in Kybernetes (2000),
vol 29, Number 7/8, 836-845
Abduction in
Language interpretation and Law making
Evelyne
Andreewsky & Danièle Bourcier
andreews@ext.jussieu.fr bourcier@msh-paris.fr
SUMMARY
The
complexity of any given cognitive phenomenon, such as “ scientific
discovery ”, “ technical expertise ”, or “ natural language
understanding ”, requires a multidisciplinary approach. We present, within the
framework of such an approach, some visible evidences of how these very
different phenomena are closely rooted in the same highly inventive
cognitive process, abduction. These evidences will be
provided out of examples from both everyday language interpretation and
law making expertise.
KEY-WORDS
Abduction,
Cognition, Complexity, Language Interpretation, Law Making.
INTRODUCTION
The computer metaphor has taken the
lead in conceptualizing the mind and providing explanations for cognitive
phenomena. With this metaphor, grounded in the most prestigious technique of
our time (i.e. computing), the human mind becomes “computational”: it processes
information. Psychology thus adopts the key-concepts of Computer Sciences (such
as information, representation or computation). Let us recall
that American Psychology (that of Behaviorism, which emphasized the study of
the responses of rats to sensorial stimuli) switches, as Arthur Koestler
claims, from “an anthropomorphic view of the rat to a ratomorphic
view of man” (before disappearing altogether). We are now dealing with a
“computomorphic” view of ourselves! This view has generated a new Science of
the Mind, consisting of a vast disciplinary enterprise, born in the 70’s. It
involves Artificial Intelligence, Neurosciences, Linguistics, Experimental
Psychology, etc.; its aim is to build the intelligibility of mind and knowledge
– hence its label of Cognitive Sciences.
Cognitive Sciences
and Systems Science have fundamentally the same interdisciplinary roots. In the
mid-century, the need to explore complex systems and to counterbalance
fragmented and compartmentalized approaches in scientific research led to interdisciplinary
attempts to model complex phenomena. Typical of such attempts was Cybernetics,
for modeling communication and control. Systems Science and Cognitive Sciences
were born of Cybernetics. The explicit aim of Cybernetics was managing
complex systems – opening therefore a way towards Systems Science.
It was also a means (though more implicitly), of gaining some understanding
of an extremely complex system - the cognitive one. This system is altogether
fascinating for researchers (in keeping with the old injunction: know
thyself), and most difficult to explain; indeed, according to Bateson, the
human mind has explained everything – but not itself. That explanation is
the whole project of Cognitive Sciences.
Aristotelian Logic and Cognitive Paradigms
Cognitive Sciences are
growing along multiple ways and paradigms. Their object, the study of human
knowledge and of cognitive abilities such as thinking or reasoning, is
obviously not a new one. Some thousand years ago in Greece, the highest form of
thinking, “Logic”, was extensively investigated, and logic became the alpha and
omega of the whole history of scientific progress. Now, since logic is, so to
speak, embodied in computers, Cognitive Sciences are the heir of the logical
approaches to reasoning. We may track the origin of these approaches to
Aristotelian classical thought, and its three modes of reasoning (“reasoning”
meaning “finding out the truth”) - deduction, induction,
and retroduction (or “abduction”):
-
“To think is to compute” - the
main cognitivist metaphor – may be linked to deduction;
-
“Thinking is an emerging property of
neural networks” - the “Connectionist” paradigm - may be linked with induction.
-
Finally, a variety of cognitive
paradigms - such as the “auto” ones (auto-organization, autopoïese,
etc.) - which reject the hypothesis of any “objective external reality”
providing information to the cognitive system - may be linked with abduction.
Deduction
The
main Cognitive Science paradigm, the symbolic - or computo-representational -
one, which takes the computer metaphor as a functional model of mind, is close
to deduction. This is particularly clear in the prototypical
computo-representational approach, that is, the system-expert model, in terms
of if…then (with the pattern of the well known Socratic
deductive reasoning: All men are mortals; if x is a man, then
x is mortal).
In this framework, the human
cognitive system is characterized by both its internal (or “mental”) states
and by the processes used to move from one state to another. These
states are representational[1]
and point to external (objective) entities.
