Prediction in Context: On the Comparative Epistemic Merit of Predictive Success

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22 Οκτ 2013 (πριν από 4 χρόνια και 18 μέρες)

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Prediction in Context:

On the Comparative E
pistemic Merit of
P
redictive Success

Martin Carrier

(Bielefeld University)

1.

Predictive Novelty vs. Coherence as E
pistemic M
erits

Prediction stands out among the
traditional criteria of
epistemic
merit in science.



Heather Douglas (2009) demanded
to supplement the emphasis on
explanation with an equal emphasis
on prediction.

Her claim is that ceteris paribus prediction is
epistemically
superior to
accommodation.



The
epistemic
role of predictions is to provide demanding test
instances for theories; predictions serve to test explanations.

Novel predictions anticipate surprising
phenomena that go
beyond
the system of
knowledge and tend to be inconsistent
with it.



Thus predictive power and coherence with
the
background
knowledge are conditions
that tend to point in opposite directions.

However: conflict between the requirement that a theory produce
novel predictions and the demand that it match the state of
knowledge.

The
existence
of competing standards suggests that the emphasis
on prediction shouldn’t be exaggerated.


What is the merit of predictions if science leaves the controlled
conditions of the lab and struggles with the intricacy of the world?

2.

Prediction in Application
-
Oriented Research Endeavors

In application
-
oriented research, prediction is assumed to play a
key role.

Targeted inter
-
vention in natural
processes requires
the ability to antici
-
pate the results of
one’s action.

=> Ambivalent views on the proper role of prediction in science:

It is complained, on the one hand, that explanation has eclipsed
prediction, whereas it is contended, on the other, that prediction
has eclipsed explanation.



=>
Question as to the relationship of predictive strength to
explanatory power.

Biotechnology of the 1990s: the ability to
anticipate the outcome of one’s inter
-
ventions is decoupled from any deeper
understanding.



P
redictive strength and theoretical under
-
standing are assessed as contrasting
virtues.

In contrast: t
he power to anticipate is more
dependent on the ability to explain than
many practitioners believe.

Use of trigger genes for getting a complex
activity
of gene expression underway.



Trigger genes act as starters so that switch
-
ing them on allows one to set a cascade of
processes in motion.

=> There is no need to disentangle this chain of events in order to
anticipate the effect.

The activation of eyeless initiates
eye formation in flies:
a
ppropriate stimulation produces
eyes in the legs or wings of flies.



The identification of trigger genes
was assumed to enable biotech
-
nologists to anticipate the out
-
come of their intervention with
-
out being able to follow out the
underlying causal chains.

Biotechnologists: prediction does not always rely on understanding.

The transition from
genomics to proteomics involves a conceptual
revolution which does not affect, however, the
technical role of the
gene.

Artificially induced eyes in drosophila

Biotechnologists: Genes can still be used as tools for bringing about
effects and anticipating the outcome of an intervention.

=> The correctness of predictions is independent, in large measure,
from the truth of more fundamental, high
-
brow theories.

However, this one
-
sided
emphasis on prediction and
the corresponding neglect of
explanation turned out to be
a failure.

In general, the
genetic
and
non
-
genetic
contexts need to
be taken into account.

“Distalless” gene: acts
in a more specific way
and affects embryonic
development dif
-
ferently.



Caterpillar embryo:
formation of legs.

developed butterfly:
wing pattern.

The
strong emphasis on prediction in application
-
oriented research
can be seen as a bias that may hurt the epistemic culture of
science.



Worse yet, neglecting
explanation
may hurt predictive strength.



Douglas
requires to bring prediction back into ph
ilosophical
accounts of explanation.

But: converse demand to
bring back explanation into
accounts of prediction.



Predictive strength needs to
be placed in a network of
other merits and achieve
-
ments.

Interconnection Processes

3.

The Role of Prediction in Expert Judgment

So far: In some
practical
contexts
predictive power does not play
the outstanding role sometimes accredited to it.



Prediction is a great team player but a lousy soloist.



Now: scientific expertise: policy advice on the basis of scientific
knowledge.

Problems in need of expertise are
often complex.



But often the details can be ig
-
nored in favor of
expounding the
distinctive features.

“Epistemic robustness”:
the
recommendation
remains un
-
changed if the
pertinent causal
factors and factual conditions
fluctuate or are unknown.



E
pistemic
robustness is an im
-
portant objective for scientific
expertise
since addressing the
minute particulars is often
immaterial for deciding about

Robustness

and what it‘s worth

how to respond to a practical challenge.



Robustness is a quality standard characteristic for
expertise. It re
-
places precision as a chief virtue of predictions in epistemic science.



The commitment to
epistemic
robustness tends to reduce the
importance of accurate predictions.

Widespread
belief:
expert recommendations may be based on
observational regularities alone.




=> Explanation and prediction are severed from each other.

However: o
bserved
regularities are para
-
digm instances of non
-
robust knowledge.



Tying prediction to
explanation is some
-
times a good guide for
enhancing the robust
-
ness of the prediction.

4.

The Significance of
Prediction in Pursuing Research



Policies

Possibility to anticipate the success of certain types of research
projects.

Prospects of demand
-
driven research policies:
outcome
-
focused pro
-
jects guided by practical
require
ments.

The successful pursuit of demand
-
driven
research programs requires to anticipate
probable
novel results
emerging
along
certain research paths.

Vannevar Bush (1945): science policy for
stimulating progress in practical matters:
funding of basic research.



The assumed impossibility of anticipating
research outcome is taken to make any
demand
-
driven research policy fail; only a
knowledge
-
driven policy can be expected to
yield fruitful results.

However: it is often deemed impossible to foresee the outcome
of research projects.

However, this unpredictability claim is in need of serious
qualification.

Successful demand
-
driven
research:

The Discovery and Use
of Giant Magneto
-
resistance

The Manhattan Project

The
Human
Genome
Project

The relevant varia
-
bility can be ac
-
counted for to some
extent by the de
-
gree to which the
pertinent theoret
-
ical framework was
staked out.

The empirical
record of attempts
to produce research
outcome on
demand is mixed.

Yet incomplete knowledge of the fundamentals does not
always thwart practical research endeavors.

The “X1,” the first
supersonic aircraft,
was designed in 1947
by ignoring fluid
dynamics and by
relying instead on
practical knowledge
and on experience
with high speeds.

=> It is a precarious endeavor to predict possible research
outcome and to assess the prospects of research
projects

Prima
-
facie plausibility: the successful stimulation of demand
-
driven research requires understanding the
epistemic
processes in
science.

Bringing Bacon’s claim
about the
relationship between understanding and
intervention to bear on science studies
produces the assertion that a targeted
intervention in science presupposes a
thorough understanding of science.



Yet Bacon’s claim is not universally true.

Some technological breakthroughs rely
on fundamental understanding but
others don’t.

5.

Conclusion

Prediction plays a less than
pronounced role in the context
of practice.



It is true, predicting the results
of human action is a key feature
of science turned practical.

However, bare predictions do
not count for much.

First, predictions need to be integrated into an explanatory frame
-
work if they are supposed to guide actions
reliably.

Second, the preference for precise predictions in epistemic research
is supplanted with the objective of specifying a robust corridor of
estimates.

Finally, it is highly uncertain to predict the success of research
projects.