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Emergence as a Conceptual Framework for Understanding
Scientific and Technological Progress
Jeffrey Alexander1, John Chase1, Nils Newman2, Alan Porter3, J. David Roessner1
1Center for Science, Technology & Economic Development, SRI International, Arlington, VA - USA
2 IISC, Atlanta, GA - USA
3Search Technology, Atlanta, GA - USA
Abstract--The global science, technology and innovation
(STI) system is characterized by some researchers as a complex
adaptive system (e.g. Heimricks, 2009). Scientific and
technological progress entails both evolutionary and
revolutionary change, with a high degree of non-linearity and
unpredictability. In the terms of complexity science, this
progress is emergentit defies a reductionist approach to
characterizing the phenomenon. We discuss the properties
associated with emergent behavior (Goldstein, 1999; Deguet et
al., 2002), and apply them to an integrative framework for
describing activities in scientific research and technological
development. We further elaborate on this framework to
suggest the developments and events in the global STI system
which may be hallmarks of technical emergence, defined as the
set of properties and phenomena observed in the development of
a concept with potential scientific and technological significance.
The conceptual framework provides a useful tool for focusing
further research on specific dynamics of scientific and
technological activity.
I. INTRODUCTION
A number of scholarly research works investigate the
details surrounding the development of “emerging
technologies” as a means of generating insights into the
nature of technical change (see, for example, [1], [2] and [3]).
Relatively fewer works look at the more fundamental
question of what qualifies a technology as “emerging.” For
example, at what point can we say that a technology is
emerging? Is there a certain point where a technology ceases
to be “emerging” and becomes, perhaps, “established?” Most
significantly for the field of technology forecasting, can we
identify certain generalized indicators of those inventions
which are likely to become “emerging technologies,” as
opposed to those which presumably disappear into obscurity?
These questions address issues at the heart of the study of
innovation and technology, including how new technologies
enter into the market and how old technologies become
obsolete. This topic is a particular focus for evolutionary
economists, who observe that technological progress seems to
follow a quasi-organic pattern of development. This
conceptual approach argues that mechanisms of technical
change are analogous to biological or ecological change [4]:
that technological changes follow a pattern of punctuated
equilibria [5]; that the creation and selection of new
technologies is constrained by their historical paths, akin to
genetic determinism [6], [7]; that competing technologies
arise and are culled through a process similar to speciation
[2], [8]; and that even knowledge and know-how follow
evolutionary pathways [9], [10]. Of course, biological
systems and socio-technical systems have important
differences, limiting the applicability of the evolutionary
analogy [4], [11]. Still, the metaphors embedded in
evolutionary economics have garnered a broad following
among researchers who study the nature of technological and
organizational change.
This paper focuses on a particular aspect of the
evolutionary view of technical change: how to identify
technologies and technical concepts which are “emergent,”
the preconditions associated with “technical emergence,” and
how the phenomenon of technical emergence relates to a
broader framework for analyzing technical change. We first
examine the origins of the idea of emergence, dating back to
the earliest debates around Darwin’s theories of evolution and
natural selection, and how the idea has gained new currency
in the field of complexity studies. We then discuss how
emergence is manifested in the global dynamics of the
scientific community and technology-focused industries. We
end with the implications of our conceptual approach for the
detection of technical emergence, identifying particular
patterns, properties, and other “signals” that could distinguish
new technologies and concepts with the potential to achieve
emergence from those which fail to live up to their promise.
II. BACKGROUND: WHY ‘EMERGENCE?’
The concept of emergence, in the sense we use it, has its
roots in the 19th-century scientific and philosophical debates
sparked by Darwinism (as noted in [12] and [13]). As we
explain in this section, it was coined to try to address a
perceived philosophical gap in the theory of evolution: the
appearance of natural phenomena with properties unlike
those found in preceding generations of phenomena. Those
early discussions of emergence were unable to advance
beyond the observation and characterization of emergent
phenomena. More recently, the formal study of “complex
adaptive systems” starting in the 1990s led to a renewed
interest in “emergence” and new attempts to understand its
role and effects in systems dynamics [12]. This reframing of
emergence lends itself to studying emerging technical
research topics and concepts, as they are the product today’s
complex systems of scientific and technological activity.
