The I-S Model of scientific explanation has five

The I-S Model of scientific explanation
has five requirements. The first is that the explanans, the thing that does the
explaining, must consist of premises of an inductively strong argument whose
conclusion is the explanandum, the thing being explained. A good explanation
must be a strong inductive argument. An example of one would be if the premises
are: Jane had strep; Jane was treated with penicillin; and almost all cases of
strep clear up when treated with penicillin. The conclusion being Jane quickly
recovered. A 2nd criterion is that the premises must be true. So it
must be true that Jane had strep and so on as the explanation should only have
true statements. The third rule is that at least one of the premises must be a
statistical law of nature that the argument cannot do without. In this case,
the statistical law is that almost all cases of strep clear up when treated
with penicillin, without it, the argument would not work. The fourth criterion
is that the explanans must be empirically verifiable. Each of the premises must
have the possibility of being tested either by observation or experiment. You
can observe that Jane had strep, was treated with penicillin that her doctor
prescribed, as well as test the effectiveness of penicillin at treating strep.
The last condition of the I-S Model is the requirement of maximal specificity
(RMS). The requirement says there needs to be maximum specification. This means
that premises must include everything, complete evidence, relevant to the
conclusion. Say if Jane had penicillin resistant strep, this would then change
the argument and the scientific explanation will now not be a very good one
because it does not explain how Jane recovered quickly.

The RMS is nonetheless needed because an
explanation should include all relevant information so that it hits all the
points. Because an argument’s strength can be changed when adding or removing
premises, all information should be given for a good explanation. Though it is
difficult to formulate the RMS, because it is difficult to classify what
information is “relevant”. So what do we include in our argument and how do we
determine that? It is difficult to specify what the RMS entails because the
criterion does not specify what relevant means. It can be the case that it
means all relevant information or what we think is relevant. Including all
relevant information is ridiculous because you can’t possibly rather all
information that relates to your argument, even the tiniest thing and some
information may make the argument bad. If it means only the things we know to
be relevant, then we are missing out on a lot of information that may be
important and it can be the case where we can avoid not knowing something to
satisfy the criterion which may make the explanation bad in reality due to those
missing points.

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Going back to the first criteria, there
is a problem with the requirement that statistical explanations be strong
inductive arguments. Although this criterion seems fine at first, there are
actually some counterexamples that are brought up; I will discuss one of them.
The problem with having a requirement of an inductively strong argument is that
it is not open to suggestions to low probability explanations even though they
can be good scientific explanations. For instance, a weak inductive argument
that satisfies the I-S Model goes: Alex had latent untreated syphilis; 30% of
cases of latent untreated syphilis turn into paresis; in conclusion Alex got
paresis. The probability of contracting paresis for any syphilis patient is
small, but we can still explain the development of paresis through syphilis. There’s
a problem then because in the I-S Model, only strong inductive arguments will
work. Although there is a small chance of occurrence, there is nonetheless a
chance that the same event will occur. This model throws out the low
probability arguments, but as there are limitations in technology and sometimes
an event occurrence is due to chance, not all arguments can be made to be
inductively strong. A high probability requirement is then not a necessary
condition for a good scientific explanation because events whose occurrence is
not probable, can still occur and those weak inductive arguments can still be
used to explain the occurrence of events.