Combining Texts

All the ideas for 'Intro to Naming,Necessity and Natural Kinds', 'Bayesianism' and 'Knowledge:Readings in Cont.Epist'

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16 ideas

2. Reason / D. Definition / 1. Definitions
The new view is that "water" is a name, and has no definition [Schwartz,SP]
5. Theory of Logic / F. Referring in Logic / 1. Naming / b. Names as descriptive
We refer to Thales successfully by name, even if all descriptions of him are false [Schwartz,SP]
The traditional theory of names says some of the descriptions must be correct [Schwartz,SP]
11. Knowledge Aims / A. Knowledge / 1. Knowledge
Perception, introspection, testimony, memory, reason, and inference can give us knowledge [Bernecker/Dretske]
12. Knowledge Sources / B. Perception / 7. Causal Perception
Causal theory says true perceptions must be caused by the object perceived [Bernecker/Dretske]
12. Knowledge Sources / E. Direct Knowledge / 4. Memory
You can acquire new knowledge by exploring memories [Bernecker/Dretske]
13. Knowledge Criteria / A. Justification Problems / 1. Justification / a. Justification issues
Justification can be of the belief, or of the person holding the belief [Bernecker/Dretske]
13. Knowledge Criteria / B. Internal Justification / 4. Foundationalism / a. Foundationalism
Foundationalism aims to avoid an infinite regress [Bernecker/Dretske]
13. Knowledge Criteria / B. Internal Justification / 4. Foundationalism / f. Foundationalism critique
Infallible sensations can't be foundations if they are non-epistemic [Bernecker/Dretske]
13. Knowledge Criteria / C. External Justification / 1. External Justification
Justification is normative, so it can't be reduced to cognitive psychology [Bernecker/Dretske]
13. Knowledge Criteria / D. Scepticism / 6. Scepticism Critique
Modern arguments against the sceptic are epistemological and semantic externalism, and the focus on relevance [Bernecker/Dretske]
14. Science / C. Induction / 5. Paradoxes of Induction / a. Grue problem
Predictions are bound to be arbitrary if they depend on the language used [Bernecker/Dretske]
14. Science / C. Induction / 6. Bayes's Theorem
Bayes' theorem explains why very surprising predictions have a higher value as evidence [Horwich]
Probability of H, given evidence E, is prob(H) x prob(E given H) / prob(E) [Horwich]
18. Thought / C. Content / 6. Broad Content
Semantic externalism ties content to the world, reducing error [Bernecker/Dretske]
18. Thought / C. Content / 8. Intension
The intension of "lemon" is the conjunction of properties associated with it [Schwartz,SP]