Combining Texts

All the ideas for 'The Evolution of Modern Metaphysics', 'First-Order Modal Logic' and 'Inference to the Best Explanation (2nd)'

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102 ideas

1. Philosophy / E. Nature of Metaphysics / 1. Nature of Metaphysics
Metaphysics is the most general attempt to make sense of things [Moore,AW]
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]
4. Formal Logic / B. Propositional Logic PL / 3. Truth Tables
Each line of a truth table is a model [Fitting/Mendelsohn]
4. Formal Logic / D. Modal Logic ML / 2. Tools of Modal Logic / a. Symbols of ML
Modal logic adds □ (necessarily) and ◊ (possibly) to classical logic [Fitting/Mendelsohn]
We let 'R' be the accessibility relation: xRy is read 'y is accessible from x' [Fitting/Mendelsohn]
The symbol ||- is the 'forcing' relation; 'Γ ||- P' means that P is true in world Γ [Fitting/Mendelsohn]
The prefix σ names a possible world, and σ.n names a world accessible from that one [Fitting/Mendelsohn]
4. Formal Logic / D. Modal Logic ML / 2. Tools of Modal Logic / b. Terminology of ML
A 'constant' domain is the same for all worlds; 'varying' domains can be entirely separate [Fitting/Mendelsohn]
Modern modal logic introduces 'accessibility', saying xRy means 'y is accessible from x' [Fitting/Mendelsohn]
A 'model' is a frame plus specification of propositions true at worlds, written < G,R,||- > [Fitting/Mendelsohn]
A 'frame' is a set G of possible worlds, with an accessibility relation R, written < G,R > [Fitting/Mendelsohn]
Accessibility relations can be 'reflexive' (self-referring), 'transitive' (carries over), or 'symmetric' (mutual) [Fitting/Mendelsohn]
4. Formal Logic / D. Modal Logic ML / 2. Tools of Modal Logic / c. Derivation rules of ML
S5: a) if n ◊X then kX b) if n ¬□X then k ¬X c) if n □X then k X d) if n ¬◊X then k ¬X [Fitting/Mendelsohn]
Negation: if σ ¬¬X then σ X [Fitting/Mendelsohn]
Disj: a) if σ ¬(X∨Y) then σ ¬X and σ ¬Y b) if σ X∨Y then σ X or σ Y [Fitting/Mendelsohn]
Existential: a) if σ ◊X then σ.n X b) if σ ¬□X then σ.n ¬X [n is new] [Fitting/Mendelsohn]
T reflexive: a) if σ □X then σ X b) if σ ¬◊X then σ ¬X [Fitting/Mendelsohn]
D serial: a) if σ □X then σ ◊X b) if σ ¬◊X then σ ¬□X [Fitting/Mendelsohn]
B symmetric: a) if σ.n □X then σ X b) if σ.n ¬◊X then σ ¬X [n occurs] [Fitting/Mendelsohn]
4 transitive: a) if σ □X then σ.n □X b) if σ ¬◊X then σ.n ¬◊X [n occurs] [Fitting/Mendelsohn]
4r rev-trans: a) if σ.n □X then σ □X b) if σ.n ¬◊X then σ ¬◊X [n occurs] [Fitting/Mendelsohn]
If a proposition is possibly true in a world, it is true in some world accessible from that world [Fitting/Mendelsohn]
If a proposition is necessarily true in a world, it is true in all worlds accessible from that world [Fitting/Mendelsohn]
Conj: a) if σ X∧Y then σ X and σ Y b) if σ ¬(X∧Y) then σ ¬X or σ ¬Y [Fitting/Mendelsohn]
Bicon: a)if σ(X↔Y) then σ(X→Y) and σ(Y→X) b) [not biconditional, one or other fails] [Fitting/Mendelsohn]
Implic: a) if σ ¬(X→Y) then σ X and σ ¬Y b) if σ X→Y then σ ¬X or σ Y [Fitting/Mendelsohn]
Universal: a) if σ ¬◊X then σ.m ¬X b) if σ □X then σ.m X [m exists] [Fitting/Mendelsohn]
4. Formal Logic / D. Modal Logic ML / 3. Modal Logic Systems / b. System K
The system K has no accessibility conditions [Fitting/Mendelsohn]
4. Formal Logic / D. Modal Logic ML / 3. Modal Logic Systems / c. System D
