Complexity Theory and Leading Organization

Week 4 Discussion: Complexity Theory and Leading Innovation0 Unread replies0 Replies

After completing the required week’s readings, including the articles on complexity theory you found, respond to the following:

What are the organizational barriers to a leader’s ability to use a systems mindset to unlock the black box between system input and system output?
What models might a leader use with complexity theory to encourage disruptive innovation in his or her organization?

Provide a rationale for your responses, supporting your discussion with APA-formatted references and citations from your readings.

Your post should be 250–500 words or more.

姝 Academy of Management Review
2010, Vol. 35, No. 3, 415–433.
INTEGRATING MODERNIST AND
POSTMODERNIST PERSPECTIVES ON
ORGANIZATIONS: A COMPLEXITY
SCIENCE BRIDGE
MAX BOISOT
ESADE
BILL MCKELVEY
University of California, Los Angeles
Competition between modernism and postmodernism has not been fruitful, and management researchers are divided in their preference, thereby undermining the legitimacy of truth claims in the field as a whole. Drawing on Ashby’s Law of Requisite
Variety, on complexity science, and in particular on power-law-distributed phenomena, we show how the order-seeking regime of the modernists and the richnessseeking regime of the postmodernists draw on different ontological assumptions that
can be integrated within a single overarching framework.
ernist strategy problematizes the relationship of
actors to observed phenomena by having language mediate it. Thus, instead of a single direct relationship between an external world, W,
and an observer, O, we now have two relationships: (1) between an external world, W, and a
descriptive language, L, and (2) between L and
an observer, O. Language is a human resource
that places the relationship between W and O in
a social context where divergent interests
(Habermas, 1972) and social power (Foucault,
1969) come into play. These shape language and
linguistic usage and, by implication, the regions
of the phenomenal world to which they give
access. Language, the postmodernists argue, is
not a neutral observation tool. It shapes observations in ways that reflect the ontological assumptions of a particular community of observers (Berger & Luckmann, 1966; Kuhn, 1962).
Postmodernism, initially a literary movement,
emerged in response to the linguistic turn in
philosophy. Its claim that “everything is text”
(Derrida, 1978) highlights the mediating role of
language linking observers to their worlds (Lyotard, 1984; Rorty, 1980).
Organization theory has been pulled in opposite directions by modernist and postmodernist
ontologies. Organizational scholars, thus, are
caught between two conflicting bases of legitimacy, with little overall consensus on what constitutes valid truth claims. Practitioners have,
The study of social systems such as organizations has long been caught between two conflicting bases of legitimacy. On the one hand,
we have positivism—a set of procedures for creating valid knowledge expressing a modernist
outlook that originated in the eighteenth century
Enlightenment project. Positivism presumes a
real, relatively stable, and objectively given
world, populated by phenomena that can be rationally known and rationally analyzed by independent observers. Such phenomena can be decomposed into observation protocols resting on
sense data and predictively related to each
other through stable laws integrated via a mathematical syntax (Benacerraf & Putnam, 1964;
Lakatos, 1976). Positivism promotes the modernist agenda: the understanding, manipulation,
and control of predominantly physical phenomena for beneficial social ends. In contemporary
social sciences, neoclassical economics remains positivism’s foremost exemplar (Colander, 2006; Friedman, 1953; Lawson, 1997; Mirowski, 1989).
On the other hand, we have postmodernism—a movement that emerged in the late 1960s
to challenge the basic tenets of modernism and
its epistemological ally, positivism. Whereas in
modernism the focus is on a phenomenal world
directly and unproblematically observed and
described by a disinterested actor who remains
external to what is being observed, the postmod415
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then, little reason to act on the research findings
of academics in open disagreement about their
discipline’s foundations. Absent any faith in
what the positivists are measuring—as they juggle with sample sizes, normal distributions,
means, variance, probabilities, and statistical
significance—managers will settle for gripping
corporate yarns that gain traction from vivid
and compelling narratives readily remembered
and retold. A good story loads on the dependent
variable with gay abandon, leveraging “samples of one” (March, Sproull, & Tamuz, 1991) into
universal managerial truths. It constitutes a
meme (Blackmore, 1999; Dawkins, 1976) that
propagates owing to its plausibility, its internal
coherence, and its alignment with the experience of its intended audience, rather than any
objective probability that it might be true.
If, following the Chicago School Pragmatists
(Dewey, 1925; James, 1907), we take knowledge to
consist of actionable beliefs, we can view modernism as attempting to substantiate these beliefs according to rationally derived principles
and rules. Postmodernism challenges this strategy as suppressing voices that fail to fit the
rationalist straitjacket (Calás & Smircich, 1999).
While it stabilizes and delineates our different
identities, modernism also limits our inherent
complexity and potentiality (Deleuze & Guattari,
1984). Order and organization are thus transient
achievements based on an infinite rather than a
limited set of possibilities, the products of what
Deleuze and Guattari call “chaosmosis” (Carter
& Jackson, 2004). The proliferation of unconstrained beliefs, however, makes them vulnerable to biases. Which ones, then, form a legitimate basis for action? Does the need to act
suggest that we should accept modernist constraints while recognizing them to be contingent? Or should we abandon these as essentially arbitrary and, following Feyerabend (1975:
296), argue that “anything goes?” If so, can organizational research still call itself a sciencebased “discipline”?
We offer a third alternative that draws on several well-known complexity principles to integrate the ordered world of modernists and the
more “chaotic” world of postmodernists. We
posit that the conjunction of adaptive tension—
the gap between the variety internally available
to a system and that which confronts it externally (McKelvey, 2001, 2008)— connectivity, and
interdependency in social phenomena reflects
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these principles and challenges the dominant
assumption that social events are independent
of each other and identically distributed (i.i.d.)
so as to yield a normal distribution. Such a
“Gaussian” default assumption underpins an
atomistic ontology, one that takes the world as
constituted by a collection of objects. Many
events connected under tension, however, are
often distributed according to a power law, as
illustrated in Figure 1, which shows two Pareto
distributions on the left and their equivalent
power-law distributions on the right. A powerlaw distribution is a Pareto distribution depicted on a log-log scale. Other (less extreme)
skew distributions, reflecting the different ways
that phenomena interact, are also possible. Here
we focus on rank/frequency power laws.
In the upper left of Figure 2, we show a stylized representation of the myriad small outcomes—such as the approximately 16,000 Californian quakes that go unnoticed each year, or
the 17 million ma & pa stores that didn’t become
Walmarts—that econometricians usually treat
as i.i.d. and summarize with a normal distribution.1 Toward the lower right of the figure, in
contrast, we see the increasingly high-ranked,
very rare, extreme outcomes that defy prediction—that is, earthquakes, floods, bankruptcies,
stock market crashes, giant firms (Microsoft,
Walmart, etc.), and giant cities.
The complex causal connections that, under
tension, generate power-law distributions do
not allow us to distinguish ex ante what is usable information from what is noise. Any one of
the tiny events located in the upper-left region of
Figure 2 could initiate a causal chain reaction,
generating an extreme outcome located in the
lower-right region of the figure. The Gaussian
default assumption is therefore easy to make.
Figure 2, however, underpins a connectionist ontology that takes the world’s fundamental constituents to be relationships. In contrast to normal distributions, power-law distributions have
long tails, potentially infinite variance, unstable
means, and unstable confidence intervals (Andriani & McKelvey, 2007). If Gaussian thinking
takes extreme events to be outliers—too different from other events in the sample to be enter-
1
“Robustness” techniques (Greene, 2002) translate skew
distributions into normal ones—that is, by making the x axis
a log scale so as to produce a log-normal distribution.
