Science vs Pseudoscience – Kevin Korb

Science has a certain common core, especially a reliance on empirical methods of assessing hypotheses. Pseudosciences have little in common but their negation: they are not science.
They reject meaningful empirical assessment in some way or another. Popper proposed a clear demarcation criterion for Science v Rubbish: Falsifiability. However, his criterion has not stood the test of time. There are no definitive arguments against any pseudoscience, any more than against extreme skepticism in general, but there are clear indicators of phoniness.

Demarcation

Science v Non-science – What’s the point? Possible goals for distinguishing btw them: Rhetorical, Political, Social Methodological: aiming at identifying methodolgical virtues and vices; improving practice How to proceed? Traditional: propose and test necessary and sufficient conditions for being science Less ambitious: collect prominent characteristics that support a “family resemblance”

What is Science?

Science is something like the organized (social, intersubjective) attempt to acquire knowledge about the world through interacting with the world. In the Western tradition, this began with the pre-Socratic philosophers and is especially associated with Aristotle.

Nature of Science Science contrasts to: Learning: individuals learn about the world. Their brains are wired for that. Mathematics/deduction: a handmaid to science, but powerless to teach us about the world on its own. Dogma, ideology, faith: These may be crucial to driving even scientific projects forward (as are good meals, sleep, etc.), but as they are by definition not tested by evidence, they are not themselves science.

A Potted History of the Philosophy of Science

Wissenschaftsphilosophie – The Vienna Circle Early 20th Century Scientific Major Success Stories: Charles Darwin (evolutionary biology) Gottlob Frege (formal logic) Albert Einstein (physics) The sciences were showing themselves as the most successful human project ever undertaken. In Vienna a group of great philosophers asked themselves: Why? How did this happen? With the Vienna Circle philosophy of science became a discipline, attempting to answer these questions.

The Vienna Circle & Logical Positivism : The beginning was the appointment of Ernst Mach as Professor of the Philosophy of the Inductive Sciences at the University of Vienna, 1895. Thereafter, Mortiz Schlick founded the Vienna Circle (and Logical Positivism) in 1922. Through the helpful activities of Adolf Hitler, the leading philosophers of science introduced the Vienna Circles ideas throughout the English speaking world.
Vienna Circle Ernst Mach Moritz Schlick Rudolf Carnap Hans Reichenbach Karl Popper Paul Feyerabend Noretta Koertge Positivismus Falsifikationismus Anarchismus
The Vienna Circle Basic Principles: Philosophy as logical analysis The logical foundation of science lies in observation & experiment e.g., Rudolf Carnap’s 1928 title: The Logical Construction of the World!! Key: Verifiability Criterion of Meaning What cannot be proven empirically, is meaningless. E.g., metaphysics, religion, superstition. {h, b e1, . . . en; e1, . . . en} verifies h
Karl Popper Objects Many scientific hypotheses are universal: E.g., light always bends near large masses. But {h, b e1, . . . e∞; e1, . . . e∞} is not even a possible state of affairs Aside from that, metaphysics is an ineliminable part of science; all science has fundamental presuppositions.
Karl Popper Falsificationism Key: Demarcation criterion for science What cannot be falsified empirically, is unscientific. E.g., Marxism, religion, psychoanalysis. {h, b e, ¬e} falsifies h Theses: We can make scientific (or social) progress alternating between Bold Conjectures and Refutations. The ideal test (severe test) is guaranteed to falsify one of two (or more) alternative conjectures. Progress: refuting more and more theories; not accumulating more and more knowledge.
Imre Lakatos Sophisticated Falsificationism {h, b e, ¬e} falsifies (h&b) Hypotheses stand or fall in networks, networked to each other and to theories of measurement, etc. = research programmes If a research programme makes novel predictions that come up true, it is progressive If a programme lies in a sea of anomalies and is dominated by ad hoc saving maneuvers, it is degenerating Unfortunately, there’s no definite point at which a degenerating research programme rationally needs to be abandoned.
Thomas Kuhn Scientific Revolutions In The Structure of Scientific Revolutions (1962) he introduced the idea that science moves (not: progresses) from “normal science” through a sea of anomalies to “revolutionary science” to a new “normal science” – from “paradigm” to “paradigm”. According to Kuhn, the process is not rational, but explained in terms of psychology, social processes and power relationships.
Paul Feyerabend Epistemic Anarchy In 1958 Feyerabend went to Berkeley, where he turned against Popper, promoting “Epistemological Anarchism” instead (Against Method, 1974). He embraced the inability to reject research programmes, promoting methodological pluralism instead. Denunciations of witchcraft, pseudosciences, etc. are mere expressions of prejudice.
Ludwig Wittgenstein Open Concepts Natural language concepts have an “open structure”, based on family resemblance, not definition.
Ludwig Wittgenstein Open Concepts One of Wittgenstein’s examples: Define “game”, in terms of the necessary and sufficient conditions. Now let’s play a game involving changing those conditions. . . Socrates’ game of taking some sophist’s definition for “love”, “knowledge”, “good” and poking holes in it could be played forever. Hence, Socrates’ phony humility in claiming that he knew nothing. The reality is that our understanding and use of language doesn’t depend on definitions.
1“Science” is an Open Concept Instead of assembling inadequate necessary and sufficient conditions, let’s collect examples of science and non-science and see what the former share in family resemblances. Leave problematic cases for later. Physics Mathematics Epidemiology Medicine Paleontology Religion Climatology Mining Evolution Theory Creationism Economics Politics Political Science Fox News
“Science” is an Open Concept I’d like to suggest the key family resemblances are: Empiricism: insistance on an empirical base versus ideological dominance Abstraction (generalization) and mathematization (when possible) versus anecdotal evidence Social processes encouraging objectivity, intersubjectivity, peer review, Popperian critical rationality versus authoritarianism
Some Pseudoscientific Arguments AGW/ecology/genetic regulatory/etc models are highly abstract, lose track of detailed reality and so are not scientific. George Box: “All models are wrong, but some are useful.” Any computer model will misrepresent continuity, but does it matter? The question is whether the property of the model of interest (mapping to reality) is preserved under model dynamics, not whether irrelevant details are carried along. The demand for “proof” in science is a good indicator of dishonesty.
Some Pseudoscientific Arguments Similarly: the model predicts overall process ok, but omits some really tiny details and therefore is wrong. Here’s an example I gave a data mining class; 120 years of data on business profits. Looks like three different trends concatenated. Let’s just regress just the points from 80-120.
Some Pseudoscientific Arguments Not bad. But some ornery shareholder says, let’s just try years 109-120 instead.
Some Pseudoscientific Arguments As we can all see profits are hardly moving; let’s turf out the board!!
Some Pseudoscientific Arguments NB: profit = global surface temperature; competitiveness = solar energy.
Some References on Scientific Method F Bacon (1620) Novum Organum Scientiarum. JS Mill (1843) System of Logic. M Gardner (1957) Fads and Fallacies in the Name of Science. Dover. T Kuhn (1962) The Structure of Scientific Revolutions. K Popper (1963) Conjectures and Refutations. R Carnap (1966) An Introduction to the Philosophy of Science. C Hitchcock (2004) Contemporary Debates in Philosophy of Science.

Slides can be found here:

My research is in: machine learning, artificial intelligence, philosophy of science, scientific method, Bayesian inference and reasoning, Bayesian networks, artificial life, computer simulation, epistemology, evaluation theory.

http://theconversation.com/is-passing-a-turing-test-a-true-measure-of-artificial-intelligence-27801

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