Pervasive complementarity among agents, variables and their relations is a strong manifestation of unity in the real world. It is explained in various ways within scientific systems and in alternative ways of viewing resource allocation from that in neoclassical economic theory and its various prototypes. Complementarity among goods, services and factors in neoclassical resource allocation is simply a localized phenomenon. Despite this, bundles of similar goods collect together to re‐establish marginal substitution with other bundles. In systems science, the cessation of complementarity among variables causes the demise of process. Indeed, the most significant influence of economic complementarity is to be found in decision‐making systems. Here strongly interactive ethical principles showing pervasive and strong complementarity reveal themselves. Hence a knowledge‐induced scientific methodology emerges. Yet these scientific dynamic methods that are merely premised on time‐phase, are found to be inadequate in explaining pervasive interactions. Instead, simulation methods reveal important and interesting results premised on the epistemological premise of systemic unity and interactions. We will examine these questions in this paper with respect to the optimal control problem of the calculus of variations, and for multi‐objective decision problems.
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1 February 2002
Research Article|
February 01 2002
Simulation versus optimization in knowledge‐induced fields
Masudul Alam Choudhury;
Masudul Alam Choudhury
College of Industrial Management, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia and the School of Business, University College of Cape Breton, Sydney, Nova Scotia, Canada
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Gabor Korvin
Gabor Korvin
Earth Sciences Department, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia
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Publisher: Emerald Publishing
Online ISSN: 1758-7883
Print ISSN: 0368-492X
© MCB UP Limited
2002
Kybernetes (2002) 31 (1): 44–60.
Citation
Alam Choudhury M, Korvin G (2002), "Simulation versus optimization in knowledge‐induced fields". Kybernetes, Vol. 31 No. 1 pp. 44–60, doi: https://doi.org/10.1108/03684920210413755
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