Ideas of Peter Lipton, by Theme

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

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2. Reason / A. Nature of Reason / 4. Aims of Reason
Good inference has mechanism, precision, scope, simplicity, fertility and background fit
2. Reason / B. Laws of Thought / 4. Contraries
Contrary pairs entail contradictions; one member entails negation of the other
11. Knowledge Aims / A. Knowledge / 2. Understanding
Understanding is not mysterious - it is just more knowledge, of causes
13. Knowledge Criteria / B. Internal Justification / 3. Evidentialism / a. Evidence
How do we distinguish negative from irrelevant evidence, if both match the hypothesis?
14. Science / A. Basis of Science / 1. Observation
The inference to observables and unobservables is almost the same, so why distinguish them?
14. Science / A. Basis of Science / 2. Demonstration
We infer from evidence by working out what would explain that evidence
Inductive inference is not proof, but weighing evidence and probability
14. Science / A. Basis of Science / 4. Prediction
It is more impressive that relativity predicted Mercury's orbit than if it had accommodated it
Predictions are best for finding explanations, because mere accommodations can be fudged
14. Science / B. Scientific Theories / 1. Scientific Theory
If we make a hypothesis about data, then a deduction, where does the hypothesis come from?
14. Science / C. Induction / 1. Induction
Induction is repetition, instances, deduction, probability or causation
14. Science / C. Induction / 3. Limits of Induction
Standard induction does not allow for vertical inferences, to some unobservable lower level
14. Science / C. Induction / 4. Reason in Induction
We can argue to support our beliefs, so induction will support induction, for believers in induction
An inductive inference is underdetermined, by definition
14. Science / C. Induction / 5. Paradoxes of Induction / b. Raven paradox
If something in ravens makes them black, it may be essential (definitive of ravens)
My shoes are not white because they lack some black essence of ravens
A theory may explain the blackness of a raven, but say nothing about the whiteness of shoes
We can't turn non-black non-ravens into ravens, to test the theory
To pick a suitable contrast to ravens, we need a hypothesis about their genes
14. Science / C. Induction / 6. Bayes's Theorem
A hypothesis is confirmed if an unlikely prediction comes true
Explanation may be an important part of implementing Bayes's Theorem
Bayes is too liberal, since any logical consequence of a hypothesis confirms it
Bayes involves 'prior' probabilities, 'likelihood', 'posterior' probability, and 'conditionalising'
Bayes seems to rule out prior evidence, since that has a probability of one
14. Science / D. Explanation / 1. Explanation / a. Explanation
Explanation may describe induction, but may not show how it justifies, or leads to truth
14. Science / D. Explanation / 1. Explanation / b. Aims of explanation
An explanation gives the reason the phenomenon occurred
An explanation is what makes the unfamiliar familiar to us
An explanation is what is added to knowledge to yield understanding
Seaching for explanations is a good way to discover the structure of the world
14. Science / D. Explanation / 2. Types of Explanation / b. Contrastive explanations
In 'contrastive' explanation there is a fact and a foil - why that fact, rather than this foil?
With too many causes, find a suitable 'foil' for contrast, and the field narrows right down
14. Science / D. Explanation / 2. Types of Explanation / c. Explanations by coherence
An explanation unifies a phenomenon with our account of other phenomena
14. Science / D. Explanation / 2. Types of Explanation / d. Lawlike explanations
Deduction explanation is too easy; any law at all will imply the facts - together with the facts!
We reject deductive explanations if they don't explain, not if the deduction is bad
Good explanations may involve no laws and no deductions
14. Science / D. Explanation / 2. Types of Explanation / e. Necessity in explanations
An explanation shows why it was necessary that the effect occurred
14. Science / D. Explanation / 2. Types of Explanation / f. Causal explanations
Causal inferences are clearest when we can manipulate things
To explain is to give either the causal history, or the causal mechanism
Mathematical and philosophical explanations are not causal
A cause may not be an explanation
Explanations may be easier to find than causes
14. Science / D. Explanation / 2. Types of Explanation / h. Explanations by mechanism
We want to know not just the cause, but how the cause operated
14. Science / D. Explanation / 2. Types of Explanation / k. Probabilistic explanations
To maximise probability, don't go beyond your data
14. Science / D. Explanation / 3. Best Explanation / a. Best explanation
Finding the 'loveliest' potential explanation links truth to understanding
Is Inference to the Best Explanation nothing more than inferring the likeliest cause?
Best Explanation as a guide to inference is preferable to best standard explanations
The 'likeliest' explanation is the best supported; the 'loveliest' gives the most understanding
IBE is inferring that the best potential explanation is the actual explanation
IBE is not passive treatment of data, but involves feedback between theory and data search
A contrasting difference is the cause if it offers the best explanation
We select possible explanations for explanatory reasons, as well as choosing among them
14. Science / D. Explanation / 3. Best Explanation / c. Against best explanation
Must we only have one explanation, and must all the data be made relevant?
Bayesians say best explanations build up an incoherent overall position
The best theory is boring: compare 'all planets move elliptically' with 'most of them do'
Best explanation can't be a guide to truth, because the truth must precede explanation
26. Natural Theory / C. Causation / 3. General Causation / c. Counterfactual causation
Counterfactual causation makes causes necessary but not sufficient