A tacit assumption common in arguments concerning the scientific method, criteria of demarcation, experimental procedures, and so forth, is that scientific methods should be like effective methods for empirical problems.
In logic, an effective method is a procedure for computing the answer to a class of problems. It requires no understanding of the problem, but just symbolic transformations according to mechanical rules. An effective method always give some answer, always gives the right answer, can always be completed in a finite number of steps, and works for all instances of its problem-class (cribbed from Wikipedia). When the truth of a statement can be determined by application of an effective method, it is said to be decidable.
Many people seem to implicitly hold science to similar standards. In this view, each step in the scientific method–from discovering theories to experimental testing–can be specified by a mechanical rule. Scientists might as well be mindless automatons obeying a procedure much like computers apply an effective method to calculate solutions to mathematical queries.
In this view, once we have the right scientific method, we need only input an empirical query into science and wait for the answer. Science will always give some answer, will always give a true (or probable) answer, can always be completed in a finite number of steps, and works for all empirical problems. If science should produce a false (or improbable) answer, then it must be because someone failed to apply the method correctly. In this view, science really might be improved if we replaced humans–biases, quirks, egos, and all–with mindless automatons.
In this context, arguments about science normally revolve around the identity of its problem-class. That is, what kind of statements are decidable by scientific methods? Or what counts as an ’empirical statement’? It is, however, normally taken for granted that whatever the problem-class, the scientific method should, like an effective method, inevitably lead us to true (or probable) conclusions. If there are no methods that can satisfy these aims, then science is considered essentially irrational (or merely instrumental).
Critical rationalists disagree entirely with this way of thinking. It is pre-Darwinian. There is no “effective method” for science. There are good and bad methods, epistemic virtues and vices, but no guarantees of truth (or even probable truth). The scientific method cannot be reduced to a sequence of mechanical rules; it is an inherently creative, unpredictable, fallible, and even artistic endeavour.
Induction was supposed to be a mechanical rule for transforming observations into scientific theories. It is a legacy of this “effective method” approach to science and the growth of knowledge–the quest for an algorithm of scientific discovery. Critical rationalists, however, take their cue from evolution. The method of conjecture and refutation is analogous–and continuous with–mutation and selection, and it bears little resemblance to an effective method.
Suppose, for example, that we proposed an “effective method” for evolution with a rule specifying how to create new mutations. Such a rule must discriminate among possible mutations; it must, therefore, prevent some from ever being realised. However, this puts the cart before the horse. The adaptive fitness of a mutation is revealed by selection pressures, not before. It turns out that we do not really have rule for creating new mutations at all, but for constraining them–it’s just another selection pressure.
Scientific theories are like mutations. We do not need to specify a mechanical rule to create new theories, but merely standards of criticism (selection pressures) to subject them to: logical consistency, falsifiability, problem-solving potential, simplicity, explanatory power, and possibly others. The ecological niche we create for our theories should be one designed to weed out error and falsehood, irrationality and redundancy. Induction, concerned as it is with the origin or source of theories, serves no purpose in such a critical discourse.
Even to this day, most epistemologies and visions of science are essentially Lamarckian. They see adaptive fitness as something induced from a reliable source. The problem, in this view, is to discover the “right” source or foundation for knowledge, and to preserve its purity by shunning guesswork and conjecture. Critical rationalism, in contrast, posits a profoundly Darwinian understanding of science and the growth of knowledge, where rationality is not about justification and “effective methods” but creativity and criticism.