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Sonquist offers little by way of
philosophical commentary; unlike sociologists such
as Parsons and Lundberg, he does not develop a
philosophy of science much less a philosophy of
language. But
there is little imperative that he philosophizes,
since the application of his AID
system is less often philosophically
controversial. In his applications there is
typically no conflict between the data inputted to
his system and the mentalistic ontology required by
Romantic sociologists, when his system is used to
process data collected by survey research consisting
of verbal responses revealing respondents' mental
states. In
such applications a conflict occurs only with those
extreme Romanticists requiring the verstehen
truth criterion for scientific criticism. In his
1963 paper, "Problems in the Analysis of Survey
Data", Sonquist considers the problem that
occurs when theoretical constructs are not the same
as the factors that the sociologist is able to
measure, even when the survey questions are
attitudinal or expectational questions, and when the
measurements that the sociologist actually uses,
often called "proxy variables" or
"indicators", are not related to the
theoretical constructs on a simple one-to-one basis.
This is a problem that occurs only in cases
in which a theory pre-exists empirical analysis, and
in this circumstance Sonquist advocates a role for
the AID system,
in which the system's empirical analyses are used
for the resolution of problems involving interaction
detection, problems which theory cannot resolve,
or which must be solved either arbitrarily or by
making untestable assumptions about the connection
between theoretical construct and measurement
factor. Later
he considers the role for the discovery system for
the development of theory, and the influence of
Robert K. Merton is evident.
In Multivariate
Model Building he states in the first chapter
that he is not attempting to deal with the basic
scientific problems of conceptualizing causal links
or with latent and manifest functions, but only with
the apparent relations between measured constructs
and their congruence with an underlying causal
structure. He defines a "theory" as sets of propositions which
describe at the abstract level the functioning of a
social system, and proposes that in the inductive
phase, ex post
facto explanations of the relationships found
within the data may form a basis for assembling a
set of interrelated propositions which he calls a
"middle range theory", that describes the
functioning of a specific aspect of a social system.
The AID system facilitates the inductive phase by identifying
interacting variables, so that mathematical
functions relating sociological variables are
correctly specified for statistical estimation.
Sonquist draws upon an introductory text,
An Introduction to Logic and Scientific Method,
written in 1934 by two academic philosophers of
science, Morris R. Cohen and Ernest Nagel.
Cohen (1880-1947) received his Ph.D. from
Harvard in 1906, and Nagel (1901-1985) studied under
Cohen at City College of New York and received his
Ph.D. from Columbia University in 1931.
The relevant chapter in the book is titled
"The Method of Experimental Inquiry",
which examines the experimental "methods"
for discovering causal relationships advanced by
Francis Bacon and later elaborated by John S. Mill.
These Baconian experimental methods are
anything but Romanticist: the two authors define the
search for "causes" to mean the search for
some invariable order among various sorts of
elements or factors, and the book gives no
suggestion that the social sciences should receive
any distinctive treatment.
Since all discovery systems search for
invariant relations, the attractiveness of the
Baconian treatment for scientists such as Sonquist
is self evident.
The propositions which Sonquist views as
constituting middle-range sociological theory and
which following Cohen and Nagel express a causal
relationship, have the linguistic form: X(1)...X(n)
implies Y. The researcher's task in Sonquist's view is to relate the
causal proposition to a mathematical functional
form, which is statistically estimated, and he
concludes that a well specified, statistically
estimated mathematical function with a small and
random error term, expresses a causal relationship
understood as the sufficient condition for an
invariant relationship between the dependent or
caused variable and the set of independent or causal
variables.
In "Computers and the Social
Sciences" and "'Retailing' Computer
Resources to Social Scientists" in American
Behavioral Scientist (1977) Sonquist and Francis
M. Sim discuss the inadequate social organization in
universities for the effective utilization of
computer resources, especially by social scientists,
whom they report are described derisively by other
academicians as "the marginal computer
users.” The authors present some arguments for changing the
professional roles and social organization of
computing in social science departments, and they
propose some organizational forms.
While the authors' sociological analysis of
computing in social science is interesting, and
while their reorganization proposals may offer
benefits, the underutilization of computer resources
and systems analysis by social scientists cannot be
remedied by such measures as academic
reorganization, so long as the prevailing philosophy
of science is still Romanticist.
Reorganizing roles could do no more for
Romantic sociology than could re-arranging deck
chairs for the sinking H.M.S.
Titanic.
Examination of Sonquist's writings in their
chronological order suggests that, as he had
attempted to expand the discovery function of his
system, he discovered that he had to move
progressively further away from the Romanticism
prevailing in contemporary academic sociology.
He would have been better served by the
contemporary Pragmatist philosophy of science, than
he had been by invoking the 1930's Positivist views
of Cohen and Nagel.