The computational
theory of mind comes directly from the cybernetic tradition. It is
based on analogies between organisms and machines and, more precisely, on an
equivalence between hardware and software, on one hand, and body and mind on
the other. An equivalence which was proclaimed as early as 1943, in keeping
with the renown W. Pitts and W. McCulloch experiment (McCulloch, 1965). It has
been taken for granted by the cyberneticians, strongly relying upon a
“computomorphic view of man”. Such a view induced many exciting hopes! First,
the hope to peer into the mysterious “black box” of the behaviorists (that is,
the cognitive system) and, conversely, to design some electronic golem
(in keeping with the feeling of those outstanding scientists such as John von
Neuman, or Norbert Wiener, when designing the first computer in the forties).
Induction
Induction, in contrast to
deduction, goes from facts to theory. It is a very usual reasoning
process, being, for instance, the (implicit) way we learn our mother tongue
(the theory - here the rules of the language - being implicitly inferred
out of day to day linguistic interactions). The connectionist paradigm is close
to this framework. Indeed, this paradigm is rooted in “learning”. A
connectionist system in the process of learning is a network of interconnected
formal neurons which modifies its connections. Patterns (in other words, facts)
are presented to the input “retina” of the system. It “learns” (for instance in
the framework of a pattern recognition problem) how to discriminate and to
class these patterns through successive modifications of the weight of
the connections between its “neurons”. At the end of the learning process the
system is provided, out of its weighted network, with a true functional
“classification theory”; it is therefore able to label any new pattern
presented to its retina as belonging to one or another class of patterns
defined through learning.
The first "neuronic
network", Rosenblatt’s Perceptron, was designed at the beginning of the
60’s. The approach was then abandoned for a variety of reasons, namely the
weakness of its formalization (Minsky & Papert, 1968). It took fifteen
years to see its re-emergence, under the name of Connectionism (or
“neo-Connectionism”), in a more elaborate theoretical framework, thanks to the
progress in non-linear Mathematics and in Systems Dynamics (Haken, 1983).
Several simulations of
cognitive activities such as character recognition, language perception, etc.,
have been attempted in the framework of the connectionist paradigm. These
simulations (McClelland et al, 1986) are no longer expressed, as are classical
computational systems, in terms of messages transmitted between some modules
of the system, but imply the activated state of the whole system,
through weighted values - numbers, and not symbols.
Connectionism
aims to explain behavior endogenously, by means of physiologically plausible
hypotheses, with a direct relation to Neurosciences. Its type of explanation is
opposed to the classical A.I. approaches, since within these latter approaches
no behavior is produced unless the set of rules underlying this behavior is
specified to the system.
The classical
computo-representational paradigm and the connectionist one are quite
different. Yet they share a “computomorphic” view of man (be it sequential,
like in the former case, or “massively parallel”, like in the later one). In
both cases, mind is defined as an information processing device
operating upon an external “objective” reality.
Abduction
(“retroduction”, for Aristotle, cf. Peirce, 1995) has multiple definitions. In
short, it is a creative process (Bourgine, 1989) which consists
of finding a plausible hypothesis to fit a given “strange” phenomenon. Indeed,
to quote von Foerster (1974), there is no information, or anomalies in
our environment. Hence, if a given phenomenon looks strange, this only means
that the theoretical framework used to interpret this phenomenon must be
revisited! The revisiting cognitive process is labeled abduction, and
its aim is to “normalize” anomalies.
The
abduction pattern refers to the fairly common experience of observing an
unexpected, anomalous and strategic datum, which becomes the occasion for
developing a new theory or for extending an existing one.
The
datum is, first of all, unexpected. It is for instance the case when a research
directed towards the test of one hypothesis yields a fortuitious by-product, an
unexpected observation which bears upon theories not in question when the
research begun.
Secondly,
the observation is anomalous, surprising, either seemingly inconsistent with
the prevailing theory or with other established facts. In either case, the
inconsistency provokes curiosity; it stimulates the investigator to make sense
of the datum, to fit it into a broader frame of knowledge.
Thirdly,
in noting that the unexpected fact must be strategic, i.e., that it must permit
implications bearing on generalized theory, we are of course referring rather
to what the observer brings to the datum, than to the datum itself. Here it
obviously requires a theoretically sensitized observer to detect the universal
in the particular.
