The Archaeology of Innovation: Lessons for Our Times
Introduction
In this paper, I have tried to bring together a number of
strands of work carried out over the last thirty years, both as an
archaeologist and as a generalist social scientist, concerned with the very
long term history of human evolution and some of its implications for the
challenges of the twenty-first century.The result is a very personal
perspective that notably differs from the contributions of very many
colleagues in that I have from the outset posited that what characterizes
modern human (
Homo sapiens sapiens )
[1] behavior and modern
human societies is information processing that includes learning and
learning how to learn (second-order learning. See Bateson 1972), as well as
categorization, abstraction, (hierarchical) organization, and related
phenomena. Moreover modern humans communicate among themselves by various kinds of symbolic
means and have the capacity to transform their natural and material
environment in many different ways, and at many spatial and temporal scales. As a result, this paper does diverge from the usual population-based
Darwinian thinking about human evolution (e.g. Boyd and Richerson 1985 etc.)
in that, for the later periods (Lane et al. 2009), it focuses on
‘organization thinking’—studying the evolution of the ways in which human
beings process information, organize themselves, and transform the world
around them.
Necessarily, this paper takes the shape of an introductory summary of many of
the underlying arguments about the trajectory of human evolution and the
aspects of that history that are particularly relevant to the present and
the future. Where possible, I have referred to papers and other publications
that elaborate my main train of thought. However, I have kept other
references to a minimum, not wanting to load the argument with the many
doubts and discussions that have occurred in the anthropological and
archaeological community over the period of gestation. I have thus been able
to reserve space to point out some of the implications of this approach for
present-day challenges, in particular the contradiction between two of
today’s favorite buzzwords: ‘innovation’ and ‘sustainability.’
The evolutionary history of the human species, and in particular its
cognitive and organizational capacity, is here seen as consisting of two
parts, the first of which is essentially biological (the growth of our brain
and its cognitive capacity), whilst the second is essentially cultural
(learning to exploit the full capacity of the brain). Hence, this paper is
divided into three major sections, describing respectively (1) the
biological evolution, (2) the cultural evolution, and (3) the implications
of the species’ past history for our present-day challenges.
It should be emphasized that each of these three sections is based on
insights and knowledge from different disciplines and sub-disciplines. The
first part derives from arguments in evolutionary biology and evolutionary
psychology, and therefore is based on an essentially life-science
epistemology and argument, and data deriving from ethology,
palaeo–anthropology and cognitive science. It attempts to reconstruct the
evolution of the human species leading up to its present-day capabilities by
comparing living primates, the fossil remains of—and the artifacts made
by—humans at various stages of their development, and the physical and
behavioral characteristics of modern human beings. This leads to a patchwork
of data-points and ideas that, in so far as it coherently holds together,
derives its principal interest from the fact that it raises new questions
and provides a basis for the arguments in the second part.
That part, on the other hand, derives from arguments in archaeology and
history, which are based on humanities and social science epistemologies
respectively, and data and insights from archaeological, written historical
and modern observational sources. It attempts to outline the development of
societal organization from small roaming gatherer-hunter-fisher bands, via
villages, urban systems and empires to the present day global society, with
a focus on the role and forms energy and information play in that
development. In doing so, I am using the constraints and opportunities
afforded by the bio-social nature of our species to explain observed
phenomena in human history, and couching the explanation in systemic terms,
which many archaeologists and most historians would have difficulty
recognizing. And to add insult to injury, I am doing so at a level of
generalization that is beyond any commonly used in these disciplines.
My justification for doing this is the fact that most, if not all,
trans-disciplinary research must aim to “constructively upset the
practitioners of all the disciplines involved” in order to raise new
questions and challenges to be considered by the communities practicing
these disciplines as well as by others, and thus to ‘stretch the envelope’
of our knowledge and insights. The direction in which I have attempted to
stretch that envelope is given by the fact that this paper intends to make a
contribution to the current sustainability debate.
In the third part of the paper, I have tried to outline how the bio-social
nature of human beings and the course of the history of the species over the
last 12,000–15,000 years have conspired to create the dilemma that we face
today: “How do we use the human capacity to innovate, the unbridled use of
which during the last three centuries has caused the unsustainability of our
current mode of life, to attain a more sustainable society?” The short
answer is clearly: “We must use our capacity to innovate in a different
way!” This third part of the paper therefore ends with some suggestions
derived from observing a fundamental weakness of our current scientific
thinking—the capacity to derive lessons from the past for the future.
Part I: The Evolution of the Human Brain
The first part of the story concerns the physical development of the human
brain and its capacity to deal with an increasing number of simultaneous
information sources. The core concept that is most relevant here is the
evolution of the short-term working memory (hereafter STWM), which
determines how many different sources of information can be processed
together in order to follow a particular train of thought or course of
action. There are different ways to reconstruct this evolution (Read and van
der Leeuw 2008, 2009). Indirectly, it can be interpolated by comparing the
STWM of chimpanzees (our closest common ancestor in the evolutionary tree
that produced modern humans) to that of modern human beings. 75% of
chimpanzees are able to combine three elements (an anvil, a nut, and a
hammerstone) in the act of cracking the nut, which leads us to think that
the STWM of chimpanzees is 2 ± 1 (because 25% of them never master this). Experiments with different ways of calculating the human capacity to combine
information sources, on the other hand, seem to point to an STWM of 7 ± 2
for modern humans. This difference coincides nicely with the fact that
chimpanzees reach adolescence after 3–4 years, and modern humans at age
13–14. It is therefore assumed that the growth of STWM occurs before
adolescence in both species, and that the difference in age of adolescence
explains the difference in STWM capacity (Figure 1. Cf. Read and van der
Leeuw 2008:1960
Another approach to
corroborating the growth of STWM is by measuring encephalization—the evolution
of the brain-to-body-weight ratio of modern humans’ ancestors through time. The
evolution of these ratios is based on the skeletal remains of each subspecies
found and, as shown in Figure 3, corresponds nicely to the evolution of the STWM
as has been established based on the way and extent to which these ancestors
were able to shape stone tools (Cf. Read and van der Leeuw 2008:164).