Both Positivism and Romanticism give a
semantically based definition of "theory"
and ontologically based criteria for scientific
criticism. On
the Pragmatist view "theory" is defined by
the pragmatics of language, its function is what
Hanson called research science as opposed to almanac
science. And
according to the Pragmatist realism practiced by
Galileo, Einstein and Heisenberg and formulated as
"ontological relativity" by Quine, every
causal claim is based exclusively on the empirical
adequacy of a tested theory.
Discovery systems therefore are not only able
to discover causality; they can also make causal
theories.
Hickey's correspondence with the editors of
the academic sociological journals described above
reveals that these editors and their selected
referees are committed to the enforcement of archaic
criteria for scientific criticism, criteria based on
an institutionalized Romantic philosophy of science. In this context Sonquist's AID
discovery system was not merely an anomaly in
academic sociology.
Developed in 1963 at the apex of Parsonian
Romanticism, the AID
system was a portent.
It signaled the widening cultural lag in
academic sociology between technological modernity
represented by the innovative computerized discovery
systems on the one hand and anachronistic philosophy
represented by an atavistic paleo-Romanticism on the
other hand. This
conflict is not merely one between alternative
theories; a multiplicity of empirically acceptable
conflicting theories is consistent with Pragmatism. This conflict is furthermore institutional; and it is
irreconcilable.
The new technology is an implementation of
ontological relativity, which subordinates ontology
to empirical criticism, while the old philosophies
subordinate empirical criticism to their peculiar
ontological dogmas.
Only one or the other type of criterion can
prevail, and the prevailing one will at least
banalize if not completely exclude the other.
Comment
and Conclusion
On
Pragmatism vs. Romanticism
Contemporary Pragmatism differs fundamentally
from Romanticism. Romanticism requires a mentalistic ontology as a criterion for
scientific criticism, such that any proposed
explanation describing a mentalistic ontology is
rejected out of hand without regard to its
demonstrated empirical adequacy.
Pragmatism on the other hand accepts only
empirical criteria for scientific criticism, and
rejects all prior ontologies as criteria for
scientific criticism.
Thus Pragmatism permits but does not require
mentalistic ontologies.
This difference is due to the different
concepts of the aim of science.
Romanticism defines the aim of cultural
science as the development of explanations having
semantics that describe mentalistic ontologies, a
semantics that Romantics call interpretative
understanding.
On the other hand Pragmatism does not define
the aim of social science in terms of any ontology. Pragmatists will accept any theory as an explanation that has
been empirically tested and not falsified regardless
of its semantics and associated ontology.
For example consumer services and nondurable
consumer goods, which together represent fifty-eight
percent of the gross domestic product of the United
States national economy.
But econometric models for these sectors are
seldom empirically satisfactory when based on
microeconomic theory.
The models’ errors in reproducing a
development sample are large and manifest
conspicuous serial correlation.
Furthermore the algebraic sign on the
statistically estimated parameter for relative price
is often positive instead of negative, and is seldom
statistically significant. But the experienced business econometrician will add
demographic variables that account for the fact that
consumers enter the market for a product at a
certain age in their life cycles, and may also exit
a market at some later age.
Trying various time lags in the number of
aggregate births or birth rates easily accomplishes
this. The
result does not conform to the Romantic philosophy
of science, because it is a rare consumer who makes
a purchase decision, and knows the size of his
demographic cohort, much less knows his place in his
society’s demographic profile. Furthermore the demographic variables so dominate the model
that their inclusion often make the relative price
and income variables statistically nonsignificant
thus requiring exclusion of these traditional
microeconomic variables.
Typically nonsignificance also occurs when
variables representing advertising and nonprice
promotions, which are paradigmatically Romantic
variables representing sellers’ attempts to
influence consumers’ product knowledge and
attitudes, are included in category or industry
models. Such
variables representing nonprice competition are
often important only for individual brands and not
for whole industries or product categories.
And even in the industry models these
Romantic variables must be statistically significant
and the model must otherwise be empirically
acceptable, or the econometrician will reject the
model.
The net
effect is that the econometric model that works well
empirically is not always the model that conforms
either to received microeconomic theory, or to the
Haavelmo agenda, or to the Romantic philosophy of
science. Of course there are other sectors of the
economy for which econometric models work well, when
the models include variables representing either
expectations data or economic conditions consciously
considered by the actors, as Haavelmo had
prescribed. But
their inclusion must be subordinated to the
Pragmatist concepts of the aim of science,
criticism, and explanation, if interpretative
understanding is have a role in scientific
explanation instead of remaining an investigative
humanities subject like history or metaphysics.
A central insight of the contemporary
Pragmatist philosophy of science is that only
empirical criteria can reveal the connection between
actions or events and their consequences in social
and behavioral sciences. Historically in successful science empirical criteria have
always had priority over all other considerations
for theory evaluation.
