| |
On
Scientific Discovery and Philosophy of Science
Before Simon and his colleagues at
Carnegie-Mellon University had developed functioning
computerized discovery systems simulating historic
discoveries, Simon had written articles claiming
that scientific discovery is a special case of human
problem solving.
In these articles he related his human
problem-solving approach for discovery, to views
published by various philosophers of science.
The articles are reprinted in his Models
of Discovery, where he comments in his
"Introduction" that the subject of
scientific discovery and of creativity generally has
always been surrounded by dense mists of romanticism
and downright knownothingness.
In his "Scientific Discovery and the
Psychology of Problem Solving" (1966) Simon
states his thesis that scientific discovery is a
form of problem solving, i.e. that the processes
whereby science is carried on can be explained in
terms that have been used to explain the processes
of problem solving.
Problem-solving thinking is an organization
of elementary information processes organized
hierarchically and executed serially, and consisting
of processes that exhibit large amounts of highly
selective trial-and-error search based on rules of
thumb or heuristics.
The theory of scientific discovery is a
system with these characteristics, and which behaves
like a scientist. Superior problem-solving scientists have more powerful
heuristics, and therefore produce adequate solutions
with less search or better solutions with equivalent
search, as compared with less competent scientists.
Science is satisficing, and to explain
scientific discovery is to describe a set of
processes that is sufficient and just sufficient, to
account for the degrees and directions of scientific
progress that have actually occurred. Furthermore, for every great success in scientific discovery
there are many failures, and a theory to explain
scientific discovery must predict innumerable
failures for every success.
In "Scientific Discovery and the
Psychology of Problem Solving” Simon also takes
occasion to criticize the philosophy-of-science
literature. He
maintains that the philosophy literature tends to
address the normative rather than the descriptive
aspects of scientific methodology, and that
philosophers are more concerned with how scientists
ought to proceed, in order to conform with certain
conceptions of logic, than with how they do in fact
proceed. And,
he adds, their notions of how scientists ought to
proceed focuses primarily on the problem of
induction. He
concludes that the philosophy of science literature
has little relevance to the actual behavior of
scientists, and has less normative value than has
been supposed.
But he finds two exceptions in the philosophy
of science literature: Russell Hanson and Thomas
Kuhn. He
says that both of these authors have made
significant contributions to the psychology and
sociology of scientific discovery, and that they
have been quite explicit in distinguishing the
process of discovery from the traditional canons
of "sound" scientific method.
He also says that he has made much use of the
views of both of these philosophers.
Simon's principal commentary on the
philosophy of Hanson is his defense of Hanson
against the view of Popper in "Does Scientific
Discovery Have a Logic?" (1973).
He notes that Popper rejects the existence of
a logic of scientific discovery in Popper's Logic
of Scientific Discovery (1934), and he says that
Popper's view is opposed by Hanson in the latter's
Patterns of Discovery (1958) and by Peirce.
Peirce used the term "retroduction",
which Simon says is the main subject of the theory
of problem solving in both its normative and
positive forms.
Simon observes that Hanson made his case by
historical examples of scientific discovery, and
that he placed great emphasis on discovery of
perceptual patterns.
In this 1973 article as well as in his
earlier "The Logic of Rational Decision"
(1965) Simon distinguishes heuristic search from
induction, and defends the idea of a logic of
scientific discovery in the sense that norms can be
derived from the goals of scientific activity.
He defines the logic of scientific discovery
as a set of normative standards for judging the
processes used to discover or test scientific
theories, where the goal from which the norms are
derived is that of discovering valid scientific
laws. And Simon emphasizes that the heuristic strategy does not
guarantee results or success, and he therefore
argues that he has not smuggled any philosophical
induction axiom into his formulation of a logic of
discovery, and that such a logic does not depend on
the solution of the problem of induction.
Simon states that discovering a pattern does
not involve induction or extrapolation.
Induction arises only if one wishes to
predict and to test whether or not the same pattern
will continue to obtain when it is extrapolated.