Whereas
both these approaches depend in fact on extrapolation and therefore do not
provide any
direct proof for our thesis, the study of the way
and extent to which the various subspecies and variants preceding modern
humans have been able to shape stone tools does provide some direct
evidence, which is summarized in Table 1. That table links the evolution of
actions in stone tool making with the concepts that they define, the number
of dimensions and the STWM involved with the stone tools that provide
examples of each stage.
In order to explain the development involved,
I will use an example: the mastering of three-dimensional conceptualization
and manufacture of stone tools (Cf. Figure 3a–d) (Pigeot 1991, van der Leeuw
2000). The first tools are essentially pebbles from which at one point of
the circumference (generally where the pebble is pointed) a chip has been
removed to create a sharper edge (Figure 3a). Removing the flake requires
three pieces of information: the future tool from which the flake is
removed, the hammerstone with which that is done, and the need to maintain
the two at an angle of less that 90º at the time of the blow. Here, we
therefore have to do with proof of S TWM 3. In the next stage, this action
(flaking) is repeated along the edge of the pebble. That requires control
over the above three variables, and a fourth one: the succession of the
blows in a line. STWM is therefore 4 (Figure 3b). Next, the edge is closed:
the toolmaker goes all around the pebble until the last flake is adjacent to
the first. By itself, this is not a complete new stage, and we have called
this STWM 4.5. But once the closed loop is conceived as defining a surface
the knapper has two options. Either to define a surface by knapping an edge
around it and then taking off the centre, or to do the reverse—take off the
centre first, and then refine the edge. The conceptual reversibility shows
that the knapper has now integrated five dimensions, and his or her STWM is
5 (Figure 3c). The next stage again develops sequentiality, but in a more
complex way. In the so-called ‘Levallois’ technique, making one artifact
serves at the same time as preparation for the next, by dividing the pebble
conceptually in two parts along its edge. And finally, the knapper works
completely in three dimensions, preparing two surfaces and then taking
flakes off the third. At this stage, STWM 7 (Figure 3d), for the first time
the knappers are able not only to work a three-dimensional piece of stone,
but also to conceive it as three-dimensional and adapt their working
techniques accordingly, greatly reducing loss and increasing efficiency.
Closely observing the tools and other traces of human existence
available around 50,000 BP indicates that, after some 2,000,000 years,
people at that time could (van der Leeuw 2000):
- Distinguish
between reality and conception
- Categorize based on
similarities and differences
- In their thinking, feed-back,
feed-forward and reverse in time (e.g. reverse an observed causal
sequence, in order to conclude from the result what kind of action could
achieve it)
- Remember and represent sequences of actions,
including control loops, and conceive of such sequences that could be
inserted as alternatives in manufacturing sequences
- Create
basic hierarchies, such as point-line-surface-volume, or hierarchies of
size or inclusion
- Conceive of relationships between a
whole and its constituent parts (including reversing these
relationships)
- Maintain complex sequences of actions in
the mind, such as between different stages of a production
process
- Represent an object in a reduced set of dimensions
(e.g. life-like cave painting)
Part II: The Innovation Explosion: Mastering Matter and Learning How to
Put the Brain to Best Use
After 50,000 BP
[2] , and especially after around 15,000 BP, we see a true
‘innovation explosion’ occurring just about everywhere on Earth. The sheer
multitude of inventions in every domain is truly astonishing, and
accelerates up to the present day. There is no reason to assume further
developments of the human STWM, as the experimental evidence indicates that
modern humans currently have the capacity to deal simultaneously with at
most seven, eight or sometimes nine dimensions or sources of information,
but even a superficial scrutiny of modern technologies, languages and other
achievements shows the wide variety of things that can be achieved with a
STWM of 7±2. We would therefore argue that for this next phase, from about
50,000 BP to the present, the biology of the mind does no longer impose any
constraints, and the emphasis is on acquiring the fullest possible range of
techniques exploiting the STWM capacity available.
The Emergence of Improved Technologies
We can distinguish several phases in that process. In
the first, the toolkit explodes, but the gatherer-hunter-fisher mobile
lifestyle remains the same. Some of the many cognitive operators that
emerge in that first stage are (van der Leeuw 2000):
- The use of completely new topologies (e.g. that of a
solid around a void, such as in the case of a pot or basket);
- The use of many new materials to make tools with.
Although it is difficult to prove that these materials were not used
earlier, nevertheless, one observes from this time onwards objects
in bone, as well as wood and other perishable materials;
- The combination of different materials into one and the
same tool (e.g. hafting small sharpened stone tools into
a wooden or bone handle);
- The inversion of the manufacturing sequence from
reductive (one that begins with a big object (a block of
stone) and successively takes smaller and smaller pieces off it) to
gain control over the shape, to additive (where tiny
particles (clay, fibers) are combined into larger, linear objects
(threads, coils) and then into a two-dimensional object (such as a
woven cloth), that is then given shape (by sewing) to fit a
three-dimensional object (a piece of clothing), etc. This implies
the cognition of a wide range of scales.
- Stretching and chunking the sequence of actions kept
in the mind: distinguishing between (complex) preparation stages
(e.g. gathering of raw materials, preparing them, shaping of
pottery, drying, decorating, firing) yet being able to link the
logic of manufacture across these stages (adapt the clay to the
firing technique, etc.)
The First Villages, Agriculture and Herding
In the next stage, ca. 13,00–10,000 BP, the continued
innovation explosion changed the whole lifestyle of many humans. The
acceleration was so overwhelming that in a few thousand years the whole
way of life of most humans on earth changed: rather than live in small
groups that roamed around, people concentrated their activities in
smaller territories, invented different subsistence strategies, and in
some cases literally settled down in small villages (van der Leeuw 2000,
2007, and references therein).
Together, these advances greatly increased the number of ways at people’s
disposal to tackle the challenges posed by their environment. That
rapidly increased our species’ capability to invent and innovate in many
different domains, allowed it to meet more and more complex challenges
in shorter and shorter timeframes, and thus substantively increased
humans’ adaptive capacity. But the other side of the coin was that these
solutions, by engaging people in the manipulation of a material world
that they now partly controlled, ultimately led to new, often
unexpected, challenges that required the mobilization of great effort to
be overcome in due time.
