On theorizing the complexity of economic systems
ON THEORIZING THE COMPLEXITY OF ECONOMIC SYSTEMS
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Magazine: Journal of Socio-Economics, Fall, 1995
ON THEORIZING THE COMPLEXITY OF ECONOMIC SYSTEMS
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ABSTRACT: Complexity challenges the « normal sciences » because they
are based on logical neopositivist epistemologies and, therefore,
have difficulty dealing with intelligible yet unpredictable
phenomena. This problem is of particular concern to neoclassical
economics. Empirical research is making it increasingly clear that
economic systems are basically complex systems and, as such,
cannot be understood by reduction and simplification. This article
discusses a theory of modeling and reasoning about complex
economic systems. It first examines « constructivist
epistemologies » as a possible foundation for the modeling of
complex systems, then presents an embryonic theory of the modeling
of complex systems, based on constructivist epistemology. Two
modeling tools–of organization by information and of intelligent
organizational decision–are proposed. The arguments presented
here suggest that economics will have to become a new science of
organizational engineering in order to utilize and contribute to
the emerging new sciences of complexity.
INTRODUCTION
Modeling is a principal–perhaps the primary–tool for studying
the behavior of large complex systems . . . Modeling, then, calls
for some basic principles to manage this complexity
–H.A. Simon (1990, p. 7)
Is Economics Ignoring Complex Economic Systems?
« The Failure of Armchair Economics » argued by H.A. Simon (1986a) is not
only a failure of « the sovereign principle of deduction prediction » or
the sovereign principle of « substantive rationality » (Simon, 1986b). It
is also a failure of a monodimensional (or closed) economic science as
discussed by Bartoli in « L’economie multidimensionelle » (1991). Whether
seen from an epistemological and methodological point of view (Simon) or
from a more historical and political point of view (Bartoli) or
discussed from an empirical (Simon) or ethical (Bartoli) viewpoint, the
arguments leading to a diagnosis of failure converge on two basic
questions: what is reasoning and what is modeling? These questions
underlie the main hypotheses which have defined scientific knowledge
since Aristotle: the hypothesis of rationality and the hypothesis of
complexity.
Other social scientists have considered some or all of the arguments
developed by Simon and Bartoli. But these two solid, different, yet
convergent studies in economics give us a sufficient understanding of
the state of the art. If the neoclassical economist is not convinced by
the works of Simon and Bartoli, I do not expect to convince him of the
« failure » of his armchair or the « scandal » of his textbooks on
microeconomics, to paraphrase Simon (1986a, p. 23).
The Reasons for the Failure to Consider
Alternative Paradigms
Before we consider alternative paradigms, we should first attempt to
explain the reasons for this persistent failure. Simon’s 1978 Nobel
Lecture offers the following suggestion (Simon, 1982, vol. 2, pp. 490-
491):
There is a saying in politics that « you can’t beat something with
nothing. » You can’t defeat a measure or a candidate simply by
pointing to defects and inadequacies. You must offer an
alternative. The same principle applies to scientific theory. Once
a theory is well entrenched, it will survive many assaults of
empirical evidence that purport to refute it unless an alternative
theory, consistent with the evidence, stands ready to replace it.
Such conservative protectiveness of established beliefs is, indeed,
not unreasonable . . . What then, is the present status of the
theory of the firm?
The last sentence, of course, could be changed to inquire as to the
status of the post-neoclassical economics paradigm with its basic
principles of monodimensionality of economics phenomena or subjective
expected utility. Simon answers his own question:
There can no longer be any doubt that the micro assumption of the
theory–the assumption of perfect rationality–is contrary to the
facts. it is not a question of approximation; they do not even
remotely describe the processes that human beings use for making
decisions in complex situations.
He adds (p. 491):
Moreover, there is an alternative. If anything, there is an
embarrassing richness of alternatives.
The challenge, of course, is to convince economists to pay attention to
the alternatives. In this regard, Simon’s Nobel Prize in economics made
a major contribution. It provoked a progressive focus of scientific
attention, not only on the inadequacy of neoclassical theory (« failure
and scandal ») but also on the conditions of the design of alternative
theories.
The Emerging New Sciences of Complexity
One of those alternatives is complexity theory. During the 1980s, a
general paradigm began to emerge for the « new sciences of complexity. »
It was epistemologically well grounded and effectively
transdisciplinary. It evolved out of work in the sciences of life
(theories of self-organizing systems), the sciences of nature (chaos
theory), the sciences of engineering (theories of networking, artificial
intelligence, and design), the sciences of man (theories of cognition
and of hermeneutics), and the social sciences (pragmatics).
A rather comprehensive presentation of this basic epistemological
breakthrough is available in the impressive works of Edgar Morin (1977,
1980, 1987, 1991, also 1990a, 1990b). A preliminary synthesis, Science
and Praxis of Complexity, was published by the United Nations University
(UNU) in 1985. Other good sources are the rich collection of articles
edited by Bocchi and Ceruti (1985) and the series of proceedings of the
Colloques de Cerisy (1983, 1990, 1991).
The growing interest in the paradigm of complexity led to the 1988
publication of the now-classical book Economy as an Evolving Complex
System, edited for the Sante Fe Institute by P.W. Anderson (Anderson et
al., 1988): An even more convincing application to economics is
Bartoli’s L’Economie Multidimensionelle (1991). After pointing out the
weaknesses of neoclassical economics, Bartoli devotes the last part of
the book to what he calls » discernible itineraries. » In short, he points
out our growing ability to model the complexity of phenomena in
economics in order to develop our collective capacity to understand them
(« the quest for meaning »). A third very useful discussion is presented
by Vullierme in Le Concept de Systeme Politique (1989).
