Narratives and transdisciplines for a post-industrial world.
T. F. H. Allen (University of Wisconsin, Madison, USA) and Mario Giampietro (INRAN,
Italy)
Abstract
As society moves to a post-industrial posture, technology rescales human activities into
new contexts where meaning is different. In that setting, complexity arises because
problems move outside prevailing disciplines, causing situations to appear undefined.
Transdisciplinary studies arise from scale mismatches, where some problem lies between
two or more disciplines. Lower level principles from one discipline fail to explain some
upper level issue in another, because of scale differences between the structures and
explanatory principles in the old disciplines. In the struggle, some new thing not
belonging to either discipline comes to be explained by a new set of principles. Ecology
emerged around 1900 as a failure of quantified data collection to link biogeography to
plant physiological adaptations. The new notion of plant community came to be
explained by a set of ecological principles. In general transdisciplines arise from
technology redefining the scale of relationships. With no paradigm at the outset to offer a
narrative, transdisciplines face complexity head on.
Complexity is all or nothing, and so measures of degree of complexity are merely of
complicatedness, that is to say degrees of simplicity. A paradigm is a narrative told to
make complexity only complicated, so that it is simple enough to model. Complexity
disappears into complicatedness upon deciding structure, significance, discontinuity and
rate-independence. But with those decisions at hand, one can make a model. Models
improve the quality of narratives. Reductionist models narrow the scope until meaning is
excluded, leaving only emergent structures. In contrast to models, narratives are exactly
about meaning. Meaning is found in two of the Aristotelian causes: in the plans of
formal causes (e.g. DNA) and in the final cause that attaches significance to the observed
structure once it is made. Aristotle’s material cause is constructed by the interaction of
his efficient cause, the force of the driving gradient (e.g. food, energy), and the plan of
the formal cause. A further complication is the difference between the time frames of the
different causalities. Understanding a complex system requires more than the dt over
which it is made, it needs also a dτ for becoming (more efficient), and a dθ for the
passage of the narrative. At the end of the narrative is death, extinction or societal
collapse, which ends the observer-observation complex with the passage of dT.
Narratives entail four Aristotelian causalities and different time domains, but they can
address complexity as the human world moves from industrial to post-industrial,
invoking post-normal science and post-modernist deconstruction. The complexity arising
from technological rescaling and redefinition our world can be addressed with
sophisticated use of narrative, a central device in systems science.
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Introduction
Systems practitioners and theorists often find themselves marginalized. Ken Boulding
reported that a review of his academic department dismissed him as having lost interest in
economics, and that at the very time he was president of the leading national society of
economists. If you take a systems posture, Boulding says that your discipline will view
you as something of a traitor. The same marginalization occurs for programs that foster
systems approaches. Academic programs of systems science and practice come, but with
startling regularity, they also go. New on the scene, and full of promise, is the
Sustainability Research Institute at the University of Leeds, where senior positions are
being filled at the time of writing. Hull is presently in good shape, but significantly
because Mike Jackson is Dean, and has administrative clout. His tenure is what keeps it
safe, and thereafter, who knows. Lancaster University, the Wharton Institute and San
Jose State University are not the champions of systems approaches they used to be. And
yet, for all this marginalization, systems practitioners and scientists know how they can
solve problems that discipline-centered colleagues cannot address. So it becomes a fair
question to ask, what does systems science have to offer society in these times of great
social and technological change?
This paper casts an eye around the contemporary human condition, and comes to the
conclusion that these are times when systems science has much to offer. It may be that
finally its time really has come, at last. The reason is for this promise is that technology
is rescaling our existence more than ever before. This leaves us with a new class of
problems and questions to answer for which systems thinking is one of the few
appropriate approaches. While normal science is still the best game in town (Allen et al
2001), it is now at its limits, and is failing to do what society wants and expects it to do.
Problems arise regularly in the space between old paradigms and discourses. Normal
scientists are spectacular in what they can achieve inside their respective paradigms, but
they are poor at finding new paradigms, even when a new intellectual frame is clearly
needed. Paradigms are narratives that set the criteria for importance and define the setting
in which multiple independent observers attempt to make their experience commensurate.
Systems science is particularly good at identifying significance between the disciplines,
and this paper makes the case that a certain facility with narrative is the key. The chronic
rescaling in all our lives is creating qualitative change in what must be done to achieve,
happiness, sustainability and even survival. Systems science can provide new stories that
will help us resonate together with the challenges and promises of the experience that is
the post-industrial world.