As part of this process, a number of fundamental changes occurred. First
of all, the relationship between societies and their environments became
reciprocal : the terrestrial environment from now on did
not only impact on society, but society impacted on the terrestrial
environment as well. As a result, sedentary societies tried to
control environmental risk by intervening in the
environment, notably by (1)
narrowing and optimizing
the range of their dependencies on the
environment , (2)
simplifying or even homogenizing
(parts of) their environments , and (3)
spatial and
technical diversification and specialization (Cf. van der
Leeuw 2000). The new subsistence techniques introduced, including
horticulture, agriculture and herding, narrowed the range of things
people depended on for their subsistence. In the process, certain areas
of the environment were ‘cleared’ and dedicated to the specific purpose
of growing certain kinds of plants. This required i
nvestment
in certain parts of the environment, dedicating those areas to
specific activities and delaying the rewards of the investment
activities. Clearing the forest and sowing resulted only a year later in
a harvest, for example.
The resulting increase of investment in the environment in turn anchored
different communities more and more closely to the territory in which
they chose to live. People now built permanent dwellings
using the
new topology (upside down containers), and devised many other
new kinds of tools and tool–making technologies facilitating the new
subsistence strategies practicable in their environment (e.g. the ard,
the domestication of animals, baskets and pottery for storage, pottery
for boiling). Without speaking of (full-time) ‘specialists,’ certain
people in a village begin to dedicate more time, for example, to weaving
or pottery making, and in doing so provide the products of their work to
others in exchange for some of the products these others produced. Differences in resource availability and technological know-how thus led
to economic diversification and, in order to provide everyone with the
things they needed, the emergence of trade.
The symbiosis that thus emerged between different landscapes and the
life–ways invented and constructed by human groups to deal with them,
by narrowing the spectrum of adaptive options open to the
individual societies concerned, drove each of them to devise more
and more complex solutions, with more and more unexpected
consequences that then needed to be dealt with in turn.
In keeping with my fundamental tenet that information processing is
crucial to such changes, I attribute the changes outlined in this
section to the beginnings of a new dynamic, in which
learning
moved from the individual to the group because the
dimensionality of the challenges to be met increased beyond the
capability of individuals to deal with them. This involved the emergence
of the following feedback loop (van der Leeuw
2007):
Problem-solving structures knowledge –– > more
knowledge increases the information processing capacity –– > that
in turn allows the cognition of new problems –– > creates new
knowledge — > knowledge creation involves more and more people in
processing information –– > increases the size of the group
involved and its degree of aggregation –– > creates more problems
–– > increases need for problem-solving –– > problem-solving
structures more knowledge … etc.
It enabled the continued
accumulation of knowledge, and thus of information-processing capacity,
which in turn enabled a concomitant increase in matter, energy and
information flows through the society, and thus the
growth of
interactive groups . But this growth was at all times
constrained by the amount of information that could be communicated
among the members of the group, as miscommunication would have led to
misunderstandings and conflicts, and would thus have impaired the
cohesion of the communities involved. Communication stress did in my
opinion provide the incentive for (a)
improvements in the means of
communication (for example by ‘inventing’ new, more precise
concepts to communicate ideas with (Cf. van der Leeuw 1982), and (b)
a reduction in the search time needed to find those one needed
to communicate with (by adopting a sedentary lifestyle).
Finally, as the social system diversified, and people became more
dependent on each other, the risk pattern increasingly included also
social stresses caused by misunderstandings and miscommunications. Handling risks therefore came to rely increasingly on social skills, and
the collective invention and acceptance of organizational and other
tools to maintain social cohesion.
The First Towns
From this point in time, I will no longer try to point
out any novel innovations or cognitive operations emerging as human
societies grew in size and towns spread over the surface of the earth. Instead, I will focus on how the feedback system that drove societal
growth as well as the conquest of the material world through innovation
posed some major challenges. Overcoming these ultimately enabled the
emergence of true ‘world systems’ such as the colonial empires of the
early modern period (van der Leeuw 2007) or the current globalized
world.
Throughout the third stage, from around 7,000 BP,
communication
remained a major constraint because more and more people were
interactive with each other as the size of settlements involved grew to
what we now call a town. This stage—again—sees the emergence of a host
of new innovations, such as writing, recurrent markets, administration,
laws, bureaucracies, specialized full-time communities dedicated to
specific activities (priests, scribes, soldiers, different kinds of
craftsmen, women, etc.). Many of these had either to do with improving
communication (such as writing and scribes), social regulation
(administration, bureaucracies, laws), the harnessing of more and more
resources (mining) or the exchange of objects and materials in part over
larger and larger distances (markets, long-distance traders, innovations
in transportation). But as larger groups aggregated, the territory
(‘footprint’ to use a modern term) upon which they depended for their
material and energetic needs expanded exponentially, and the effort
required to transport foodstuffs and other materials did the same.
To deal with this constraint, an interesting core-periphery dynamic
emerged to exploit that ever-growing footprint—the exchange of
organization against energy. Around towns, dynamic ‘flow structures’
emerged in which organizational capacity was generated in the towns and
then spread around them, extending the town’s control over a wider and
wider territory; in turn, the increasing quantities of energy collected
in that territory (in the form of foodstuffs and other natural
resources) flowed back towards the city to feed the ever-increasing
population that kept the flow structure going by innovation (creation of
new organization and information-processing capacity). These ‘flow
structures’ became the ‘bootstrapping’ drivers that created larger and
larger agglomerations of people and the territories to go with them.
What enabled the urban populations to keep innovating, and thus to
maintain the flow structures, was—again—the growing capacity of more and
more interacting minds to identify new needs, novel functions and new
categories, as well as new artifacts and challenges. Underpinning that
dynamic is one that we know well in the modern world. Invention is
usually (and certainly in prehistoric and early historic times)
something that involves either individuals, or very small teams. Hence,
in its early stages it is related to a relatively small number of
cognitive dimensions—it solves challenges that few people are aware of. As such inventions become the focus of attention of much larger numbers
of people, they simultaneously become cognized in many more dimensions
(people see more uses for them, ways to slightly improve them, etc.),
and this in certain cases triggers an ‘innovation cascade’—a string of
further innovations, including new artifacts, new uses of existing
artifacts, and new forms of behavior and social and institutional
organization. In this process, clearly, towns and cities are more
successful than rural areas because of the greater number of interactive
individuals in such aggregations. That is corroborated by the fact that
when scaling a number of urban systems of different sizes against
respectively metrics of population, energy and innovation, population
scales linearly, energy sub-linearly and innovation capacity
super-linearly (Bettencourt et al. 2006)
Empires
The above ‘flow structures’ kept growing (albeit with
ups and downs) until, after several millennia (from about 2500 BC in the
Old World, and about 500 BC in the New), they were able to cover very
large areas, such as the prehistoric and early historic empires (The
Chinese, Achaemenid and Macedonian and Roman Empires, for example, in
the Old World, the Maya and Inca Empires in the New World, and later the
European colonial Empires), which concentrated large numbers of people
at their center (and, in order to feed them, gathered treasure, raw
materials, crops, and many other commodities from their hinterlands).