It took about three hundred years for natural
scientists and philosophers to recognize this.
Apparently it will take another hundred years
or more for the retarded slow learners in the social
sciences to institutionalize it also.
On
Pragmatism vs. Psychologism
Is
computational philosophy of science conceived as
cognitive psychology a viable agenda for
twenty-first century philosophy of science?
Both Simon and Thagard recognized the lack of
empirical evidence needed to warrant claims that
their computational cognitive systems are anything
more than very rough approximations to the
structures and processes of the human mind.
In fact Simon furthermore admitted that in
some cases the historical discoveries replicated
with the discovery systems described in his Scientific
Discovery were actually performed differently
from the way in which the discovery systems
performed the rediscoveries.
Recognition of this variance amounts to the
falsification of the cognitive psychology claims.
Yet Simon did not explicitly reject his
colleagues’ discovery systems as empirically
falsified psychology.
Rather the psychology claims were tacitly
ignored, while the systems continue to be developed
without independent empirical research into
psychology to guide new cognitive system
development.
Others have also found themselves confronted with
such a conflict of aims.
In "A Split in Thinking among Keepers of
Artificial Intelligence" the New
York Times (18 Jul. 1993) reported that
scientists attending the annual meeting of the
American Association of Artificial Intelligence
expressed disagreement about the goals of artificial
intelligence. Some
maintained the traditional view that
artificial-intelligence systems should be designed
to simulate intuitive human intelligence, while
others maintained that the phrase "artificial
intelligence" is merely a metaphor that has
become an impediment, and that AI systems should be
designed to exceed the limitations of intuitive
human intelligence.
The article notes that the division has
fallen along occupational lines with the academic
community preferring the psychology goal and the
business community expressing the alternative goal,
and also that large AI systems have been installed
in various major American corporations.
This alignment is not inherent, since the
academic community need not view artificial
intelligence exclusively as an agenda for
psychology. But
the alignment is understandable, since the business
community financially justifies
artificial-intelligence systems pragmatically as it
does any other computer system, and it has no
interest in faithful replicas of human limitations
such as the computational constraint described in
Simon's thesis of bounded rationality or the
semantical impediment described by Hanson and called
the “cognition constraint” by Hickey.
This is the same pragmatic justification that
applies generally in basic scientific research. Scientists will not use discovery systems to replicate the
scientist's limitations, but to transcend these
limitations to enhance the scientist’s research
capability, productivity and performance.
Artificial
intelligence may have outgrown its original home in
academic psychology.
The functioning of discovery systems
developed to facilitate basic science research is
more adequately described as constructional
language-processing systems with no psychological
claims. The
relation between the psychological and the
linguistic perspectives can be illustrated by way of
analogy with man’s experience with flying.
Since primitive man first saw a bird intended
for dinner spread its wings and escape the hunter by
flight, mankind has been envious of birds’ ability
to fly. This
envy is illustrated in ancient Greek mythology by
the character Icarus, who escaped from the labyrinth
of Crete with wings he made of wax.
The story describes him as having flown too
near to the hot sun, so that he fell from the sky as
the wax melted, and then was drowned in the Aegean
Sea. Icarus’
fatally flawed choice of materials notwithstanding,
his basic design concept was a plausible one in
imitation of the evidently successful flight
capability of birds. Call Icarus’ design concept the “flapping-wing”
technology. A
contemporary development of the flapping-wing
technology with superior materials might serve well
for an investigation of how birds fly, but it is not
the technology used for modern flight by mankind.
Successful human flight has evolved very
different technologies, such as initially the
hot-air balloon, then the Wright brothers’
fixed-wing motor-powered propeller airplane, then
later the rotary blades of the helicopter, and most
recently the jet and the rocket.
It is noteworthy that none of these
successful technologies imitate the capabilities of
the birds - a fact that is not surprising, when one
recalls that the motivating aim was not to
investigate birds, but rather to enable men to fly.
When
proposed imitation of nature fails, pragmatic
innovation prevails, in order to achieve the
motivating aim.
Therefore when asking how a computational
philosophy of science should be conceived, it is
necessary firstly to ask about the aim of philosophy
of science and whether or not computational
philosophy of science is adequately characterized as
normative cognitive psychology.
Contemporary Pragmatist philosophy of science
views the aim of basic science as the production of
a linguistic artifact having the status of an
“explanation”, which is a theory that has been
proposed and not falsified when tested empirically.
Then the aim of a computational philosophy of
science in turn is derivative from the aim of
science: to enhance scientists’ research practices
by developing and employing mechanized procedures
capable of achieving the aim of basic science.