Law discovery only means finding patterns in
the data that have been observed; whether or not the
pattern will continue to hold for new data that are
observed subsequently will be decided in the course
of predictive testing of the law, and not in
discovering it.
It may be noted that after Simon's colleagues
had created functioning discovery systems based on
heuristic search, Simon often described some of
those systems as using inductive search.
However, in his Scientific Discovery Simon explicitly rejects the search for
certainty associated with attempts to justify
inductivism.
Simon subscribes to Popper's falsificationist
thesis of scientific criticism, and in his
"Ramsey Eliminibility and the Testability of
Scientific Theories" (1973) reprinted in his Models
of Discovery Simon proposed a formalization of
Popper’s requirement that admissible theories be
falsifiable. This
formalization is his "FITness" criterion,
which is a neologism containing an acronym for
"Fit and Irrevocable Testability.”
According to this requirement a theory should
be admissible for consideration if and only if (1)
in principle it is falsifiable by a finite set of
observations, and (2) once falsified, additional
observations cannot rehabilitate it.
In a footnote at the end of this paper Simon
adds that the FITness criterion is only a
requirement for falsifiability, and that it says
nothing about the disposition of a theory that has
been falsified, such that the FITness criterion is
compatible with what Lakatos calls
"methodological falsificationism", because
methodological falsificationism permits a falsified
theory to be saved by modifying the auxiliary
hypotheses that connect it with observables.
In his "Methodology of Scientific
Research Programmes" in Criticism
and the Growth of Knowledge (1970) Imre Lakatos
distinguished "dogmatic falsificationism"
from "methodological falsificationism",
and within the latter type he further distinguished
"naive" and "sophisticated"
subtypes. Simon's
reference to auxiliary hypotheses in his footnote
suggests he believed that his FITness criterion is
of the "sophisticated" subtype.
But he later apparently reconsidered; in Scientific
Discovery he called his FITness criterion
"naive falsification", and gave two
reasons: Firstly the criterion postulates that there
are wholly theory-free observation sentences, whose
truth status can be determined by observations that
have no theoretical presuppositions.
Secondly his criterion is too strict to be
applied to any theory.
Simon maintains that there are no wholly
theory-free observation sentences.
Simon's comments on Kuhn's philosophy are
principally concerned with Kuhn's distinction
between normal and revolutionary science.
Kuhn maintained that the revolutionary
transition is a gestalt
switch, while Simon defends his own view that
heuristic search procedures apply to revolutionary
changes as well as to normal science.
In his "Scientific Discovery and the
Psychology of Problem Solving" Simon says that
his theory of scientific discovery rests on the
hypothesis that there are no qualitative differences
between the processes of revolutionary science and
those of normal science, between work of high
creativity and journeyman work respectively.
Simon points to the fact that trial and error
occurs in both types of work.
He argues that trial and error are most
prominent in those areas of problem solving where
the heuristics are least powerful, that is, are
least adequate to narrow down the problem space,
such that the paths of thought leading to
discoveries often regarded as creative might be
expected to provide even more visible evidence of
trial and error than those leading to relatively
routine discoveries.
Later in his Scientific Discovery Simon develops the idea of the amount of
trial-and-error search into the distinction between
"strong" methods, which he says resemble
normal science, and "weak" methods, which
resemble revolutionary science.
He identifies expert systems based
principally on productions, where there may be
almost no search needed for problem solving, as
paradigmatic cases of strong methods exemplifying
normal science.
Simon's argument that trial and error is used
in all types of discovery is his defense of the
heuristic method.
But method is only one aspect of his theory
of problem solving; there is also the definition of
the problem space.
He acknowledges that scientific work involves
not only solving problems but also posing them, that
correct question asking is as important as correct
question answering. And he notes that Kuhn's distinction between normal and
revolutionary science is relevant to the relation of
question asking and question answering.
In the 1966 article Simon identifies the
problem space, which is the problem solver's point
of view of the outer environment, with Kuhn's idea
of paradigm, and he identifies the definition of the
problem space with the process of problem formation.
