"An advantage of a deterministic theory is that, assuming sufficient knowledge, there is no uncertainty in the evolution of the state of the system. In practice, measurements are not perfectly precise, so there is always uncertainty as to the value of any variable." (Edward R Dougherty, "The Evolution of Scientific Knowledge: From certainty to uncertainty", 2016)
"Foretelling the future is the crux. A model may fit existing data, but the model must incorporate mathematical machinery that makes it predictive across time to be scientifically valid." (Edward R Dougherty, "The Evolution of Scientific Knowledge: From certainty to uncertainty", 2016)
"Limitations on experimentation can result in limitations on
the complexity or details of a theory. To be validated, a theory cannot exceed
the experimentalist’s ability to conceive and perform appropriate experiments.
With the uncertainty theory, modern physics appears to have brought us beyond
the situation where limitations on observation result only from insufficient
experimental apparatus to a point where limitations are unsurpassable in
principle."
"In the classical deterministic scenario, a model consists of a few variables and physical constants. The relational structure of the model is conceptualized by the scientist via intuition gained from thinking about the physical world. Intuition means that the scientist has some mental construct regarding the interactions beyond positing a skeletal mathematical system he believes is sufficiently rich to capture the interactions and then depending upon data to infer the relational structure and estimate a large number of parameters." (Edward R Dougherty, "The Evolution of Scientific Knowledge: From certainty to uncertainty", 2016)
"Parameter estimation is a basic aspect of model construction
and historically it has been assumed that data are sufficient to estimate the
parameters, for instance, correlations that are part of the model; however,
when the number of parameters is too large for the amount of data, accurate parameter
estimation becomes impossible. The result is model uncertainty."
"[…] people attempt to use highly flexible mathematical
structures with large numbers of parameters that can be adjusted to fit the
data, the result often being models that fit the data well but lack structural
representation of the phenomena and thus are not predictive outside the range
of the data. The situation is exacerbated by uncertainty regarding model
parameters on account of insufficient data relative to model complexity, which
in fact means uncertainty regarding the models themselves. More importantly
from the standpoint of epistemology, the amount of available data is often
miniscule in comparison to the amount needed for validation. The desire for
knowledge has far outstripped experimental/observational capability. We are
starved for data."
"Scientific knowledge is worldly knowledge in the sense that
it points into the future by making predictions about events that have yet to
take place. Scientific knowledge is contingent, always awaiting the possibility
of its invalidation. Its truth or falsity lies in the verity of its predictions
and, since these predictions depend upon the outcomes of experiments,
ultimately the validity of scientific knowledge is relative to the methodology
of verification."
"The foundations of a discipline are inseparable from the rules of its game, without which there is no discipline, just idle talk. The foundations of science reside in its epistemology, meaning that they lie in the mathematical formulation of knowledge, structured experimentation, and statistical characterization of validity. Rules impose limitations. These may be unpleasant, but they arise from the need to link ideas in the mind to natural phenomena. The mature scientist must overcome the desire for intuitive understanding and certainty, and must live with stringent limitations and radical uncertainty." (Edward R Dougherty, "The Evolution of Scientific Knowledge: From certainty to uncertainty", 2016)
"The model (conceptual system) is a creation of the imagination, in accordance with the rules of the game. The manner of this creation is not part of the scientific theory. The classical manner is that the scientist combines an appreciation of the problem with reflections upon relevant phenomena and, based on mathematical knowledge, creates a model." (Edward R Dougherty, "The Evolution of Scientific Knowledge: From certainty to uncertainty", 2016)
No comments:
Post a Comment