According to Piaget & Garcia (1983) there is a functional
continuity between every day cognitive elaboration and scientific ones, given
that we rely on our expertise to deal with the circumstances
under hand, both in everyday life and in scientific research. Cognitive every
day interpretation somehow recalls reasoning, featuring highly scientific
domains. Indeed, both are dealing with the production and testing (falsifying)
of hypothesis and theories, both resort to abduction, the process aiming to
suggest a hypothesis able to explain a given unexpected phenomenon.
According to Peirce (1995), such hypotheses or theories are “ new
suggestions, even if all their elements were already in mind, since we never
dream to put these elements together ”. In such a framework, interpreting
everyday unexpected sentences, or thinking of new laws to handle
unexpected behaviors, is something like suggesting scientific theories.
It leads -like in theories - to the emergence of something new,
i.e., some relevant hypotheses on what is at hand within given circumstances.
The abductive framework
emphasizing pragmatic hypotheses (that is, hypotheses capable of verification
or justification) fitting circumstances, is a main creative cognitive process,
as will be illustrated hereafter by
Language Interpretation and Judicial examples.
1. Abduction and language interpretation
Several
anomalies (or problems) emerge from the traditional “objective”
approaches to meaning. These cognitive problems, insofar as they are inherent
to understanding, may be highlighted through the difficulties of natural
language understanding systems (A. I.). This type of framework presupposes
an “objective” approach to meaning postulating namely, that all sentences have
a meaning independent of the interlocutors… This is obviously not the
case since, for instance, a statement such as the following: it’s better to
give than to get, “takes” two completely opposite meanings, depending on
whether it’s made by a priest during a sermon, or by a pugilist in the context
of a boxing match! How can one then “construct” the meaning of this statement
without being dependent on these circumstances (potentially infinite), that is
to say without taking into account the creative dimension of
understanding?
Along this dimension,
the abductive framework best fits everyday life examples, showing that “ the ”
meaning of a given word may be strongly driven by the domain of experience in
which this word occurs. Word meaning may indeed entirely change from one
occurrence to the other, according to each new domain at hand, and is therefore
likely to differ from any earlier meaning. This is obviously the case of
a word occurring in a new “ metaphor ” (Lakoff & Johnson, 1980).
But it can also be the case for a word occurring in any sentence. Thus the need
to "create" and to recreate these words meanings. This may be
illustrated with very common words and sentences, such as for instance
“ water ” in the claim:
There is no more water!
Such a claim,
interpreted in the framework of many different domains of experience, is
endowed with many different meanings. When uttered in a Supermarket, it usually
means that there are no more bottles of mineral water left on the shelves
- the lexical item water is therefore to be interpreted as
“ bottles ”. If one is lost in the desert, this item comes to mean
something like “ a drink vital for survival ”, and the claim is a cry
of despair. It would turn into a cry of joy when bailing out a boat; water
being now an undesirable ballast slowing down the boat. In the framework
of a scientific article, commenting some “ memory of water ” data
(likely to change our conceptualization of the liquid), water is
something like “ our traditional concept of H2O ”, and so
forth.
Such a strong “ updating ” of the meaning of
water in each new domain of experience implies a recurrent creation
of these meanings, and therefore a recurrent abduction.
Such
an endless creation of meaning stresses the complexity of language
understanding. Word meaning, far from pointing to any objective external
reality, is made up of our complex subjective and inter-subjective internal
one. In this framework, it stands for a nice metaphor of cognition, in keeping
with Wygotski's (1995) claim: the meaning of a word reflects, in its
simplest mode, the unity of Language and Thought; individual Cognition[2]
is reflected in a given word as the sun in a drop of water. A word endowed with
meaning is a microcosm of Human Cognition.
2. Judicial
abduction: legal proof, legal presumption and experimental norms
The judicial system is obliged to
anticipate but it cannot foresee all: neither future events, nor the necessary
rules, ror the effect of the implemented norms. We will focus on three cases
where law relies upon abductive reasoning for the establishment of facts, the
invention of principles, or the creation of norms.