Firstly Simon notes that normal science need
not pose its own questions, because its questions
have already been formulated for it by the current
paradigm produced by the most recent scientific
revolution. The
problem space is thus given by the current state of
the science; the problematic case is the scientific
revolution, which establishes the new paradigm.
Simon argues that it is not necessary to
adduce entirely new mechanisms to account for
problem formulation in revolutionary science,
because, as Kuhn says, the paradigms of any given
revolution arise out of the normal science of the
previous period.
Normal science research leads to the
discovery of anomalies, which are new problems that
the prospective revolutionaries address.
Therefore Simon argues that there is no need
for a separate theory of problem formulation.
He states that a theory of scientific
discovery adequate to explain revolutionary as well
as normal science must account not only for the
origin of the problems, but also for the origins of
representations, the problem spaces or paradigms.
Representations arise by modification and
development of previous representations as problems
arise by modification and development of previous
problems. A
system that is to explain human problem solving and
scientific discovery does not need to incorporate a
highly powerful mechanism for inventing completely
novel representations.
Even in revolutionary science the problems
and representations are rooted in the past, and are
not cut out of whole cloth.
Later in his "Ramsey
Eliminability..." article Simon considers
another objection pertaining to the problem space in
revolutionary developments. The objection is that in revolutionary science the
range of alternative hypotheses that constitute the
problem space or representation cannot be delimited
in advance. He
states that this objection rests on a commonly drawn
distinction between well defined problems, which are
amenable to orderly analysis such as those in normal
science, and ill defined problems, which are thought
to be the exclusive domain of creativity, such as
those in revolutionary science.
Simon argues that the force of the objection
depends on the distinctions being qualitative and
not just matters of degree.
He replies that there is no reason to deny
that revolutionary hypotheses can be the result of
some kind of recursively applicable rule of
generation. He
cites as an example of a revolutionary discovery
Mendeleev's periodic table, which does not involve a
notion of pattern more complex than that required to
handle patterned letter sequences.
The problem space of possible patterns in
which Mendeleev was searching was of modest size,
and at least half a dozen of Mendeleev's
contemporaries had noticed the pattern independently
of him, although they had not exploited it as
systematically or as vigorously as he did.
Simon concludes that before one accepts the
hypothesis that revolutionary science is not subject
to laws of effective search, one should await more
microscopic studies than have generally been made to
date of the histories of revolutionary discoveries.
He says that the case of Mendeleev may pose
to be not at all exceptional.
Later in "Artificial Intelligence
Research Strategies in the Light of AI Models of
Scientific Discovery" in Proceedings
of the Sixth International Joint Conference on
Artificial Intelligence (1979) Simon can refer
to operational discovery systems.
He states that discovery systems are
distinguished from most other problem-solving
systems in the vagueness of the tasks presented to
them and of the heuristic criteria that guide the
search and account for selectivity, and that because
their goals are very general, it is unusual to use
means-end analysis commonly used for well structured
tasks and to work backward from a desired result.
The discovery system solves ill structured
tasks and works forward inductively from the givens
of the problem and from the new concepts and
variables generated from the givens. He does not reference Kuhn in this context, but the
implication is that discovery systems can routinely
produce revolutionary science.
Then still later in his Scientific Discovery he reconsiders his earlier correlation of well
structured problems with normal science and ill
structured problems with revolutionary science.
He notes that normal science is described by
Kuhn as involving no development of new laws but
simply of applying known laws or developing
subsidiary laws that fill in the dominant paradigm.
He concludes that all discovery systems that
develop new laws directly from data and not from a
dominant paradigm must be productive of
revolutionary science.
Simon's difficulties in relating his ideas to
Kuhn's originate with Kuhn's ideas, not with
Simon's. The
most frequent criticism of Kuhn's Structures
of Scientific Revolutions in the philosophy of
science literature is that his distinction between
normal and revolutionary science is so vague, that
with the exception of a few paradigmatic cases his
readers could not apply the distinction to specific
episodes in the history of science, unless Kuhn had
identified a particular episodes himself.
