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Empirical Laws and Foreseeing the Future of Technological Progress
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Orientador(es)
Resumo(s)
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.
Descrição
Palavras-chave
Moore’s Law Prediction Entropy Pseudo-state space