2. 1. Legal proof and abductive reasoning
The detective, the policeman, the
paleontologist reconstitute the past by means of clues from which they put
forward hypotheses. Certain authors have studied the relations between
scientific reasoning and the detectives' procedures. The story of Oedipus could
for example be interpreted as that of an examining magistrate inquiring on his
personal case. Umberto Eco has proven that Conan Doyle used abductive reasoning
as if it possessed an absolute logic. Sherlock Holmes in The Sign of Four
constructs the following type of reasoning: the fact that Watson enters with a
certain color of mud on his shoes allows the former to "deduce" that
Watson has sent off a telegram from a certain post-office... The aim is to
produce rigorously linked arguments, whose exposition is in the interest of the
story. The policeman, who is Darwin's brother-in-arms, discovers the criminal
via his foot-tracks by means of a mental process identical to the one used by
Cuvier to reconstitute the aspect of his Montmartre fossils by the simple means
of their bone debris (Huxley, 1988).
Policemen and examining magistrates
do not learn de visu about facts relevant to a case or a file. The "truth" can hence only be
established after traces, fingerprints on objets, memories of witnesses, or by
experiment. The knowledge obtained
remains indirect, however, and depends on the aptitude to decipher and
interpret the traces which may under certain conditions become judicial proofs.
This notion of proof must be
distinguished from that of proof in Logic. Logic has thought as its basis, that
is, reasoning and not facts. In Logic, proof (or deduction) is the result of a
reasoning, which serves to demonstrate the accuracy of a thesis. This is why
Logic does not recognize abduction as valid reasoning. The separation between
factual proof, logical proof and judicial presumption deserves our special
attention.
2. 2. Presumption as an abductive reasoning
Law is not merely preoccupied with
facts: the judge, the administrator, "qualifies" - i.e., matches
facts and hypotheses with the legal condition of the application of a
rule. Facts can be infinite and the
judge only takes into consideration those which produce a judicial
consequence. Using a comparison with
scientists: the chemist or physician is only interested in details as far as
they help to confirm or infirm a certain theory or hypothesis (Perelman, 1975)
A second characteristic is that in
the search for facts, law has regulated the administration of proof, subject to
a specific procedure. If this procedure is not followed, the proof provided is
not acceptable even if the "truth" is glaring. For instance, law has
created a presumption which makes it difficult to search for facts leading to a
proof of fatherhood. In fact, the husband is the presumed father of a child
conceived during marriage: he is the only person who can disown the child and
he only has six months to do so. What purpose do these presumptions serve? They
allow to conclude without proof. It devolves on the person who refutes the
results to bring the proof.
In law, presumption is defined as
the technique allowing to provide solutions under the uncertainty of proofs.
What reasoning will the judge follow
in the case of contention? He will draw from a known fact (the date of birth,
or of marriage) the consequences of the unknown fact: the fatherhood or
non-fatherhood of x. Such a fact, if not true, is at least becoming likely out
of a first group of facts or clues. This idea of likelihood is interesting in
our case. It limits impossible induction and delirious interpretations (the famous "interpretation delirium").
One may thus know the likelihood of
a hypothesis and the acceptability of a conclusion.
Let us consider the schemas of
Peirce in the case of fatherhood:
The
father is the mother's husband (plausible)
x is
y's child (known fact)
ABDUCTION y is the husband of x's mother (possible fact)
y
is the husband of x's mother
x
is y's child
INDUCTION The
father is the mother's husband (probable)
The
father is the mother's husband (legal presumption)
y
is the husband of x's mother
DEDUCTION x
is y's child
The abduction which started off from
a simple presumption becomes thus a legal presumption, that is, a rule of law
which, while basing itself on factual truth, forces the truth mechanism.
2. 3. Law making and experimental
norms
The legislator may have foreseen the
facts and the rules but not the citizens' reactions to a new law. Any normative
system can produce effects which are unexpected and even conflicting with the
initial aim. In the framework of law making, unexpected effects can indeed
emerge. They can be positive (improving the law), or negative (bypassing the
law). In this framework, we must rely upon abduction to cope with such law
effects.
Legal technique has already foreseen
this possibility of palliating the unexpected effects of texts, or of finding
solutions to new situations, by developing so-called experimental laws.