To accomplish this, the computational
philosopher of science should be at liberty to
employ any technology - any computer hardware or
software design irrespective of psychology or
neurology - that facilitates production of testable
theory proposals and therefore of scientific
explanations. And
it might be added that the empirical test is
prediction, which in the event is a contribution to
basic science.
Since a computer-generated explanation is a
linguistic artifact, the computer system may be
viewed as a constructional language-processing
system. Psychology
or neurology may or may not suggest some tentative
hypotheses to this end.
But the aim of basic science does not require
reducing a computational philosophy of science to
the status of a specialty in either psychology or
neurology, any more than the aim of aerospace
science need be reduced to a specialty in
ornithology. Thus
to construe computational philosophy of science as
normative cognitive psychology, as Thagard would
have it, is to have lost sight of the aim of basic
science. And
to date attempts at a cognitive psychology of
science appear to have offered basic science no
better prospects for improvement of research
practices, than did the Icarus wing-flapping
technology for manned flight.
In retrospect the thesis that it could, might
be labeled the “Icarus fallacy.” Accordingly in
computational philosophy of science the phrases
“cognitive psychology” and “artificial
intelligence” are rendered as irrelevant as
“engineering ornithology.”
The developers of the practical and
successful discovery systems have been practicing
researchers in the sciences for which they have
developed their discovery systems.
They have created systems that have produced
serious and responsible proposals for advancing the
contemporary state of the empirical sciences in
which they work.
To date none have been cognitive
psychologists.
Those fruitful discovery systems are
Sonquist’s AID
system, Litterman BVAR
system, and Hickey’s METAMODEL
system. If
they have not been cognitive psychologists, neither
have they been academic philosophers.
In some cases their understanding would have
benefited from the contemporary Pragmatist
philosophy of science perspective.
Sonquist, who developed the AID
system, is a practicing research sociologist.
His inadequacy in contemporary philosophy of
science led him to turn to Positivism, in order to
evade the obstructionist Romanticism still
prevailing in academic sociology.
Pragmatism would have served him better.
Now known as the CHAID
system, Sonquist’s discovery system is probably
the most widely used of all the discovery systems
created to date.
It is also the earliest.
For Litterman, evasion of the Romantic
philosophy was easier.
He is an economist who developed his BVAR
system under teachers at the University of Minnesota
who were rational expectations advocates.
Ironically their economic “theory”
notwithstanding, they were economists who had
rejected Haavelmo's structural-equation agenda,
thereby rendering Romanticism inoperative for
determining the equation specifications for
econometric model construction.
But Litterman would have had a better
understanding of the significance and value of his
work for economics, had he understood the
contemporary Pragmatist philosophy of science.
His system is still used by the Federal
Reserve Bank of Minneapolis.
Hickey was more fortunate, since he is both
an Institutionalist economist and –
notwithstanding the reformist obstructionism of the
University of Notre Dame philosophy faculty – a
contemporary Pragmatist philosopher of science. In the thirty years since he created his system, he has
applied his METAMODEL
discovery system for market analysis of both
consumer and industrial products, for consumer
credit risk analysis, for macroeconomics, for
regional economics, and for macrosociology.
The practical discovery systems developed by
Sonquist, Litterman, and Hickey also reveal a
distinctive strategy.
Their designs, procedures, and computer
languages remained close to the analytic practices
actually used by researchers in their respective
sciences. The
difference between these systems and those developed
by Simon, Thagard, and other cognitive
psychologists, echoes the old philosophical issue
between the ideal-language and the ordinary-language
philosophers in the twentieth century.
What may be called the ordinary-language
computational philosophy-of-science approach used by
Sonquist, Litterman, and Hickey is based on the
analytical techniques that are ordinary in their
sciences. The
ideal-language cognitive-psychology philosophers of
science use computer languages that are not used in
the sciences in which they implemented their systems
and that furthermore often have structures
resembling the Russellian symbolic logic.
The Logical Positivists’ used Russellian
logic for philosophy of science, and their approach
is now of interest only to the antiquarian or
historian. Today
the ideal-language discovery systems are the
exclusive property of the academic cognitive
philosophers of science.
The world of science still awaits their
contribution to the advancement of the contemporary
state of science.
Computational philosophy of science is the
wave of the future that has arrived.
But some philosophers will have to make
fundamental adjustments in their psychologistic
views in philosophy of language. The psychologistic turn has in no small part been due to
their doctrinaire nominalism built into their
Orwellian newspeak, the Russellian symbolic logic.
Yet nothing precludes a linguistic
computational philosophy of science that views the
discovery systems as language-processing systems and
recognizes a three-level semantics enabling
philosophers to speak unabashedly about mental
concepts without having to make pretentious
psychologistic claims.
Cognitive psychology of science is still a
promissory note issued by a profession having no
payment or credit history, and science awaits
evidence of its redeeming cash value.
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