The move to a post-industrial society
The change to post-industrialism has a technological component. In all technology, the
human is rescaled relative to the problem. Allen was asked forty years ago on his final
examination as a undergraduate, “How has the plastic bag changed plant systematics?”
The answer is “Greatly,” because plants in a plastic bag remain alive and fresh, until the
botanist can get back to the laboratory. This rescales the practice of plant collection.
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Often such rescaling not only makes some process relatively faster, it changes options
qualitatively. Computers used to be sold on the promise that they would do what you do
now, but much faster. In fact, users do something different. The technological changes
in the contemporary world are rescaling human existence in a way that creates
qualitatively new activities, problems and achievements.
A technological innovation that caused an important rescaling about a century ago was
the quadrat, used in quantitative data collection while making field surveys of vegetation
(Pound and Clements, 1898). Laying four measuring staffs on the ground to make a
square of vegetation may not seem much of a technological innovation. However,
modest as it might seem, the quadrat yielded data about a new theretofore unseen
structure, the plant community. The whole enterprise was to plot out the physiological
explanation for the biogeography of plants in Nebraska. That plan failed, because the
quadrats were too small to capture the large issue but too heterogeneous to address the
lower level principles. But the new structure, the plant community, caused ecology to
emerge as a new self-conscious discourse, situated in between, and not reducible to,
either biogeography or physiology. Rescaling through technology thus created ecology
as a transdisciplinary discourse. As a rule, transdiciplines appear when technology
rescales an issue to make it qualitatively different. Contemporary technology, like the
quadrat a century ago, is doing exactly that.
Clements was part of the Nebraska Natural History Survey to describe the vegetation of
the newly captured territory. Their mission was to get a description of nature before it
went under the mechanical plow or under urban sprawl. It was a time of great change
from agricultural America to an urban industrial nation. Draves and Coates (2004)
identify a large cultural change from 1890 to 1920. This is the shift to which Clements
was responding in his fashioning of the new transdiscipline of ecology.
In their book, “Nine Shift,” Draves and Coates (2004) suggest we are presently in a
change of similar dimensions a century after the shift from agrarian to urban America.
Instead of the motor car, they say that the internet will be the agent of change. To
emphasize how large was the change into the industrial society, Draves and Coates point
to Frankfort, Kansas, in 1907. It is now a small empty backwater, but then it supported
four a department stores stocking 150 ladies capes, and the best line of shoes in the world
for gentlemen, ladies, adolescents, children and infants. Today one cannot buy any shoes
in Frankfort. In 1907 there were six banks, owned by millionaire bankers. There was
even an opera house. Where, one might imagine, would be the closest opera house to
that one? The surprise is that it was just eleven miles down the road, in Blue Rapids,
Kansas. But all that had disappeared by 1920, and Frankfort was left behind. The
existence of Frankfort and towns like it was iconic to the agricultural society. The
principal means of production in the United States was agriculture, a diffuse system that
captures a low quality energy, sunlight. Frankfort was one of the many first order
collection centers for agricultural activity. At first the motor car, dependent on high
quality energy, gave Frankfort ready access to goods. But soon enough, the motor car
bypassed the little town, and it shuffled off into history. “Nine Shift” offers statistics on
the shift, noting that 75% of all lives radically changed then, as people moved into
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industrial production, living in suburbs. They argue that the internet will have an effect
by 2020 as great as did the automobile in changing most peoples’ lives from rural to
suburban.
In the first decades of the twentieth century, the people changed. The majority of
workers started on farms, but became factory workers. The ideal woman at the turn of
the century was Gibson Girl, elegant, poised and effective. She gave way to the flapper
by 1920, a girl of very different character. Meanwhile, men who were companions of the
Gibson Girl were characterized as a bit meek and inept, a sort of Berty Wooster fellow.
By 1920, the boys of the turn of the century had turned into men of achievement, captains
of industry, and effective mechanics. Thus “Nine Shift” characterizes the social turmoil
in values a century ago. Turning to contemporary revolution, it notes that boys have
slipped in their performance in school work from about par with girls in 1980 to some
20% lower scores on aptitude tests today. But boys are instead self-taught wizards at
computing and the internet. So it was a century earlier, when boys were failing to take
interest in their tradition agricultural roles, and instead were messing around with
machines. As they became adults, it was these boys who drove America into an
industrial machine-oriented posture, starting motor car factories. In 1910, Studebaker
made only carriages. At one time or another 2,020 companies have made motor cars. By
1920, there were 80 companies, by 1940 only 40, and by 1970 in America we were down
to the “Big three.”