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FIGURE 1
From Pareto to Power-Law Distributions: Two Examples—Pareto on Left, Power Law on Righta
a
Reproduced from Glaser (2009).
tained as probable and, thus, to form part of the
distribution being studied—power laws incorporate outliers as a significant part of the disFIGURE 2
Stylized Power-Law Distribution
Paretian world
Gaussian
world
Log
of
event
frequency
Power law
negative slope
Mean
Log of event size
tribution and therefore meriting attention. Even
if they cannot make them probable— unstable
means and potentially infinite variances prevent it—power laws signify the existence of
scale-free phenomena worthy of our consideration. Their scalability—that is, the causal dynamics stemming from multiplicative subunit
interactions to produce similar outcomes at multiple hierarchical levels (e.g., network organizations such as the Internet)—renders them plausible.
Numerous complexity researchers (Andriani &
McKelvey, 2007, 2009; Newman, 2005; West &
Deering, 1995) have found power-law distributions to be ubiquitous in social no less than in
natural systems. They have captured social phenomena ranging from the large number of statistically similar entities located in one tail of
the distribution to the N ⫽ 1 extreme outcomes
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best studied by hermeneutics methods in the
other. We argue that modernists and postmodernists have each got hold of one tail of a distribution in which extreme outcomes are not
random outliers as interpreted by Gaussians
but, rather, the product of tension and connectivity effects. These shift a distribution from an
i.i.d.-based normal distribution to a power-law
distribution. While other distributions are, of
course, possible, omitting these from the discussion does not affect the thrust of our argument.
Understanding what drives the distribution of
social phenomena at different levels of organization allows us to integrate the seemingly opposed modernist and postmodernist epistemologies into a unitary representation. Locating
them both along a single causal continuum enhances the epistemic legitimacy of each in the
eyes of the other.
The structure of our article is as follows. First,
we briefly provide working definitions of the
modernist and postmodernist positions. To show
where they differ, we present these as idealizations, hoping that readers will see beyond the
resulting simplifications. Following this, we
draw on Ashby’s concept of requisite variety to
offer a complexity perspective on the challenges
of adaptation. We argue that such a perspective
illuminates the modernist/postmodernist debate. We then apply our analysis to organizations and explore its implications for organizational research. We end with a conclusion.
MODERNISM VERSUS POSTMODERNISM
Defining Modernism
Modern science is one of the fruits of the Enlightenment’s modernist project. Insofar as the
social sciences promote the understanding and
use of science to improve modern society, they
also pursue a modernist agenda (Israel, 2001).
While positing the epistemological and moral
unity of mankind (Hollinger, 1994), the modernist
project “assumes that human beings are autonomous subjects, whose interests and desires are
transparent to themselves and independent
from the interests and desires of others” (Calás
& Smircich, 1999: 653). Bacon and Descartes are
considered to be the main proponents of this
“atomistic” ontology (Hollinger, 1994).
Modernism sought knowledge outside religious revelation; Baconian science argued for
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the empirical rather than the faith-based justification of truth claims. Truth arose from a correspondence between a claim and empirically observed facts, rather than divinely sanctioned
revelations transmitted through sacred—and,
hence, unmodifiable—texts. This required the
repeatability or replicability of facts and the
rejection of one-shot events such as miracles.
Objectivity, however, could only be fully
achieved by an independent and decontexualized observer endowed with a god’s eye view—a
“view from nowhere” (Shapin & Schaffer, 1985).
If modernism constituted a world view, the
rise of positivism at the end of the nineteenth
century provided it with a methodology. Ernst
Mach’s rebellion against Hegelian idealism
gave rise in 1907 to the Vienna Circle, a group of
physicists and mathematicians whose dream
was the attainment of absolute verified truth
(“verificationism”) based on a rigid correspondence (“correspondence theory”) between operational measures and theory terms (Suppe, 1977).
Modernism and its methodological handmaiden, positivism, have long underpinned the
epistemic legitimacy of the natural sciences. Being essentially concerned with what Reichenbach (1938) called “the context of justification,”
however—justification being one of Plato’s prerequisites for genuine knowledge—modernism
and positivism showed little interest in what
Reichenbach called “the context of discovery.”
If, for Bacon, genuine knowledge yielded prediction and control, and hence a basis for action,
these forms of justification would separate science from superstition, alchemy, religion, and
faith-based revealed truth. Into this world of
apodictic certainties, Reichenbach introduced
the idea that probabilistic thinking offered a
more realistic basis for justification. With
Brown’s “Brownian Motion” in 1827 (Ford, 1992),
Boltzmann’s statistical mechanics of 1877 (Boltzmann, 1887), Gibbs’s statistical actuarial tables
for the insurance industry in 1902 (Gibbs, 1902),
and Fisher’s statistics of 1916 (Fisher, 1918), a
shift occurred, endorsed by Reichenbach (1938),
from exact to probabilistic representations.
In a noisy world the structures underpinning
the replicability of independent events are captured statistically by the mean. In the case of
normal distributions, the variance could often
conveniently be treated as mere noise—something to be got rid of rather than explored. Over
time, the normality of a distribution became the
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default assumption—the taken for granted signature of a universal reality that yielded stable,
manipulable objects. Gaussian statistics, the
statistics of the normal distribution now widely
applied in the social sciences (e.g., Greene,
2002), delivers stable means, finite variances,
and independent data points (Andriani & McKelvey, 2007; Taleb, 2007). The social sciences,
epitomized by neoclassical economics, thus created for themselves the stable and (mostly) computationally tractable social objects that had
been the focus of Newtonian physics (Colander,
2006; Friedman, 1953; Mirowski, 1989), while at
the same time eschewing the more complex,
messy interactive and dynamic social processes
characterizing human social behavior.
Gaussian-inspired statistical truths artificially structure the world so as to achieve significant reductions in complexity, a demultiplication of explanatory entities, and a consequent
reduction in the required degrees of freedom—
that is, the number, n, of observed events that
are free to vary minus the number of necessary
relations, r, obtainable from these observations
(Walker, 1940). In line with Occam’s razor—the
explanans should always be more compact than
the explanandum—they achieve compressibility
and parsimony (Hempel, 1965). In modern cosmology the search for a theory of everything
illustrates this concern with compressibility and
parsimony (Guth, 1997; Weinberg, 1992). In addition to its parsimony, a theory’s worth is also
based on its predictive power. Predictability as
such, however, does not always require understanding (Bridgeman, 1936). As Feynman famously pointed out, despite its remarkable
predictive achievements, “No one really understands quantum mechanics” (1967: 129).
The modernist approach has not gone unchallenged. Unlike the physical sciences, the social
sciences have to deal with the fact that although
the people they study are subject to physical
forces, they act primarily on the basis of representations and interpretations of the world that
make meaning central to explanations of their
behavior. The inability of the physical sciences
to deal with the vexing question of meaning led
to the rejection of the modernists’ stance as a
whole by many social scientists. After all, what,
exactly, constitutes “replicability” when dealing
with a complex social or organizational phenomenon? In what respect might two complex
social outcomes be sufficiently similar to justify
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a claim of replicability? And how robust is the
concept of intersubjective objectivity—modernism’s substitute for the god’s eye view— given
the social distribution of power, influence, and
bias (Foucault, 1969; Shapin & Schaffer, 1985)? If
questions like these suggest an unbridgeable
gulf between the natural and the social sciences, sociologists of science go further, pointing out that in the natural sciences no less than
in the social sciences, problems of interpretation, meaning, status, and power effectively contaminate all claims to objectivity (Callon, 1986;
Golinski, 1998; Latour, 1988). No student research
assistant in any physics or biology laboratory
long remains unaware of what results the professor wants to see!