□P → P is not valid in D (Deontic Logic), since an obligatory action may be not performed [Fitting/Mendelsohn]
The system D has the 'serial' conditon imposed on its accessibility relation [Fitting/Mendelsohn]
4. Formal Logic / D. Modal Logic ML / 3. Modal Logic Systems / d. System T
The system T has the 'reflexive' conditon imposed on its accessibility relation [Fitting/Mendelsohn]
4. Formal Logic / D. Modal Logic ML / 3. Modal Logic Systems / e. System K4
The system K4 has the 'transitive' condition on its accessibility relation [Fitting/Mendelsohn]
4. Formal Logic / D. Modal Logic ML / 3. Modal Logic Systems / f. System B
The system B has the 'reflexive' and 'symmetric' conditions on its accessibility relation [Fitting/Mendelsohn]
4. Formal Logic / D. Modal Logic ML / 3. Modal Logic Systems / g. System S4
The system S4 has the 'reflexive' and 'transitive' conditions on its accessibility relation [Fitting/Mendelsohn]
4. Formal Logic / D. Modal Logic ML / 3. Modal Logic Systems / h. System S5
System S5 has the 'reflexive', 'symmetric' and 'transitive' conditions on its accessibility relation [Fitting/Mendelsohn]
4. Formal Logic / D. Modal Logic ML / 4. Alethic Modal Logic
Modality affects content, because P→◊P is valid, but ◊P→P isn't [Fitting/Mendelsohn]
4. Formal Logic / D. Modal Logic ML / 5. Epistemic Logic
In epistemic logic knowers are logically omniscient, so they know that they know [Fitting/Mendelsohn]
Read epistemic box as 'a knows/believes P' and diamond as 'for all a knows/believes, P' [Fitting/Mendelsohn]
4. Formal Logic / D. Modal Logic ML / 6. Temporal Logic
F: will sometime, P: was sometime, G: will always, H: was always [Fitting/Mendelsohn]
4. Formal Logic / D. Modal Logic ML / 7. Barcan Formula
The Barcan says nothing comes into existence; the Converse says nothing ceases; the pair imply stability [Fitting/Mendelsohn]
The Barcan corresponds to anti-monotonicity, and the Converse to monotonicity [Fitting/Mendelsohn]
5. Theory of Logic / F. Referring in Logic / 3. Property (λ-) Abstraction
'Predicate abstraction' abstracts predicates from formulae, giving scope for constants and functions [Fitting/Mendelsohn]
9. Objects / F. Identity among Objects / 7. Indiscernible Objects
The Indiscernibility of Identicals has been a big problem for modal logic [Fitting/Mendelsohn]
10. Modality / E. Possible worlds / 3. Transworld Objects / a. Transworld identity
□ must be sensitive as to whether it picks out an object by essential or by contingent properties [Fitting/Mendelsohn]
Objects retain their possible properties across worlds, so a bundle theory of them seems best [Fitting/Mendelsohn]
10. Modality / E. Possible worlds / 3. Transworld Objects / c. Counterparts
Counterpart relations are neither symmetric nor transitive, so there is no logic of equality for them [Fitting/Mendelsohn]
11. Knowledge Aims / A. Knowledge / 2. Understanding
Understanding is not mysterious - it is just more knowledge, of causes [Lipton]
11. Knowledge Aims / C. Knowing Reality / 3. Idealism / b. Transcendental idealism
Appearances are nothing beyond representations, which is transcendental ideality [Moore,AW]
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]
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]
26. Natural Theory / C. Causation / 9. General Causation / c. Counterfactual causation
Counterfactual causation makes causes necessary but not sufficient [Lipton]