“Nine Shift” predicts that trains will replace cars. Suburbs will give way to compact selfsufficient neighborhoods, wherein people will work much more significantly from home.
Income differentials between the top and bottom of the work force will return to the more
equitable pay scales of the mid-twentieth century. Already, workforce hierarchies have
given way to network, wherein almost nobody has a foreman anymore. Top down offices
are already changing their character, turning into diffuse, electronically connected
intranets. Education moves haltingly on line, but it will become much more web-based.
And finally, what amounted to cheating on homework in the late twentieth century will
become praised as effective collaboration. While Draves and Coates in “Nine Shift” will
be significantly wrong in the particulars, they accurately describe the size and speed of
societal change that the new technology is imposing on society.
The emergence of ecology as a transdisciplinary discourse was in response complexity
appearing in a rapidly changing world, rescaled by technology. Clements’ world was
being technologically turned upside down. So is ours. While Clements’ research was
facilitated by the motor car that he and his wife used in the field, our vehicle is faster and
moves in a virtual space. Our turn around too is leading to transdisciplinary problems,
much as they did for Clements. Such new intellectual devices appear when
disciplinarians face complexity. The rescaling caused by technological change takes us
out of defined situations. Without definition, complexity imposes itself on our problems
and discourses, and then systems science is it its element.
Complexity and Narrative
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Robert Rosen (2000) insists that complexity cannot be modeled. This makes complexity
a discrete category, because you can either model something or you cannot. Degrees of
complexity would be at odds with Rosen’s statement, so what then is meant by the
various measures of complexity? It appears that we need a distinction between
complexity ala Rosen and the measurable degree of elaboration that is called complexity
in common parlance. While we cannot model complexity, we can deal with it through
narrative. After deciding on the terms of the narrative, then we can look to see what had
to be done to simplify the situation enough for models to apply. It is indeed these
translations into narrative that give the degrees of “complexity” that are embodied in
diversity, chaos, connectivity, algorithmic complexity, and all the other measures used in
the vernacular and in complexity science.
As a precursor to telling a story, one must specify a level of analysis. It may change as
the story moves forward, and that too must be made explicit. A level of analysis includes
assertions that some parts of the continuous flux are frozen long enough for us to
recognize them as structures. Once it is restricted to being a structure, it possible to
assign an identity to an observed system. This identity is in terms of a finite selection of
attributes which are expected when observing or fabricating an individual specimen of
that general system type. Structures invoke a level at which material is aggregated in
discrete packages as opposed to a continuous distribution. Thus structures demand a
qualitative treatment in a pre-analytic phase, before they can be quantified. There are
decisions to be made about structure versus behavior, discrete versus continuous,
significant versus incidental change, and what is rate-dependent versus rate-dependent.
Only then have levels of analysis have been asserted sufficient to start measuring and
counting how elaborate is the situation in what are commonly called measurements of
complexity.
Captured in a narrative are relationships. A narrative invokes a hierarchy that links large
to small, fast to slow, and different types of objects. For instance, the narrative of germ
theory links microbes and disease. The measures of complexity that are asserted in
defiance of Rosen (2000) give an account of how many levels there are in the hierarchy,
and how many bits and types of bits exist in the whole. More bits and more types and
bigger scalar differences are all taken to mean greater complexity in the vernacular.
More organization also implies greater elaboration in the hierarchy in terms of a more
structured set of controls. But notice that number and variety of parts, and degree of
organization are not measures of the Rosenean complexity, they are measurements of
what we have to acknowledge so as to get some coherent account of complexity.
Defining variety and order prescribes aspects of the observation space within a formal
language. This formalization guides the perception of the organizational pattern in the
observed.
A paradigm is a relevant narrative that makes all the above decisions of discrete
significance with regard to some set of phenomena. Again, germ theory is an example.
Paradigms assert structure and relationship simply enough so that models can be tested.
Pasteur can then test implications for germ. Notice that while, paradigms simplify
complexity, they do not usually reduce the situation to something that is plain and simple.
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Paradigms can be complicated, in fact they usually are, but they are still simplifications
of complexity. They are not technically complex, they are merely complicated, which is
a different matter. So there is genuine Rosen complexity, and then there are narrative
accounts of situations that are more or less simple, to which measures of complexity are
applied.
Unlike models, narratives make no pretense of being objective and unambiguous.
