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Why are Good Theorys Good- Review

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Presentation about the Paper "Why are Good Theories Good.Reflections on epistemic values, confirmation, and formal epistemology "

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Why are Good Theorys Good- Review

  1. 1. WHY ARE GOOD THEORIES GOOD ? REFLECTIONS ON EPISTEMIC VALUES , CONFIRMATION, AND FORMAL EPISTEMOLOGY Sinu G S Student MICS Selected Topics in Artificial Intelligence University of Luxembourg
  2. 2. THEME OF PAPER This paper discusses about  Comparison of Theory of Confirmation and Theory of Verisimilitude or Truthlikeness.  Connection between Logic of Confirmation with Logic of Acceptability  Connection of Confirmation Theory with Naturalism,Intertheoretic Reduction and Explanation. 2
  3. 3. AGENDA OF PRESENTATION Introduction  Approaches of Confirmation  Problems in Confirmation  Huber‟s Theory of Confirmation  Confirmation and Truthlikeness  Many Senses of Confirmation  Naturalism and Bayesian Tinkering  Belief Framework for modeling realistic cognitive agents (Research Assistant Systems)  3
  4. 4. Introduction  Approaches of Confirmation  Problems in Confirmation  Huber‟s Theory of Confirmation  Confirmation and Truthlikeness  Many Senses of Confirmation  Naturalism and Bayesian Tinkering  Belief Framework for modeling realistic cognitive agents (Research Assistant Systems)  4
  5. 5. INTRODUCTION  Epistemology   Theory of Knowledge and justified belief Theories and Evidence Theory  Real World  Data(Evidence)  Predictions   Confirmation 5
  6. 6. Introduction  Approaches of Confirmation  Problems in Confirmation  Huber‟s Theory of Confirmation  Confirmation and Truthlikeness  Many Senses of Confirmation  Naturalism and Bayesian Tinkering  Belief Framework for modeling realistic cognitive agents (Research Assistant Systems)  6
  7. 7. APPROACHES OF CONFIRMATION  Inductive Logic  Induction proceeds from the specific case to the general case: “probable inference”  All swans we have seen have been white; therefore all swans are white. 7
  8. 8. INDUCTIVE LOGIC METHOD Initial observation Prediction suggests generates hypothesis NO, modify hypothesis experiments and data New observations Do new observations match predictions? YES, confir m hypothesis “Accepted truth” 8
  9. 9. APPROACHES OF CONFIRMATION  Deductive Logic  Deduction proceeds from the general case to the specific case: “certain inference”  For every action, there is an opposite and equal reaction. This rifle will recoil when it is fired. 9
  10. 10. HYPOTHETIC-DEDUCTIVE LOGIC METHOD Initial observation suggests hypothesis Prediction A hypothesis hypothesis Prediction B Prediction C New observations NO, falsify hypothesis Do new observations match predictions? hypothesis Prediction D YES, repeat attempts to falsify 10 “Accepted Multiple truth” failed falsifications
  11. 11. Introduction  Approaches of Confirmation  Problems in Confirmation  Huber‟s Theory of Confirmation  Confirmation and Truthlikeness  Many Senses of Confirmation  Naturalism and Bayesian Tinkering  Belief Framework for modeling realistic cognitive agents (Research Assistant Systems)  11
  12. 12. PROBLEMS OF CONFIRMATION  What makes an observation count as evidence ? This piece of copper conducts electricity  This confirms (increases the credibility of) Hypothesis “All pieces of copper conduct electricity” Law Like Hypothesis  This man performs scientific experiments  This confirms(increases the plausibility) of all man perform scientific experiments Accidental Hypothesis  idea 12
  13. 13. PROBLEMS OF CONFIRMATION How do observations confirm a scientific theory ?  You can know only what you observed and you have never observed a “Law of Nature”  Russell‟s Chicken Story   Moral of the story: You cannot always induce the truth from past experience! 13
  14. 14. PROBLEMS OF CONFIRMATION  Raven‟s Paradox P1 : All ravens are black.  P2 : Everything that is not black is not a raven  E1 : This raven, is black.  E2 : This red (and thus not black) thing is an apple (and thus not a raven).  14
  15. 15. PROBLEMS OF CONFIRMATION Moral of Raven‟s Paradox  Theory of Confirmation “With in certain limits,what is the true of evidence statements is true of the whole „universe of discourse‟  Evidences may depends on Context.  15
  16. 16. PROBLEMS OF CONFIRMATION  A logical consequence of any theory T is T or S. “Earth is center of solar system or I am 23 years old”  I am actually 23 years old.  This means Earth is center of Solar System (Logical Sequence of I am 23 years old ) is confirmed by observing „ I am 23 years old‟   Can nature of solar system be confirmed by my age ? 16
  17. 17. Introduction  Approaches of Confirmation  Problems in Confirmation  Huber’s Theory of Confirmation  Confirmation and Truthlikeness  Many Senses of Confirmation  Naturalism and Bayesian Tinkering  Belief Framework for modeling realistic cognitive agents (Research Assistant Systems)  17
  18. 18. HUBER‟S THEORY OF CONFIRMATION  Problem of Theory of Theory Assessment  How we compare and evaluate theories in the light of available evidence? Given Hypothesis or Theory H  Set of data,the Evidence E  Some Background information B  How good is H given B ?  What is the value of H in view of E and B ?  18