Throughout this period communication and energy remained the
main constraints, impacting on cities, states and empires. Thus we see advances in the harnessing of animal energy (including
slavery), wind power (for transportation in sailing vessels and for
driving windmills), falling water (for mills), etc., but also in the
facilitation of communication, (e.g. long-distance ‘highways’ over land,
the sextant and compass to facilitate navigation on the sea), and in all
kinds of ways to create and concentrate wealth serving to defray the
costs of managing societal tensions, maintaining an administration and
an army, etc.
Those costs effectively limited the extent of Empires in space and time. Tainter (1988), for example, argues convincingly that only the treasure
accumulated outside the Roman Empire in the centuries before the Roman
conquest enabled Rome to maintain the large armies and bureaucracies to
keep its Empire. As soon as there was no more treasure to be gained by
conquering, and the Empire was thrown back upon a dependency on
recurrent (in essence solar) energy, he argues that it could no longer
maintain the flow structure. This reduced the advantages of being part
of the Empire, so that it began to lose control over its wide territory,
causing people to fall back on smaller, regional or local networks. Thus
disaffection, or even dispersion of the population, followed the
cessation of the flows that generated the coherent socio-economic
structure of an Empire in the first place.
Part III: The Last Three Centuries
The last three centuries have seen the (provisional)
culmination of the trajectory I have outlined in Part II. That trajectory
shows how the constraints and opportunities afforded by the bio-social
nature of our species explain a number of observed phenomena if human
history is conceived in systemic terms. In that sense, these last three
centuries do not differ from what went before, but they have seen an
unbridled acceleration of our species’ innovative activity, initially
because the ‘taming’ of fossil energy removed the energetic constraint
from much human activity, and subsequently because the introduction of
electronics enabled the separation of information from most of the
substrates used for its transmission until then. These two developments
together have engendered a ‘quantum jump’ or ‘state change’ in societal
dynamics, which has been at the root of many of today’s challenges, but
also introduces potential ways to deal with these that were not
available thus far.
The Introduction of Fossil Energy and Society’s Dependence on
Innovation
The (for the moment) last phase of this long-term
process of social evolution through innovation involves the last two and
a half centuries, in which first the energy constraint was removed by
the introduction of plentiful fossil energy, and recently the
communication and information processing constraint is in the process of
being removed due to the development of new technologies. The
introduction of fossil energy first brings in its wake new technologies
to enable, facilitate or reduce the cost of transportation (railroads
steamers, cars, etc.), manufacturing (steam-driven factories), and
energy itself, as well as (later) technologies to reduce the amount of
energy needed to fulfill societal needs.
Without immediately having a clear explanation, however, I would like to
signal another emergent driver that, in this period, transforms
innovation from a demand-driven to a supply-driven activity. For most of human (pre-)history, it
seems that inventions were either the result of perceived needs or were
not really introduced at a large scale in societies until such a need
emerged. It took, for example, roughly one thousand years after the
invention of ironworking to actually see that technique spread
throughout Europe at a fairly rapid pace (Cf. Sørensen and Thomas 1989). In that case, the initial brake on the transformation of this invention
into an innovation seems to have been related to the social structure of
society. In the Bronze Age, hierarchies emerged that controlled wide
exchange networks because they controlled the sources of bronze, which
was relatively easy to do because accessible sources to this metal were
relatively few and far between. That is not the case with iron—it can be
found in virtually every water-rich place in Europe, and once the
technology to use it spread, no one could any longer derive riches from
controlling the manufacture of iron tools. The introduction of iron
technology therefore enabled large numbers of people to manufacture and
use much better tools and weapons and had, in a sense, a democratizing
effect.
Between the eighteenth and the twentieth century, and particularly in the
second half of the latter, with respect to innovation
the balance
between supply and demand shifted in favor of supply . Rather
than societal needs driving innovation, innovation came to drive
societal needs. Companies competed to lay their hands on inventions (or
developed them internally), and then created markets for them, forcing
their use on society in order to enhance their profit.
This has
led to a situation in which innovation has become endemic to our
societies, and those societies, through their dependency on
ever-increasing GDP and profit figures, have become dependent on
innovation for their continued existence . This is a novel
dynamic that has major consequences for the way we might deal with the
challenges of the twenty-first century, sustainability among them. I
will come back to this in a later section.
This phenomenon has emerged in a period that saw the transformation of
our society’s perspective on time. Whereas until the seventeeth century,
the most frequent vision explained the present by invoking ‘History’ or
‘The Past’ or ‘It has always been like this’, whereas
invoking something ‘new’ or ‘an innovation’ was socially heavily frowned
upon. With the enlightenment this changed, ultimately leading to our
current attitude, in which the ‘new’ is mostly preferred over the ‘old’,
the ‘proven’ or the ‘heritage’ (Girard 1990). Interestingly enough, this
change in perspective was accompanied by the institutionalization of the
universities and academic disciplines as ‘research crucibles’, initially
on the expectation that, ultimately, something useful would be invented,
but increasingly with the expectation that such economic advantages are
what research exists for.
Separating Information from its Material and Energetic
Substrates
Although ‘information technology’ has been in existence
for many thousands of years, in the form of gestures, language, writing,
accounting, and many other things including North American smoke signals
and African tamtams, the second half of the twentieth century saw the
definition of the concept of ‘information’ (Shannon and Weaver 1948) and rapidly
thereafter the mechanization of information processing, initially in the
domain of communication, but then also in the domains of calculation,
representation, and many others. Hence, the current emphasis in certain
quarters on our present-day society as the ‘information society’ is
misguided—every society since the beginning of human evolution has been
an ‘information society.’
