science enjoys periods of stable growth punctuated by revisionary revolutions, to which he added the controversial ‘incommensurability thesis’, that theories from differing periods suffer from certain deep kinds of failure of comparability.
The central idea of this extraordinarily influential—and controversial—book is that the development of science is driven, in normal periods of science, by adherence to what Kuhn called a ‘paradigm’. The function of a paradigm is to supply puzzles for scientists to solve and to provide the tools for their solution. A crisis in science arises when confidence is lost in the ability of the paradigm to solve particularly worrying puzzles called ‘anomalies’. Crisis is followed by a scientific revolution if the existing paradigm is superseded by a rival. Kuhn claimed that science guided by one paradigm would be ‘incommensurable’ with science developed under a different paradigm, by which is meant that there is no common measure of the different scientific theories. This thesis of incommensurability, developed at the same time by Feyerabend, rules out certain kinds of comparison of the two theories and consequently rejects some traditional views of scientific development, such as the view that later science builds on the knowledge contained within earlier theories, or the view that later theories are closer approximations to the truth than earlier theories.
He claims that normal science can succeed in making progress only if there is a strong commitment by the relevant scientific community to their shared theoretical beliefs, values, instruments and techniques, and even metaphysics. This constellation of shared commitments Kuhn at one point calls a ‘disciplinary matrix’
The most interesting response to crisis will be the search for a revised disciplinary matrix, a revision that will allow for the elimination of at least the most pressing anomalies and optimally the solution of many outstanding and unsolved puzzles.
The phenomenon of Kuhn-loss does, in Kuhn's view, rule out the traditional cumulative picture of progress. The revolutionary search for a replacement paradigm is driven by the failure of the existing paradigm to solve certain important anomalies. Any replacement paradigm had better solve the majority of those puzzles, or it will not be worth adopting in place of the existing paradigm.
For the novel puzzle-solution which crystallizes consensus is regarded and used as a model of exemplary science. In the research tradition it inaugurates, a paradigm-as-exemplar fulfils three functions: (i) it suggests new puzzles; (ii) it suggests approaches to solving those puzzles; (iii) it is the standard by which the quality of a proposed puzzle-solution can be measured
Kuhn's contrasting view is that we judge the quality of a theory (and its treatment of the evidence) by comparing it to a paradigmatic theory. The standards of assessment therefore are not permanent, theory-independent rules. They are not rules, because they involve perceived relations of similarity (of puzzle-solution to a paradigm). They are not theory-independent, since they involve comparison to a (paradigm) theory.
Kuhn (1977, 321-322) identifies five characteristics that provide the shared basis for a choice of theory: 1. accuracy; 2. consistency (both internal and with other relevant currently accepted theories); 3. scope (its consequences should extend beyond the data it is required to explain); 4. simplicity (organizing otherwise confused and isolated phenomena); 5. fruitfulness (for further research).
First, it has been argued that Kuhn's account of the development of science is not entirely accurate. Secondly, critics have attacked Kuhn's notion of incommensurability, arguing that either it does not exist or, if it does exist, it is not a significant problem. Despite this criticism, Kuhn's work has been hugely influential, both within philosophy and outside it. The Structure of Scientific Revolutions was an important stimulus to what has since become known as 'Science Studies', in particular the Sociology of Scientific Knowledge (SSK).
Kuhn's influence outside of professional philosophy of science may have been even greater than it was within it. The social sciences in particular took up Kuhn with enthusiasm. There are primarily two reasons for this. First, Kuhn's picture of science appeared to permit a more liberal conception of what science is than hitherto, one that could be taken to include disciplines such as sociology and psychoanalysis. Although, he says, the natural sciences involve interpretation just as human and social sciences do, one difference is that hermeneutic re-interpretation, the search for new and deeper intepretations, is the essence of many social scientific enterprises. This contrasts with the natural sciences where an established and unchanging interpretation (e.g. of the heavens) is a pre-condition of normal science. Re-intepretation is the result of a scientific revolution and is typically resisted rather than actively sought. Another reason why regular reinterpretation is part of the human sciences and not the natural sciences is that social and political systems are themselves changing in ways that call for new interpretations, whereas the subject matter of the natural sciences is constant in the relevant respects, permitting a puzzle-solving tradition as well as a standing source of revolution-generating anomalies.
Beginning with somewhat beer-soaked origins, we've recently been trying to think about what makes for a good problem in our field. Why are some questions more tractable than others? How do you identify a good problem in advance? Are there common elements that can be identified in different fields? Apart from a slightly groan-worthy title, what we will do for the class is not very well defined yet. Some of the specific questions we'd like to ponder:
-Why are some problems and hypotheses more likely to lead to enlightenment (or to the reduction in ignorance), while others are more likely to further obscure the truth? How does one construct a hypothesis that has the intrinsic property of knowability?
-What are the roles of intuition and experience/deduction in formulating a question that is knowable when it is probed using scientific reasoning?
-When do models build knowledge? What types of models are most influential in shaping the way we think? Are they the same models that keep the scientific invesigation on the pathway to truth?
-How does one avoid working on a problem that "dies when the investigtor dies" (Michelangelo)?
If these questions seem like they'd be interesting to sit around a table and cogitate on, let us know, and one thing we'd like you to start thinking about is a paper, or papers, that you've found to be good examples of elegant approaches to important problems. By starting on this now, we hope to build up a series of case studies we can all explore together and gain from everyone else's experiences and ideas.
It is not clear we will be able to come up with concrete or world-shattering answers, but we do think these are important questions to think about. Attached below are some more thoughts resulting from a mixture of caffeine and hops.
David and Gerard