by Mitch Kokai
Senior Political Analyst, John Locke Foundation
Richard Fernandez writes at PJ Media about a problem that plagues much of today’s scientific research.
A map as big as the world is useless because any economical map of a chaotic, open system must be smaller than the world itself. But what if at maximum compression it’s full size? It’s a problem Edward R. Dougherty of the Center for Bioinformatics and Genomic Systems Engineering, who wrote to me, takes up:
today’s major scientific and engineering problems—in biology, medicine, environmental science, etc.—involve enormous complexity, and it is precisely this complexity that runs up against the limits of what is scientifically knowable. To understand the issue, one must appreciate the radical break with antiquity that occurred with the birth of modern science in the Seventeenth Century, the problems of knowledge and truth engendered by modern science, and the evolution of scientific thinking through the Twentieth Century.
He calls it the “Crisis of Complexity … as technology provides more detailed observation, Nature is becoming more unfathomable”. His book, an excellent history of the scientific method explains how we got to the present dilemma. Beginning with Newton’s Hypotheses Non Fingo scientific truth began to detach itself from intuition. Mathematical prediction and reproducible results were all we could know of nature, nothing more. “The advent of quantum mechanics in the first part of the Twentieth Century brought it to light: a theory may be preposterous from the perspective of human intelligibility but lead to predictions that agree with empirical observation—and therefore be scientifically valid. Man can possess knowledge beyond the limits of his physical understanding.”
It worked for a while then the objects of scientific inquiry went into the big league. The relatively simple subjects of 20th century science became not only unintuitive but vast beyond human comprehension. The really complex domains of the 21st century science are thorny because there are so many variables we cannot populate the data set in the classic way. We can’t build maps as big as the world bit and don’t know what else to do.
Dougherty proposes the replacing impractical models with a heuristic guided by a Bayesian, replacing science with engineering, looking for methods to partition problems and simplifying it. But he has no illusions. In his view we are living through a second crisis of reason, from which we cannot escape until another scientific resolution arrives. It is not the first crisis of reason. Another occurred at the end of the Roman empire “with the failure of reason to bring material well being and spiritual contentment” to an epoch that had suddenly stopped working.