Clearly, as we are only at the beginning of a process that will
eventually harness electronic and other forms of information processing
throughout all aspects of our thinking and our society, and offer many
new solutions to existing challenges and equally many new challenges, we
cannot presently outline the higher-level ‘drivers’ that may emerge as a
result of that process. However, we do note that, again, these will
accelerate the dependency of our society on innovation. Indeed, massive
information collection and treatment, as well as the application of the
concept of information to physical, biological and societal processes,
is emerging as a new challenge: the NBIC ‘revolution’, under which we
understand the encounter (and potential interaction) of nano- bio-
information- and communications technology.
However that may be, after the mastering of ‘matter’ by devising ways to
conceptually separate manipulating it from the time/space in which that
process occurred, which took humanity about two million years, and the
mastering of energy by separating it conceptually from movement and
change, which took the next seven thousand years, it took only two
hundred years more to conceptualize information by separating it from
its material or energetic substrate. Our collective capability to
process information has therefore accelerated more or less
exponentially, as has the size of Earth’s human population and—more
important from our perspective—the size and number of the cities that
are the principal hearths of new inventions and innovations. Having
identified the driver behind this process, as with any such exponential
growth, we have to ask: “How much longer can this go on?” In order to
answer that question, we must look at the long-term consequences of the
‘innovation explosion’ from the Neolithic to the present.
Part IV: The Challenge of the Future—Innovation, Sustainability and
‘Unanticipated Consequences’
One way to introduce this topic, to which we will devote the
last part of this paper, is to point out the contradiction between the fact
that innovation is seen as the way out of the present syndrome of
overpopulation, looming or current resource shortage, omnipresent pollution,
etc., even though two centuries of unbridled innovation are responsible for
bringing about the consumer society as well as the current sustainability
challenge. One must conclude that innovation as it is presently embedded in
our societies is hardly the panacea to get us out of the sustainability
predicament that many claim it is. That, then, in turn prompts the question
whether there are any alternatives to ‘innovating ourselves out of trouble,’
and if there are, what could they be?
It seems to me that the root of this challenge lies in the relationship
between the fundamental limitations of the human mind, whether collective or
individual, and the complexity of the world outside us. I would argue that,
over the millennia that relationship has changed as a result of the
innovation explosion itself. In order to understand the nature of that
change, we need to look at the relationship between people and their
environment.
Human cognition, powerful as it may have become in dealing with the
environment, is only one side of the (asymmetric) interaction between people
and their environment, the one in which the perception of the
multidimensional external world is reduced to a very limited number of
dimensions. The other side of that interaction is human action on the
environment, and the relationship between cognition and action is exactly
what makes the gap between our needs and our capabilities so dramatic. The
crucial concept here is that of ‘unforeseen’ or ‘unanticipated’
consequences. It refers to the well-known and oft-observed fact that, no
matter how careful one is in designing human interventions in the
environment, the outcome is never what it was intended to be. It seems to me
that this phenomenon is due to the fact that every human action upon the
environment modifies the latter in many more ways that its human actors
perceive, simply because the dimensionality of the environment is much
higher than can be captured by the human mind. In practice, this may be seen
to play out in every instance where humans have interacted in a particular
way with their environment for a long time—in each such instance, ultimately
the environment becomes so degraded from the perspective of the people
involved that they either move to another place or change the way they are
interacting with the environment.
How does this happen? Imagine a group of people moving into a new
environment, about which they possess little knowledge, such as the European
settlers into the Eastern North American forests (Cronon 1983). After a
relatively short time, they will observe challenges or opportunities to
interact with this environment, and they will ‘do something’ about them. Their action upon these challenges is based on an impoverished perception of
them, which mainly consists of observations concerning the short-term
dynamics involved. Yet these same actions transform the environment in ways
that affect not only the short-term, but also the long-term dynamics
involved in unknown ways. Over time, little by little all the frequent
challenges become known and are modified by the society’s interaction with
the environment, while the unknown longer-term challenges that are
introduced accumulate. Or to put this in more abstract terms, due to human
interaction with the environment, the ‘risk spectrum’ of the
socio-environmental system is transformed into one in which unknown,
long-term (centennial or millennial) risks accumulate to the detriment of
shorter-term risks.
Ultimately, this necessarily leads to ‘time-bombs’ or ‘crises’ in which so
many unknowns emerges that the society risks being overwhelmed by the number
of challenges it has to face simultaneously. It will initially deal with
this by innovating faster and faster, as our society has done for the last
two centuries or so, but as this only accelerates the risk spectrum shift,
this ultimately is a battle that no society can win. There will inevitably
come a time that the society drastically needs to change the way it
interacts with the environment, or it will lose its coherence. In the latter
case, after a time, the whole cycle begins anew—as one observes when looking
at the rise and decline of firms, cities, nations, empires or
civilizations.
What is the effect of an exponential increase in information processing
capacity on this asymmetry between human understanding and human action? Clearly, as the information processing capacity increases, the total number
of (collectively) cognized dimensions involved in the process does so more
or less commensurately. The human actions on the environment therefore
affect more and more dimensions of the processes going on in that
environment. As the multiplier between cognized human dimensions and unknown
environmental dimensions affected by human actions is large, this implies
that due to the exponential increase in the number of human cognized
dimensions, the number of affected environmental dimensions grows even more
rapidly, posing ever more rapidly ever more complex environmental challenges
for humankind to deal with.
This permanent, and increasing, tension between the total cognitive capacity
of a society and the complexity of its environment has in itself been a, if
not the, major driver behind the increase in information processing capacity
of human beings and societies. As such, it has had important consequences
for the information processing structure of the societies involved. Several
of these have already been mentioned in this paper: population increase,
aggregation of human populations in villages and then cities, the invention
of writing, markets, administration and other phenomena accompanying
urbanization, etc. But others have not been given much attention; such as
its impact on our language and the way we have done (and often still do)
science.