Defining Postmodernism
Alvesson and Deetz see modernism as
the instrumentalization of people and nature
through the use of scientific-technical knowledge
(modeled after positivism and other “rational”
ways of developing safe, robust knowledge) to
accomplish predictable results measured by productivity and technical problem-solving leading
to the “good” economic and social life, primarily
defined by accumulation of wealth by production
investors and consumption by consumers (1996:
194).
Postmodernists hold that such scientific
knowledge, shaped by local historical and cultural contexts, represents one story among many
(Calás & Smircich, 1999)—a social construction
serving the ideological agenda of powerful
elites (Koertge, 1998). The postmodern perspective challenges the Enlightenment project by introducing a radical subjectivity and the exercise
of power as irreducible constraints on our access to an objective world (Foucault, 1975). To
postmodernists, the world— especially the social world—is not objectively given. It is kaleidoscopic and unstable, and its constituent components are elusive. The stability that we
impute to it and from which we derive laws and
theories is partly shaped by our interaction with
other observers. Postmodernists therefore distrust the modernist’s summary Gaussian descriptions and the confident narratives these
produce (Lyotard, 1984).
Postmodernist epistemology is profligate
rather than parsimonious. By entertaining multiple representations of phenomena (“voices”) as
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equally valid alternatives, postmodernists shun
what they see as the exclusions and repressions
underpinning the modernists’ claims to singular
objective representations. Postmodernists seek
“infinite conversations” undistorted by power
considerations (Derrida, 1978; Foucault, 1975;
Rorty, 1989). Their emphasis on “playfulness” is
designed to counter a desire to control everything and the despair at not being able to do so.
Life in all its richness and messiness is more
important to postmodernists than the impoverished conceptions of it found in psychology, economics, and other positivist-leaning social sciences. Expressed as a statistical strategy,
postmodernists invite us to focus on the rich
promises latently present in the variance rather
than on an impoverished mean. They believe
that either the social sciences accommodate the
theses of postmodernity or they become irrelevant.
Postmodernism is a broad church that accommodates a multiplicity of views—not always
harmoniously (Jenks, 1992). It challenges modernism’s unitary vision of science and society,
deconstructing the modernist object of study, revealing the fragility of the assumptions underpinning its stability, and greeting modernism’s
metanarratives with incredulity. Lyotard (1984)
would replace these with petit récits—modest
narratives—which, like Merton’s theories of the
middle range (Merton, 1949), would be of limited
spatiotemporal reach. Yet while these might
promote awareness and reflexivity, they render
theorizing elusive (Calás & Smircich, 1999)
since, trapped as they are in local Wittgensteinian language games, there is no basis for choosing between competing representations: meaning now becomes undecidable. In fact,
modernism overreaches precisely when its allencompassing metanarratives—Marxist, Parsonian, and so forth— encounter Lyotard’s local
petit récits. Being embedded and contextual, the
latter, far from scaling up into metanarratives,
constantly challenge the former’s relevance and
validity.
In contrast to the natural sciences, postmodernism massively increases the variety of phenomena that social scientists are required to
deal with. These can be viewed as manifestations of complexity at work; they point to higher
levels of interaction and interdependence
among phenomena and to the irreversible effects of time and path dependency. In effect,
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postmodernism is a theory of social complexity
(Cilliers, 1998). Assumptions of independence
among phenomena are here challenged by the
operation of dense feedback loops— both positive and negative— generated as much by how
intentional agents construe events (Dennett,
1989) as by physical causal links among them.
Given complex interdependencies, focusing exclusively on the mean of a distribution becomes
dysfunctional and misleading since its variance
now contains much of the relevant information;
it is more than just noise.
Given complex interdependencies within
densely connected causal networks, how do we
proceed? The connectionist ontology implicitly
underpinning postmodernism massively increases the number of plausible patterns needing
causal analysis and interpretation. For postmodernists, however, computational convenience
does not constitute an epistemic justification for
a reductionist stance, so Occam’s razor is of
little use; the complexity must be absorbed and
lived with rather than reduced (Boisot & Child,
1999). Postmodernists are interested in unpredictable and emergent phenomena rather than
predictable regularities—in process rather than
structure. Their methodological preference is for
qualitative case-based research. In any tradeoff between understanding and prediction, understanding should take precedence.
Postmodernism itself, however—the pursuit of
“infinite conversations”— has also come under
fire. The relativism resulting when one theory is
deemed as good as another and equal airtime is
given to all (Hollis, 1982), or when paradigms
cannot be reconciled (Kuhn, 1962), makes it impossible to compare, evaluate, and select from
competing alternatives. The primacy postmodernism accords to the chaotic nuances generated by the swaying of individual “trees” at the
expense of patterns discernible in the “forest”
effectively paralyzes theory choice, thus undermining justification and practitioner relevance.
Yet without a timely and “justifiable” consensus, productive social action becomes impossible. This poses a challenge to management inquiry interested in both valid truth claims and
actionable outcomes. How can it contribute to
practical action if (1) truth claims cannot be disentangled from the “situated” interests that give
rise to them; (2) truth claims are framed in incommensurable languages; (3) competing alternatives are incommensurable across observers;
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and (4) any convergence achieved across alternative truth claims reflects the influence of status, power, repression, and coercion?
In what follows we argue that modernism and
postmodernism are not so much competing alternatives as alternative moments in a single
dynamic process of human adaptation to both
natural and social phenomena.
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guishable from randomness residing in phenomena and an “effective complexity” residing
in the regularities underpinning their structure.
By focusing on effective complexity, a system
can respond in selective and discriminating
ways to the massive variety it confronts (McKelvey & Boisot, 2009).
The Ashby Space
ADAPTING TO COMPLEXITY
In the human case, adaptation is about how to
respond intelligently to the threats and opportunities embedded in the variety of natural and
social phenomena confronting us as a species.
To repeat, variety is often the surface manifestation of complexity at work. Since what distinguishes modernists from postmodernists is how
they approach this variety, we take it as the
starting point for our discussion. In biology the
issue is often framed in evolutionary terms—
alternative framings are possible (Dooley & Van
de Ven, 1999). We draw on the biological approach and apply it to the human and organizational realms.
The Law of Requisite Variety
Ashby’s Law of Requisite Variety states that
“ONLY VARIETY CAN DESTROY VARIETY” (1956: 207). The
law holds that for a biological or social entity to
be adaptive, the variety of its internal order
must match the variety imposed by environmental constraints. We treat variety as a proxy for
complexity (McKelvey & Boisot, 2009). Gell-Mann
(1994) holds that emergent complexity is a function of the variety present in phenomena. Wherever the variety externally imposed on an adaptive biological or social system exceeds that
internal to the system, there emerges an adaptive tension (McKelvey 2001, 2008) within it that
fills the gap between what the environment requires of the system to ensure its integrity or
survival and what it can actually deliver at a
given moment.