By the end of the 1980s, the attention of economists working on this
frontier was converging on two related topics: the rationality of the
reasoning processes involved in economic behavior (first argued by
Simon; see also Hogarth & Reder, 1986) and the complexity of the
modeling processes of those economic systems (recently illustrated by
Bartoli). Today, this convergence gives us the basic principles on which
we can organize the search for a paradigm of economic systems perceived
as complex systems. The aim is not to eliminate the neoclassical
paradigm but to design an alternative that can help the economic actors
to « understand what they do » and to « do things which make sense. » The
remainder of this article discusses the main features of this new state
of the art.
THE EPISTEMOLOGICAL FOUNDATIONS OF THE MODELING
OF COMPLEX SYSTEMS
Complexity: The Essential Unpredictability
The essential feature of the concept of complexity is its intelligible
unpredictability. In other words, a complex system can be modeled as an
« open » system, a systemic model which can exhibit incompletely expected
behavior in a way that is understandable to the model builder. Such a
property is unacceptable to the « classical » scientific disciplines
because their aim is precise understanding and explanation of the
currently unpredictable behavior of the phenomena they are studying.
There are many other useful definitions of complexity. I discussed some
of them in a paper titled « Conception de la complexite et compexite de
la conception » (Le Moigne & Orillard, 1990). However, I will not discuss
them here because I believe that the unpredictability argument is strong
enough to summarize all of the other features of a complex system,
particularly an economic system.
The classical epistemologies reject this conceptualization of complexity
as a scientific concept because they assume two hypotheses: the
ontological hypothesis and the deterministic hypothesis. They assume
that the phenomena described by scientific knowledge have an essential
reality, independent of the observer, and that the reality is completely
explainable by causal laws which have to be « discovered. » From those two
hypotheses emerges a definition of « scientific truth, » and from that
understanding of truth (or « necessity »), there emerges a procedure to
reason about truth. Since Aristotle, this procedure has been known as
the « logic syllogism, » based on the « principle of the excluded middle. »
This principle is usually considered to be a natural principle which has
its own ontological status. This implies that the scientific models
would be enclosed or exhaustively described in a given and determined
universe–the universe of discourse of the corresponding scientific
discipline.
The Founding Hypotheses of the Constructivist
Epistemologies
The need for alternative epistemological hypotheses in order to model
complexity has been recognized since the very beginning of the sciences
(as far back as the pre-Socratics). In the modem age, this conclusion
has come from a number of disciplines ranging from quantum physics to
genetic psychology. Perhaps most importantly, it emerges from the
difficulties that arise with the rational application of « Cartesian
dualism. » That is, can we assume the complete separability of the
observing subject and the observed object when we consider only the
discursive knowledge formulated by the observer acting on (or in, or by,
or with) an object? Given that the ontological status and deterministic
characteristics of the observed object are hypotheses, it seems
legitimate to consider alternative hypotheses. This, in fact, has often
been done in the past 30 years, and among the alternatives suggested are
two hypotheses which appear to offer an adequate basis upon which to
model complex systems. Those two hypotheses are the Designed Universe
Hypothesis and the Teleological Hypothesis. The conjunction of these two
hypotheses leads to a definition of a « scientific plausibility » (or
« possibility, » or « feasibility; » « Verum et Factum Recipocrantur »–the
criteria and the rule of the true in the doing–as Vico put it in 1710
[Vico, 1987]).
The Designed Universe Hypothesis assumes that the modeler (or the
Observing System, according to Von Foerster, 1984) designs his own
experience of the world. That is, « he knows that he doesn’t know » the
world, which has an independent ontological status (the ontological
hypothesis). Instead, he knows only his own representations (or models)
of his usually constrained perception of his actions. That is, he knows
not the « real world, » but the real (for him) representation of a world
in which he perceives himself as acting. And he knows that he designs
(or constructs) these representations (which can be « complex, » including
some explicit unpredictabilities).
The Teleological Hypothesis was reintroduced in 1943 by N. Wiener, the
founder of Cybernetics, and his colleagues in a famous paper titled
« Behavior, Purpose and Teleology » (Weiner, Rosenblueth, & Bigelow, 1943)
purposeful system, usually aiming to pursue his own, changing purposes.
The Modeler also assumes that the modeled phenemona can possibly be
assumed to be purposive. In other words, instead of searching for an
« efficient cause » (« because »), the modeler searches for some « final
causes » (« in order to »).
The constructivist epistemologies based on those two hypotheses have
been developed in various formulations. J. Piaget, who reestablished the
concept of constructivism in 1967, spoke mainly of genetic epistemology.
Simon speaks of empirical epistemology (see, e.g., Sieg, 1990). Morin
talks about an epistemology of complexity. And von Glasersfeld (1988)
calls for a radical constructivism.
As is the case with the positivist paradigm, there is not universal
agreement on the definition of constructivism. Hayek, for instance,
considered that « the whole positivist (belief), of which legal
positivism is but a particular form, is entirely a product of that
Cartesian constructivism » (1967, p. 104). The local epistemological
dispute between positivism and realism sometimes leads to an
assimilation of positivism with constructivism, even though the first
two only refer to the ontological and deterministic hypothesis.
Nevertheless, after the 1967 publication of the Epistemologic
Encyclopedia edited by Piaget, we can correctly denote as constructivist
the epistemologies based on the two hypotheses of the Designed Universe
and Teleological Behavior. (For more recent comments, see Inhelder &
Voneche, 1985; Watzlawick, 1981; von Foerster, 1984; Segal, 1990; von
Glasersfeld, 1988; Le Moigne, 1990b, 1991; Ceruti, 1992).