These laws aim at testing the effects of new norms or institutions concerning
social or technical evolutions (such as abortion in France, pre-legal
euthanasia in the Netherlands), or likely to have important economical effects
(new taxes). These laws are evaluated, extended, modified or definitely adopted,
following the ensuing results.
Let us observe, for example, how, in
the case of euthanasia, new rules based on practice are emerging in the
Netherlands.
How can one use the present social
system and normative framework to anticipate or interpret a new, surprising,
controversial and complex phenomenon? Progress in science and medical
technology has developed the situation of euthanasia. Formerly, in the
Netherlands, like in France today, euthanasia was practiced in "petit
cabinet" by means of an overdose of morphine and registered as
"natural death". At present euthanasia is "legally"
organized in the following way (Griffiths, 1998):
Euthanasia has always been
considered as murder in the existing juridical system. Nonetheless, since 1990,
a partial "legalization" has been installed: this is assorted with
the physicians' duty to prepare a case report and to transmit it to the public
prosecutor for evaluation of all necessary conditions. This procedure was even
validated by the Parliament in 1993.
The court either definitely files and disposes of the case, or
prosecutes the physician who is then judged. Though considered guilty, he may
not, however, be sentenced.
Social laboratories and professional
communities are used to help promote the process of learning and to encourage
the emergence of rules representing maximal consensus.
The same type of evaluative approach
was used in France in the case of child abortion (I.V.G.: Voluntary pregnancy
interruption). Abortion was "experimented" by a temporary law in
1975. This phase was considered as very important, not so much for the
uncertainty of its effects (an increase in abortions, for example), as for the
social resistance and the strong political opposition in Parliament (the
project was only approved by 99 members of the majority which then counted
291).
In the Netherlands, in the case of
euthanasia (or aid in committing suicide, its other alternative) we observe a
different type of elaboration and adjustment process. There is no preliminary law, but experimental practice allowing
abductive approaches of a problem considered case by case, following protocols
established by physicians, and the personal demands of patients. A new law was
then proposed. The social corpus (in this case, the physicians) took the risk
of elaborating the first rules of the game.
The behaviors and reactions (unknown
at the start) instigated by the new facts will be interpreted in order to allow
the emergence of an adequate procedure leading to the establishment of a new
rule. This procedure develops the
emergence of shared knowledge (the aim being to render the procedure visible
and hence to incite the physicians to prepare medical reports) when faced with
unknown situations. This knowledge is mutually co-determined, and it
facilitates consensus via temporary abductive mechanisms.
CONCLUSION
We
have tried to present how abduction patterns refer to the fairly common
experience of dealing with an unexpected phenomenon, which calls for developing
a new interpretation or for extending an existing one.
Anyone
testing a given hypothesis and observing an anomaly that surprises because it
conflicts with his knowledge and views, normally reacts by suspecting a
mistake. When he has excluded this possibility, his second sensible reaction is
to find an ad hoc interpretation to explain the anomaly. If this
interpretation is interesting, probable, simple, elegant and testable enough,
he can experiment to verify this new hypothesis. A scientific investigation
runs on two legs, one leg for hypothesis testing, and a second leg for anomaly
explaining, out of abduction (Merton R. K., 1957).
The
same holds for Language understanding, either in everyday life or within the
Legal framework: clearly, one cannot simply rely on ready made meanings,
neither for usual nor for Legal Language where, in both cases, understanding is
a creative mechanism able to cope with unexpected phenomena.
Scientists
have long been regarded as using mainly the method of hypothesis testing; yet,
from Aristotle to Peirce, a number of them have espoused the method of anomaly
explaining (involving abduction processes). This is how both the scientist and
the layman reason.
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[1]The representational theory of mind
goes back at least to Descartes. For the Cognitive Sciences, the representation
of knowledge in human memory constitutes a main research problem; one of the
first models of semantic memory (Quillan, 1968) was built on the hypothesis of
an hierarchical organization of the concepts stored in memory, according to
some cognitive economy. From that time, many cognitive representational models
have been developed in classical Artificial Intelligence (I. A.), to cope
with behavioral data (Shanon, 1993).
[2] "Cognition" is "sosnanie" in Russian; and means also "consciousness".