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Entropy 2016, 18(6), 217; doi:10.3390/e18060217

Empirical Laws and Foreseeing the Future of Technological Progress

1
UISPA–LAETA/INEGI, Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, Porto 4200-465, Portugal
2
Institute of Engineering, Polytechnic of Porto, Department of Electrical Engineering, Rua Dr. António Bernardino de Almeida, 431, Porto 4249-015, Portugal
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Academic Editor: Kevin H. Knuth
Received: 17 April 2016 / Revised: 27 May 2016 / Accepted: 30 May 2016 / Published: 2 June 2016
(This article belongs to the Special Issue Computational Complexity)
View Full-Text   |   Download PDF [530 KB, uploaded 2 June 2016]   |  

Abstract

The Moore’s law (ML) is one of many empirical expressions that is used to characterize natural and artificial phenomena. The ML addresses technological progress and is expected to predict future trends. Yet, the “art” of predicting is often confused with the accurate fitting of trendlines to past events. Presently, data-series of multiple sources are available for scientific and computational processing. The data can be described by means of mathematical expressions that, in some cases, follow simple expressions and empirical laws. However, the extrapolation toward the future is considered with skepticism by the scientific community, particularly in the case of phenomena involving complex behavior. This paper addresses these issues in the light of entropy and pseudo-state space. The statistical and dynamical techniques lead to a more assertive perspective on the adoption of a given candidate law. View Full-Text
Keywords: Moore’s Law; prediction; entropy; pseudo-state space Moore’s Law; prediction; entropy; pseudo-state space
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Lopes, A.M.; Tenreiro Machado, J.A.; Galhano, A.M. Empirical Laws and Foreseeing the Future of Technological Progress. Entropy 2016, 18, 217.

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