It is likely that discussions among supporters of the different
formulations of constructivism will continue for the next few decades.
The new epistemological questions raised by the emerging « sciences of
cognition » are already acting as a sort of catalyst for those
discussions (« Isn’t cognition an ‘experimental epistemology,’ » asked W.
McCulloch in 1965 (1988, p. 359).
« The Search for the Middle » as an Operating Principle
If we agree to base the search for knowledge on constructivist
epistemologies, we have to accept the consequence with respect to the
definition of the reasoning process: the operating principle of the
« excluded middle » is no longer required. We reason on models or symbolic
representations and not on separated and independent natural entities.
In 1920, Bogdanov wrote (1980, pp. 63-65):
It was long ago noticed that man in his activity, practice and
cognition, only joins and separates some given elements on hand. .
unifies elements of complexes of experience; the discerning
efforts separates them; nothing else, going beyond these limits,
can exist here. No logic or methodology was able to this day to
find anything else. But further investigation reveals that these
two acts, joining and separation, do not play an equal part in the
activity of man, or occupy in it an equal place. One of them is
primary: the act of joining. . . The other is derivative. The one
can be direct; the other is always only a result. . . Separation
is also secondary . . . A completely independent act of separation
which is not induced by some act of joining together cannot exist.
In other words, reasoning can be guided by any form of syllogism (or
conjunction), be it dialectical or rhetorical. The reasoning process can
be guided by the Principle of the Search for some « middles » (or « means, »
as suggested by Aristotle, 2d An, 10-30) instead of the Principle of the
Excluded Middle. When the aim of science changes from « objective truth »
to « intelligibly feasible, » the cognitive processes of reasoning have to
be more intelligible–that is, discursively reproducible or programmable-
-rather than exclude many avenues of search.
From Analytic to Systemic Modeling Methodology
The constructivist paradigm has a parallel consequence in terms of
modeling processes. The reductionist principle associated with nearly
all the positivist epistemologies was legitimized by its main aim: if
truth lies in the ontological reality of the observed object, the parts
of that object also have to be real or true. Therefore, in order to know
that reality, we can reduce it to its « simpler » parts and then describe
those parts. In this manner, the reductionist principle excludes the
hypothesis of the complexity of the observed phenomenon. Since the
publication of Descartes’ famous Discours de la Methode (1637), it has
been customary to apply this principle under the name of « the
methodology of analytical modeling. »
The modeling methodologies associated with the constructivist hypothesis
are not necessarily, nor even preferably, analytical. They are methods
for designing « created representations, » not for analyzing « discovered
realities. » Their purpose is to design possible symbolic modes that make
sense to the observer. The observers claim that they can establish some
correspondence between their empirical perceptions of their own
experiences (physical and cognitive) and such artificially designed
models. They know that they cannot develop a « proof of the truth » of
those models because they cannot correctly define the epistemological
concept of « certain truth. »
The ability to « design »–or to search for some middle (symbolic) terms
which meaningfully relate the behaviors and the purposes of the
observing system–has been studied for centuries. Aristotle’s Les
Topiques (1983b) illustrates this designing capability of the human mind,
as do the Leonardo da Vinci Handbooks (1987) read by Valery (1972 [1885])
design » which studies the process through which a cognitive system (seen
as an « information processing system ») elaborates, or creates or
« designs, » symbolic models. And the case of « self-organizing cognitive
systems » or « information self-processing systems, » or « autopoietic
systems » has been more specifically studied by several other authors
(von Foerster, 1984; Atlan, 1972; Maturana & Varela, 1980).
The emerging conclusion is the concept of « possible models » (see Jacob,
1981). For many classical scientists (and particularly for neoclassical
economists), this is a rather provocative argument. The deterministic
hypothesis leads them to believe that their theories are « true »
explanations of some natural, or real, unambiguous phenomenon. The
possibility that various alternative theories might be able to interpret
the same phenomenon is excluded.
The modeling methodology implied by the teleological hypothesis takes
into account this ability of the observing system to design various
possible models of its own experience and to interpret those models in
various possible ways. We recognize here a form of the contingency
theory attached to the theory of design (or modeling) of complex
systems.
In the last 40 years, the experience gained in the modeling of complex
systems has progressively led to a focus on one family of design
methods. That is the Theory of General Systems, seen as a Theory of
Modeling (see Le Moigne, 1977, 1991). This approach gives us a
methodology which is well grounded on the rich modeling experience of
nearly all the scientific disciplines. It can be seen as a sort of « New
Rhetorics » (Perelman & Olbrechts-Tyteca, 1970) or « New Dialects » (Barel,
1989) or « Natural Logic » (Grize et al., 1983). It is a tool for the
designers of systems conceived as multi-criteria models of complex
phenomena. It leads to another basic concept, the concept of « project »
or « complex project » (seen by Piaget, 1970, p. 20, as the « interaction
between the subject and the object »). Here, the teleological hypothesis
is of direct concern for the intelligibility of the designing process.
We cannot consider the classical criteria of objectivity or subjectivity
of a model, but we are concerned with a « new » criterion, the complex
criterion of productivity which, as observed by Aristotle, can often be
seen as a useful criterion of intersubjectivity.
These discussions of the methodology of design of (perceived) complex
phenomena have progressively led to a general formulation of an
alternative methodology of modeling known currently as the Theory of
Systemic Modeling (Le Moigne, 1990a). It is a well-grounded alternative
to the familiar Theory of Analytic Modeling. Scientists working in the
fields of socioeconomics have made important contributions to systemic
modeling (see, e.g., Lesourne, 1991). The works of E. Morin in Europe
and H.A. Simon in North America provided the decisive impetus which made
it possible to go beyond the too-limited resources of « cybernetics
modeling » (Kilt & Valach, 1966) and the related techniques often called
Systems Analysis or Systems Dynamics. These modeling techniques were
based on a basic hypothesis of permanent closure of the models. The
concepts of feedback loop and circular causality are useful, but not
sufficient to render an account of the complex behavior of many economic
phenomena.