Narratives are about the meaning of experience, they are not about any objective account
of some material reality. While models are often used to achieve prediction of some
posited reality, narratives are devices for constructing a commensurate experience
between the story teller and the listener. Pompous pronouncements of “new paradigms”
are often misplaced, because the storyteller alone is not the arbiter. In an established
paradigm, the identity of the story teller and the identities of those that are listening must
be compatible, to the extent that the paradigm is shared. All players must agree on the
relevance of the selected narrative for their common good. Prediction highlights the
modernist view, which uses external reality as its benchmark. Prediction stands in
contrast to commensurate experience which emphasizes a post-modern view where
understanding is constructed from interpretation of an observer’s experience (Funtowicz
and Ravetz, 1992; Allen at al., 2001).
Reductionist narrative and replicant knowledge.
In the face of complexity, reductionist models narrow the focus so that meaning is finally
eliminated, such that only emergence is left. Complexity science makes much of
emergence, and it is indeed an important part of complexity, being the source of new
levels in a hierarchical structure. However, emergence is only the thermodynamic half of
elaborate structure. The other half, which is commonly missed in complexity science, is
planned control. Planning is expressed through constraints that are coded in some sort of
language (e.g. DNA, hormones, statutes or understood cultures). Biological and social
systems all invoke complex systems that possess models of their own. People have
models, but so do foals just born have a model of mother, suckling and grass. Foals do
not find food at random, they have a set of triggers that amount to a model. An organism
enters the world with a model of the world, even though it has in itself not yet had much
experience. Furthermore, the world for which the organism has a model, itself contains
other organisms, which themselves possess models of the world, including models for the
first organism. Fish eggs mean food for other fish. Foals have meaning for even first
time mares. These models in the object of study make for complexity. So reductionism,
in eschewing semantics, is explicit in side-stepping the critical issues of complexity,
which maneuver comes at a certain cost.
Sometimes the models that come from reductionism are very useful, but they have an evil
twin. Sound reductionism identifies some specific process that fits into a more general
condition. The specifics are related to the general condition by a narrative that is the
meaningful context of the local reductionist knowledge. That narrative might be
evolution, which is viewed as responsible for the equivalence of some biochemical
function, such that it might be expected to arise in some new organism. It is also
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reasonable to expect this equivalence in organisms yet untested for that local function.
The usefulness and validity of reductionisms turns on models of “what is.” The
challenge in economic complexity is the frequent introduction of what “ought to be.”
Even the natural scientists’ “what is” has value laden experience that has constructed the
scientist before the project has even begun. But the “ought to be” that arises in the
application of economic models is much more overt in the values it imposes. Such
heavily value-laden statements embody a danger of narrowing models to give some
insight independent of a narrative for the new place where the model is applied. “Is” is
more robustly connected to a narrative than is “ought.” The evil twin to reductionism
arising in economics sneaks through misplaced values that misplace a narrative.
When the narrative is misplaced, Giampietro refers to “Replicant Knowledge.” This term
comes from the human replicants in the movie Blade Runner (Giampietro, Allen and
Mayumi in press). In the film, replicants live a short time, and have no memories of
whence they came, because they were made in adult form. Replicants are very effective
in extraterrestrial battles, precisely because they have no narrative that would lead them
to have a conscience, remorse or other worries. But on Earth replicants are a danger to
society, and in the movie they are hunted down. The replicants lack a narrative, but note
that does not mean they are inconsequential. An economic model might invoke the
positive value judgments ascribed to larger GNP. When this is taken out of the context
where its narrative applies, usually bad things happen as a result of actions recommended
by the model. For instance, thrusting development money into a small Third World
economy does increase GNP, but the money goes away with the expatriates who do the
building, leaving behind only environmental damage and debt. The narrative of “larger
GNP is good,” comes from experience in the First World, which depends on inexorable
increase in GNP. But applied to some small economy the model fails, because the
narrative that worked well in the US economy is importantly out of place. Ecological
economics without proper narratives runs the risk of violating the complexity of the
economies that they visit. A discussion of models versus narratives is not simply an
esoteric issue. The success of reductionist focus in biology should not be taken as a green
light for social situations, because absent a proper narrative, things come unraveled.