Although Ashby’s law tells us nothing about
the nature of the external complexity a system
must respond to, the fact that systems such as
ourselves adapt and survive suggests that
within a certain range such complexity must be
manageable. Not all of it will be relevant to the
system’s survival. Gell-Mann (1994) distinguishes between a “crude complexity” indistin-
We explore the difference between crude and
effective complexity in a diagram (Figure 3) that
we label the Ashby Space (Boisot & McKelvey,
2007). The vertical axis measures the variety of
external stimuli that register with an agent; the
horizontal axis measures the variety of responses generated by that agent. The diagonal
indicates where the variety of responses
matches that of incoming stimuli and is therefore adaptive. Above the diagonal, the variety of
the responses fails to match that of incoming
stimuli; below it, the variety of responses is excessive relative to what is adaptive and wastes
energetic resources. We now partition the vertical axis of the Ashby Space into different regimes: chaotic, complex, and ordered. We could
also partition the horizontal axis, but we do not
need to do so. In the chaotic regime incoming
stimuli exhibit no obviously discernible regularities; in the complex regime they exhibit some,
even if these still have to be teased out; in the
ordered regime one can subordinate all the variety encountered in incoming stimuli to some
ordering principle—for example, algorithmic
FIGURE 3
The Ashby Space
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compression—as when, for example, the sequence a,b,a,b,a,b,a,b,a,b,a,b,a,b,a,b,a,b,a,b can
be reduced to 10(a,b).
Using this diagram, we offer a complexitydriven interpretation of Ashby’s law. The vertical arrow going down from A to C describes a
cognitive process of variety reduction that aims
to filter out crude complexity and focus on effective complexity. This, according to modernists,
requires interpretation and selection—that is,
algorithmic compression. If successful, it reduces the variety calling for responses and
gradually moves them into the ordered regime.
A postmodernist distrusts such moves, arguing
that what constitutes effective complexity lies in
the eye of the agent and that where adaptive
responses need to be collective, the modernist’s
reductionist strategy is often coercive rather
than cognitive in nature.
Although stimuli can appear at any point
along the vertical axis, the horizontal arrow proceeding from point A to point B offers the clearest illustration of a postmodernist implementation of Ashby’s law. Since postmodernists here
find themselves in the chaotic regime, their default assumption is that there will be no effective complexity to be teased out—that is, no
robust underlying structure. All is crude complexity. Lacking any agreed upon basis for interpreting the stimuli—that is, for reducing their
variety by traveling down the space prior to
formulating a response—postmodernists allow
the variety of responses to expand until it
matches that of incoming stimuli. They, thus, are
willing to remain in the chaotic regime until
“Nature shows her hand.” In sum, in contrast to
modernists, whatever the regime they find themselves in, the postmodernists’ default preference
is to move horizontally across rather than vertically down the Ashby Space. Yet although
Ashby himself does not distinguish between
crude and effective complexity— between variety that should be treated as noise by the agent
and variety that has relevance for it—in attempting to accommodate all variety and refusing to be selective, the postmodernists’ response
is likely to be costly in terms of energy expended
and may well overshoot point B on the diagonal
where adaptation is achieved.
While there will be many situations in which
agents confront regimes that are either wholly
chaotic or wholly ordered, many management
and organizational research challenges arise in
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the complexity regime, where both effective and
crude complexity operate. Intelligent agents in
this regime initially move down from point A
toward point C but are led to turn right toward
point D when they encounter irreducible uncertainty. Organizational researchers entering this
region of the Ashby Space need to tolerate
higher levels of epistemic variety than modernists but must then be willing to select from
it. In effect, they must become evolutionary
epistemologists, progressing toward a higher
probability of truth by slowly weeding out inferior theories (Hahlweg & Hooker, 1989; McKelvey, 1999; Radnitzky & Bartley, 1987). They
must, however, defer to postmodernist sensibilities by making their selection more forgiving than modernists would wish, but then devise effective procedures to home in on the
most promising interpretive schemata. Where
a collective interpretation is possible, some of
these can gradually be moved into the ordered
regime.
Phase Transitions and Scalability
Our discussion so far has centered on the respective responses of modernists and postmodernists to stimuli appearing high on the vertical
axis of Figure 3, where the world will be experienced as chaotic. But what determines where
on the vertical scale stimuli will actually appear? Complexity science studies elements in
interdependency—and increasingly in living
systems (Gell-Mann, 2002). Absent such connectivity, one has atomistic aggregations to which
i.i.d. assumptions apply. Complexity increases
with the number of interacting elements and the
density and nonlinearity of the interdependent
outcomes (Holland, 1988, 2002). Beyond certain
thresholds, complexity can lead to phase transitions toward either emergent order—that is, dissipative structures that maintain themselves in
existence by continuously importing free energy
from their environment and exporting bound energy back into it (Nicolis & Prigogine, 1989)— or
greater chaos (Kauffman, 1993; Kaye, 1993).
Some scholars study interdependencies
among heterogeneous agents—these could
range from nucleotides to individual human beings and to organized collectivities of these—
operating at what was early on called the “edge
of chaos” but is now seen as a region of emergent complexity between the “edge of order”
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and the edge of chaos. For Nicolis and Prigogine
(1989), the edge of order is the “1st critical value”—a level of energy sufficient to cause phase
transitions in many physical phenomena (as
when the level of heat in a teapot causes a
rolling boil). This region of complexity, varying
in size (and separating ordered from chaotic regimes), is what Kauffman (1993) labels the melting zone. When, through the amplification of
feedback, connectivity-enabled interdependencies reach a specific intensity, they can trigger
phase transitions from one of the regimes of
Figure 3 to another. In some systems new order
emerges from such phase transitions when existing structures come to be dominated by unstable modes that become order parameters
for a new regime; Haken (1983) describes these
as becoming enslaved. His “slaving principle”
constitutes a disruption of equilibrium (symmetry breaking; Mainzer, 2007/2004) that reflects choices made by agents within the system.
In social systems such choices may reflect the
exercise of power by those in a position to reduce critical uncertainties within the system
(Crozier, 1964). Often, the connections themselves are established and amplified when the
system is put under adaptive tension—is forced
across the edge of order into the melting zone.
Here we see tension, such as that between supply and demand, which causes entrepreneurs to
start up possibly innovative new enterprises—in effect, phase transitions out of the status
quo. Social systems put under tension, through
recession, poverty, migration, ethnic conflict,
and so forth, can also be torn apart by forces
starting with tiny initiating events. Given these
conditions, the initiating event may add pressure to neighboring interdependencies so as to
take the system up to, if not over, the edge of
chaos. An analogy is with a fishing net lying
loosely in a pile. Cut one of its cords and nothing
happens. Now stretch it taut and cut one of its
cords; the cut of one link transmits tension to
neighboring links, propagating a tear across the
net.
Yet interdependencies are raw materials for
any kind of organization. Bak (1996) argued that,
to survive, a system must be able to stay within
the melting zone, in a state that precariously
maintains its effective complexity near the edge
of chaos, which he called “self-organized criticality” (SOC). Bak illustrated SOC with a
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sandpile. Keep adding grains of sand to a sandpile, thus increasing the adaptive tension it is
subjected to, and at some critical point the slope
becomes steep enough that tiny to large avalanches occur that reduce the steepness of the
slope and restore stability. At this point the
causal influences generated by the tension become scalable and propagate throughout the
sandpile in unpredictable ways, influencing
grains far removed from each other. The sizefrequency distribution of avalanches in the
sandpile follows a power law that Bak claimed
to be universal. They also exhibit a fractal structure—they are self-similar across a range of
scales—meaning that their appearance and the
underlying causal dynamics are essentially the
same across multiple scales or hierarchical levels (Mandelbrot, 1982).