Will Economics Abandon Its Energetic Metaphors?
>From the standpoint of economics, one important cultural consequence of
this shift in the general modeling paradigm is the rejection of the
classical Energetics Metaphor insofar as it is used as an explanatory
principle instead of a useful metaphorical heuristic. In their classical
theories, economists believe in the importance of a number of concepts
borrowed from energetics. Examples are the concepts of equilibrium,
entropic process, flow regulation, rate, throughput, speed, acceleration,
engineering efficiency, yield, potential, and power. Economists
sometimes believe that they have given to physical energetics one of its
important concepts, the Principle of Least Action, often called the
« Economic Principle » or the « Natural Parsimony Principle » (see Le Moigne,
1990b). And economists often argue that an economic theory based on an
energetics theory (mechanical, hydrodynamical, thermodynamical, etc.) is
scientifically « better » than another which does not explicitly refer to
energetics concepts, even if the theory still uses sophisticated
mathematics.
The use of the energetics metaphor as an explanatory hypothesis is, at
least in some cases, acceptable from a positivist point of view. However,
economists now realize that it cannot provide adequate understanding of
the behavior of the complex socioeconomics systems that they have to
deal with. Consequently, when they agree to shift their modeling and
reasoning processes to the framework of a constructivist epistemology,
they must be ready to abandon the simplifying hypothesis suggested by
the energetics paradigm. In the words of Bateson (1972), « To consider
social organizations as energetic phenomena and to interpret them in
terms of energetic theory is pure nonsense. »
In practice, the abandonment of the energetic hypothesis as the basis
for modeling economic systems should provide a useful incentive for new
epistemological discussions in economics. The argument of this article
is that those discussions should lead economists to establish guidelines
or canonical principles which focus on the related concepts of
Organization, Information, and Decision Making. Such a focus should help
economists design and use symbolic models of possible behaviors in
complex contexts.
AN EMBRYONIC THEORY OF THE MODELING OF COMPLEX ECONOMIC
SYSTEMS: THE INFORGETIC THEORY
Modeling and Reasoning: What and How to Model
Such a theory of complex systems modeling can be presented in two sets
of arguments:
1. First, the identification of « what to model. » This set of
arguments articulates the main operating concepts involved in the
« designing » of complex systems models.
2. Second, the search for the « how » in the modeling process. This
set of arguments recapitulates the « reasoning » processes involved
in the modeling processes in order to simulate feasible expected
behaviors.
In practice, those two sets of arguments have a strong cognitive
interelationship. They are the two faces of the same coin and we do not
expect to invent or discover a « new » argument. The arguments have long
been a part of the modeling experience of the human race. A reading of
some of the works of Aristotle will make this clear (e.g., 1983a, 1967).
The presentation of the embryonic theory which follows will focus mainly
on the arguments that relate to the social and political sciences. The
purpose is the modeling of complex economic systems, but our evolving
cultural understanding of complexity does not allow us to define an
economic system in a way that clearly differentiates it from a social or
political system (the so-called « intellectual autarchy of economics; »
Nelson & Winter, 1982, p. 405). We will conclude that theory of complex
economic systems modeling has to be a theory of complex economics
organization engineering. That is, we are primarily concerned about its
« projects » or « methods » or « engineering. » These are generally defined by
its specific modeling and reasoning resources, or its Ingenium (Vico,
1987 [1710]).
The Modeling of Complex Systems is the Modeling
of Actions, Not of Things or Objects
The first operating proposition suggested by the constructivist paradigm
is that we do not perceive things but actions. « We only perceive and
represent acts, or operations, » wrote Valery in his Cahiers (1979).
Perceiving and modeling are themselves operations, purposeful
operations. « To represent a tree, » observed Leonardo da Vinci, « we are
forced to represent some context in which it reacts. We perceive and we
represent the interaction between the tree and its context. » This
alternative way of modeling is not familiar to most people. They are
accustomed to a fixed worldview and therefore search for immutable
things rather than temporal actions. One group that should feel
comfortable with the notion of modeling actions is economists. Most of
the concepts used in economics are verbal substantives. They are names
of actions, such as production, cooperation, competition, distribution,
equilibration, regulation, and capitalization. Economic phenomena are
usually first perceived and modeled as processes or operations.
Since the time of Aristotle, philosophers have known that the modeling
of perceived phenomena can be done through an active evolving form,
joining its actual movement and its potential final cause, or dunamis.
And this conjunction can be usefully interpreted in teleological terms:
the behavior being intelligible through its final causes, which are
« potentialized » or « memorized » through its successive behaviors. This
evolutionary modeling of economic phenomena–seen as both active (or
functioning) and evolving (or self-transforming)–is illustrated by the
« evolutionary theory of economic change » developed by Nelson and Winter
(1982).