Aristotle to the rescue
So how can we use meaning and narrative to address complex situations safely and
effectively? It is important to deconstruct how meaning interacts with mechanics. There
are three levels of concern here: level N at which the structure in question resides; level
N+1, its context; and level N-1, its parts. There are two sides to each level. One is
thermodynamic, where there is no plan or meaning, rather things just happen that way
(Figure 1). Note that there is no plan for a whirlpool, nor for the degradation of high
quality material into detritus, it just happens. The second law of thermodynamics causes
a gradient from high to low quality to appear. The other side at each level is where
meaning resides. We do not wish to argue that the mechanics of the material world has
meaning in itself. Tainter and Lucas (1983) have been explicit in avoiding meaning as
intrinsic to archeological sites for preservation, and forcefully argue against intrinsic
meaning in their special area, and by extension in other discourses. Rather, meaning
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comes from interpretation by the human observer. The molecules in an ecosystem do not
care, and neither do those in an organism. As Rosen (1979) points out in his works on
anticipatory systems, we cannot understand life or society without purpose and goals,
because there are linguistic components, models that are part of the functioning of these
systems. We cannot interpret models without invoking a preferred outcome. Life and
society are not just complicated physics and chemistry. Bottom line is we are dealing
with interpretation of complex systems, not with complexity in itself.
At this point Aristotle’s causalities as invoked by Ulanowicz (1997) are useful. These
causalities can be situated in the three level, two-sided scheme sketched above. At the
highest level on the linguistic, meaningful side is the final cause. For a house it would be
the need for housing. The need for housing explains why there is a house. In biology or
social studies, the meaning in the final cause might indicate a shortage of a resource.
This meaning would invoke a planning activity two levels down at N-1, the linguistic
level of the plan for the structure. On the other hand that upper level meaning might be
that there are opportunities to capture some extra resource, such as when a society
achieve military might to facilitate pillage. When the presence of a new abundant
resource is the issue, the thermodynamic gradient in the environment, at level N+1, offers
more. Thus, moving across, from the final cause to N+1 on the thermodynamic side, one
finds the efficient cause, sitting as a thermodynamic twin to the upper level meaning on
the linguistic side. The efficient cause is the force applied to the material to put it in a
certain configuration. In building a house, this external force would be delivered by the
builders. In biology it might be food, in a society it might be gold.
The linguistic meaning at level N itself would be the ego in a sentient being, but there are
equivalent less tangible equivalents in non-sentient beings. It would be a self-identity of
a corporation or of the people in a nation state in a large social system. At the lower
level, N-1, there is planning, and so a plan appears. The plan is the formal cause, the
blueprint on a building site. This plan might be embodied in DNA or in regulations in a
society. It imposes a set of constraints on the lower level thermodynamic side. These
constraints might be genetic, as to which proteins form under protein synthesis in gene
expression. The constraints coming from a plan in a social group may form relationships
between, say, individuals in families in response to family values. Constraints coming
from the plan impose limits on the lower level components in the system, exerting control
over the bits. These constrained lower level components become players in the
mechanisms whereby the whole is to be made.
But mechanism cannot drive itself. The constrained lower level components must have a
thermodynamic gradient applied to them, say as food ingested from the environment.
The top of the gradient in the environment is high quality resource, which when ingested
comes to form high energy metabolites inside the organism. The high energy metabolites
form the top end of a thermodynamic gradient inside the organism. In societies, oil
imports or money might be equivalent to food. With the internal gradient in place, the
mechanism is thus driven. The driven mechanisms create the material thing at level N,
the material cause, the configuration of the stuff of which the whole is made. In a
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building the material cause is the bricks of a building, as they constitute walls. In
organisms the material cause is the protoplasm that resides in patterned structures.
But with the Aristotelian material cause of the system in place, we are still not finished.
The full formed structure at level N has meaning in its context at the upper level, and this
takes us back to level N+1 on the linguistic side (Figure 2). The newly created structure
at level N may have a different meaning than it did in its earlier manifestation. Under
such a change, the system has become something else. In all of these dynamics of both
behavior and emergence, the hard part is keeping track of all the changes that occur on
different levels. The issue for complexity becomes, what stays the same when everything
is different?
The holon as an organic dynamic
Arthur Koestler (1968) coined the term holon, for the way that systems are
simultaneously autonomous wholes, while being at the same time parts bound within a
larger scheme. The thing itself is Janus-faced and open in both directions, the skin
between the parts and the external world. Figure 3 shows the holon and the external
gradient. Inside the holon is contained the stuff of which it is made, the material cause.
The planning element inside is the formal cause, the blue print for making the whole.
The external gradient is the efficient cause. Once made, the holon has meaning for its
context, the final cause. The cycles around the Aristotelian causalities of figure 2 can be
seen in the system cast as a holon.