A. Origins of the concept of emergence
The term “emergence” as it is used here is attributed to the
psychologist G. H. Lewes, from his work Problems of Life
Alexander, J., Chase, J., Newman, N., Porter, A., Roessner, J.D. (2012) "Emergence as a conceptual framework
for understanding scientific and technological progress." Proceedings of PICMET 2012: Technology Management
for Emerging Technologies, Vancouver, B.C., 29 July - 2 August.
and Mind [13], [14]. Lewes proposed that the phenomenon
of emergence explained the complex properties of the human
mind and consciousness—that the mind and its abilities could
not be explained by studying its evolutionary heritage or its
component structures. In this era, emergence was a reaction
to an increasingly reductionist and mechanistic worldview,
where science attempted to explain natural phenomena
through fine-grained analysis. The “emergentist” school
argued that while evolution could account for the incremental
appearance of a new structure in nature, at some point the
cumulative complexity of that structure produces a new
phenomenon qualitatively different in its properties and
capabilities from its predecessors [15]. This “qualitative
novelty” is not immediately recognizable or attributable to
any of the components of that new structure, but instead
“emerges” from the interaction among those components
within the structural environment. This trait of “emergent”
phenomena is popularly phrased as, “the whole is greater than
(or, at least, different from) the sum of its parts.”
One implication of this theory of emergence is that a
complex system must be understood at two different levels—
the level of the system, or the “whole,” and the level of its
component elements, or “parts.” Since the behavior of the
“whole” is distinct from the behavior of the “parts,” an
observer must be aware of the specific aspects of both levels,
as well as interaction between the two levels. Emergentist
theory referred to that interaction as “downward causation” or
“supervenience”—that association with the “whole” will
change the nature of the “parts,” just as the parts influence the
emergent properties of the whole.
These early theorists further claimed that emergence
defied any kind of scientific analysis, as it stemmed from
interactions and systems too complicated to be studied. This
opened the emergentists to criticisms from the likes of
Bertrand Russell, who countered that emergent phenomena
could be understood through reductionist analysis, at some
point—the only issue was that the analytical tools and
methods were not available at that time. Also, if emergence
did defy scientific analysis, then it was not a topic for
scientific inquiry, and thus emergence was marginalized
within the scientific community as a concept more
appropriate for philosophy.
B. Re-framing of emergence in complexity theory (1980s to
present)
Emergence as a concept re-entered into scientific
discourse to a significant degree thanks to a new field of
inquiry called “complexity science.” Advances in nonlinear
mathematics and computer simulation provided tools for
analyzing “complex adaptive systems,” in physics,
economics, biology, and other disciplines, and seemed to
offer the promise of understanding the nature of emergence in
a more scientific manner [16], [17]. Complexity science
examines how large, unordered sets of actors (or agents) self-
organize and, in doing so, develop unpredicted patterns of
behavior—patterns now termed “emergent.” Examples of
emergent behavior include the coordinated movements of
flocks of birds, convection currents in gases, and “bubble
markets” in investments. Each of these phenomena occurs
under circumstances that are difficult to foresee simply by
analyzing the rules governing the behaviors of the individual
actors involved. To observers, these behaviors appear
spontaneously in systems structured with a particular degree
of complexity. Complexity science proposes that there is
something inherent in the way that the elements of the system
react to one another and to the overall system environment
that leads to emergent patterns of behavior.
While the concept of emergence in complexity science has
achieved a formalism beyond its original use in the 19th
Century, critics point out that there is still no widely-accepted
definition of emergence [16]. A number of works have tried
to fill that void by developing a generalized definition of
emergence, often by synthesizing elements of definitions
suggested by others [12], [13], [18], [19]. Complexity
scientists themselves confound the attempt to develop a
standardized definition of emergence by describing it as
“ostensive”—meaning, in part, that emergence is recognized
when it is observed (or, in common parlance, “I know it when
I see it”). While we will not attempt to offer our own
definition of emergence, we note that emergent phenomena
are identified by the following traits:
• They arise from complex systems of elements or “agents,”
and are characterized by ordered but unpredictable
patterns in the behaviors of those agents
• These patterns create effects at a broader system level (the
“whole”) different from effects at the local level
• The emergent phenomena cannot be isolated from the
system—if one were to remove the agents exhibiting
emergent behavior from their surrounding system, that
behavior would change or would cease altogether
• They persist over time with some degree of coherence, but
may change in form or behavior during that time period
(in other words, they are dynamic)
One property commonly ascribed to emergence is that of
“radical novelty,” meaning that they exhibit behaviors not
previously seen within that system [13]. We question
whether all emergent phenomena must be “novel” in this
sense. Rather, we believe that there are phenomena which
can emerge periodically in a recurring manner, but the timing
and conditions of that recurrence are not fully predictable.