Andriani and McKelvey (2007, 2009) identified
such connectivity-based outcomes extending
across thirty-two magnitudes of physical phenomena, twenty-seven magnitudes of biological
phenomena, and eleven magnitudes of social
phenomena. They also showed how pervasive
power laws are in physical, biological, social,
and organizational phenomena—listing over
100 of the latter. Barabási (2002) saw power laws,
scalability, and fractal structures operating in
social networks. Here, connections are often established through communication, and their effects, both positive and negative, are amplified
through the operation of feedback loops. A power-law distribution includes many social “loners” at one network extreme and a single highly
connected “star” at the other. As Brunk observed, “Instead of the bulk of the data being
produced by one process and the ‘outliers’ by
another, all events— both minuscule and the
historically monumental—are produced by the
same process in an SOC environment” (2002: 36).
Two Ontologies: Friends or Foes?
Our analysis suggests that we do not have to
choose between connectionist and atomistic ontologies. The high variety and low variety they
engender, pursued respectively by postmodernists and modernists, are but transitory moments
in a broader process in which each has its place.
Connectionism and atomism are lenses that we
bring to bear on events for particular purposes.
Complexity theory—about the dynamics of connectivity and interdependence—provides us
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with an overarching conceptual framework that
accommodates both. Within it, Gell-Mann’s concept of effective complexity is well placed to
fruitfully integrate modernist and postmodernist
insights.
Atomistic and connectionist ontologies are
thus complementary and contingent, rather than
alternatives. Under certain circumstances, although they do not have to, phenomena can
connect. When this yields extreme events, we
account for them through a detailed retracing of
causal connections presented as a historical
narrative or a case study: the fall of Constantinople, the Cuban Missile Crisis, etc. Yet since
these causal connections and the resulting
causal patterns are improbable, they do not lend
themselves to systematic replication and experimentation. The lessons of history are thus
rarely unequivocal. The causal components of
an extreme event, taken individually, may lend
themselves to systematic replication and experimentation, but the predictions yielded by such
an atomistic approach remain strictly limited in
scope, offering little purchase on the more richly
connected patterns typically covered by historical accounts. All attempts at grand narratives
ignore this point (Lyotard, 1984). We know, for
example, that beyond a certain threshold, social
tensions and instability, for good or evil, can
throw up charismatic leaders, but we cannot
predict when or how. Anticipation rather than
prediction is, then, the best that we can hope for.
BRIDGING TO ORGANIZATIONS
To summarize, modernism advances knowledge when phenomena are independent of each
other or can be made so via controlled experiments. It targets the ordered regime in the
Ashby Space, one in which phenomena can be
predicted and responded to efficiently. What we
have called an atomistic ontology takes the independence of phenomena as its default assumption, allowing them to be described by a
normal distribution. In the case of socially produced knowledge, postmodernism takes this assumption to be an unwarranted simplification of
realities that include coercive social processes.
It emphasizes the idea that new order creation
draws on the arbitrary—and sometimes illegitimate— use of power (Foucault, 1975). Postmodernism implicitly builds on a connectionist ontology and power-law dynamics to argue that
July
there exists no socially legitimate basis for moving down the Ashby Space. Yet modernists and
postmodernists are like blind people who have
each seized different parts of the complexity
elephant, little realizing that their ontologies
complement rather than compete with each
other. The challenge is to understand when
each applies.
Existing Discourse
Modernist discourses seek to maintain a high
level of generality that becomes increasingly
unsustainable as they travel down the powerlaw slope of Figure 2, toward ever-smaller samples of ever-larger and more extreme outcomes.
In so doing, however, they often impose oversimplified interpretations (i.e., unjustified algorithmic compressions) on the data that may obscure the effects of power and bias. Seeing this,
postmodernists challenge the legitimacy of theorizing even in those regions of the power-law
slope—the upper-left region of Figure 2—where
Gaussian assumptions may actually be warranted. However, by arguing that error-eliminating statistical strategies eliminate more than
just errors—they also eliminate “weak voices”—
postmodernists underplay the methodological
value of replicability and explanatory coverage
(Mayo, 1996) that makes some theories more
plausible than others.
Since for postmodernists all theory choice is,
at base, politically driven, they find no convincing basis for moving down the Ashby Space of
Figure 3. Yet the “infinite conversations” they
advocate are a luxury that a practical resourceconstrained manager can ill afford; they constitute counsels of perfection that have little adaptive potential. Thus, just as the truth claims of
the atomistic ontology underpinning modernist
discourse become increasingly suspect when
made too far down the power-law slope of Figure 2, so the connectionist ontology underpinning the postmodernist discourse overreaches
itself when it claims that meaning— belonging
as it does to the realm of language and social
interaction effects—remains unconstrained by
the real-world dynamics operating in the figure’s upper-left-hand regions.
Both modernists and postmodernists aim for
reliable knowledge, but, holding competing ontologies, they end up talking right past each
other. Figure 2, however, suggests that there is a
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time to be atomistic and a time to be connectionist and that it is the degree of adaptive tension
present in a system—as determined by some
order parameter—that influences the degree of
connectivity present among phenomena. The
seemingly incompatible ontologies can thus be
reconciled. Given connectivity, one has to accept the possibility of power-law-distributed, occasionally extreme, and unpredictable outcomes and, hence, be willing to settle for being
roughly right rather than precisely wrong. In the
connectionist world of living systems, the “justification” of knowledge resides primarily in its
contribution to efficacious adaptability and survival rather than to the attainment of a predictive law-like truth (Gell-Mann, 2002). Falsification, Popper’s (1935) criterion of demarcation
between science and nonscience, remains in
force since “false” knowledge threatens both adaptation and survival. Sooner or later, reality
kicks back (Popper, 1983).
In pursuit of a stable and predictable order,
modernists who find themselves in the chaotic
regime of Figure 3 aim at reaching point C,
located in a region of the Ashby Space where
compact statistical representations have purchase. Postmodernists finding themselves in the
same regime, in contrast, are drawn toward
point B, located in a region where the description of events is incompressible and only detailed narrative is possible. The complexity perspective, however, identifies point D as more
relevant to organization science. Given adaptive tension of some kind, intelligent, interdependent agents in the Ashby Space constitute
complex adaptive systems (CASs; Holland, 1988,
2002) striving for improved fitness, growth, and
survival via self-organizing processes that we
associate with the complex regime of Figure 3.
Many of their interdependent behaviors give
rise to scale-free dynamics and result in power
laws. In order to economize on scarce energetic
and computational resources, for example,
many agents typically seek out the ordered regime of Figure 3. Yet because of their own collective actions and unpredictable events, they
often find themselves in the chaotic regime.
Organizational researchers study phenomena
that typically fall somewhere within the complex regime—that is, they are neither so lacking
in structure as to remain stuck in the chaotic
regime nor so structured as to end up in the
ordered regime. The complex regime is the one
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in which the power-law distributions of Figure 1
(stylized in Figure 2) make their appearance.
Here, compact symbolic representations coexist
with more discursive narrative ones. Yet while
the ebb and flow of adaptive tension causes
behaviors to shift toward the upper left or lower
right along the distribution, modernist thinking
wants to draw organizational research permanently down into the ordered regime of Figure 3.