Environments are Interactions
In focusing on processes (both synchronic and diachronic or functioning
and transforming), the theory of complexity has led to a sort of
ecological theory of the concept of environment. The modeler cannot
consider that there exists some sort of « different things » in which the
phenomenon would be active. Instead, the modeled phenomenon is seen as
interacting with other phenomena. In other words, our classical
conception of environment (or substratum) « separated » from the
phenomenon has to be abandoned and replaced by a theory of a system’s
environment seen as a « carpet of various and tangled processes » (Bruter,
1974). Simon (1981) has suggested reversing the modeling procedure and
considering the phenomenon under study as an « internal environment »
which does not differ from the « external environment » except through the
focus of the modeler’s attention. This argument represents a change from
the focus of the classical cybernetic modeling, for which « clearly the
inner variables are the variables under the control of the system »
(Simon, 1990, p. 10). Systemic (or dialectic) modeling takes into
account the self-behavior of the ecosystem. The conditions of autonomy
of the phenomenon are in solidarity with the interrelated systems. « To
be autonomous, » said Morin (1980), « the system must be dependent. »
The Irreversibility Principle
The third characteristic of complexity modeling is the principle of the
irreversibility of time. The postulate of the irreversibility of time
has a long history stretching back to Heraclite (Prigogine & Stengers,
1988). It is implied by the concept of complexity seen as « essential
unpredictability, » the unpredictability of the creation. « Time is
creation, or it is nothing, » said Bergson ([1907] 1971). « Real things, »
if they do exist, may be reversible (or eternal?). Thus, one could refer
to « this stone » being the same now, a century ago, and a century into
the future. But the modeling of action is a modeling of action through
time. Action is represented by changes in space (movement or cinematics)
and in form (morphogenesis or dynamics). The teleological hypothesis
implies the irreversibility principle and the irreversibility principle
forbids use of the classical ceteris paribus reasoning (« all other
things being equal »).
The Reemerging Concept of « Organiz-action »
The concepts used to model the complexity of economic systems can be
theoretically articulated by the teemerging concept of organiz-action
(suggested by Morin, 1977). This concept is probably the main feature
and the main product of the theory of complexity modeling so far.
Organiz-action refers to conjunctions such as the following:
* the process and its results
* the observing and the observed system
* order and disorder
* whole and parts
* possibility and necessity
* actual and potential
* autonomy and solidarity
* differentiation and coordination
* cooperation and competition
* processing fields and processed flow
* the articulation and the gaps (or the slacks)
* the perceived world (the constraints) and the designed world
(the projects)
* regularities and singularities
* knots and links
* levels of networks and network of levels
* physical action and symbolic action
* communication and control (Wiener)
* information and decision
* assimilation and accommodation (Piaget)
* the channel and the code (Shannon)
* selection and reproduction
Such conjunctions are perceived in their inseparability. Such
dialectical reasoning has been used since the time of the pre-Socratics.
But it has tended to be ignored in the twentieth century because of the
dominance of analytical modeling, with its concept of structure (or
group, in formal mathematical terms).
The Canonical Form of « Organiz-action »
The concept of organiz-action dates back to the father of general
systems theory (Bogdanov, 1980 [1913-1920]; his name is actually a
pseudonym for A. Malinowski, Russian economist, biologist, medical
doctor, and political figure). It has been progressively redesigned and
« complexified » by a large number of prominent scientists, including E.
Morin, J. Piaget, and H.A. Simon. Their common objective was to guide
modelers in representing, in some intelligible fashion, the unseparable
key conjunctions perceived in complex systems.
Morin (1977, 1980) has proposed a canonical (or paradigmatic) form of
the concept, showing that it always gives account of any combination of
six archetypal actions: three transitive ones and three recursive ones.
The six are:
to maintain AND self-maintain
to relate AND self-relate
to transform AND self-transform
The dual consideration of the synchronic and diachronic characteristics
of the recursivity property of the organization can be expressed by the
conjunction of the self and the reorganization involved in any active
organization. That is an eco-organization. So, each time we consider an
organization, we have to consider it as:
an Eco-Auto-Re-Organization.
And, recalling that the concept of organiz-action (like the concept of
system) is designed as a tool for modeling complex phenomena, we focus
on its representational characteristics:
an organization is a representation
a representation that we are able
to model as an organiz-action
An organiz-action is a representation of a teleological system seen as
active, organized, and organizing, through its own information-
processing activity, both informed by the organiz-action which forms it
and informing–and transforming-this organization. The principle of self-
organizing systems (von Foerster, 1984 [1959]; Atlan, 1972; Morin, 1980)
or the corresponding principle of « organizational equilibration » (Piaget,
1968, 1975) suggests the following first step (or level) in the modeling
process, which can be presented as the constitutive correspondence
between information and organization:
INFORMATION ARROW RIGHT, ARROW LEFT ORGANIZATION
This basic and recursive correspondence can be called the Inforgetic
Paradigm. The name metaphorically evokes the Energetics Paradigm used by
the natural sciences. In the universe of discourse about the designed
representation of perceived complexity, we can state that information is
to organization (Org) what matter is to energy (Erg) in the universe of
discourse about the given and determined real world.
The first practical result of this metaphor is to offer us an
alternative paradigm (or epistemological parapet) each time we need to
refer to an energetics concept (such as flow or yield). The second
result is to suggest a sort of framework for our theory-building
endeavor. We can identify a first principle of this inforgetic theory,
the principle of organizational equilibration or of self-organizing.
This also can help us to take into account another key epistemological
assumption in order to model complex phenomena: the teleological
hypothesis.