This corresponds with the distinction between emergence in
relatively simple systems, or “simple” emergence, and
“complex” emergence, as proposed by Halley and Winkler
[20]. In organic systems, for instance, certain phenomena
“emerge” quite commonly (such as insect colonies), but the
circumstances under which the specific instances of those
phenomena emerge are not reducible to specific aspects of
the agents within the system.
C. Implications for emergence detection
The current view of emergence in complexity science has
some significant implications for understanding the
circumstances surrounding the appearance of emergent
phenomena—which, as we will discuss later, also influences
our understanding of the emergence of new research fields
and technologies.
The first implication is that identifying and understanding
emergence requires an awareness of the interaction between
the system and its elements, and the role of that interaction in
the emergent phenomenon. The mechanism enabling the
“whole” to be different from the sum of its “parts” can be
described as “synergy,” where synergy is “combined or
cooperative effects produced by the relationships among
various forces, particles, elements, parts or individuals in a
given context—effects that are not otherwise attainable” [14].
Therefore, evidence of emergence will be seen at the level of
the system (the appearance of the emergent phenomenon) and
the level of the agents (the changes in the behaviors of the
agents associated with that emergence).
A second implication is that in social systems, synergy
can be a combination of both spontaneous and planned
interaction between the agents in the system. Planned
interactions can, for example, generate “leverage,” where the
agents involved are sharing resources and risks to achieve
capabilities beyond those available to a single agent. In this
way, the routines of self-organization are an important
component in emergence. Evidence of self-organization—
where the agents reconfigure themselves in new or unusual
patterns—should be a hallmark of emergence.
A third implication is that emergence is a function of the
dynamics of the complex system in which it appears.
Complex adaptive systems are defined as systems in a
constant state of flux, where that flux is the product of self-
organizing behavior [21]. This self-organization is closely
associated with emergence. Therefore, emergence can be
identified as a distinctive shift in the dynamics of the system,
where that shift appears to be a coordinated movement
although it is fundamentally the product of spontaneous
change.
The work towards a more general theory of emergence
may help to inform our understanding of emergence in
science and technology—but only to the degree that science
and technology operates in a system provides a setting where
emergence is likely to occur. In the next section, we discuss
how the modern systems of science and technology are more
likely to enable “emergence” today than in the past, and how
emergence is manifested in those systems.
III. EMERGENCE IN TECHNICAL DOMAINS
We use the concept of emergence in the domain of science
and technology to delineate a more specific concept of
“technical emergence,” which addresses a particular gap in
the analysis of technological change. Technical emergence is
distinct from the concept of invention, which commonly
refers to a technical creation which embodies both utility and
novelty (see [22] for a discussion of the distinction between
inventions and non-inventions). It is also different from
innovation, which encompasses all of the activities involved
in bringing a new technology to the point where it can be
implemented in a product or process [23]. In contrast, a
concept has achieved technical emergence when it has
generated sufficient interest in a particular community that
the community members (researchers and inventors) will
devote time, resources and energy to explore the potential for
that concept to have an enduring impact on science and
technology.
Technical emergence is somewhat orthogonal in this sense
to discussions of invention and innovation. A purely
scientific technical concept, such as a new research approach,
can become emergent without being embodied in a particular
invention. Conversely, a technical idea can progress from
invention to innovation within a single organization, and
therefore might only “emerge” within the scientific and
technical community after it has been introduced to the
market. An illustrative example is the development of
Kevlar, which resulted from a serendipitous discovery within
Dupont’s corporate research laboratory, and was not broadly
known in the scientific and technical community until it was
commercialized [24].
