Combining Philosophers

All the ideas for Rescher,N/Oppenheim,P, Peter Lipton and Gregory L. Murphy

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81 ideas

2. Reason / A. Nature of Reason / 4. Aims of Reason
Good inference has mechanism, precision, scope, simplicity, fertility and background fit [Lipton]
2. Reason / B. Laws of Thought / 4. Contraries
Contrary pairs entail contradictions; one member entails negation of the other [Lipton]
9. Objects / C. Structure of Objects / 8. Parts of Objects / c. Wholes from parts
A whole must have one characteristic, an internal relation, and a structure [Rescher/Oppenheim]
11. Knowledge Aims / A. Knowledge / 2. Understanding
Understanding is not mysterious - it is just more knowledge, of causes [Lipton]
12. Knowledge Sources / B. Perception / 5. Interpretation
Research shows perceptual discrimination is sharper at category boundaries [Murphy]
13. Knowledge Criteria / B. Internal Justification / 3. Evidentialism / a. Evidence
How do we distinguish negative from irrelevant evidence, if both match the hypothesis? [Lipton]
14. Science / A. Basis of Science / 1. Observation
The inference to observables and unobservables is almost the same, so why distinguish them? [Lipton]
14. Science / A. Basis of Science / 2. Demonstration
Inductive inference is not proof, but weighing evidence and probability [Lipton]
We infer from evidence by working out what would explain that evidence [Lipton]
14. Science / A. Basis of Science / 4. Prediction
It is more impressive that relativity predicted Mercury's orbit than if it had accommodated it [Lipton]
Predictions are best for finding explanations, because mere accommodations can be fudged [Lipton]
14. Science / B. Scientific Theories / 1. Scientific Theory
If we make a hypothesis about data, then a deduction, where does the hypothesis come from? [Lipton]
14. Science / C. Induction / 1. Induction
Induction is repetition, instances, deduction, probability or causation [Lipton]
Induction is said to just compare properties of categories, but the type of property also matters [Murphy]
14. Science / C. Induction / 3. Limits of Induction
Standard induction does not allow for vertical inferences, to some unobservable lower level [Lipton]
14. Science / C. Induction / 4. Reason in Induction
An inductive inference is underdetermined, by definition [Lipton]
We can argue to support our beliefs, so induction will support induction, for believers in induction [Lipton]
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) [Lipton]
My shoes are not white because they lack some black essence of ravens [Lipton]
A theory may explain the blackness of a raven, but say nothing about the whiteness of shoes [Lipton]
We can't turn non-black non-ravens into ravens, to test the theory [Lipton]
To pick a suitable contrast to ravens, we need a hypothesis about their genes [Lipton]
14. Science / C. Induction / 6. Bayes's Theorem
Bayes seems to rule out prior evidence, since that has a probability of one [Lipton]
Bayes is too liberal, since any logical consequence of a hypothesis confirms it [Lipton]
A hypothesis is confirmed if an unlikely prediction comes true [Lipton]
Bayes involves 'prior' probabilities, 'likelihood', 'posterior' probability, and 'conditionalising' [Lipton]
Explanation may be an important part of implementing Bayes's Theorem [Lipton]
14. Science / D. Explanation / 1. Explanation / a. Explanation
Explanation may describe induction, but may not show how it justifies, or leads to truth [Lipton]
14. Science / D. Explanation / 1. Explanation / b. Aims of explanation
An explanation gives the reason the phenomenon occurred [Lipton]
An explanation is what makes the unfamiliar familiar to us [Lipton]
An explanation is what is added to knowledge to yield understanding [Lipton]
Seaching for explanations is a good way to discover the structure of the world [Lipton]
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? [Lipton]
With too many causes, find a suitable 'foil' for contrast, and the field narrows right down [Lipton]
14. Science / D. Explanation / 2. Types of Explanation / c. Explanations by coherence
An explanation unifies a phenomenon with our account of other phenomena [Lipton]
14. Science / D. Explanation / 2. Types of Explanation / e. Lawlike explanations
Deduction explanation is too easy; any law at all will imply the facts - together with the facts! [Lipton]
We reject deductive explanations if they don't explain, not if the deduction is bad [Lipton]
Good explanations may involve no laws and no deductions [Lipton]