The Second Principle of Inforgetic Theory: The Intelligent
Organizational Decision
The interactions between information and organiz-action are governed by
two sets of forces. The first is the chance and law dialectics, to use
the term introduced by yon Bertalanffy (1961; see also Monod, 1970;
Dupuy, 1982). The second is the projects, or the teleological behavior,
of the actors involved in the organiz-action, informing it and informed
by it. The entire behavior of the organization is not produced by a
deterministic law of spontaneous social order. instead, it is a result
of human design and of human action. This hypothesis is at least as
plausible as the famous alternative hypothesis of social organization
formulated by Hayek (1967). Hayek argued that what we observe are « the
results of human action but not of human design. »
The Information-Organization genotype has to be complexified in order to
model this capacity of human organizations to purposefully design their
behavior. (By genotype, I mean a structure, a mechanism, or a rule by
which to play the game.) This genotype is capable of engendering some
local models, according to Dupuy (in UNV, 1985). To do so, we must
consider the autonomous decision-making processes that are involved in
any organizational and informational process, according to Simon (1976
[1943]). Simon’s hypothesis has been examined by numerous other social
scientists who have concluded that the decision-making process cannot be
defined outside of its interactions with the informational and
organizational context in which it occurs (see, e.g., McGuire & Radner,
1972; March, 1988). Therefore, we need to complexify our initial
genotypic model:
from loop * 1:
INFORMATION ARROW RIGHT, ARROW LEFT ORGANIZATION
(I) (O)
to a new paradigm that takes into account the teleological decision-
making process involved in it:
to loop * 2:
Clearly, loop 2 is a more complex paradigm. The difference between it
and loop 1 can be compared to the Karl Marx’ famous distinction between
the bee and the architect (1965, p. 718). In this more complex paradigm,
the decision-making process must first organize the social organization
(O-D in the above model). In that process, the decision making is the
decision to inform itself, constructing and selecting symbols. At the
same time, it is itself formed and informed by the informational process
(loop D-I, generated by the initial loop, I-O). Those three interacting
genotypic loops constitute the basic framework through which we can
model any socioeconomic system in its perceived complexity.
This general definition of what I call the Inforgetics Paradigm does not
lead to a « problem-solving procedure. » Here lies the difference with the
physical energetics paradigm, which leads, for example, to the « problem-
solving » theory of gravitation. The basic argument of the new paradigm
is to propose a problem-setting procedure which can lead in some
meaningful ways to the modeling of complex, heterogeneous,
multidimensional socioeconomic systems.
This intelligible complexification of the inforgetic paradigm from:
allows us to theorize about complex organizations in conceptual terms
which can be named: the « Inforgetic Theory. » We have previously
recognized: as the first principle of this theory the « Self-Organizing
Principle » or the « Equilibration Principle » (also referred to as the
« Complexity from Noise Principle » or the « Organization from Information
Principle »).
The second principle of Inforgetic Theory is what I and others have
called the « Principle of General Intelligent Action » (see Newell & Simon,
1976; Le Moigne, 1990c, 1992). This principle arises from the
conceptualization of the adaptive behavior of the teleological
organization as proposed by Piaget (1975) and Simon (1980, 1981). It
takes into account the self-designing capabilities of the complex symbol
processing involved in the decision-making process. According to this
second principle, a complex adaptive system is an intelligent system,
« able to invent, » through its symbol-processing capabilities, some « new »
possible answers when purposefully dealing with unexpected and
unpredictable conditions. Empirical data often show such observable
invention behavior in complex contexts. However, classical and
neoclassical theories do not provide any way of modeling such behavior.
Modeling Complexity: Intelligent Reasoning
The understanding of intelligent organizational behavior requires more
thinking about the reasoning processes (the rationality) involved in its
modeling. In this regard, it is helpful to recall Piaget’s observation
(1977 [1937], p. 311) that « intelligence organizes the world by
organizing itself. » Rereading that sentence 40 years later, yon
Glasersfeld observed that it constitutes one of the « key arguments of
the constructivist epistemology » (1988). intelligence is here understood
as human intelligence–that is, it is the capability of the human mind
to build « mental » (or « symbolic ») representations of the world it
perceives and to « reason on it. » To organize the world is to build
organized and organizing representations of the perceived world–and, in
doing so recursively, to organize itself to process those
representations or to purposefully reason on them. In a constructivist
epistemology, this interpretation of the recursive behavior of a
cognitive organization appears as plausible. We know that many empirical
observations corroborate Piaget’s initial theory about organizing
intelligence.
In cooperation with Newell, Simon developed a major extension of the
theory of intelligence from the « intelligence of the human mind » to the
« intelligence of social organization » (see Simon, 1981, p. 7; Klahr &
Kotovsky, 1989). The key feature of the theory is to propose an answer
to the « how » question–how an intelligent activity is able to identify
either a « problem » or a « difference » in a perceived ill-structured
context, or how it is able to invent some alternative feasible
« solutions » to the problem and how it is able to evaluate the relevance
of those solutions vis-a-vis the purposes of the system.
The final stage, the choice of the solution to implement (the action or
the change of behavior), is, paradoxically, rather easy to understand.
However, the first stages of the processes are often perceived as
confusing. The pragmatic answers to the question « what to decide? » as
proposed in the various forms of syllogisms and the old wisdom of the
« Topics » do not help us to understand (and to learn) the procedural
answers to the question « how to decide? » In a seminal article titled « On
How to Decide What to Do, » Simon observed 1982, vol. II, p. 460):
Economics, which has traditionally been concerned with what
decisions are made rather than with how they are made, has more
and more reason to insert itself in the procedural aspects of
decision, especially to deal with uncertainty, and more generally,
with non-equilibrium (today we might use the term « evolutionary »):
phenomena. A number of approacheds to procedural rationality have
been developed in such fields as operations research and
management science, artificial intelligence, computational
complexity and cognitive simulation which might be of considerable
value to economics as it moves in this new direction.