We will discuss the relationship of emergence to
invention and innovation more fully in Section IV. Before
that discussion, it is important to understand how emergence
can occur in the global scientific and technical enterprise.
Emergence is a function of structural complexity, and
therefore technical emergence derives from the increasingly
complex nature of scientific and technological activity.
A. Complexity in the global science system
The global science system has not always been
characterized as “complex.” In the pre-industrial period,
there were few scientists in the world, and they tended to
communicate in very close-knit communities isolated from
one another. That situation contributed to the phenomenon of
“simultaneous discovery,” where new ideas were developed
independently by scholars in different parts of the world, but
ended up being credited only to the one who was able to
attract enough attention to his works [25].
As recently as the early 20th century, scientific research
activity was concentrated in a small number of companies,
universities, and independent institutes [26]. Since the end of
World War II, however, the scientific community has become
increasingly globalized and interconnected, particularly since
the advent of digital scientific communication, at least where
codified scientific knowledge is concerned [27], [28]. The
basic organizational unit of science is now the collaborative
network, rather than the individual investigator [29], [30].
Advances in communication and transportation led to the
growth and integration of previously-disparate scientific
communities, forming them into a set of inter-dependent
complex networks—i.e., a complex adaptive system [31].
The global system of science in the digital age takes on a
particular character relevant to the concept of emergence.
Scientific discovery is now described as a particular set of
search regimes: researchers integrate existing knowledge
into mental “maps,” and those maps help to expose “idea
gaps” (topics which have not been the focus of sufficient
prior research). Those “idea gaps” constitute opportunities to
exploit existing knowledge in novel ways, or to discover new
knowledge [32], [33]. These search processes are occurring
continuously in parallel across multiple communities and
subcommunities, and new scientific breakthroughs are
frequently the product of the cognitive integration of
knowledge from separate disciplines, coupled with the
invention of new modes of discovery [34], [35], [36]. This
makes the processes of scientific discoveries difficult to
analyze or predict, illustrating the complex structure of
modern scientific research.
Note that we are not necessarily claiming that the
knowledge structures that constitute science are complex.
Rather, the organizational structure of scientific activity is the
complex system. An emergent scientific phenomenon, then,
is one that arises within that system for organizing scientific
inquiry. A new science is “emergent” as a function of the
complexity of the scientific system. While a new scientific
concept or discovery may be deceptively simple, the fact that
it is produced within a complex system means that it is likely
to be produced in an emergent manner, rather than a
predictable, mechanistic fashion.
B. Complexity in technology
Similarly, technological advance today occurs within a
complex system closely linked to the scientific system,
although distinct from that system as well. Modern
technological progress has grown more dependent on science,
starting in the late 19th century [37], and is often the product
of a collective effort by networks of inventors [38], [39] and
organizations [40]. A number of recently-emerged
technologies, such as biotechnology, tissue engineering, and
advanced vaccines, are characterized by the co-evolution of
science and technology [1], [3], [32]. The dynamics of
technological change, like the global scientific system, can be
described as a complex adaptive system [41].
Rycroft and Kash [42] describe the relationship between
the complexity of technical systems, and the complexity in
the organizational forms and routines involved in developing
new technologies. A substantial amount of analysis examines
the development of organizational forms for innovation,
where an emerging technology is implemented in a particular
product or process. This line of inquiry includes the study of
techno-economic networks and technology delivery systems
[43], [44]. Even the development of the initial technical
concept, before any thought is given to a specific application,
inventors rely on the research and technical expertise of
others to guide the search for the particular principles and
capabilities that they integrate into their inventions [45], [46],
[47].
As technical progress is highly interdependent upon the
interactions between technical communities of
engineers/inventors and communities of scientist/researchers,
such activity is more likely to exhibit the characteristics of
synergy and leverage described in Section I.C above. The
development of a given technology can progress through
planned coordination among research groups, as is the case
with the development of new semiconductor manufacturing
techniques under the International Technology Roadmap for
Semiconductors, or through the more spontaneous
competition among independent research efforts, as seen in
the “race” between the U.S. federal government and Celera
Genomics to map the human genome. As technological
development has become global in scope, the diversity of
communities involved has also increased, providing more
plentiful opportunities to encounter “breakthrough” ideas that
provide the impetus for rapid technical advances [48]. The
result is an increase in the velocity of technical change,
enabling technical systems to be improved such that it
reaches its theoretical maximum level of performance in
shorter and shorter timespans via paths that are more and
more difficult to foresee. Thus, global technology
development is now embedded in a complex adaptive system
of agents.
