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.

[1]  Haim H. Permuter,et al.  Interpretations of Directed Information in Portfolio Theory, Data Compression, and Hypothesis Testing , 2009, IEEE Transactions on Information Theory.

[2]  K. Popper.  Objective Knowledge: An Evolutionary Approach , 1972 .

[3]  A. Kolmogorov.  Three approaches to the quantitative definition of information , 1968 .

[4]  Masud Mansuripur,et al.  Introduction to information theory , 1986 .

[5]  W. Powell,et al.  The New Institutionalism in Organizational Analysis , 1993 .

[6]  Thomas M. Cover,et al.  Some equivalences between Shannon entropy and Kolmogorov complexity , 1978, IEEE Trans. Inf. Theory.

[7]  S. Winter,et al.  An evolutionary theory of economic change , 1983 .

[8]  Gregory J. Chaitin,et al.  On the Length of Programs for Computing Finite Binary Sequences , 1966, JACM.

[9]  Hans Christian,et al.  Warmth disperses and time passes : the history of heat , 1999 .

[10]  W. H. Zurek.  Complexity, Entropy and the Physics of Information , 1990 .

[11]  U. Beyer.  Complexity Entropy And The Physics Of Information , 2016 .

[12]  Ray J. Solomonoff,et al.  A Formal Theory of Inductive Inference. Part II , 1964, Inf. Control..

[13]  Sabrina Kruger,et al.  The Origin And Evolution Of Cultures , 2016 .

[14]  Thomas M. Cover,et al.  Elements of Information Theory , 2005 .

[15]  Viktor Mayer-Schnberger,et al.  Big Data: A Revolution That Will Transform How We Live, Work, and Think , 2013 .

[16]  César A. Hidalgo,et al.  The building blocks of economic complexity , 2009, Proceedings of the National Academy of Sciences.

[17]  L. Brillouin,et al.  The Negentropy Principle of Information , 1953 .

[18]  D. North.  Structure and Change in Economic History , 1983 .

[19]  Ming Li,et al.  An Introduction to Kolmogorov Complexity and Its Applications , 2019, Texts in Computer Science.

[20]  Sang Joon Kim,et al.  A Mathematical Theory of Communication , 2006 .

[21]  R. Swinburne.  OBJECTIVE KNOWLEDGE: AN EVOLUTIONARY APPROACH , 1973 .

[22]  L. Levin,et al.  THE COMPLEXITY OF FINITE OBJECTS AND THE DEVELOPMENT OF THE CONCEPTS OF INFORMATION AND RANDOMNESS BY MEANS OF THE THEORY OF ALGORITHMS , 1970 .

[23]  Adolfo Ibáñez,et al.  LINKING INFORMATION , KNOWLEDGE AND EVOLUTIONARY GROWTH : A MULTILEVEL INTERPLAY BETWEEN NATURAL SELECTION AND INFORMED INTERVENTION , 2013 .

[24]  S. Winter,et al.  An Evolutionary Theory of Economic Change.by Richard R. Nelson; Sidney G. Winter , 1987 .

[25]  Fazlollah M. Reza,et al.  Introduction to Information Theory , 2004, Lecture Notes in Electrical Engineering.

[26]  P. Richerson,et al.  The Origin and Evolution of Cultures , 2005 .

[27]  C. P. Goodman,et al.  The Tacit Dimension , 2003 .

[28]  Eric Gossett,et al.  Big Data: A Revolution That Will Transform How We Live, Work, and Think , 2015 .

[29]  John L. Kelly,et al.  A new interpretation of information rate , 1956, IRE Trans. Inf. Theory.

[30]  A. Tversky,et al.  Judgment under Uncertainty: Heuristics and Biases , 1974, Science.

[31]  E. Schrödinger,et al.  What is life? : the physical aspect of the living cell , 1946 .

[32]  Stanislas Leibler,et al.  The Value of Information for Populations in Varying Environments , 2010, ArXiv.

[33]  Ray J. Solomonoff,et al.  A Formal Theory of Inductive Inference. Part I , 1964, Inf. Control..

[34]  M. Hilbert,et al.  Linking Information, Knowledge and Evolutionary Growth: A Multilevel Interplay between Natural Selection and Informed Intervention , 2013 .

[35]  Zurek,et al.  Algorithmic randomness and physical entropy. , 1989, Physical review. A, General physics.

[36]  Carl T. Bergstrom,et al.  The fitness value of information , 2005, Oikos.

[37]  HE Ixtroductiont,et al.  The Bell System Technical Journal , 2022 .

[38]  野中 郁次郎,et al.  The Knowledge-Creating Company: How , 1995 .