Probability

Download Against Coherence: Truth, Probability, and Justification by Erik J. Olsson PDF

By Erik J. Olsson

It really is tempting to imagine that, if a person's ideals are coherent, also they are prone to be precise. This fact conduciveness declare is the cornerstone of the preferred coherence conception of information and justification. Erik Olsson's new booklet is the main vast and particular research of coherence and possible fact to this point. surroundings new criteria of precision and readability, Olsson argues that the worth of coherence has been generally over priced. Provocative and readable, opposed to Coherence will make stimulating analyzing for epistemologists and an individual with a major curiosity truthfully.

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Additional info for Against Coherence: Truth, Probability, and Justification

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Two testimonies are conditionally independent just in case, once the truth-value of the hypothesis is known, what the one witness has said does not affect the probability of what the other witness will say. The assumption of conditional independence has two parts, corresponding to assuming the hypothesis true or assuming it false: P(E1/H ) ¼ P(E1/H,E2) and PðE1 =:HÞ ¼ PðE1 =:H, E2 Þ. These two assumptions serve to simplify calculations tremendously and yet this is not their main motivation. e. 14 What reasons are there for thinking that conditional independence in the sense just referred to is an adequate probabilistic representation of testimonial independence?

Applying Lewis’s congruence definition, moreover, gives exactly the desired result: assuming the one element of this ordered set as given premiss raises the probability of the other; indeed it raises it to 1. The latter fact can even be taken in support of ascribing to Lewis acceptance of (3): full agreement is not just coherent; it is very coherent. There is a complication that, although it needs to be addressed, does not affect the points just made. While the representation in terms of 10 Whether or not we have coherence at the level of ‘assertions of supposed facts’ will depend, as in the examples just given, on what assumptions are made concerning the reliability of the testimonies.

It is 13 The relevant probability is the chance that the witness gives a false testimony multiplied by the chance that he picks out Forbes falsely. A further reasonable assumption needs to be made in order for the former chance to equal 1 À i. Let E1,k ¼ ‘Smith says that suspect k did it’ and, similarly, E2,k ¼ ‘Jones says that suspect k did it’. Further, let Hk ¼ ‘Suspect k did it’. The extra assumption is P(E1,k /Hk) ¼ P(E2,k / Hk) ¼ i. For the chance that a given witness—say, Smith—gives a false testimony equals 1 minus the chance that he gives a true testimony: 1 À [P(E1,1/H1)P(H1) þ Á Á Á þ P(E1,n /Hn)P(E1,n /Hn)] ¼ 1 À i.

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