Computational model of Collective Intelligence for meta-level analysis and prediction of free or quasi-free market economy

• Autorzy: Szuba T. , Szydło S. , Skrzyński P.
• Języki publikacji: EN

Abstrakty

EN

This paper encourages the use of a computational model of Collective Intelligence as a major (meta-level) tool to analyze and predict behavior of socio-economical systems like free (or quasi-free) markets are. Researchers are aware, that economics is a study of human behavior, but lack of a proper formal tool has shifted research in economics into the language of money, production, consumption, etc. From an economic point of view, when analyzing free (quasi-free) markets, more important is group behavior than individual behavior because they result in changes of market indexes. Group behavior leads in specific cases to the emergence of "group intelligence" with the most famous case named "A. Smith invisible hand of market". A computational model of Collective Intelligence allows for the formal extraction of the "system of inference processes" which run in an unconscious way in socio-economic structures. The construction of a proper formal and simulation model of such Collective Intelligence inferences allows us to take an attempt to predict outcomes in terms of economical results. The paper will present a formal basis, methodology of constructing Collective Intelligence systems for given socio-economic structures.

Słowa kluczowe

EN

collective intelligence; computational model; free market; quasi-free market; economics; human behavior; simulation model

• Wydawca: AGH University of Science and Technology Press
• Czasopismo: Decision Making in Manufacturing and Services
• Rocznik: 2012
• Tom: Vol. 6, no. 1-2
• Strony: 41--51
• Opis fizyczny: Bibliogr. 8 poz.

Twórcy

autor

Szuba T.

• AGH University of Science and Technology, Poland

autor

Szydło S.

• AGH University of Science and Technology, Poland

autor

Skrzyński P.

• AGH University of Science and Technology, Poland

Bibliografia

• Berry, G., Boudol, G. (1992), The chemical abstract machine, Theoretical Computer Science.
• Fontana, W., Buss, L. W. (1994), The arrival of the fittest, Bulletin of Mathematical Biology
• 56(1), 1–64.
• Giarratano, J., Riley, G. (1998), Expert systems, PWS Pub. III ed.
• Prokhorov, A. B. (2001), Nonlinear Dynamics and Chaos Theory in Economics: a Historical
• Perspective, https://www.msu.edu/~prohorov/paper.pdf.
• Szuba, T. (1998), A Molecular Quasi-random Model of Computations Applied to Evaluate
• Collective Intelligence, Future Generation Computing Journal 14(5–6), 321–339.
• Szuba, T. (2001a), A formal definition of the phenomenon of collective intelligence and its
• IQ measure, Future Generation Computing Journal 17(4), 489–500, Elsevier.
• Szuba, T. (2001b), Computational Collective Intelligence, Wiley Series on Parallel and Distributed
• Computing. Wiley & Sons, February, 2001.
• Skrzyński, P. (2011), Collective Intelligence theory applied to describe phenomena of A. Smith
• invisible hand of market. Ph. D. dissertation. AGH Univ., January, 2011.