The Two Hypotheses of Intelligent Reasoning:
Symbol and Search
As Simon argues, the modeling of the reasoning processes involved in an
« information-processing system » (such as a social organization) dealing
with « ill-structured (or complex) evolutionary phenomena, » can be seen
as the modeling of an « intelligent » system, because « Intelligence is
closely related with adaptability, with problem solving, learning and
evolution. A science of intelligent systems has to be a science of
adaptative systems . . . so long as we do not confuse adaptability with
the ability to attain optimal solutions » (Simon, 1980, p. 45). The two
key features of such a theory of intelligent systems have been
identified by Newell and Simon in a seminal contribution known as their
« Turing Lecture » (1976). Those two main hypotheses (or, as they also say,
« laws of qualitative structure ») are: the physical symbol hypothesis and
the heuristic search hypothesis.
The physical symbol hypothesis assumes that it is possible to represent
a « general intelligent action » (or a purposeful behavior in a complex
context) as a « physical symbol system » (« a machine that produces through
time an evolving collection of symbol structures which can design and
interpret »). The heuristic search hypothesis assumes that a physical
symbol system exercises its intelligence by search–that is, by
generating and progressively modifying symbol structures until it
produces a solution structure (by symbol computation).
The constructivist modeling of a reasoning process based on those two
« laws of qualitative structures » leads to the description of a procedure
in which the invention and the choice of the next step of the reasoning
process is seen as partially determined by the « results » of the previous
step. As such, it cannot be anticipated. Here lies one of the roots of a
fundamental distinction raised by Simon (after Aristotle) between the
two forms of rationality: the substantive and the procedural (Simon,
1982). Substantive rationality does not need any form of « intelligence. »
The sequence of the steps of the reasoning process is completely and
unambiguously described. It is usually called an « algorithm » or a
« regulation’s rule. » It is presumed to be independent of the
evolutionary behavior of the system in which and for which it is
running. It does not care about the context, which is once and for all
determined to be appropriate to the achievement of given goals within
the limits imposed by given conditions or constraints. Substantive
rationality appears as a form of reasoning relatively well fitted to the
case of well-structured, stable situations. It cannot truly be seen as
an « intelligent » form of reasoning since it does not require any form of
« invention » and it does not adapt itself to any unexpected changes in
the context. Simon observes that the Theory of Subjective Expected
Utility Maximization, endorsed by most neoclassical economists, is a
theory of the substantive rationality type. He asks whether or not we
can expect such a theory to fit the reasoning processes dealing with the
perceived complexity of economic systems. He concludes (Simon, 1986b, p.
39), « In this kind of complexity there is no single sovereign principle
for deductive prediction. »
Intelligent Reasoning with Complex Systems:
Procedural Rationality
The theory of intelligent systems gives us a guide to describe and model
the behavior of purposeful reasoning systems dealing with complex
situations. We can represent such behavior as an heuristic search
process generating and modifying symbol structures. Let us consider, for
instance, a very classical economic problem: the complex problem of the
efficient distribution of revenue. Empirical observations show that the
global amount of revenues to be shared depends upon the sharing rules
used by the « producers » of the revenues, who are also its « consumers »
(« the size of the cake depends on the sharing of the cake ! »). Are we
unable to cognitively take into account this empirical fact by arguing
that there exists no stable algorithm which can determine the
unpredictable form of the relationship between the GNP and the policy of
revenue distribution? We know that by trial and error, or means-end
analysis, we can use our own reasoning process to search one step at a
time. We cannot predict or predetermine the « result » of the process (the
efficient policy of revenue distribution, for instance), but we can
describe the procedure, or « how we intend to behave » at each step of the
reasoning process. This paradigm of « procedural rationality » was
proposed by Simon, who also suggested the metaphor of the « appropriate
deliberation of a jury. » Nobody can predict the end result with
certainty, but we assume that it will be produced by an effective
reasoning process. This is a process which can be modeled as the
« heuristic searches of a symbolic system, » that is, as « an intelligent
system » (Le Moigne, 1989a, 1990c)
Bounded Rationality: The Engineering of Complex
Economic Systems
The theory of procedural rationality clearly reveals the importance of
the practical limitations of the cognitive (or computational) capacities
(or resources) of any information-processing system. In developing this
argument, Simon introduced the concept of Bounded Rationality. It is not
the rationality in itself that is bounded. Instead, it is the processing
capacity of the computational system of physical symbols which seeks to
behave according to a given form of rationality, be it substantive or
procedural. This argument has become familiar to most economists.
Bounded Rationality affects the capacity of an adaptive system to
effectively manage its ability to generate symbols (the organizational
engineering of symbolization), to memorize symbols (the engineering of
memorization), and, correspondingly, to manage its own « attention » (the
engineering of the scarcest resource: organizational attention; see
Simon, 1988). None of these resources (symbolization, memorization, and
attention) are « given. » They are cognitively designed and built through
symbols computation and chunking (Newell, 1990) by the modelers who are
observing actors of their observed socioeconomic evolutionary
organization. Pitrat (1991, p. 338) observes:
[As] as an intelligent system must and can observe and model its
own teleological behavior, [it may] build a model of itself, and
in doing so, plan the search for some solutions to problems while
taking its own capabilities into account, monitor and oversee its
search processes, and understand why (or at least how), it has
found some result, so that it can (interpret) it and learn from
its successes and failures.
Complexity Means Many Satisficing Behaviors
This interpretation of the self-modeling activities of a complex system
helps us to understand the reason for the basic characteristics of the
behavior of an intelligent system. It searches for satisficing
solutions. That is, it knows that there is no unique solution to a
complex problem. Because it knows that, it deals with evolving goals and
evolutionary situations which it perceives (and models) as
multidimensional; it assumes the rational conclusion that no unique best
solution simultaneously fits the various criteria and various
representations of the problem. it can search and find some
« satisficing » solutions but it cannot compare those solutions in
absolute terms. All of them can be rationally, justified; all of them
appear as satisficing. This rational lack of determination is still hard
for most economists to accept, even though it is firmly based on
rationality and its epistemological roots.