This accommodates normally distributed phenomena located in the upper-left “Gaussian” region of Figure 2. Postmodernist thinking, on the
other hand, believes that the natural home of
organization research is the chaotic regime—
the region that occasionally produces the
unique and sometimes extreme events located
in the lower right of Figure 2. Yet since low-tohigh variations in adaptive tension often cause
scalable outcomes to progress from upper left to
lower right down the power-law slope, so should
management and organizational analysis. Traveling left up the slope, one deduces observable
behaviors from underlying patterns in causal
dynamics. Traveling from upper left down the
inverse slope, however, requires more than induction. It calls for an inferential strategy that
we label scalable abduction. Scalability is what
causes the target phenomenon to spiral out into
the extreme outcomes located on the lower right
of the power-law slope.
Scalable Abduction
According to Peirce, “Abduction . . . consists of
examining a mass of facts and in allowing these
facts to suggest a theory” (1935: 205). Abduction
seeks inference toward the best explanation,
one that turns on the coherence with which a
novel or anomalous event can be related to a
background theory (Aliseda, 2006; Thagard, 2006;
Thagard & Shelley, 1997). The observed behavior
of workers in the Hawthorne experiments, for
example, was anomalous relative to prevailing
background theories of worker motivation
(Roethlisberger & Dickson, 1964). These theories
then had to be either modified or broadened to
“explain” the anomaly. Scalable abduction infers toward the best scalable explanation. As
outcomes move down the power-law slope, scalable abduction calls for explanations based on
theories about causes operating in the same
manner across the multiple levels/scales of a
system (Gell-Mann [2002: 23] called this “middle-
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level” theorizing); it focuses on tiny initiating
events coupled with scale-free causes operating
from the very small to the very large so as to
explain infrequently occurring extreme outcomes. Thus, associating a Gaussian epistemology with the upper-left region of Figure 2 and a
“narrative” epistemology with the lower-right
region, scalable abduction offers an inferential
engine that can travel between them and track
the dynamics by which certain tiny events get
amplified into extreme outcomes.
When applied to distributions of phenomena
governed by power laws, scalable abduction allows one to derive limited but nonetheless useful expectations concerning scale-free dynamics
and the causal processes that underpin them.
Scale-free dynamics emerge from myriad lowerlevel “tiny initiating events” (Holland, 2002),
some of which propagate out causally and explode into the larger events that make up one
end of the power-law distribution (Andriani &
McKelvey, 2007, 2009; Gell-Mann, 2002). The metaphor is of a butterfly flapping its wings over
eastern Brazil and ultimately triggering a tornado in Texas—a “butterfly event” (Lorenz,
1972). Here, events uncovered at one scale justify
some forms of extrapolation out to less frequent,
more extreme events at another.
Lying between idiosyncratic inductions and
predictions based on deductive tests, scalable
abduction offers anticipation. Anticipation is
“softer” than prediction, bridging between the
strong predictive claims achievable in, say,
classical physics and the unpredictable, often
seemingly chaotic press of singular events confronting us daily at the human scale. Both prediction and anticipation shape our expectations
and orient our responses. Both draw on evidence
for their justification, although anticipation, often only expressible in a loose, narrative form,
achieves less precision than prediction. While
predictability is problematic given complexity,
anticipation remains fluid with respect to
changing conditions and tensions, thereby facilitating adaptive action and survival.
IMPLICATIONS FOR MANAGEMENT
RESEARCH
Four points emerge from our analysis:
1. The atomistic and connectionist ontologies
that respectively underpin modernist and
July
postmodernist positions have been treated
by organizational researchers as being antagonistic to each other (McKelvey, 2003).
2. They each occupy different end points of a
power-law distribution that reflects complex dynamics such as SOC and new order
creation (McKelvey, 2004).
3. Under adaptive tension, these dynamics
connect hitherto disconnected small events
so as to produce ever-larger, more complex,
but less frequent outcomes (Andriani &
McKelvey, 2007).
4. A power-law distribution thus reconciles
the two antagonistic ontologies in a single
overarching ontology that makes the appropriateness of either modernist or postmodernist perspectives contingent on the degree of tension and connectivity present in
the system (stylized in Figure 2).
What implications do the above points carry
for organizational researchers? We identify five:
1. Engage with the properties of power-law
distributions and the different epistemic strategies in the Ashby Space that these suggest. In
this space, for example, the chaotic regime describes the world of Heraclitus, who famously
said, “The river where you set your foot just now
is gone. Those waters giving way to this, now
this” (Haxton, 2001). Frequently, ours is an epistemically fragile world of unique yet connected
phenomena that unfold unpredictably and can
only be narrated, not analyzed into simplistic
formulas. To the extent that living (social) systems exhibit any regularities—that is, phenomena that repeat—we can move down into the
complex regime where connections become contingent and some analysis becomes possible.
Here we discover the world of power laws—
distributions in which small events sometimes
scale up into extreme outcomes. Such phenomena cannot be summarily summarized by the
means and standard deviations of “normal”
Gaussian statistics. Instead of analyses and
theories based on our conventional statistical
methods, we need scalable abduction and
scale-free causal theories (Gell-Mann’s middlelevel theories [2002]). In Table 1 we briefly define
eight of the fifteen scale-free theories that
readily apply to organizations (Andriani & McKelvey, 2009).
Epistemic robustness may only be achievable
in the ordered regime of the Ashby Space, where
normally distributed phenomena are sufficiently similar and disconnected that the statisticians’ i.i.d. assumptions apply. They can then
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TABLE 1
A Sample of Scale-Free Theories of Naturea
Theory
Definition
Phase transition
Exogenous energy impositions cause autocatalytic interaction effects such that new
interaction groupings form (Prigogine & Stengers, 1997)
Heterogeneous agents seeking out other agents to copy/learn from so as to improve fitness
generate networks; with positive feedback, some networks become groups, and some
groups become larger groups and hierarchies (McKelvey & Lichtenstein, 2007)
Given newly arriving agents in a system, larger nodes with an enhanced propensity to
attract agents will become disproportionately even larger (Barabási, 2002)
Multiple exponential or log-normal distributions or increased complexity of components
(subtasks, processes) sets up, which results in a power-law distribution (Newman, 2005;
West & Deering, 1995)
Word frequency is a function of ease of usage by both speaker/writer and listener/reader
(Zipf’s [power] Law [1949]), now found to apply to firms and economies in transition
(Ishikawa, 2006; Podobnik, Fu, Jagric, Grosse, & Stanley, 2006)
Surfaces absorbing energy grow by the square, but organisms grow by the cube, resulting
in an imbalance; fractals emerge to balance surface/volume ratios (Carneiro, 1987)
As cell fission occurs by the square, connectivity increases by n(n ⫺ 1)/2, producing an
imbalance between the gains from fission and the cost of maintaining connectivity;
consequently, organisms form modules or cells so as to reduce the cost of connections
(Simon, 1962)
Under constant tension of some kind (gravity, ecological balance), some systems reach a
critical state where they maintain stasis by preservative behaviors, such as Bak’s small
to large sandpile avalanches, which vary in size of effect according to a power law (Bak,
1996)
Spontaneous order creation
Preferential attachment
Combination theory
Least effort
Square-cube law
Connection costs
Self-organized criticality
a
We use eight out of fifteen scale-free theories discussed in Andriani and McKelvey (2009).
be aggregated into stable classes and their behavior deductively predicted. Although these
conditions can only be met by some of the phenomena that humans encounter as they go
about their business, they do bring better understanding of behavior when applicable. While no
natural boundaries separate the three regimes—they interpenetrate—the first is the natural home of the historian, the second of social
scientists and biologists, and the third of scientists who study nonliving phenomena. Needless
to say, since effective representations in each
regime will call for a different mix of narrative
and abstract symbolic resources, epistemic flexibility and tolerance are called for.