Figure 4 shows the passage around the causalities. There are two paths, one that takes the
plan of the formal cause for granted, and another path that involves changing plans to
deal with limited resources. Systems that take the plan as set are driven and explained by
the thermodynamics of the efficient cause. Allen et al. (2001) call these high gain
systems, because they make a profit with modest expenditure of effort applied to high
quality raw materials. The effort to concentrate the raw material was expended by some
other process, as when geological forces make oil from dead plants. Systems that change
the plan, to become more efficient, take the energy flux as fixed or declining, and are
explained by coded plans and the changes therein. Allen et al. 2001 call these low gain
systems, because they apply greater effort to a poor quality resource. Low gain systems
take a low quality resource and themselves concentrate it to make fuel. For instance,
plants take a diffuse energy source, light, and concentrate its free energy into sugar for
burning. Of course the system always has both thermodynamic and coded elements, and
which is seen as dominant is a matter of the comparisons being made in the narrative that
the biologist or social scientist tells.
Any given problem invokes a story. In an apparent paradox, more total resource is
captured from low quality resources, and more work is done with them. The paradox is
resolved by there being more raw material available in a low quality state, for instance as
gold dust is to nuggets. As the system moves around the causes, it may switch between
high or low gain, or it may persist in one phase of another. For instance, information
services are offered presently in the US principally to an elite, willing to pay a premium
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for cable television and internet connection. Note that wireless broadband is focused in
elite settings, such as frequent flier lounges. Information services are presently in high
gain mode, exploiting a high quality resource, moneyed technocrats. The situation has
been created by the thermodynamics of an emerging flux of information, and information
service have been growing for a couple of decades in high gain. But that market is
saturating, and the market begins to cool. Therefore, information services will have to
move to low gain, by organizing services for the larger mass of people with less
disposable income. The problem for information service providers is to access resources
now paid by the mass of people, for instance, to fast food outlets, corner convenience
stores and bargain super-stores. In another apparent paradox, being poor is expensive,
because the savings of bulk buying possible for elites is not available in a pay check to
pay check family economy. The interest on tax refund anticipation loans and payday
loans is very high, suggesting that there is a large mass of resource to be found in those of
moderate means. Less loan sharking and more information services to increase
efficiency in disposing of modest would seem to be a good thing. If the elite are gold
nuggets to the prospectors in the service industry, then the gold dust of the service
industry is to be found in the pockets of those with modest incomes. The challenge for
the service industry is to move into low gain mode, by modifying the plan, the formal
cause. That shift will be achieved by crafting a new post-industrial narrative that
captures the complexity of a world where dense and rich information becomes
commonplace for almost everyone.
The journey around the Aristotelian causes is not a simple circuit that the system
completes just once. In the end the observer-observation relationship comes to an end, in
death, extinction, or societal collapse. But until that happens, there is a continuous
cycling around the causes. The cycles continue through growth, maturity and senescence
(Figure 5). The plan is executed, but there are unplanned thermodynamic processes of
emergence. The consequences of mistakes in the plan lead to a new experience. New
experience embodies change in the significance of the new whole structure. Those
changes might require the system to become more efficient in low gain mode. And each
time around may lead to the evolution of yet more efficient behavior. In this way,
biological and economic systems may move to ever higher efficiency and the use of ever
more unlikely resource material. The evolution of termites started with an unwilling food
resource, wood, and has moved to ever less rewarding resources thereafter. In an
extreme adaptation termites appear to have reached the end of the road. Many tropical
species have abandoned wood altogether, and subsist on eating soil, a most unlikely
resource. While some systems become extremely low gain, others expand under high
gain. These systems enter self-perpetuating episodes of expansion under high gain, as
booty funds armies to preempt yet more resources. In all of this, the system is changing
and becoming at several levels.
Note that there are different processes involved in the move from one Aristotelian cause
to another (figure 2). We might expect these processes to move forward, not just at
different rates, but with different temporal frames for reference. The development of the
material cause under the plan of the formal cause might be at rate measured by dt.
Meanwhile, entering a new planning cycle, because of a new experience in the final
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cause, is a process of becoming, perhaps becoming more efficient. The tempo here is
completely different from the dt of development. We need a new time for the system
becoming, dτ. But then there is a narrative which moves forward each time the system
changes its formal cause, as it changes its significance. We deal with an observer
observation complex, and the narrative is told to the observer. A change in significance
is part of a narrative which moves forward in yet another time, requiring dθ. Taking a
post-modern view, we see understanding as being constructed, as each experience opens
the mind to new sorts of experience. There is the pace at which the understanding of the
narrator and the listener are constructed by the telling of the story, and this would require
a different sort of dθ (Figure 5).