Let us look at language first. Initially, as small groups lived together most
of the time, humans had the opportunity and time for multi-channel
communication—spoken language, gestures, body language, eye context and any
other kind of communication. This allowed for the long-term accumulation of
trust and understanding that allows for the reduction and correction of a
wide range of communication errors. But as the groups involved grew, and the
time devoted to each interaction therefore shortened, fewer channels of
communication were available, and spoken language won out as the main
channel of communication between people meeting each other infrequently and
for short periods of time, mainly because spoken language is a relatively
precise way to communicate concepts. Ultimately, as networks of
communication grew even further, the need to avoid misunderstandings and
errors must also have had an impact on language itself, requiring the
communities concerned to develop more and more precise ways of expressing
themselves in a shorter and shorter time. That impact, it seems to me, must
have been visible in a proliferation of more and more, but ever narrower,
concepts (categories) at any particular level of abstraction—thus reducing
the number of dimensions in which these concepts could be interpreted. The
multiplication of meanings attached in different contexts to the same
words—or the same roots—that one sees in any etymological dictionary bears
testimony to this process, as does the proliferation of artifact categories
through time, each with more and more precise and limited functions. Simultaneously, an increase in the number of levels of abstraction itself
did compensate for this fragmentation, so that one could still find ways to
‘lump’ over these increasingly narrow concepts along crosscutting
dimensions. ‘Information’ is but one of the last major abstractions
introduced.
In Western science, similar processes of fragmentation are observable at
least since the fourteenth century, and for very similar reasons (Cf. Evernden 1992). During these centuries, science has emphasized the need to
solidify as much as possible the relationship between observations and
interpretations, and thus between the realm of the real, with its infinite
number of dimensions, and the realm of ideas, in which only a limited number
of dimensions is cognized. Much scientific explanation therefore consisted
of reducing the large number of dimensions involved in the processes
observed into a much more limited number that was manageable in the
(individual or collective) human brain, and could thus be shaped into a
coherent and comprehensible narrative. Hence the fact that such science was
generally ‘reductionist’. A corollary of this is the fact that, particularly
in empirical science, each complex phenomenon was ‘broken up’ into component
parts in the hope that once these components had been explained, they could
be put together to explain the whole phenomenon in all its complexity. This
led to the same kind of
fragmentation that occurred in
languages in general, observable at the highest level in the current
division of human inquiry into disciplines, sub-disciplines,
specializations, etc., each practiced by its own community that has
developed its own epistemology, perspective, language, concepts, methods,
techniques and values.
We now see that fragmentation as one of the main handicaps in our attempts to
understand the full complexity of the processes going on around us. Moreover, the interpretations linked the phenomena investigated to processes
that proceeded the time at which these phenomena were observed, rather than
to what was still to come (and therefore could not be observed). Scientific
reasoning therefore emphasized the explanation of extant phenomena in terms
of chains of cause-and-effect and (much later) an emphasis on feedback
loops, in both cases linking the progress of processes through time to their
antecedent trajectory. In particular, it has emphasized thinking about
“origins” rather than “emergence”, about “feedback” rather than
“feed-forward”, about “learning from the past” rather than “anticipating the
future”. Hence, it is no surprise that ‘thinking about the future’, whether
one calls it ‘futurology’, ‘forecasting’, ‘scenario construction’ or
‘foresighting’ is actually a stepchild in our current academic and research
institutions, and is principally developed in industry or government.
As a result of these tendencies, both in our societies’ communication and
culture, and in our scientific research, we have now come to a point where
the unanticipated consequences of our interventions in the environment
threaten to overwhelm us because of their complexity. So many unknown
dimensions are involved in the dynamics of our socio-natural environment
that we increasingly feel we no longer have any means to understand, limit
or control their effects. That feeling is experienced as a ‘crisis’, and we
encounter it more and more frequently—whether in the financial domain, or in
those of food security, natural hazards, the security of our societies from
terrorism or other undermining activities, etc.
One could effectively define such ‘crises’ as temporary incapacities of our
society to process the information necessary to deal adequately with the
external and internal dynamics it is engaged in. In our perspective, these
incapacities are the result of the fact that the gap between the number of
dimensions cognized in the society and the number of dimensions playing a
role in the socio-natural dynamics it is involved in is crossing a threshold
beyond which the former is inadequate to deal adequately with the latter. In
the run-up to that threshold, a clear ‘early warning’ signal is the fact
that the society increasingly suffers from ‘short-termism’, a focus on the
immediate challenges that it encounters, without taking the longer term into
account. In other words, the fact that tactics come to prevail over strategy
in much decision-making.
The core of the challenge seems to be that we must find ways to turn lessons
from the past into lessons for the future! To do so, we must devise ways to
argue coherently—and as far as possible falsifiably in Popper’s (1959)
sense—from the simple to the complex in order to better anticipate the
complex consequences of our actions. That would enable us to re-emphasize
long term, strategic thinking and a holistic vision that favors intellectual
fusion between different scientific communities and perspectives. To do so,
we must crucially acquire the capacity to increase, rather than reduce, the
number of dimensions that we can harness in order to understand complex
phenomena, so that we may attain a better understanding of the consequences
of our actions because we can consider more dimensions in our
decision-making about interventions in the environment.
Conclusion: Is There a Way Out?
It initially seems as if our intellectual and scientific
tradition, the size of our interactive population, the nature of many of our
languages, the under-determination of our theories by our observations (Cf. Atlan 1992, van der Leeuw 2007) and the limitations of our human short-term
working memory are as many challenges to our capacity to fundamentally
change the nature of our thinking, and more specifically to our capacity to
explicitly focusing on the future and extrapolating new dimensions from the
ones we know at any particular point in time, there are many examples of
individuals or (small) groups of people who have nevertheless done so with
some degree of success, from classical Greek philosophers via Leonardo da
Vinci to eighteenth and nineteenth century science-fiction authors (such as
Jules Verne or Paul Deleutre
[3] ). They have been able to design utopias or to extrapolate
positively from their lifetime observations into the future, even though
some of these ideas were never implemented or only realized years or
centuries later. Inventors have also been able to anticipate, and most of us
call on our “intuition” when we need to do so.
