2000 HSS/PSA Program 1 - History of Science Society

PSA Abstracts
theories, due to Arntzenius (1994) and Maudlin (1994). The main idea of both
arguments is that if these theories were possible, causal loops would also be.
But, it is argued, the consistency conditions of such loops would exclude the
very possibility of these theories. I argue that Arntzenius and Maudlin s lines
of reasoning fail because they rely on untenable assumptions about the nature
of probabilities in causal loops.
Michael Bishop Iowa State University
J.␣ D. Trout Loyola University of Chicago
50 Years of Successful Predictive Modeling Should be Enough:
Lessons for Philosophy of Science
This paper has two aims. The first is to describe a line of research that we believe
philosophers have for too long ignored or not properly appreciated. The second
is to extract some implications from this research for philosophy of science. In
his classic 1954 book, Paul Meehl reported on 20 experiments in which human
experts (clinical prediction) and actuarial formulas (a prediction is arrived at by
a straightforward application of an equation to the data) made a prediction about
a social phenomenon. Meehl found that in every non-ambiguous case, when
both types of prediction were based on the same evidence, the actuarial predictions
were more reliable. Since then, the evidence has expanded in three directions.
First, every fair test since 1954 on a problem of social prediction has supported
Meehl’s conclusion. Second, giving human experts the “advantage” of certain
kinds of information (e.g., unstructured interviews) typically degrades their
reliability, and so doesn’t help experts beat the formulas. And third, certain very
simple, improper predictive models (models that are not constructed so as to
best fit a large set of data) are also more reliable on problems of social predictions
than human experts. (In fact, there is new and highly suggestive research in the
area of “fast and frugal” heuristics indicating that in real world environments,
there are extremely simple heuristics that consistently are about as reliable as
computationally expensive, ideal models, such as multiple regression and
Bayesian models.) The implications of these findings range from the concrete
(how should we make predictions about whether a prisoner will commit violent
crimes if paroled?) to the rarified (what is the nature of scientific rationality?).
We will comment on three of the more rarified implications. Theories of scientific
reasoning / rationality. A result of these findings is that for some problems of
scientific reasoning, the most reliable, tractable reasoning strategy available to a
scientist will be profoundly counterintuitive, violating some deeply held
principles of rational enquiry. Some views of how scientists ought to reason are
incompatible with the contention that scientists ought to use improper predictive
models. Explanation. One reason people seem to prefer clinical predictions to
actuarial predictions is that the former seems to provide more satisfying
explanations. But given the often limping narrative causal descriptions that pass
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