For clearer view of the different times moving forward see Figure 6. To start there is a
simple time in which the plan is executed. The representation is acted upon to make a
structure whose meaning may be projected through transduction. Here the model
expects, and the works of the system establishes, the material being. Being once made,
the material whole then has an experience. The experience embodies the meaning of the
whole for its context. All of this moves forward with dt. We have already mentioned the
dτ of becoming, as the cycle of establishment is repeated. In becoming, the system plays
out a role.
Rosen (2000) uses concept of essence, which lies behind the realized structure. In
biology it is not DNA, but something impossible to define, because it has Rosenean
complexity. While the list of US presidents is a defined, closed set, the Presidency is
neither closed nor defined. The Presidency is accessed via the material realizations,
individual presidents, created over dt. The temporal frame there is in elections. The
Presidency is an essence, changed by its realizations, for instance, Richard Nixon and the
Watergate scandal. The process of assessing the essence, as best we can, is via the
narrative. There are two times for the narration, as we mentioned before, dθnarrator for the
change in the listener and narrator, and dθnarrative for the forward progression of the
narrative itself.
Finally there is the extinction of the system, or the end of the observer’s interest in it, and
for that we need a dT, to mark the passage beyond the end of the story. Bill Cronon
(1992) reports the end of the autobiography of Plenty Coups, Chief of the Crows. The
Chief said, “When the buffalo went away, the hearts of my people fell to the ground, and
they could not lift them up again. After this nothing happened.” And Cronon goes on to
identify Plenty Coups as the meta-observer who decides the end (Figure 5).
Although the Crows continued to live on their reservation and although their
identity as a people has never ceased, for Plenty Coups their subsequent life is all
part of a different story. The story he loved best ended with the buffalo.
Everything that has happened since is part of some other plot, and there is neither
sense nor joy in telling it… Just the nothingness that follows the end of a story.
(Cronon 1992: 1366-7)
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Conclusion
So all this might feel a bit esoteric. But, if we do not deconstruct the scaling issues in
complexity, in a manner similar to that prescribed here, there is an invitation to commit
error. As technological re-scaling makes for qualitative changes in our context,
prevailing disciplines become impotent or even irrelevant. Traditional disciplines are
secured by paradigms, but in the absence of a paradigm, complexity comes to overwhelm
us. The tool of choice to create paradigms and achieve transdisciplines is the narrative.
Importantly, narrative is not about telling of objective truth, but is rather about finding a
consonance of meaning between independent observers. The narrative helps the storyteller and the listener find commensurate experience. While prediction is lauded as the
acid test of science and understanding, we feel it is over-rated. The real power of
prediction is in convincing others of the usefulness of certain improved narratives,
leading to a powerful commensurate experience. The problem with prediction is that it is
about the behavior of something acknowledged as separate, and so something ultimately
not directly accessible. Prediction pertains to the observer-observed duality, which we
see as a primitive intellectual device, a mealy-mouthed compromise. It is much better to
deal with the observer-observation complex, because we do indeed have direct access to
our own experience. Prediction can be a useful device, but in the service of gaining
commensurate experience between independent observers.
Narratives have a unique character that is quite different from predictive models. Susan
Stanberg on National Public Radio interviewed Janos Starker, the renowned cellist
(/www.npr.org/templates/story/story.php?storyId=4164583). He insisted that one should
not be able to predict what is coming next in great music, but when it is over, the whole
should feel inevitable. And so it is with a compelling narrative, it feels inevitable. If he
had known it was his mother, Oedipus would not have married her. But once he had
done it, all the rest seems so inevitable, as it will always be in a compelling story.
If Einstein was surprised that the world yields to mathematics, we are taken aback and
overjoyed that so much in our experience is commensurate with what others tell us is
their experience. Even better, we appear to be able to work on building
commensurability by means of narratives. Models improve the quality of our narratives
on two accounts (for issues of quality see Funtowicz and Ravetz, 1992; Pirsig 1974,
1992). Models bring quantified precision to narratives, unequivocal constraints, and
explicit boundary conditions, thus improving the structural quality of narratives to which
the models are applied. Models also improve the dynamical quality by challenging the
narrative with alternatives. So systems scientists must stand shoulder to shoulder with
the technical modelers as well as with natural scientists. We have the tools to work
toward commensurate experience.