Reasoning in Economics and the Sciences of Cognition
The engineering of cognition involved in the modeling of the procedural
forms of rationality which is developed in the framework of the
inforgetics paradigm also leads to some promising developments in the
areas of the new Natural Logics and the related field of the new
Dialectics and the new Rhetorics. When the reasoning processes are not
formally constrained to confuse the negation with the contradiction, the
« rational modelers » (be they logicians, mathematicians, or economists)
rediscover three old Aristotelian forms of rationality–recursive
reasoning, hologramorphic reasoning, and the fractalization procedure.
Recursire reasoning is perfectly described by an old rhetorial figure,
« the chiasmus. » Hologramorphic reasoning expresses a constitutive
relationship between the whole and the parts (see, e.g., Pinson et al.,
1985; Morin, 1990b). The fractalization procedure expresses the micro-
macro-cosmic duality involved in systems modeling, which is basically
different from the classical fragmentation procedure defined by
classical reductionism. All of the above are examples of contemporary
works on rationality which inforgetic economics can usefully tap to
enrich its own resources in terms of heuristic search dealing with
complex systems. (For additional comments on this topic, see Le Moigne,
1990a, 1990c; UNU, 1985, pp. 35-61, as well as the readings cited
therein.)
CONCLUSION: ECONOMICS INFORGETIC THEORY CONTRIBUTES
TO THE NEW SCIENCES OF COMPLEXITY
Inforgetic Theory is a tool to guide the economist in dealing with
complex problems. It answers the need for some alternative to the
neoclassical paradigm in economics. But how can we be sure that it is an
effective and relevant alternative? The answer lies in the doing.
Inforgetic Theory is a procedural type. It does not give any definite
answers. instead, it provides general searching procedures without any
guarantee of a successful search.
This article has reviewed and interpreted the work of some of the best
contemporary social scientists who have dealt with the complexity of
socioeconomic phenomena and concluded that given the present state of
knowledge, the inforgetics theory appears to be useful. However, it is
best seen more as a problem-setting approach rather than as a problem-
solving theory.
Here is, perhaps, the most important conclusion regarding the science of
complexity. It cannot be a positive science. Instead, it has to be a
fundamental science of engineering (or of design, as Simon said, 1981).
It has to be a science based on a project of knowledge, or a science of
« how to artificially design » a system which will, once built, behave in
a manner that is intelligible for its designers.
Neoclassical economists will undoubtedly have some difficulty in
accepting this idea that our discipline has to be considered as an
engineering science (which has nothing in common with an applied science)
deal with the engineering of symbolization. The recent design of the
symbolic concept of sustainability gives us a good example.
Sustainability is not a positive thing we expect to find in nature. We
also have to deal with an engineering of memorization and with an
engineering of attention. More generally, we have to deal with the
engineering of interaction between organization, decision, and
information. All of these must be seen in their intelligible and
irreducible complexity and also in their evolutionary interdependencies
with each other.
As they begin to understand the concept of the inforgetic theory of
complex systems, many economists will probably conclude that they are
like Monsieur Jourdain, the famous Moliere hero. That is, « they practice
it even though they did not previously know its name. » In this, they
will sometimes be correct, at least when they refer to their own
cognitive search process and reference it to some constructivist
epistemology. Once this admission is made, it is to be hoped that
economists will finally begin to deal effectively with the perceived
complexity of the world, even if they formulate their theory of modeling
in terms other than the inforgetics theory.
Simon summarized the problem and the opportunity with the following
assessment of the work of economists (1990, p. 13):
(T)he situations they wish to model are of orders of magnitude
more complex than the most elaborate models that supercomputers of
the present and future will sustain. We need to apply keen
intelligence whether or people or computers, to make sure that we
capture in our models the aspects of the world’s systems that are
important to us.
This article modestly suggests that Simon’s challenge can be met by
collectively joining the modeling resources of the social sciences and
of the engineering sciences to develop some form of inforgetic theories.
Such theories will enable us to deal with complexity in a much more
intelligent manner than is possible with the currently fashionable
energetics theory. Furthermore, the new inforgetics theories will be
learnable through practice, even if they are not yet easily teachable.
Acknowledgements: The inforgetic theory, including the analysis of its
epistemological foundations and its relationship to organizational
engineering, was born and developed in the research group GRASCE
(Research Group on Economic Adaptation, Systemics and Complexity) at the
University of Aiz-Marseillies III/CNRS. This paper owes its ideas to all
GRASCE members, past and present, junior and senior. I particularly
thank H.A. Simon and E. Morin for their personal encouragement and
stimulating contributions to the new science of complexity. A debt is
owed to my colleague, Robert Delorme, who was persuasive in convincing
me to write an English summary of this still-embryonic theory. Warm
thanks also go to Jean-Pierre Van Gigch and to R. E. Hartwick, who
contributed many improvements to the expression of these ideas in
English. As usual, the responsibility for any errors lies entirely with
the author.
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~~~~~~~~
By JEAN-LOUIS LE MOIGNE, Universire d’Aix-Marseille III
Direct all correspondence to: Jean-Louis Le Moigne, Grasce-Ura CNRS 935,
Faculte d’Economie Appliquee, Centre Forbin Austerlitz, 15-19 Allee
Claude Forbin 13627, Aix en Provence, Cedex 1, France.
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Source: Journal of Socio-Economics, Fall95, Vol. 24 Issue 3, p477, 23p,
2 diagrams.
Item Number: 9603152323
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