14. Science / D. Explanation / 2. Types of Explanation / f. Necessity in explanations
An explanation shows why it was necessary that the effect occurred [Lipton]
14. Science / D. Explanation / 2. Types of Explanation / g. Causal explanations
A cause may not be an explanation [Lipton]
To explain is to give either the causal history, or the causal mechanism [Lipton]
Mathematical and philosophical explanations are not causal [Lipton]
Explanations may be easier to find than causes [Lipton]
Causal inferences are clearest when we can manipulate things [Lipton]
14. Science / D. Explanation / 2. Types of Explanation / i. Explanations by mechanism
We want to know not just the cause, but how the cause operated [Lipton]
14. Science / D. Explanation / 2. Types of Explanation / l. Probabilistic explanations
To maximise probability, don't go beyond your data [Lipton]
14. Science / D. Explanation / 3. Best Explanation / a. Best explanation
Is Inference to the Best Explanation nothing more than inferring the likeliest cause? [Lipton]
Best Explanation as a guide to inference is preferable to best standard explanations [Lipton]
The 'likeliest' explanation is the best supported; the 'loveliest' gives the most understanding [Lipton]
IBE is inferring that the best potential explanation is the actual explanation [Lipton]
Finding the 'loveliest' potential explanation links truth to understanding [Lipton]
IBE is not passive treatment of data, but involves feedback between theory and data search [Lipton]
A contrasting difference is the cause if it offers the best explanation [Lipton]
We select possible explanations for explanatory reasons, as well as choosing among them [Lipton]
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? [Lipton]
Bayesians say best explanations build up an incoherent overall position [Lipton]
The best theory is boring: compare 'all planets move elliptically' with 'most of them do' [Lipton]
Best explanation can't be a guide to truth, because the truth must precede explanation [Lipton]
18. Thought / D. Concepts / 1. Concepts / a. Nature of concepts
The main theories of concepts are exemplar, prototype and knowledge [Murphy]
18. Thought / D. Concepts / 4. Structure of Concepts / c. Classical concepts
The theoretical and practical definitions for the classical view are very hard to find [Murphy]
The classical definitional approach cannot distinguish typical and atypical category members [Murphy]
Classical concepts follow classical logic, but concepts in real life don't work that way [Murphy]
Classical concepts are transitive hierarchies, but actual categories may be intransitive [Murphy]
The classical core is meant to be the real concept, but actually seems unimportant [Murphy]
18. Thought / D. Concepts / 4. Structure of Concepts / d. Concepts as prototypes
There is no 'ideal' bird or dog, and prototypes give no information about variability [Murphy]
Prototypes are unified representations of the entire category (rather than of members) [Murphy]
The prototype theory uses observed features, but can't include their construction [Murphy]
The prototype theory handles hierarchical categories and combinations of concepts well [Murphy]
Prototypes theory of concepts is best, as a full description with weighted typical features [Murphy]
Learning concepts is forming prototypes with a knowledge structure [Murphy]
18. Thought / D. Concepts / 4. Structure of Concepts / e. Concepts from exemplars
The most popular theories of concepts are based on prototypes or exemplars [Murphy]
The exemplar view of concepts says 'dogs' is the set of dogs I remember [Murphy]
Exemplar theory struggles with hierarchical classification and with induction [Murphy]
Children using knowing and essentialist categories doesn't fit the exemplar view [Murphy]
Conceptual combination must be compositional, and can't be built up from exemplars [Murphy]
The concept of birds from exemplars must also be used in inductions about birds [Murphy]
18. Thought / D. Concepts / 4. Structure of Concepts / f. Theory theory of concepts
We do not learn concepts in isolation, but as an integrated part of broader knowledge [Murphy]
Concepts with familiar contents are easier to learn [Murphy]
Some knowledge is involved in instant use of categories, other knowledge in explanations [Murphy]
People categorise things consistent with their knowledge, even rejecting some good evidence [Murphy]
26. Natural Theory / C. Causation / 9. General Causation / c. Counterfactual causation
Counterfactual causation makes causes necessary but not sufficient [Lipton]