Moreover, there are some (shy) beginnings of a wider trend in this direction
that we can point to. The kind of reductionist, fragmented and ‘explanatory’
science that resulted from these tendencies has in the last twenty-five
years come under increasing attack from the ‘Complex Systems’ perspective
emerging in the 1980’s (e.g. Mitchell 2009). It assumes that in order to get
a realistic representation of reality, we need to study emergence,
feed-forward and develop a generative perspective to which the amplification
of the number of cognized dimensions is essential. In other quarters,
‘foresighting’ is spreading from the relatively limited field of industrial
and economic decision-support tools to academic practitioners who actually
delve into the epistemological and other challenges that need to be met for
this kind of science to flourish (Wilkinson and Eidinow 2008, Selin 2006). And yet elsewhere, under pressure from the looming environmental challenges
of the twenty-first century, the scientific community is beginning to look
ahead at ‘unanticipated consequences’ and what these may imply for the
challenges of the future (e.g. Ostrom 2009). This seems to indicate that the
current predicament is more due to over-investment in the long-standing
reductionist approach than anything more fundamental, and that, at least in
theory, it should be possible to transcend our relative incapacity to deal
with the complexities of the dynamics we are involved in.
Overcoming the Limitations of Human STWM
Although I am not an expert in the field at all, it
seems to me that the ICT (Information and Communication Technology) revolution has indeed created the conditions
for us to overcome the limitations to our cognitive capacities that are
inherent in our short-term working memory. Present-day computers do have
the capacity to deal with an almost unlimited number of dimensions and
information sources in real time, and thus to overcome what appeared at
first sight to be the most fundamental of the barriers mentioned above. But that capacity has not fully been exploited because of our
long-standing and ubiquitous scientific and intellectual tradition,
which has emphasized the use of such equipment as part of the process of
dimension-reduction that provides acceptable explanations, rather than
as a tool to increase the number of dimensions taken into account in our
understanding of complex phenomena. Under the impact of complex systems
science this is clearly changing (as seen, for example, in the increased
use of high-dimensional Agent Based Models, but much more needs to be
done, mainly in developing conceptual and mathematical tools as well as
appropriate software.
Overcoming the Under-determination of Our Theories by
Observations
Similarly, and with the same caveat that I am not a
professional in this field, I am under the impression that the very
recent revolution in IT (Information Technology) capacity to continuously monitor processes
on-line, and to treat and store the exponentially increased data streams
that are generated by such monitoring, points to the fact that we may
indeed be on the brink of (at least partly) overcoming the
under-determination of our theories by our observations that is the
corollary of the dimension-reduction traditional science practices
(Atlan 1992). The reduction in the size and cost of the monitoring
equipment is quickly bringing such massive data collection within reach. Simultaneously, the development of novel data-mining techniques is
helping us to make sense of the data thus collected, or at least in
selecting the appropriate data to be scrutinized in order to better
inform our theories.
Transforming Our Scientific and Intellectual Tradition
Although I am not among those who fall easily for
panaceas, I do believe that the complex (adaptive) systems approach is a
useful first step on the way to fundamentally transform our scientific
and intellectual tradition from studying stasis and preferring simple
over complex explanations, to studying dynamics, with an emphasis on
emergence and inversion of Occam’s razor (increasing the number of
dimensions taken into account). Clearly, we have a long way to go in
this domain, but the rapid and substantive advances in certain fields,
including physics, biology and economics, coupled with the rapid recent
spread of this approach in Universities in many parts of the world and
the growing awareness of the need for more holistic approaches in such
domains as sustainability and health, cause me to be moderately
optimistic about our chances of transforming our scientific and
intellectual tradition.
The Communication Challenge
The underlying communication challenge is how to
communicate other than linearly and in writing or speech with an
increasingly large number of partners at very variable distances. This
is the trend that was in my mind responsible for the particular
development referred to above: narrower and narrower concepts, and the
consequent fragmentation of our perspective on the world. Contrary to
some, I do not think language is subject to deliberate change—it adapts
itself to human needs and ideas in a ‘bottom-up’ process. But even if it
were possible to transform the ways in which we speak and write, we
would still have an essentially linear communication tool. The question
is therefore whether the radically different ways of interactively
communicating that are enabled by modern communications technologies,
and in particular the collective building of knowledge using multimedia,
as is enabled in web 2.0, will allow us to communicate non-linearly and
in more dimensions. This would entail the directed use of visuals, which
generally can communicate more dimensions simultaneously than spoken or
written language.
Transforming Our Thinking
The kind of reductionist thinking that I am referring to
is so heavily ingrained and so widely spread in out culture and out
kinds of science that changing our thinking will require a major effort. Our world view, our language, our institutions all militate against such
a change, and most importantly, we are for the moment lacking a coherent
alternative way of thinking against which we can leverage our
present-day science. By far the greatest challenge from the perspective
of human and financial capital and effort appears to me therefore to be
in the domain of education, from the earliest childhood throughout
university and into adult life. The current education system in the
developed world is, overall, no longer adapted to the challenges of the
twenty-first century, among which sustainability looms so large. We have
to move away from knowledge acquisition aimed at question-driven
research towards challenge-focused education that aims to help deal with
substantive challenges, from ‘linear explanation’ in terms of
cause-and-effect to ‘multi-dimensional projection’ in terms of
alternatives, from one-to-many teaching (in which an instructor tells
students what to do, what is right and what is wrong), to many-to-many
teaching in which instructors and students all interact, learn and
teach. At the same time, we must develop education systems that
stimulate the acquisition of creativity, risk-taking and diversity
rather than conformity and risk-averseness. In doing so we must harness
the tools referred to above, but more than anything we must ‘bend’ minds
around to thinking in new, uncharted, ways. In doing so, we are
handicapped by the fact that economics, career structures, evaluations,
disciplinary momentum and many other factors and dynamics are stacked
against success in this area. There is a lot of work to be done!
Bibliography
Alp, I. E.
1994. “Measuring the Size of Working Memory in Very Young
Children: the Imitation Sorting Task.” International
Journal of Behavioral Development 17:125–141.
Atlan, H.
1992. “Self-organizing Networks: Weak, Strong and
Intentional. The Role of their Underdetermination.” La
Nuova Critica, N.S. 19-20 (1/2):51-70.
Bateson, G.
1972. Steps to an Ecology of Mind.
New York.
Bettencourt, L.M.A., Lobo, J., Helbing, D., Kühnert,
C.,
West, G. B.
