Abstract
Pseudoscience is error, substantive or methodological, parading as science. Obvious examples are parapsychology, “intelligent design,” and homeopathy. Psychoanalysis and pop evolutionary psychology are less obvious, yet no less flawed in both method and doctrine. The fact that science can be faked to the point of deceiving science lovers suggests the need for a rigorous sifting device, one capable of revealing out the worm in the apple. This device is needed to evaluate research proposal as well as new fashions. Such a device can be designed only with the help of a correct definition of science, one attending not only to methodological aspects, such as testability and predictive power, but also to other features of scientific knowledge, such as intelligibility, corrigibility, and compatibility with the bulk of antecedent knowledge. The aim of this paper is to suggest such a criterion, to illustrate it with a handful of topical examples, and to emphasize the role of philosophy in either promoting or blocking scientific progress. This article is a revised version of a chapter in the author’s forthcoming book Matter and Mind (Springer). [The Appendix on inductive logic was written at the request of the editors in order to elaborate claims made in #10 (4).]
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Notes
Thomas Kuhn notwithstanding scientific revolutions are partial, not total. For example, Einstein’s special relativity was based on Maxwell’s classical electrodynamics, and Darwin’s evolutionary biology left physiology, cellular biology and biochemistry intact.
For detailed criticism of Kuhn’s relativism and constructivism, see Bunge (1999) Chaps. 8, 9.
On the philosophical and social follies of this school, see Bunge (1996).
See Appendix for a summary of the fatal flaws of the inductive logic programme, including its Bayesian components.
More in Bunge (1966).
As I argued first in 1938 in an unpublished book manuscript, and then elaborated in my Scientific Research (Bunge 1967).
More on the genetic changes brought about by the invention of agriculture about 10,000 years ago in Cochran and Harpending (2009).
Here is a possible elucidation of the relative plausibility concept. Let K designate a body of antecendent or background knowledge, and h a new hypothesis to be evaluated in the light of K. Then Plaus (h|K) = (h ∈ K) v ¬(∃q)[q ∈ K &¬(q&p)].
The ‘its from bits’ myth, or digital physics and metaphysics, is the thesis that the basic constituents of the universe are the symbols 0 and 1. The proponents of this piece of magical thinking do not explain how the universe could have existed before symbols were invented, nor how they combine to constitute things endowed with energy and other physical properties.
For examples and arguments, see, e.g., Stove (1991).
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Appendix: Why Inductive Logic is Elusive (See #10 (4) Above)
Appendix: Why Inductive Logic is Elusive (See #10 (4) Above)
No one doubts that we perform plenty of inductive generalizations. We do this on occasions of two sorts: when generalizing from a set of data, and when evaluating a scientific hypothesis in the light of the relevant empirical data. But the occurrence of induction does not warrant the possibility of building a logic of induction dealing with the probability of hypotheses, as in the attempts of Reichenbach and Carnap. The obstacles to such an enterprise are the following.
Nobody knows the meaning of propositions of the form “the probability of proposition p is such and such”. If what is meant is the probability that p be true, then they are meaningful but absurd, because propositions do not acquire (or lose) their truth value by chance, but as a result of hard research. Let us call this the first semantic objection.
The Bayesians, such as de Bruno de Finetti and James Savage, claim all probabilities are credences or belief strengths. And Patrick Suppes and others have claimed that probability is a generalization of causality, so that strict causation corresponds to the particular case when the probability in question equals one (1). Either interpretation is free from the first semantic objection. But does either work? Let us check them analyzing the AIDS–HIV relation. Since not every bearer of the HIV virus has AIDS, presumably both schools admit that one ought to calculate the probability that an individual with HIV has developed AIDS, or P(A|V), and will claim that Bayes’ theorem applies. This theorem, a rigorous part of the probability theory, states that the a posteriori probability of A given (or assuming) V equals the “inverse” probability of V given A, times the (prior) probability of A, divided by the (prior) probability of V:
The Bayesians will interpret the unknown P(A|V) as the strength of their personal belief that HIVs are also sick with AIDS, whereas the Suppesians will interpret it as the objective propensity or disposition of HIVs to develop into AIDS patients. The radical Bayesians will assign the probabilities at will, whereas the Suppesians will identify the probabilities with the relative frequencies gotten from epidemiological statistics. If we reject the former procedure as unscientific for being arbitrary, we are left with the second method, which is used not only by the Suppesians but also by the so-called objectivist Bayesians. Now, these statistics tell us that all AIDS patients are infected by V, i.e., P(V|A) = 1. Finally, with luck, statistics will tell us the value of the ratio P(A)/P(V) of the priors. But what is the use of this exercise? None, because what we need to know is the mechanisms of infection and immunity, and this is a matter for scientific research, not for logic. And if these mechanisms turn out to be causal rather than stochastic, we will not need the probability calculus either. We may call this objection the argument from barrenness.
No system of inductive logic includes rules or prescriptions for assigning probabilities to propositions. It is usually assumed that the probability of a tautology equals 1, and that of its contradictory equals 0, but nothing is said about the non-denumerable infinity of propositions in between. In particular, no inductive logician has told us what the probability of Newton’s law of motion is; nor a fortiori, what the probabilities of its relativistic or quantum counterparts are. Consequently, the whole apparatus of the probability calculus, which inductive logics presuppose, is invoked in vain. Let us call this the empty formalism objection.
The most powerful and interesting scientific hypotheses are not low level or empirical generalizations, such as “All adult dogs bark”, gotten from data such as “Fido barks”, “Rover barks”, and so on. The high-level scientific hypotheses, such as Newton’s laws of motion, contain concepts, such as those of mass and force that do not occur in the relevant empirical data, such as information about positions of bodies in the case of mechanics. Hence no logic can possibly perform the miracle of generating high-level hypotheses from empirical data. (Dynamics entails kinematics, not the other way round.) We call this the second semantic objection.
A popular example of inductive logic is the following informal (non-deductive) argument:
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(i)
99% of the swans in the world are white.
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(ii)
This is a swan.
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(iii)
∴The probability that his swan is white equals 0.99.
Since this the probability of a fact, the above is not open to the first semantic objection. Indeed, we understand what the meaning of “the probability that a swan be white” provided the swan in question be picked at random. So, the above argument is valid on condition hat the randomness condition be added. But the concept of randomness (or chance) is ontological and scientific, not logical. Anyway, swans are born either white of black: they get their color from their genomes, not from playing a lottery. Indeed, a random item, such as a binary sequence of 0’s and 1’s (or Bernouilli trials), can only be produced by a randomizer such as a coin flipper, which is a physical device. Mathematics can handle sequences whose general (or nth term) is well-defined, such as (−1)2n+1 in the case of the oscillating sequence 1, −1, 1, −1, … Indeed, after a few terms one adds the general term (−1)2n+1, then dot–dot–dot, and finally the phrase “and so on”. But if the sequence in question has been generated by a randomizer, there will be no general term. That is, in this case there is no “so”, hence no “so on”. In other words, it is impossible to predict the next term of a random sequence. In conclusion, a theory of induction cannot be a priori just like deductive logic.
In sum, the inductive logic project has five flaws, any one of which is fatal. Why then pursue it after more than half a century of failures?
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Bunge, M. Knowledge: Genuine and Bogus. Sci & Educ 20, 411–438 (2011). https://doi.org/10.1007/s11191-009-9225-3
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DOI: https://doi.org/10.1007/s11191-009-9225-3