2. Explore the power-law distribution before
you exploit it. Complexity and power-law thinking offer researchers and practitioners a choice
of strategies. A move toward the world of Heraclitus leads them to samples of one and epistemic fragility. Finding themselves in unfamiliar territory, they are in March’s (1991)
exploratory mode of learning and must behave
like hunter-gatherers. A move toward the world
of normal distributions, in contrast, leads them
toward large i.i.d. samples and epistemic ro-
bustness. Here the territory is more familiar, allowing them to operate in March’s exploitative
mode of learning and to behave like settled
farmers (Hurst, 1995). It is in the world of powerlaw distributions, however, that management
and organizational researchers operate in frontier scientific territory and have to balance out
exploration and exploitation as described by
March— call this “homesteading.”
Good science requires us to deploy a research
strategy appropriate to our epistemic circumstances. Hans Reichenbach, a friend of the Vienna Circle, claimed that exploration— he
called this “discovery logic”—was of no interest
to the philosophy of science. Only exploitation—
“justification logic”—was of relevance (Reichenbach, 1938). Yet the positivists advocated so
stringent a conception of knowledge that neither
the natural nor the social sciences could satisfy
it. But we don’t get to exploit anything unless we
have paid our dues in the coin of exploration.
Homesteading precedes farming, and a long period of hunter-gathering may, in turn, precede
homesteading. Effective research requires us to
travel up and down the Ashby Space—and, by
implication, in both directions along the stylized
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power-law distribution of Figure 2. There is
scope for a division of labor since differences in
the cognitive style of researchers will push them
into different regions of the space. Useful knowledge creation, however, ultimately requires
such labor to be coordinated and integrated.
3. Do not privilege one part of the power-law
distribution at the expense of another. The world
is a dynamic place, subject both to the emergence of order and, according to the second law
of thermodynamics, its erosion. The first law of
thermodynamics holds that the conservation of
energy drives the creation of matter. In neoclassical economics the first law fostered an equilibrium-focused mathematics (Colander, 2006;
Mirowski, 1989). The second law of thermodynamics holds that ordered energy-based structures eventually deteriorate into randomness—a process called “entropy production”
(Swenson, 1989). While some structures temporarily stabilize, others rapidly disintegrate. To
understand organizational phenomena is to understand these opposing processes.
If we view organizations through a network
lens (Boisot & Lu, 2007), we see that organizational research studies the regularities that govern the interdependencies among different
nodes in a network—that is, the structure and
the dynamics of their connectivity. Since nodes
can be individuals, departments within an organization, or whole organizations, we see that
many of these regularities are scalable (Barabási, 2002). And since connectivity is a variable
that reflects the level of adaptive tension in the
network, organizational research must engage
with the power-law distribution as a whole,
without privileging one particular region at the
expense of another. It cannot therefore presume
that studies of “average” or “typical” organizations accurately reflect organizational properties ranging across an entire power-law distribution. Just as Axtell (2008) invoked the
power-law distribution of firm size in the United
States to claim that there is no such thing as the
typical firm, so we hypothesize that the “average” organization does not exist.
4. Study the causal dynamics that call for scalable abduction. Power laws are the signature of
SOC in natural and social systems. By focusing
on circumstances under which independent
events and processes connect, scalable abduction becomes the inferential strategy of choice
for studying SOC in particular and organization-
July
al phenomena in general. Scalable abduction
turns out to be the basis of Dilthey’s (1959) Verstehen (understanding), a diacritical concept
distinguishing natural from cultural sciences.
While scalable abduction does not necessarily
yield strong or precise predictions (Burrell &
Morgan, 1979), it offers a new answer to the old
question of whether a science of history is possible. Historicism argues that history is subject
to laws that allow prediction. From a modernist
perspective, however, samples of one— unique
events— cannot exhibit law-like behavior and,
hence, remain beyond the reach of prediction
(Popper, 1945).
Yet do we not also hear that those who fail to
learn the lessons of history are condemned to
repeat them? Although it does not allow the
levels of prediction achievable in some of the
natural sciences, for living systems like organizations, abductive inference offers a useful level
of anticipation, one that can be efficaciously
adaptive. A key challenge here is to separate
the small events that are likely to remain independent and random from the small events that,
driven by tension-induced connectivities, are
likely to become scalable. Such events don’t
usually come with labels attached, so without
some understanding of how scalable causal dynamics arise in organizations (see Table 1), it is
hard to distinguish butterfly events ex ante from
random, independent ones. Here, by their finegrained analyses of individual events, the narrative strategies advocated by postmodernists
come into their own. They invite us to proceed
cautiously and to avoid the premature closure
that overhasty, statistically driven hypothesizing can produce.
5. Link the methodologies available for studying different points on the power-law slope. Graham Allison’s (1971) analysis of the Cuban Missile Crisis illustrates some of the issues we are
discussing. As Allison tells it, the crisis came
very close to generating the ultimate extreme
event: a thermonuclear war. Given the race
against time, the level of adaptive tension was
extremely high—at certain moments during the
crisis, the slightest mishap could have triggered
a nuclear missile exchange between the Soviet
Union and the United States. Had Kennedy not
engaged with the causal texture of the events
that made up the crisis at the appropriate level— had he not been sensitive to the presence of
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Boisot and McKelvey
butterfly events—the world would have plunged
into a nuclear abyss.
In the book Allison develops three different
models through which the crisis can be analyzed: Model I, the rational actor; Model II, organizational process; and Model III, governmental politics. Model I treats the state as a stable
and independent object whose behavior is rational and predictable, Model II unpacks the
state to reveal a more complex and organized
entity subject to divergent rules and routines
that undermine some of the rationality imputed
to it by Model I, and Model III subordinates the
behavior of the different components that make
up the state to the games played by political
actors. Each model adds a layer of complexity—
and, by implication, of narrative richness—to
the analysis and improves its explanatory
power. The first seeks predictability; the third
understanding.
Model III is the most complex—not to say chaotic— of all three models. As Allison points out,
the information needed by Models II and III
dwarfs that required by Model I. In fact, to advocates of Model I, the information requirements
of Model III reflect an “undue concern with subtlety” (Allison, 1971: 251). Model I is coarse
grained, offering an informative summary of
tendencies, whereas model III is fine grained.
Allison’s three models complement each other.
The best foreign policy analysts weave strands
of the three models into their accounts. The key
difference between Allison’s and our approach
is that whereas he produced three different perspectives that happened to fruitfully complement each other—after all, they may well have
turned out to be based on incommensurate paradigms—we locate our three ontologies along a
single continuum that theoretically integrates
the different perspectives we have discussed
and identifies the inferential conditions for moving along the continuum in either direction.
Historical case-based narratives such as Allison’s look at the way events have connected. But
history as currently conceived only delivers useful lessons if events connect this way again; it
then has predictive value. Heraclitus, however,
tells us that events never connect in the same
way twice. Anticipation is both less demanding
and more demanding of history. It does not ask
how things will connect but how they could connect. It is less demanding in that it does not seek
predictive accuracy or precision. It is more de-
429
manding because it has to explore a much
larger space of possibilities than prediction requires.
