Cosmic Confusions Not Supporting versus Supporting Not-

1. Cosmic ConfusionsNot SupportingversusSupporting Not- John D. Norton Department of History and Philosophy of Science Center for Philosophy of Science University of Pittsburgh www.pitt.edu/~jdnorton CARL FRIEDRICH VON WEIZSÄCKER LECTURES UNIVERSITY OF HAMBURG June 2010

2. Fragments of inductive logics that tolerate neutral support displayed. Non-probabilistic state of completely neutral support. Artifacts are introduced by the use of the wrong inductive logic. “Inductive disjunctive fallacy.” Doomsday argument. This Talk Bayesian probabilistic analysis conflates neutrality of evidential support with disfavoring evidential support. Wrong formal tool for many problems in cosmology where neutral support is common.

3. CompletelyNeutral Evidential Support

4. Background evidence is neutral on whether h lies in some tiny interval or outside it. h 1 2 3 5 0 4 Unconnected Parallel Universes: Completely Neutral Support Same laws, but constants undetermined. h = ? c = ? G = ? … h = ? c = ? G = ? … h = ? c = ? G = ? …

5. Background evidence strongly disfavors h lying in some tiny interval; and strongly favors h outside it. very probable very improbable very probable h 1 2 3 5 0 4 Parallel Universes Born in a Singularity: Disfavoring Evidence Stochastic law assigns probabilities to values of constants. P(h1) = 0.01 … P(h2) = 0.01 … P(h3) = 0.01 …

6. How to RepresentCompletelyNeutral Evidential Support

7. P(not-H|B) P(H|B) P(H|B) P(not-H|B) Small. Strong disfavoring. Large. Strong favoring. Large. Strong favoring. Small. Strong disfavoring. Probabilitiesfrom 1 to 0 span support to disfavor P(H|B) + P(not-H|B) = 1 No neutral probability value available for neutral support.

8. …Fails Logic of all evidence Underlying Conjecture of Bayesianism… Logic of physical chances

9. I I I I I h 1 2 3 5 0 4 I I I Argued in some detail in John D. Norton, "Ignorance and Indifference." Philosophy of Science, 75 (2008), pp. 45-68. "Disbelief as the Dual of Belief." International Studies in the Philosophy of Science, 21(2007), pp. 231-252. [A|B] = support A accrues from B Completely Neutral Support [ |B] = I “indifference” “ignorance” any contingent proposition

10. I I I I I I I I I h rescale h to h’ = f(h) 1 2 3 5 0 4 I Equal support for h’ in equal h’-intervals. h’ 1 2 3 5 0 4 [ h in [0,1] OR h in [1,2] | B] = [ h in [0,1] | B] = [ h in [1,2] | B] The principle of indifference does not lead to paradoxes. Paradoxes come from the assumption that evidential support must always be probabilistic. Justification… I. Invariance under Redescription using the Principle of Indifference Equal support for h in equal h-intervals.

11. I I I Equal (neutral) support for h in [0,2] and outside [0,2]. h 1 2 3 5 0 4 [ h in [0,1] OR h in [1,2] | B] = [ h in [0,1] | B] Justification… II. Invariance under Negation I Equal (neutral) support for h in [0,1] and outside [0,1]. h 1 2 3 5 0 4

12. Neutrality and Probabilistic Independence

13. P(Ai|E&B) = P(Ai|B) all i For incremental measures of support* inc (Ai, E, B) = 0 [Ai|B] = I all contingent Ai Binary function Tertiary function Presupposes background probability measure. Presupposes NO background probability measure. * e.g. d(Ai, E, B) = P(Ai|E&B) - P(Ai|B)s(Ai, E, B) = P(Ai|E&B) - P(Ai|not-E&B)r(Ai, E, B) = log[ P(Ai|E&B)/P(Ai|B) ]etc. Neutrality of (total) support Probabilisticindependence vs. For a partition of all outcomes A1, A2, …

14. Objectivevs Subjective

15. In each evidential situation, Many conditional probability represents opinion + the import of evidence. Only one conditional probability correctly represents the import of evidence. Initial “informationless” priors? Pick any. They merely encode arbitrary opinion that will be wash out by evidence. Impossible. No probability measure captures complete neutrality. Ignorance and Disbelief or Neutrality and Disfavor mad dog Bruno de Finetti Subjective Bayesianism degrees of belief Objective Bayesianism degrees of support

16. 1. Subjective Bayesian sets arbitrary priors on k1, k2, k3, … Pure opinion. 2. Learn richest evidence = k135 or k136 3. Apply Bayes’ theorem 0.00095 P(k135|E&B) P(k135|B) = = 0.00005 P(k136|E&B) P(k136|B) P(k135|E&B) = 0.95 P(k136|E&B) = 0.05 Pure Opinion Masquerading as Knowledge Endpoint of conditionalization dominated by pure opinion.

