文档介绍：Why Probability Does Not Capture the Logic of Scientific Justification
1. Introduction
Here is the usual way philosophers think about science and induction. Scientists do many
things— aspire, probe, theorize, conclude, retract, and refine— but essful research culmi-
nates in a published research report that presents an argument for some empirical conclusion.
In mathematics and logic there are sound deductive arguments that fully justify their conclu-
sions, but such proofs are unavailable in the empirical domain because empirical hypotheses
outrun the evidence adduced for them. Inductive skeptics insist that such conclusions cannot
be justified. But “justification” is a vague term— if empirical conclusions cannot be estab-
lished fully, as mathematical conclusions are, perhaps they are justified in the sense that they
are partially supported or confirmed by the available evidence. To respond to the skeptic,
one merely has to explicate the concept of confirmation or partial justification in a systematic
manner that agrees, more or less, mon usage and to observe that our scientific con-
clusions are confirmed in the explicated sense. This process of explication is widely thought
to culminate in some version of Bayesian confirmation theory.
Although there are nearly as many Bayesianisms as there are Bayesians, the basic idea
behind Bayesian confirmation theory is simple enough. At any given moment a rational
agent is required to assign a unique degree of belief to each proposition in some collection of
propositions closed under “and”, “or”, and “not”. Furthermore, it is required that degrees of
belief satisfy the axioms of probability. Conditional probability is defined as follows:
P (h and e)
P (h|e) = .
P (e)
Confirmation can then be explicated like this:
evidence e confirms hypothesis h (for agent P ) if and only if P (h|e) > P (h).
In other words, confirmation is just positive statistical dependence with respect to one’s
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degree