A continuation of the roles and connections between reductionism, science and academic freedom.
From the first article in this series we saw the long way that Science has travelled from its origins. Some of the principles of research, including the scientific method, as is popularly known, have since then run into some paradoxes.
From the first article in this series we saw the long way that Science has travelled from its origins. Some of the principles of research, including the scientific method, as is popularly known, have since then run into some paradoxes.
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The Origin of Reductionism
Auguste Comte |
Cafe Central, Vienna |
Tree of Knowledge by Gregg Henriques |
This idea that all the sciences are unified has not been universally adopted. Jerry Fodor is the most famous critic of it.
Although many believe that this kind of Positivism has fallen into disuse in the sciences, we do not agree. We will list some of the tenets of what Hillary Putnam called the "received view."
A focus on science as a product, a linguistic or numerical set of statements;
A concern with axiomatization, that is, with demonstrating the logical structure and coherence of these statements;
An insistence on at least some of these statements being testable, that is amenable to being verified, confirmed, or falsified by the empirical observation of reality; statements that would, by their nature, be regarded as untestable included the teleological; (Thus positivism rejects much of classical metaphysics.)
The belief that science is markedly cumulative;
The belief that science is predominantly transcultural;
The belief that science rests on specific results that are dissociated from the personality and social position of the investigator;
The belief that science contains theories or research traditions that are largely commensurable;
The belief that science sometimes incorporates new ideas that are discontinuous from old ones;
The belief that science involves the idea of the unity of science, that there is, underlying the various scientific disciplines, basically one science about one real world.This sounds very much to us as the view of most working scientists today. The wikipedia article on Positivism goes on to say that, "By the end of the twentieth century, nearly every one of those claims or beliefs had been severely criticized or put into question, so much so that they can be regarded now as being untenable, or at least in need of many qualifications and caveats." Although we believe this statement is true, we do not think that this information has filtered down to the average science lab or science instruction. Our view is echoed by this same article when it glibly states, that this view is, "...still alive among many scientists and others who are not well-versed in, or knowledgeable about, what has occurred in technical philosophy since the 1950s."
The ultimate expression of this idea of the unity of science is in what has been called the "Theory of Everything" (TOE). This approach attempts to unify all events in the entire universe to certain basic physical principles, represented by grand unifying mathematical formulas. From Archimedes to Hawking, scientists have been trying to find this grand unifying theory to no avail.
Paul Feyerabend |
The typical view of a reductionist nature is reflected in the view towards "variables." The view espouses that the closer one gets to the most basic components the less "noise" or variables there will be. These variables are seen as anomalies that do nit in the model or prediction. Normally, they are associated with improper measurements in the experiment, or some other form or operator error. At times, it is thought that with more precise measurements, more understanding of the MOST basic components of the thing being studied, these anomalies will disappear.
The whole point assumes that one does not wish variability in any experiment. Thus the answer to this problem is seen as a further reductionist approach to be able to understand the missing, detailed components that will eliminate the noise in the experiment. This is usually displayed in the desire for better instruments of measure, more powerful microscopes, huge colliders, etc. The noise is seen as a distraction from what is really happening.
In our next part of this series we will mention some of the classic problems which are bringing down a reductionist approach in favor of complex systems.
4 comments:
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You got great points there, that's why I always love checking out your blog.
My blog:
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Monica thanks for the wonderful comments and insightful information. I will look at these videos carefully. It seems you and I are very much in agreement on many things.
I gave a talk called "Science Beyond Reductionism" at Future Salon a while back. Video available at http://videos.syntience.com/index.html . The alternative to Reductionism is to use Model Free Methods; the life sciences are increasingly doing this. The term Model Free Methods was first used in 1935 by Lionel S. Penrose. In the talk, I use the NetFlix competition as an example of a complex task that can be done both using Models and in a Model Free manner. I also provide a small zoo of Model Free Methods.
I also examine Reduction as a process in my second article for hplusmagazine.com . Its title is "Reduction Considered Harmful" and I make the limited claim that for the purpose of creating a true Artificial Intelligence (AGI) Reduction in the target problem domain is impossible. In the next article I will show that it is also unnecessary, and the fourth article will discuss Model Free Methods as the alternative. As of posting time that second article is still in the publishing queue.
Reductionism has had an excellent run since the 17th century with peaks in the 17th and the 20th centuries. But it happens to be close to useless for what I call "the remaining hard problems" and the switch to Model Free Methods currently underway needs to be recognized as a necessary progression for all disciplines and not just the Life Sciences. Artificial Intelligence Research is (for predominantly historical reasons) both one of the most Reductionist disciplines and the one that most needs to change; the mismatch between problem domain constraints (or lack of them :-)) and methodology in AI research is appalling and fatal and pretty much obvious to any observer outside the field.
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