green numbers give full details.     |    back to list of philosophers     |     expand these ideas

Ideas of Peter Lipton, by Text

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

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