17. Inductive Disjunctive Fallacy

18. Disfavoring Neutral support prob = 0.01 prob = 0.02 prob = 0.03 … prob = 0.99 I I I … I Disjunction of very many neutrally supported outcomes a strongly supported outcome. is NOT Strongly disfavoring support Completely neutral support conflated with a1 a1 or a2 a1 or a2 or a3 … a1 or a2 or … or a99

19. Probability zero. “As improbable as anything can be.” Probability one. As probable as anything can be. van Inwagen, “Why is There Anything At All?” Proc. Arist. Soc., Supp., 70 (1996). pp.. 95-120. One waynot to be. Infinitely many waysto be. …

20. “Anthropic reasoning predicts we are typical…” “… [it] predicts with great confidence that we belong to a large civilization.” Our Large Civilization Ken Olum, “Conflict between Anthropic Reasoning and Observations,” Analysis, 64 (2004). pp. 1-8. Fewer wayswe can be in small civilizations. Vastly more ways we can be in large civilizations. …

21. Hence our space is infinitely more likely to be geometrically infinite. Our Infinite Space Informal test of commitment to anthropic reasoning. Fewer wayswe can be observers in a finite space. Infinitely more ways we can be observers in an infinite space. …

22. Inductive Logics that Tolerate Neutrality of Support

23. Postulate same rule in a new, non-additive inductive logic. Conditionalizing from Complete Neutrality of Support If T1 entails E. T2 entails E. [T1|B] = [T2|B] = I then [T1|E&B] = [T2|E&B] Discard Additivity, Keep Bayesian Dynamics If T1 entails E. T2 entails E. P(T1|B) = P(T2|B) then P(T1|E&B) = P(T2|E&B) equal priors Bayesianconditionalization. equal posteriors

24. Apply rule of conditionalization on completely neutral support. E = k135 or k136 [k135|E&B] = [k136|E&B] [k135|B] = [k136|B] = I Nothing in evidence discriminates between k135 or k136. Bayesian result of support for k135 over k136 is an artifact of the inability of a probability measure to represent neutrality of support. Pure Opinion Masquerading as Knowledge Solved “Priors” are completely neutral support over all values of ki. [k1|B] = [k2|B] = [k3|B] =… = [k135|B] = [k136|B] = … = I No normalization imposed. [k1|B] = [k1 or k2|B] = [k1 or k2 or k3|B] =… = I

25. The Doomsday Argument

26. Bayes’ theorem p(T|t&B) ~ p(t|T&B) . p(T|B) Compute likelihood by assuming t is sampled uniformly from available times 0 to T. p(t|T&B) = 1/T Variation in likelihoods arise entirely from normalization. p(T|t&B) ~ 1/T Support for early doom Entire result depends on this normalization. Entire result is an artifact of the use of the wrong inductive logic. Doomsday Argument (Bayesian analysis) time = 0 we learn time t has passed For later: which is the right “clock” in which to sample uniformly? Physical time T? Number of people alive T’?… time of doom T What support does t give to different times of doom T?

27. E = T>t [T1|E&B] = [T2|E&B] [T1|B] = [T2|B] = I The evidence fails to discriminate between T1 and T2. Apply rule of conditionalization on completely neutral support. Doomsday Argument (Barest non-probabilistic reanalysis.) Take evidence E is just that T>t. T1>t entails E. T2>t entails E. time = 0 we learn time t has passed time of doom T What support does t give to different times of doom T?

28. Unique solution is the “Jeffreys’ prior.” p(T|t&B) = C(t)/T Infinite probability mass assigned to T>T*, no matter how large. Evidence supports latest possible time of doom. Disaster! This density cannot be normalized. Doomsday Argument (Bayesian analysis again) Consider only the posterior p(T|t&B) Require invariance of posterior under changes of units used to measure times T, t. Invariance under T’=AT, t’=At Days, weeks, years? Problem as posed presumes no time scale, no preferred unit of time. time = 0 we learn time t has passed time of doom T What support does t give to different times of doom T?

