Van Fraassen The Scientific Image Explanation Notes

Bas van Fraassen - The Scientific Image

Chapter Five: The Pragmatics of Explanation

The language of explanation

• A theory T can explain a fact E without being either true or empirically adequate

  • thus to say that T explains E does not commit us to either the realism or the empirical adequacy of T

  • so the question of the acceptability of an explanation is separate

    • when we say we ‘have an explanation’ this implies that our explanation is acceptable

• The grammar of explanation is such: fact E explains fact F relative to theory T

  • e.g. the gravitational pull of the moon explains the ebb and flow of the tides in Newton’s theory

A biased history (of theories of explanation)

1. Hempel

• States two criteria for what constitutes an explanation

  • explanatory relevance - the explanatory information must give good grounds for believing that the phenomena occurs

  • testability - the statements of a scientific explanation must be empirically testable

• Explanatory relevance:

  • this information is of two types:

    • the laws supplied by theory

    • factual information

  • in non-statistical theories, the information implies the fact that is explained, in statistical theories, the information bestows high probability on that fact

• Explanatory relevance is neither necessary nor sufficient to explanation

  • insufficient - we can have good grounds to believe, e.g., that a galaxy is receding from us if its light exhibits a red shift, without it explaining that phenomenon. Red shift is a consequence of the galaxy moving away from us, not the reason for the motion

  • unnecessary - we can explain without giving good grounds to believe that a phenomena will occur - in cases of low probability. e.g. paresis - only those with syphilis get paresis, but far from all of those with syphilis get is (say 1 in 10). this means that we can warn someone with untreated syphilis that they may contract paresis (explanation), but doesn’t give them good grounds to believe that they will do so

• We would have to modify the account such that the explanatory information gives us good and relevant grounds for believing the phenomena has, does or will occur

  • this invites us to the problem of what is meant by relevance

• Testability is met by all scientific theories, so can’t help ameliorate the difficulties with Hempel’s account

2. Salmon: Statistically Relevant Factors

• For Salmon, explanation is not an argument, but an assembly of statistically relevant factors

  • a factor F is statistically relevant to an event E if F alters the probability of E occurring

    • whereas Hempel’s model was too strong, requiring the probability of E given F to be greater than 0.5, Salmon’s is not - it allows for the probability of E given F to be lower than that of E simpliciter

    • this can explain the paresis example

  • BUT statistical relevance cannot explain, for example, why an event happens at one particular moment rather than another

• Statistical relevance is neither necessary nor sufficient for explanation

  • insufficient - we can assemble the statistically relevant factors without always explaining

    • e.g. if we spray some ivy with a poison that is 90% effective, we can explain the death of some ivy with the statistically relevant factor, but we can’t explain why the 10% is still alive by saying ‘because it was sprayed with poison’

  • unnecessary - we can explain without reference to statistically relevant factors

    • e.g. if paresis can be contracted from either syphilis or epilepsy with a probability of paresis given either equalling 0.1. John belongs to a family in which everyone has either syphilis or epilepsy. If he develops paresis, we will surely explain it by saying ‘Because he had syphyilis/epilepsy’. This is an explanation, despite the fact that extra knowledge that he had, say, syphilis, would not alter the probability of him developing paresis at all

• It seems that both Hempel and Salmon see explanatory power as nothing more than empirical adequacy and empirical strength

  • thus explaining an event is indistinguishable from a) showing its occurrence to not constitute an objection to the empirical adequacy of one’s theory, and b) providing information entailed by the theory and relevant to the event’s occurrence

Global properties of theories

• Friedman views scientific explanation not as a question of explaining individual phenomena, but of making sense of broader phenomena through global theories

  • hence we evaluate something as an explanation of P relative to an assumed background theory, K

• Problems:

  • what is included in the background theory - just laws, or information as well?

    • if information, it can’t include all our information, because we know that P when we ask for an explanation of P

  • an explanation relative to K implies that P is true - but of course, the point to explain might be the non-occurrence of P: cf. Salmon

  • the question of whether we have an explanation of P hinges on this account on whether K gives us information about facts other than P. why should explanation rest on this?

The difficulties: asymmetries and rejections

• Two cases that none of these theories of explanation can deal with:

  • where the request for explanation is rejected, despite the case lying within a theory’s domain

    • e.g. Newton’s theory did not contain an explanation of gravity, only a description

    • not everything within a theory’s domain is a legitimate topic for why-questions

  • asymmetry: even if a theory implies that one condition obtains when and only when another does, it may be that one condition is explained in terms of the other and not vice-versa

    • redshift and flagpole examples

Causality: the conditio sine qua non

• In modern philosophy, causation is seen as a relation between events. But what is the nature of the causal relation?

  • a cause is not a sufficient condition for its effect - ivy/poison example

    • rather, cause is seen as the ...

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