  19. 19. HUBER‟S THEORY OF CONFIRMATION Qualitative Theory of Hypothetico-Deductivism  (H&B) E  Aims at informative theories  Increasing Function of Logical Strength of Theory Quantitative Theory of probablistic inductive logic  P(H|E&B)>=r , r (.5,1)  Aims at plausible or true theories  Decreasing Function of Logical Strength of Theory 19
  20. 20. HUBER‟S THEORY OF CONFIRMATION Conflicting Concepts of Confirmation  Informativeness  If E confirms H and H0 logically implies H, then E confirms H0  . E |∼ H, H0  H ⇒ E |∼ H0.  Plausibility  If E confirms H and H logically implies H0, then E confirms H0.  E |∼ H, H  H0 ⇒ E |∼ H0  A good theory is true & informative 20
  21. 21. HUBER‟S THEORY OF CONFIRMATION  2 virtues a good theory Truth (or „plausibility‟)  Strength (or „informativeness‟)   f (H, E, B) Epistemic value of Hypothesis If E entails H → H’, then f (H, E) ≤ f (H’, E)  If ¬E entails H’ → H, then f (H, E) ≤ f (H’, E)  f (H, E) = p (H, E) + p (¬H,¬E)  21
  22. 22. Introduction  Approaches of Confirmation  Problems in Confirmation  Huber‟s Theory of Confirmation  Confirmation and Truthlikeness  Many Senses of Confirmation  Naturalism and Bayesian Tinkering  Belief Framework for modeling realistic cognitive agents (Research Assistant Systems)  22
  23. 23. CONFIRMATION AND TRUTHLIKENESS  Knowledge  Justified True Belief Belief  An Idea Some one has about the world  True  Corresponds with facts  Justified  Not just a coincidence  23
  24. 24. CONFIRMATION AND TRUTHLIKENESS Karl Popper‟s view  Belief    What if every one lost all their beliefs about engineering Justified  Circular Argument Why A ? Because B  Why B ? Because A  Criticise Beliefs,Don‟t Justify them  Knowledge is useful truth.  24
  25. 25. CONFIRMATION AND TRUTHLIKENESS  Acceptable theories Not Only High degree of confirmation  But Also Capacity of explaining or predicting the empirical evidence   Epistemic value of a theory depends on two factors Coherence or Similarity between H and E  How Informative our Empirical Evidence is ?  Vs (H, E) = [p (H&E) /p (HvE)] [1/p (E)] = p (H, E) /p (HvE) 25
  26. 26. CONFIRMATION AND TRUTHLIKENESS High Similarity High Informative H is more verisimilar than H‟ 26
  27. 27. CONFIRMATION AND TRUTHLIKENESS  Properties of Empirical Verisimilitude 27
  28. 28. CONFIRMATION AND TRUTHLIKENESS  Properties of Empirical Verisimilitude 28
  29. 29. Introduction  Approaches of Confirmation  Problems in Confirmation  Huber‟s Theory of Confirmation  Confirmation and Truthlikeness  Many Senses of Confirmation  Naturalism and Bayesian Tinkering  Belief Framework for modeling realistic cognitive agents (Research Assistant Systems)  29
  30. 30. MANY SENSES OF CONFIRMATION  Acceptance of Theory  H can be „acceptable‟ in the sense that the community allows that individual scientists accept H Best one confirmed  H can be „acceptable‟ in the sense that the community commands its members to accept it Mostly Confirmed What makes theory so good that it is legitimate to accept it ? Alternative Theorems  What makes theory so good that it is compulsory to accept it ?  Certified Knowledge 30
  31. 31. MANY SENSES OF CONFIRMATION  X ,the set of all possible mutually exclusive sets of consequences that the choice of a demarcation level β will have for scientist i, then,the optimal choice for that scientist will correspond to: pi(x,b) is obviously the probability with which i judges that the choice of b will lead to consequences x,  ui(x) is the utility that i would experiment under x.  31
  32. 32. Introduction  Approaches of Confirmation  Problems in Confirmation  Huber‟s Theory of Confirmation  Confirmation and Truthlikeness  Many Senses of Confirmation  Naturalism and Bayesian Tinkering  Belief Framework for modeling realistic cognitive agents (Research Assistant Systems)  32
  33. 33. NATURALISM AND BAYESIAN TINKERING Problem of theory evaluation‟ is not a „philosophical‟ problem, but a problem for the communities of fleshand-bone scientists  Inter Theoretical Reduction increases epistemic values  Showing that a theory can be reducible to another increases the verisimilitude of both theories  33
  34. 34. EXPLANATORINESS AND CONFIRMATION  A Theory H explains the facts F for the scientific community C if and only if F can be derived from H by C, and the members of C understand H, i.e., if H is „intelligible‟ for them  Coeteris paribus, if X is easier to understand than Y, then p(Y) < p(X). 34
  35. 35. Introduction  Approaches of Confirmation  Problems in Confirmation  Huber‟s Theory of Confirmation  Confirmation and Truthlikeness  Many Senses of Confirmation  Naturalism and Bayesian Tinkering  Belief Framework for modeling realistic cognitive agents (Research Assistant Systems)  35
  36. 36. CONCEPTUAL BELIEF FRAMEWORK (RESEARCH ASSISTANT SYSTEMS) Acceptability Hypothetico Deductive Supportability Truthfulness Background Knowledge Informativeness Inductive Supportability 36
  37. 37. CONCEPTUAL BELIEF FRAMEWORK (RESEARCH ASSISTANT SYSTEMS) Acceptability Relativism Truthfulness Evidence Informativeness Totality(among set of enclosure) 37
  38. 38. CONCEPTUAL BELIEF FRAMEWORK (RESEARCH ASSISTANT SYSTEMS) Relativism Acceptability Supportability Truthfulness Theory Informativeness Reducibility 38
  39. 39. 39

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