takes the form of Shannon´s mutual information. In the case of a deterministic dynamic, uncertainty reduction takes the form of a predictable algorithm, which is quantified through Kolmogorov complexity. The length of such algorithm represents the knowledge about the unfolding dynamic. This shows that we might be able to eventually describe both, economics and technological information systems within the same analytical framework.