2007. “Growth, Innovation, Scaling, and the Pace of Life in
Cities.” Proceedings of the National Academy of
Sciences (US) 104 (17):7301-7306.
Boyd, R., and Richerson, P.J.
1985. Culture and the Evolutionary
Process. Chicago.
Carlson, S. M., Moses, L. J., and Breton, C.
2002. “How Specific is the Relation between
Executive Function and Theory of Mind? Contributions of Inhibitory
Control and Working Memory.” Infant Child
Development 11, 73–92.
Corrêa, L. M. S.
1995. “An Alternative Assessment of Children’s
Comprehension of Relative Clauses.” Journal of
Psycholinguistic Research 24:183–203.
Cronon, W. 1983. Changes in the Land: Indians, Colonists, and the Ecology of New
England. New York.
Diamond, A. and Doar, B.
1989. “The Performance of Human Infants on a Measure of
Frontal Cortex Function, the Delayed-response Task.” Developmental Psychobiology 22:271–294.
Epstein, H. T.
2002. “Evolution of the Reasoning Brain.” Behavioral Brain Science 25:408–409.
Evernden, N.,
1992.
The Social Creation of Nature,
Baltimore.
Johnson, J., Fabian, V. and Pascual-Leone, J.
1989. “Quantitative Hardware Stages that Constrain Language
Development.” Human Development
32:245–271.
Kemps, E., De Rammelaere, S. and Desmet, T.
2000. “The Development of Working Memory: Exploring the
Complementarity of Two Models.” Journal of Experimental
Psychology 77:89–109.
Kidd, E. and Bavin, E. L.
2002. “English-speaking Children’s Comprehension of
Relative Clauses: Evidence for General–cognitive and Language-specific
Constraints on Development.” Journal of
Psycholinguistic Research 31:599–617.
Lane, D., Maxfield, R., Read, D.W., and van der Leeuw,
S.E.
2009. “From Population Thinking to Organization Thinking,”
in: Complexity Perspectives on Innovation and Social
Change (eds. Lane, D., Pumain, D., van der Leeuw, S.
E., and West, G.) 11-42. Berlin.
Luciana, M. and Nelson, C. A.
1998. “The Functional Emergence of Prefrontally-guided
Working Memory Systems in Four-to-eight-year Old Children.” Neuropsychologia 36:273–293.
Martin, R. D. 1981. “Relative Brain Size
and Basal Metabolic Rate in Terrestrial Vertebrates.” Nature 293:57–60.
Mitchell, M. 2009. Complexity: a Guided Tour. New York.
Ostrom, E.
2009. “A General Framework for Analyzing Sustainability of
Social-Ecological Systems.” Science 325 (5939)
419-422.
Pigeot, N.
1991. “Reflexions sur l'histoire technique de l'homme: De
l'évolution cognitive à l'évolution culturelle.” Paléo 3:167-200.
Popper, K.
1959. The Logic of Scientific
Discovery. London.
Read, D.W., Lane, D.A. and van der Leeuw, S.E,
2009. "The Innovation Innovation," in Complexity Perspectives on Innovation and Social Change
(Lane, D.A., Pumain, D., van der Leeuw, S.E., West,
G. eds.). Berlin.
Read, D. W. and van der Leeuw, S.E.
2008, “Biology Is Only Part Of The Story …”, Philosophical Transactions of the Royal Society,
Series B 363:1959-1968.
Read, D. W. and van der Leeuw, S.E.
2009. “Biology Is Only Part Of The Story …”, in Sapient Mind (Renfrew, A. C. and
Malafouris, L. eds.) 33-49. Oxford.
Rightmire, G. P.
2004 “Brain Size and Encephalization in Early to
Mid-Pleistocene Homo.” American Journal of Physical
Anthropology 124:109–123.
Ruff, C. B., Trinkhaus, E., and Holliday, T.W.
1997 “Body Mass and Encephalization in Pleistocene Homo.”
Nature 387:173–176.
Selin, C.
2006. “Trust and the Illusive Force
of Scenarios.” Futures 38 (1):1-14.
Shannon, C.E. and Weaver, W.
1948. “A Mathematical Theory of Communication,” Bell System Technical Journal, 27:379-423 and
623-656.
Siegel, L. S. and Ryan, E. B.
1989. “The Development of Working Memory in Normally
Achieving and Subtypes of Learning Disabled Children.” Child Development 60:973–980.
Sørensen, M. L. S., and R. Thomas, eds
.
1989
.
The Bronze Age–Iron Age Transition in Europe: Aspects
of Continuity and Change in European Societies, c. 1200 to 500
B.C
.
BAR International Series
483. Oxford.
Tainter, J.A.
1988. The Collapse of Ancient
Societies. Cambridge.
van der Leeuw, S.E.
1986. “On Settling Down and Becoming a
'Big-Man,'” in van Bakel, M.A., Hagesteijn, R.R.,
and van de Velde, P. eds. Private
Politics: a Multi-disciplinary Approach to 'Big-Man' Systems
33-47. Leiden.
van der Leeuw, S.E. 1990. “Archaeology, Material
Culture and Innovation.” SubStance
62-63:92-109.
van der Leeuw, S.E.
2000. "Making Tools from Stone and Clay," in
Anderson, A., Murray, T., eds Australian Archaeologist: Collected Papers in Honour of Jim
Allen. Canberra.
van der Leeuw, S.E.
2007. “Information Processing and its Role in the Rise of
the European World System” in Costanza, R., Graumlich, L. J.,
and Steffen, W., eds.
Sustainability or Collapse? 213–241. Cambridge,
MA.
Wilkinson, A. and Eidinow, E.
2008. “Evolving Practices in Environmental Scenarios: A New
Scenario Typology,” Environmental Research
Letters, 3 (4) 045017.
Footnotes
Note 1
The distinction between humans ( Homo
sapiens ) and modern humans ( Homo
sapiens sapiens ) referred to here follows current custom
among paleo-anthropologists. The transition is estimated to have
occurred somewhere around 200,000 years BP.
Note 2
All the dates mentioned in this paper are not only approximate, and
differ between different parts of the world, but are also continually
subject to revisions as archaeological research progresses.
Note 3
Writing under the pseudonym Paul d’Ivoi, this French author anticipated
the idea of modern telecommunications (wireless and television)