As we said in the section on replicant knowledge, some of the complexity we face comes
from the fact that organisms and societal groups themselves possess models and tell
narratives. We are in a dialogue with those entities in a larger observer-observation
12
complex. As the narrative of animals improve, they can begin to lie, as when mimics lie
predators as to how suitable they are and prey items. One narrative is that cockroaches
taste good to birds. The second narrative tells of how the brightly colored, red and black
cockroach is really a noxious ladybug, the animal that the cockroach mimics. All this is
very post-modern, in that the perception of the predator becomes the reality. Belief in the
story is all that matters. Self-knowledge is not possessed by the mimic itself, but is
achieved through the narrative that the predator tells the cockroach. You do not know
you are an item of prey until a predator treats you that way. The narrative of a mimic is
two dimensional (taste good, taste bad). Therefore the story that the predator tells is a
three dimensional narrative conveying self-knowledge to the mimic. The stories that
natural systems tell can be intricate and high dimensional.
Allen, Zellmer and Wuennenberg (2005) identify that science invokes similar high
dimensional narratives. First the scientist insists that the prevailing narrative be a one
dimensional track that must pass through a set of zero-dimensional models embodied in
thought experiments. As the narrative improves, scientists are in a position to lie.
Performing an experiment amounts to lying. We know better, but try it on anyway. The
lie is to the world, when the experimenter says in the experimental design, “No, really, it
is just that simple.” Then, in the experimental results, the world gives thumbs up or
down on the story. Particularly if the experiment fails to behave as expected, the story
the world tells improves the self-knowledge of the scientist. Thus progress in science is a
four dimensional narrative that continues to improve scientists’ self-knowledge of their
assumptions. Scientists get a better account of the situation when the observerobservation complex informs them that they are mistaken. Thus scientists tell narratives
and create models much in the manner of the biological and social things that they study.
Nature and science work in the same way. While natural selection is the manifestation of
commensurate experience in biology, scientific progress invokes models and narratives to
improve human commensurate experience.
So the post-industrial world imposes overwhelming issues upon us, but we should not
stand as the deer in the headlights of the oncoming complexity. Systems science allied
with normal science can use narratives to overcome the difficulties imposed by the
rescaling of new technology. In business, society and our dealings with nature and
environment will need many new transdisiplinary efforts. In this uncomfortable setting,
systems science may now come into its own as one of the best tools we have.
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Figure 1. The three layer, two-facetted scheme wherein exist the Aristotelian Causes.
Level N where the system itself resides. Level N+1 offers the respective contexts. Level
N-1 invokes the level of the parts. The right side is meaningful and linguistic, whereas
the left side is thermodynamic, and just is. The various planning and creation phases are
identified, as are the placements of the different causalities.
14
Figure 2. The same pattern in figure 1 is represented here to show the path around the
Aristotelian causes. If the meaning of a system is that it can suddenly preempt more high
quality resource, the plan is generally taken as working well. Therefore the passage is
from final cause at top right across to a changed efficient cause at top left. In this case
the plan embodied in the formal cause taken as given. But if final cause implied that the
resources are limited and declining, then the system must alter its plan, in a track down
the linguistic side to N-1. The new plan lets the system survive by becoming more
efficient.
15
Figure 3. If the system is bounded by the oval in the center, it has two aspects to it. The
material cause is the stuff of which it is made. The formal cause is the meaning inside
the system that codes for the constraints on the parts. The whole is driven by an external
gradient, the efficient cause. Once constructed, the whole takes on meaning for its
environment, the final cause.
16
Figure 4. The passage around the causes in figure 2 is mapped onto figure 3.
17
Figure 5. The changes in final cause in figure 4 amount to the consequences of
unexpected emergence in the material cause. The change in final cause can be seen as
experience, which then causes the system to become more efficient. There are four
different levels of time here. The execution of the plan is in simple dt. The increases in
efficiency are the level of becoming, which demand a different time measured by dτ. The
changes in meaning at the level of formal cause amount to the narrative of the system,
which moves forward at a different rate of time dθ. Finally, at the end of the observer
observation complex’s life expectancy, death, extinction, societal collapse, and the end of
the grant, all cause the evolutionary demise at a rate of measured by dT.
18
Figure 6. The cycles of change in figure 5 are displayed here, showing how the time of
the narrative moves forward, both in terms of the telling of the story in the change of
essence, and as it changes the narrator and the listener.
19
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