29. [T1,T2|t&B] = [T3,T4|t&B] = I for all T1,T2, T3,T4 A Richer Non-Probabilistic Analysis Consider the non-probabilistic degree of support for T in the interval [T1,T2|t&B] time = 0 Presume that there is a “right” clock-time in which to do the analysis, but we don’t know which it is. So we may privilege no clock, which means we require invariance under change of clock: T’ = f(T), t’ = f(t), for strictly monotonic f. we learn time t has passed time of doom T What support does t give to different times of doom T?

30. Inductive inference the right way

31. A Warrant for a Probabilistic Logic Ensemble Randomizer + No universal logic of induction Material theory of induction: Inductive inferences are not warranted by universal schema, but by locally prevailing facts. The contingent facts prevailing in a domain dictate which inductive logic is applicable. Mere evidential neutrality over the ensemble members does not induce an additive measure. Some further element of the evidence must introduce a complementary favoring-disfavoring. An ensemble alone is not enough.

32. Just like the microcanonical distribution of ordinary statistical mechanics? No: there is no ergodic like behavior and hence no analog of the randomizer. “Giving the models equal weight corresponds to adopting Laplace’s ‘principle of indifference’, which claims that in the absence of any further information, all outcomes are equally likely.” Gibbons, Hawking, Stewart, p. 736 Ensemble without randomizer Probabilities from Multiverses? Gibbons, Hawking, Stewart (1987): Hamiltonian formulation of general relativity. Additive measure over different cosmologies induced by canonical measure. Gibbons, G. W.; Hawkings, S. W. and Stewart, J. M. (1987) “A Natural Measure on the Set of All Universes,” Nuclear Physics, B281, pp. 736-51.

33. No universal formal logic of induction. Inductive strength [A|B] for propositions A, B drawn from a Boolean algebra deductive structure of the Boolean algebra. “Deductively definable logics of induction” is defined fully by Large class of non-probabilistic logics. No-go theorem. All need inductive supplement. Formal results: Independence is generic. Limit theorem. Scale free logics of induction. • Read • "Deductively Definable Logics of Induction." Journal of Philosophical Logic. Forthcoming. • “What Logics of Induction are There?” Tutorial in Goodies pages on my website.

34. Winding Up

35. Fragments of inductive logics that tolerate neutral support displayed. Non-probabilistic state of completely neutral support. Artifacts are introduced by the use of the wrong inductive logic. “Inductive disjunctive fallacy.” Doomsday argument. This Talk Bayesian probabilistic analysis conflates neutrality of evidential support with disfavoring evidential support. Wrong formal tool for many problems in cosmology where neutral support is common.

36. Read all about it…

39. Commercials

41. Finis

42. Appendices

43. Level I multiverses. Many clones of Penzias and Wilson measure 3oK cosmic background radiation in other parts of space. Which is our Penzias and Wilson? Self-Sampling Assumption: “One should reason if as one were a random sample from the set of of all observers in one’s reference class.” (Bostrom, 2007, p. 433) The self-sampling assumption imposes probabilities where they do not belong by mere supposition. Evidence on which is our PW is neutral. No warrant for a probability measure. The Self-Sampling Assumption Penzias and Wilson measure 3oK cosmic background radiation.

44. = q <<1 back-ground is 100oK back-ground is 100oK back-ground is 100oK Our PW measure 3oK measure 3oK i-th PW measure 3oK P( | ) P( | ) P( | ) back-ground is 100oK someone somewhere measures 3oK P( | ) is (near) one. Introduce self-sampling to reduce this probability by allowing that our PW is probably not the “someone somwhere.”  P( ) i-th PW is our PW = = q i q 1/n Recover the same result without sampling or calculation just by applying (L) directly to case of “our PW.” If n = infinity, the computation fails. “1/n = 1/infinity = 0” The failure is an artifact of the probabilistic representation and its difficulties with infinitely many cases. Why have the Self-Sampling Assumption? “(L)”A physical chance computed in a physical theory. Very many trials carried out in the multiverse.

45. No reason to expect observed values. The values are improbable and therefore in need of explanation. How do we decide what is in urgent need of explanation and what is not? We decide post hoc. Only after we have the new explanatory theory do we decide the cosmic parameter in urgent need of explanation. We cannot demand that everything be explained on pain of an infinite explanatory regress. A Tempting Fallacy in Modern Cosmology Prior theory is neutral on the values of some fundamental cosmic parameters: • Non-inflationary cosmology provides no reason to expect a very flat space. • Fundamental theories give no reason to expect h, c, G, … to be the values that support life.