IV. IMPLICATIONS FOR DETECTION OF TECHNICAL
EMERGENCE
As both scientific discovery and technology development
have been shown to be activities carried out in complex
adaptive systems, we can apply the concept of “emergence”
to the study of emerging fields of research and
technologies—which we collectively label “technical
concepts.” This concept has particular implications for how
we might be able to identify future emerging technical
concepts, and how we understand the underlying dynamics of
those concepts.
In thinking about how scientific research fields and
technologies “emerge,” we focus on a particular type of
emergence which we term “technical emergence.” We define
technical emergence as follows:
Technical emergence is the phase during which a concept
or construct is adopted and iterated by a members of an
expert community of practice, resulting in a fundamental
change in (or significant extension of) human understanding
or capability.
In this definition, the “community of practice” is
understood to be any relevant subset of the global system of
scientific research and technology development.
Communities of practice in science and technology can be
relatively stable and well-defined, such as disciplinary
communities, but ethnographic studies of research
environments show that they are better understood to be
dynamic assemblies of multiple research teams working in
coordination around a given topic [49]. Therefore,
identifying an emerging technical concept requires the
identification of the community of practice relevant to that
concept.
A. Key signals of technical emergence
The idea of the community of practice is a critical aspect
of technical emergence. While scientific discovery and
invention was once the domain of individual investigators,
rapid advances in a field today are enabled by harnessing the
collective intellect, resources, and interest of networks of
scientists and engineers. Therefore, we can expect that any
technical concept of significance will only “emerge” when it
has been adopted by a particular community of adherents.
The communitarian aspect of technical emergence
emphasizes the importance of understanding the particular
level of analysis where emergence is detected. The
community of practice around software encryption, for
example, is part of the broader community in information
security. Concepts that “emerge” among cryptographers may
never register much interest across that broader community,
but may still have downstream significance to information
security as a technical domain. We must understand that
technical emergence may be detected at the subcommunity
level, and only later have an impact at the level of the larger
community. Similarly, when we identify a potentially-
emerging “technical concept,” we must consider the
relationship of the concept to the broader knowledgebase or
technical system in which it resides. For example, the
concept of fuel injection for internal combustion engines
could not “emerge” until complementary changes in engine
technology took place to accommodate fuel injection
technology (such as advances in the computer control of
engine systems).
In complexity theory, emergence is characterized by
intense dynamism—the agents in the complex system exhibit
distinctive and novel patterns of behavior different from the
patterns in a previous time period. We expect that technical
emergence will be characterized by a similar dynamic change
within the community of practice relevant to the technical
concept. In other words, the critical nature of emergence is
not inherent in the concept itself, but in the reaction of the
community to that concept. Throughout the history of
science and technology, new concepts have been conceived,
but failed to gain any traction with the scientific community
or technology developers and so faded into obscurity. Even
those concepts that inspire a change in the community may
not become successful innovations—but it is the collective
action of that community that makes an emerging research
field or technology truly “emergent.”
In scientometrics, then, we should focus our attention on
measures of dynamism—how the networks of agents are re-
organizing around an emerging concept, for example, or how
the level of productivity changes as the concept “emerges”
within a community. We should also be aware that the
dynamism may not always be productive. Changes may be
multi-directional, self-canceling, or may simply fail to
produce lasting changes in human knowledge or ability. It is
the nature of the change that denotes emergence, rather than
its final result.
We thus offer the following propositions:
P1: A technical concept can emerge only if a relevant
community forms around that concept to fuel its
elaboration and development
P2: A technical concept will emerge from the activity of a
particular subset of a broader community, and the
dynamics at the broader level are an important
influence on the emergence at the level of the
technical concept.
P3: Emergence of a technical concept will be detectable as
a function of the dynamics of the community and
system in which the concept is embedded. Change is
the critical signal of emergence, rather than absolute
levels of activity or organization.
