Philip Kitcher Belief without Proof

Philip Stuart Kitcher  Philosopher of science, now at Columbia University.  Has written on creationism, sociobiology, the idea of scientific progress and on the implications of biotechnology for the future of humans.

Against simple distinctions  It seems easy to draw a sharp line between evidence, reason and science on one hand and faith and religion on the other.  Tennyson: ‘believing where we cannot prove’.  Creationists accept this line, but insist that evolution is a matter of faith just as much as believing in the Bible (or the Quran) is.

Too simple a dichotomy  “(S)cience is not a body of demonstrated truths. Virtually all of science is an exercise in believing where we cannot prove. Yet, scientific conclusions are not embraced by faith alone.” (269)  So what is this middle ground between proof and faith?

Ideals of certainty  Mathematical proof  Direct observation  But recall Descartes– if we work at it, we can muster up a little doubt even about things as obvious as these!  Sometimes eyewitnesses are just wrong (Dawkins’ Gorilla).  And some ‘theorems’ have proven false.  Science, too, has often got things wrong.

Past errors in science  Phlogiston  Caloric  Ether  Protein as the ‘genetic material’  All reasonable conclusions at the time, but now known to have been mistaken…  Science always reaches beyond the evidence we have. Consider F=ma…

The search for evidence  But science finds many clever ways of testing its theories, and extending those tests to new conditions and cases.  In the end we really do get evidence about how things we can’t even see function.  But the conclusions scientists draw are always open to new challenges.  Even Newton’s mechanics has been overturned.

So?  Should we just give up and be outright skeptics?  Or is there some middle ground here?  We have to reject over-confidence in science, but we don’t want to completely dismiss it either.  We can have good (even excellent) evidence without certainty.

Theories  “All theories are revisable, but not all theories are equal.”  In particular, the evidence for evolution is as good as it gets– in that sense, we can justify saying that evolution is a fact.  The long time frame of evolution is no less accessible to test and observation than atoms, molecules, distant galaxies or fundamental laws of physics.

The value of predictions  The Hay Fever strategy: A theory about hay fever makes some odd predictions.  Suppose those predictions turn out to be true.  Wouldn’t it be reasonable to suppose that the theory is ‘on the right track’?  A real case: Pascal’s confirmation of Torricelli’s atmospheric hypothesis.

Hypothetico-deductive tests  Theories can be thought of as collections of ‘claims or statements’.  We can reason with those claims and statements, reaching various conclusions.  Some of those conclusions are the kinds of claims we can test by observation.  When these conclusions are confirmed by observation, the theory they follow from is also confirmed.

On deduction vs. ordinary reasoning  A deductively valid argument is such that, if its premises are all true, its conclusion must also be true. There’s no way (even in an imaginary world) that the premises can hold without the conclusion holding too.  “It was -20 last night, so the bird bath is frozen.” This is not deductively valid, but it is a good argument!

The temptation of falsificationism  On one hand, if a theory is true, and some observational consequence follows deductively from that theory, then that consequence must also be true.  But on the other hand, if all of a theory’s observational consequences that we can check are true, the theory could still be true.  So truth of observational consequences is at best weak evidence for a theory’s truth, but the falsehood of just one is conclusive evidence against the theory’s truth!

Sir Karl Popper ( )  A scientific theory must be falsifiable– it must make some observable predictions whose falsehood would refute it.  But no theory is really confirmed by its successful predictions.

Charges against evolution  That it makes no predictions (so it’s not scientific at all).  That it makes false predictions (so it’s been refuted).  That it doesn’t make the kinds of predictions it should (the kinds of experiments and observations evolutionary biologists make ‘have no bearing’ on the theory’)

Ad Hoc  Roughly means, ‘to this’. An ad hoc response to a problem is one that we make up just for that problem.  An ad hoc defence of a theory is a claim that we have no independent support for, and that (somehow) alters what the theory predicts so that it no longer makes a prediction we know to be false.

The trouble with falsificationism  It’s either far too demanding or it’s far too loose as a criterion for science.  Newton’s mechanics includes three laws: inertia, F=ma and action-reaction, plus gravity. But by themselves they predict nothing (nothing rules out other forces)!  But if we extend the mechanics to make it falsifiable, we fall into the guru’s trap: ‘quietness is wholeness in the center of stillness’ is now equally ‘scientific’!

Pierre Duheme  Hypotheses are tested in bundles.  When a prediction fails, we have options– any claim in the bundle that produced the false prediction could be the false one.

Scientific Success  If it can’t be tested at all, it surely isn’t science.  Consider celestial mechanics and the case of Uranus.  Scientists couldn’t fit its orbit into their Newtonian models.  But the answer wasn’t ‘give up on Newton’– it was, ‘find the unknown planet that’s altering Uranus’s orbit’: a success!

Auxiliary hypotheses  We test theories by combining them with auxiliary hypotheses (only gravity affects the motions of the planets; the bodies whose gravitational mass significantly affects the orbit of Uranus are the sun, Jupiter and Saturn,…).  Adams and Leverrier changed an auxiliary hypothesis (adding Neptune to the story), and used the resulting calculations to predict where Neptune should be seen.  Their new auxiliary was independently testable.

Good Auxiliaries  Hypotheses are tested in bundles.  But they can be part of different bundles.  When we use an auxiliary to ‘save’ a theory from refutation, we should be able to test it as part of a different bundle (with components that are otherwise considered reliable).  Newton’s auxiliaries (and his strategy for applying them) worked, not just one case at a time, but over and over for many different cases, in celestial mechanics and beyond.

Good theories  Use independently testable auxiliaries  Are unified (apply similar auxiliaries and strategies for solving a wide range of problems).  Are fecund (lead us to discoveries that we hadn’t anticipated– new planets (Neptune and, later, Pluto), new/better measurements (improvements in clocks, etc. guided by mechanics)…

Openness and risk  Science is always open/incomplete, and its practitioners are almost always focused more on what’s not (yet) settled than on what is settled (for now).  This also means that it’s always at risk of failing the test. New and better observations and measurements can force us to give up old theories and invent new ones.

Pseudo-science  When auxiliaries aren’t independently testable,  And problem-solving strategies are developed independently for each problem the theory faces,  And no new predictions or improved measurements or other nice results are obtained, then,  We don’t have a scientific theory at all.

On the other hand,  When auxiliaries are testable separately from the theory,  When the theory applies its problem-solving strategies systematically to a wide range of problems,  And when the results are successful predictions (even when applied to entirely new bodies of evidence) and ideas for still more observations that the theory makes predictions about, then  We have a good scientific theory.