The attractiveness of Kuhn's book at the time
of its appearance was not its unimpressive
conceptual clarity; it was its welcome redirection
of the philosophy profession's interest to the
history of science at a time when many philosophers
of science had come to regard the Logical Positivist
philosophy with hardly any less cynicism than Ovid
had shown toward the ancient Greek and Roman
mythology in his Metamorphoses. Simon's discovery systems offer analytical clarity that Kuhn
could not provide, with or without the Olympian
irrelevance of the Russellian symbolic logic.
Nonetheless Simon’s psychological approach
shares difficulties with Kuhn’s sociological
approach. The
reaction against Kuhn’s sociological approach was
often based in the recognition that conformity to
and deviance from a consensus paradigm may explain
the behavior of scientists without explaining the
success of scientists.
In due course Simon’s cognitive psychology
agenda for philosophy of science will be carried
further than Simon or his colleagues had expected,
and will result in artificial-intelligence systems
that are more than just discovery systems.
One noteworthy example of the psychological
approach can be found in the artificial-intelligence
systems developed by Thagard and his associates,
which are considered below.
But firstly consider the discovery systems
developed by Simon and his colleagues at
Carnegie-Mellon University.
The
Theory of Discovery Systems
Simon's principal work on discovery systems
is his Scientific
Discovery: Computational Explorations of the Creative
Processes (1987) co-authored with three
colleagues. In
the introductory section on the theory scientific
discovery he says that the central hypothesis is
that the mechanisms of scientific discovery are not
peculiar to that activity, but can be subsumed as
special cases of the general mechanisms of problem
solving. Thus
the approach taken is to exhibit a series of
computer systems capable of making nontrivial
scientific discoveries, which are actually
rediscoveries of historic scientific laws and
theories including empirical generalizations.
And the method of operation of these computer
systems is the method of heuristic search. The
theory of scientific discovery is also in his view
therefore a theory in cognitive psychology.
Simon says that he seeks to investigate the
psychology of discovery processes, and to provide an
empirically tested theory of the
information-processing mechanisms that are
implicated in that process.
He states that an empirical test of the
systems as psychological theories of human discovery
processes would involve presenting the computer
programs and some human subjects with identical
problems, and then comparing their behaviors.
The computer system can generate a
"trace" of its operations, and the human
subjects can report a verbal and written protocol of
their behavior, while they are solving the problem.
Then the system can be tested as a theory of
behavior by comparing the trace with the protocol.
Simon states that the computer systems
described in his book incorporate heuristic search
procedures to perform the kinds of selective
processes that he believes scientists use to guide
them in their search for regularities in data.
But he also admits that his book provides
little in the way of detailed comparison with human
performance. And
in discussions of particular applications involving
particular discoveries, he says that in some cases
the historical discoveries were actually performed
differently than the systems performed the
rediscoveries.
The principal interest in this book is
actually system design rather than psychological
testing and reporting, and Simon states that he
wishes to provide some foundations for a normative
theory of discovery, that is to say, to write a
how-to-make-discoveries book.
He explains that by this he does not mean a
set of rules for deriving theories conclusively from
observations. Instead,
he wishes to propose a set of criteria for judging
the efficacy and efficiency of the processes used to
discover scientific theory.
Accordingly Simon sets forth a rationality
postulate for the scientist: to use the best means
he has available - the best heuristics - for
narrowing the search down to manageable proportions,
even though this effort may result in excluding good
solution candidates.
If the novelty of the scientific problem
requires much search, this large amount of search is
rational if it employs all the heuristics that are
known to be applicable to the domain of the problem.
Thus, his rationality postulate for the
scientist is a bounded rationality postulate, not
only due to the limits imposed by memory capacity
and computational speed, but also due to the limit
imposed by the inventory of available heuristics.
|
|
