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Ideas of Peter Lipton, by Text

[American, 1954 - 2007, Professor at Cambridge University.]

2004 Inference to the Best Explanation (2nd)
01 'Descr' p.17 16803 Bayes is too liberal, since any logical consequence of a hypothesis confirms it
01 'Descr' p.17 16802 Bayes seems to rule out prior evidence, since that has a probability of one
01 'Descr' p.17 16804 Induction is repetition, instances, deduction, probability or causation
01 'Descr' p.17 16801 A hypothesis is confirmed if an unlikely prediction comes true
01 'Underd' p.5 16799 Inductive inference is not proof, but weighing evidence and probability
01 'Underd' p.7 16800 An inductive inference is underdetermined, by definition
02 'Reason' p.23 16807 An explanation gives the reason the phenomenon occurred
02 'Reason' p.24 16808 An explanation is what makes the unfamiliar familiar to us
02 'Reason' p.27 16809 Good explanations may involve no laws and no deductions
02 'Reason' p.27 16810 Deduction explanation is too easy; any law at all will imply the facts - together with the facts!
02 'Reason' p.28 16811 An explanation unifies a phenomenon with our account of other phenomena
02 'Reason' p.29 16812 An explanation shows why it was necessary that the effect occurred
02 'Underst' p.20 16806 An explanation is what is added to knowledge to yield understanding
03 'Fact' p.30 16814 Understanding is not mysterious - it is just more knowledge, of causes
03 'Fact' p.30 16813 To explain is to give either the causal history, or the causal mechanism
03 'Fact' p.31 16815 Mathematical and philosophical explanations are not causal
03 'Fact' p.33 16816 In 'contrastive' explanation there is a fact and a foil - why that fact, rather than this foil?
04 'Attractions' p.66 16822 Seaching for explanations is a good way to discover the structure of the world
04 'Attractions' p.66 16823 Standard induction does not allow for vertical inferences, to some unobservable lower level
04 'Attractions' p.70 16824 Is Inference to the Best Explanation nothing more than inferring the likeliest cause?
04 'Spelling' p.56 16817 Best Explanation as a guide to inference is preferable to best standard explanations
04 'Spelling' p.57 16818 The 'likeliest' explanation is the best supported; the 'loveliest' gives the most understanding
04 'Spelling' p.58 16819 IBE is inferring that the best potential explanation is the actual explanation
04 'Spelling' p.61 16820 Finding the 'loveliest' potential explanation links truth to understanding
04 'Spelling' p.62 16821 Must we only have one explanation, and must all the data be made relevant?
05 'A case' p.76 16825 How do we distinguish negative from irrelevant evidence, if both match the hypothesis?
05 'A case' p.81 16826 With too many causes, find a suitable 'foil' for contrast, and the field narrows right down
05 'Explanation' p.83 16827 If we make a hypothesis about data, then a deduction, where does the hypothesis come from?
05 'Explanation' p.83 16828 IBE is not passive treatment of data, but involves feedback between theory and data search
05 'Explanation' p.86 16829 We reject deductive explanations if they don't explain, not if the deduction is bad
05 'Unsuitable' p.97 16832 If something in ravens makes them black, it may be essential (definitive of ravens)
06 'The Method' p.101 16836 My shoes are not white because they lack some black essence of ravens
06 'Unsuitable' p.92 16831 A theory may explain the blackness of a raven, but say nothing about the whiteness of shoes
06 'Unsuitable' p.98 16833 We can't turn non-black non-ravens into ravens, to test the theory
06 'Unsuitable' p.99 16834 To pick a suitable contrast to ravens, we need a hypothesis about their genes
07 'friends' p.110 16840 To maximise probability, don't go beyond your data
07 'The Bayesian' p.103 16837 Bayes involves 'prior' probabilities, 'likelihood', 'posterior' probability, and 'conditionalising'
07 'The Bayesian' p.104 16838 Bayesians say best explanations build up an incoherent overall position
07 'The Bayesian' p.107 16839 Explanation may be an important part of implementing Bayes's Theorem
08 'From cause' p.132 16846 A cause may not be an explanation
08 'From cause' p.133 16848 Causal inferences are clearest when we can manipulate things
08 'From cause' p.133 16847 Counterfactual causation makes causes necessary but not sufficient
08 'From cause' p.137 16849 Explanations may be easier to find than causes
08 'Improved' p.127 16844 A contrasting difference is the cause if it offers the best explanation
08 'the guiding' p.122 16842 We want to know not just the cause, but how the cause operated
08 'the guiding' p.122 16841 Good inference has mechanism, precision, scope, simplicity, fertility and background fit
09 'Is the best' p.156 16854 Contrary pairs entail contradictions; one member entails negation of the other
09 'Is the best' p.156 16855 The best theory is boring: compare 'all planets move elliptically' with 'most of them do'
09 'The two-stage' p.150 16853 We select possible explanations for explanatory reasons, as well as choosing among them
09 'Voltaire's' p.142 16850 Explanation may describe induction, but may not show how it justifies, or leads to truth
09 'Voltaire's' p.146 16851 The inference to observables and unobservables is almost the same, so why distinguish them?
09 'Voltaire's' p.147 16852 Best explanation can't be a guide to truth, because the truth must precede explanation
10 'The fudging' p.172 16856 It is more impressive that relativity predicted Mercury's orbit than if it had accommodated it
11 'Circularity' p.184 16857 Predictions are best for finding explanations, because mere accommodations can be fudged
11 'Circularity' p.189 16858 We can argue to support our beliefs, so induction will support induction, for believers in induction
Pref 2nd ed p.-4 16798 We infer from evidence by working out what would explain that evidence