B. Characterizing technical emergence
Our approach to understanding emergence in this context
also differs somewhat from previous theory on technical
change. Evolutionary economists who write about emerging
technologies tend to link the “emergence” of such
technologies to a discrete event—the “speciation” of a
technology, described as the point where a technology
bifurcates between an existing technology and a newer-
generation of technology that at some point ends up
competing against its “parent” [2], [8]. This view adopts the
“gradualist” perspective of classic Darwinian theory. The
development of the new technology is constrained by the
aforementioned influence of “path dependance,” since a
speciation event will produce a new technology which is
similar to the parent [6], [7], [50]. Indeed, the new
technology may be identical to the older technology, but
simply applied to a new domain of use, or incorporating a
complementary technical capability [51]. To the extent that a
new technology represents a more fundamental shift in the
evolutionary “path,” it is generally the cumulative result of
attempts to improve the extant technology through
elaboration and extension, to the point that a move to a new
approach is the most feasible and economical option for
achieving greater performance [47], [52]. These speciation
events correspond to the notion in complexity of “punctuated
equilibria”—where the natural evolution and maturation
processes in an ecosystem are disrupted periodically by the
introduction of new mutations producing species with
superior adaptability [5], [53].
By adopting the concept of emergence from complexity
theory, we propose a different framework for understanding
technical change. Recall that emergence is posited by some
theorists to occur in two different forms: simple and complex
emergence. We argue that in technical change, the concepts
of incremental and radical innovation are analogous to those
two forms of emergence, thus giving us the distinction
between incremental and radical emergence. A common
model of technical change uses the Fisher-Prye or “S” curve
pattern to characterize improvements in performance over
time. Our view of technical emergence focuses attention on
two key types of inflection points: the “take-off” phase,
where the pace of performance improvement increases
dramatically, and the shift from one S-curve in an existing
technology to a different S-curve representing a subsequent
radical change in technology (see Figure One). An inflection
point on a known S-curve can be viewed as incremental
emergence: the change that results from such emergence is
expected and to an extent predictable a priori, although the
process may not be. In contrast, radical emergence will be
seen in situation where both the change and the process
leading to that change are not predictable a priori.
Fig. 1: Idealized Representation Of Technical Emergence
Using Technology S-Curves
V. FUTURE DIRECTIONS
Based on this new understanding of technical emergence,
we expect to be able to develop more refined systems for
identifying emerging technologies and distinguishing them
from technologies which are “pre-emergent.” We also expect
to be able to develop more precise systems for detecting cases
of emergence in scientometrics, by focusing on system
dynamics and second-order changes in the behavior of
scientists and engineers as reflected in the literature. Finally,
we aim to investigate and refine the distinction between
radical and incremental emergence, and thus provide a more
nuanced understanding of how radical technical change
occurs.
We propose to explore the propositions presented in this
paper by examining patterns of “technical emergence” as
evidenced in digitized scientific and technical literature—
peer-reviewed journal articles, granted patents, and other
documentary artifacts. These documents provide particular
stylized modes of communicating the processes and content
of research and development. We believe that new
approaches to text analytics can be integrated into a testbed
system to determine how scientific and technical
communities identify new technical concepts, and how those
communities self-organize around new research topics and
discoveries to bring about the type of “technical emergence”
we describe here. This type of automated document
processing can be compared against ethnographic studies of
technology development and other case histories to validate
how emergence is manifested in today’s global science and
technology systems.
Our aim is not to forecast new technological development
per se, but to at least detect significant changes that as they
unfold or immediately thereafter, thus providing a more
comprehensive situational awareness of changes in the global
scientific and technological environment.
ACKNOWLEDGEMENT
This paper was supported by the Intelligence Advanced
Research Projects Activity (IARPA) via Department of
Interior National Business Center contract number
D11PC20155. The U.S. government is authorized to
reproduce and distribute reprints for Governmental purposes
notwithstanding any copyright annotation thereon.
Disclaimer: The views and conclusions contained herein are
those of the authors and should not be interpreted as
necessarily representing the official policies or endorsements,
either expressed or implied, of IARPA, DoI/NBC, or the U.S.
Government.
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