Today, high-powered, agent-based simulation
models make it possible to engage in such explorations. If deduction was the inferential strategy of choice of a prestatistical age and induction that of the statistical age (Stigler, 1986), we
hypothesize that scalable abduction will become the inferential strategy of choice in the
age of computational modeling (Epstein & Axtell, 1996; North & Macal, 2007; Tesfatsion & Judd,
2006). It allows one to move methodically across
levels of resolution and analysis and to explore
statistical and narrative data in ways that were
not available to Allison. As he put it in the concluding section of his book,
What we need is a new kind of “case study” done
with theoretical alertness to the range of factors
identified by Models I, II, and III (and others) on
the basis of which to begin refining and testing
propositions and models (Allison, 1971: 273).
CONCLUSION
By accommodating the dynamics of tension
and connectivity, an epistemology based on
complexity science offers management and organizational researchers a more encompassing
legitimacy than either modernist or postmodernist epistemologies on their own— one that is
well aligned with emerging concepts of organizational complexity (Allen, Maguire, & McKelvey, in press; Lewin, 1999; Maguire, McKelvey, Mirabeau, & Öztas 2006). If effective
organizational complexity lies between order
and chaos, then, by implication, so does the
effective legitimacy of management research.
This location implies a methodological expansion out from the world of stable, normally distributed entities toward the more kaleidoscopic
and problematic world captured by power-law
distributions.
Ours is a plea for a new direction in organization and management research—and more
broadly in the social sciences. The paradigmatic
competition between modernism and postmodernism has not been fruitful. Natural scientists
and neoclassical economists continue to espouse a modernist stance, and many social scientists continue to espouse that of postmodernism (Colander, 2006; Kelso & Engstrøm, 2006;
Mirowski, 1989; Ormerod 1994, 1998). Conse-
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quently, the legitimacy of management research’s would-be truth claims remains stuck in
an epistemological quagmire. Morin (1992), however, pointed out that the new complexity sciences are now dissolving the distinction between the natural and the social sciences. The
complexity perspective suggests that where prediction is problematic, anticipation offers usefully adaptive information and, hence, becomes
a legitimate goal for scientific endeavors. Thus,
while the criteria of demarcation that separate
science from nonscience need not be abandoned—as advocated by Feyerabend (1975) and
some postmodernists—they need to be rather
more accommodating than those promulgated
by modernists.
Organizational researchers study interacting,
interdependent agents—individuals, departments, firms, etc. These simply do not behave
like a collectivity of autonomous agents. Informed interdependencies are the stuff of organization and, indeed, of life itself. Postmodernist
organizational researchers are right in thinking
that the complexity that results is not well captured by the analytical tools forged by modernist thinking. They are, however, wrong in thinking that such complexity is beyond the reach of
any kind of managerially useful analysis.
Agent-based simulation modeling, for example,
today provides both natural and social scientists with tools for studying the complex scalable processes outlined in this paper (Epstein,
2007; Epstein & Axtell, 1996; North & Macal, 2007;
Tesfatsion & Judd, 2006). It is ideally suited to
exploring the wide range of possible outcomes
out of which more probable ones might emerge.
Such possibility thinking—Kauffman (2000) calls
it the “adjacent possible”—would place organization scholars more firmly in the context of
discovery (Reichenbach, 1938) so long shunned
by modernists, without in any way undermining
the case for a subsequent justification. The narrative strategies of the postmodernist would
then be used by scholars to select the most plausible of these possibilities—those that square
abductively with their prior experience. The approach would, in effect, legitimate a more creative, exploratory approach to organizational research that prevails in many Ph.D. programs,
one that acknowledges the contingent nature of
many organizational processes even as it seeks
a robust understanding of their exploitable regularities.
July
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Max Boisot (max.boisot@gmail.com) is a professor at the ESADE business school in
Barcelona; an associate fellow at the Said Business School, University of Oxford; and
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received his Ph.D. from Imperial College, London University. His current research is on
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Bill McKelvey (mckelvey@anderson.ucla.edu) is a professor at the UCLA Anderson
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philosophy of science, organization science, complexity science, agent-based computational modeling, and complexity leadership.
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Emergence: Complexity & Organization. Emergence: Complexity & Organization.
Social complexity theory for sense seeking
Unearthing leadership mindsets for unknowable and uncertain times
March 31, 2015 · Academic
Heather Davis1
1 University of Melbourne
Davis H. Social complexity theory for sense seeking: Unearthing leadership mindsets for unknowable and
uncertain times. Emergence: Complexity & Organization. 2015 Mar 31 [last modified: 2015 Apr 4]. Edition 1.
doi: 10.emerg/10.17357.f1a65666920193baad6eb91f442349df.
Abstract
This exposition considers perspectives underpinning contemporary leadership studies given we are located in
what Hawking describes as the ‘century of complexity’, also understood as a Knowledge Era. Social complexity
as context allows consideration of the turbulence our times without looking for guaranteed, certain, or ‘right’
answers and allows us to work with these conditions, rather than succumb to threat rigidity, pretend they do not
exist, or think they are someone else’s problem. To make sense of these conditions requires ontological and
cognitive shifts of mindset that more closely match the ‘requisite variety’ of the complexities of our times. The
paper draws upon a PhD interpretive inquiry which identified cogent leadership literacies for the 21st century
and explored them within Australian university settings. Various cognitive frames feature in this paper and
serve to illuminate possibilities for scholars and practitioners seeking fresh approaches for leadership studies for
a Knowledge Era. Whilst there are many contemporary scholars already doing so it is also clear that the
ontological shifts are not easy and that archaic mindsets are difficult to dislodge even in light of wicked
problems like the Global Financial Crisis of 2008 or environmental disasters.
Introduction
This exposition considers current perspectives underpinning contemporary leadership studies given we are
located in what Hawking describes as the ‘century of complexity’ 17 (p. 29). Indeed Berman & Korsten 8 find
complexity to be the ‘greatest challenge’ facing leaders in the short to medium term:
The world’s private and public sector leaders believe that a rapid escalation of ‘complexity’ is the
biggest challenge confronting them. They expect it to continue, indeed, to accelerate in the coming
years. They are equally clear that their enterprises today are not equipped to cope effectively with
this complexity in the global environment (p. 3).
The paper begins with a preamble providing contextual factors for the work of leadership in the early part of the
21st century. In particular these contexts focus on the mindsets and hegemony of the Industrial and Knowledge
Eras to tease out assumptions underpinning leadership studies and practice today. Next, the context of trying to
seek sense of the work of leadership as sites of social complexity is explored with particular emphasis on the
work of Paul Cilliers as a pathway for turning social complexity theory towards leadership studies. This is
followed by considering leadership approaches that have been illuminated by social complexity theories. These
are more fully explained through the introduction of a speculative typology, constructed as a sense seeking
frame to encourage ‘working with’ such contexts rather than trying to tame and order them, named as Worldly,
Sustaining, Leadingful, Relational and Learningful Leadership Literacies.
The paper draws upon a PhD study that began just prior to the Global Financial Crisis in 2008. This interpretive
Emergence: Complexity & Organization
1
inquiry identified cogent leadership literacies for the 21st century and explored them within Australian
university settings. I was initially drawn to my research, as a practice-led researcher, by “a sense of a problem,
of something going on, some disquiet, and of something there that could be explicated” 92 (p. 9) about the
detrimental effects on practice and people in light of yet another restructure.
The concept of knowledge intensive work, workers and the place where this occurs is encapsulated in the term
‘knowledge intensive enterprise’. The provenance for these can be traced to Peter Drucker 25. More recently
Knowledge Intensive Firms (KIFs) have been characterized as places:
where symbolic work—using ideas and concepts—is crucial…Theory-guided cognitive activity is
important—or at least makes a difference—in more situations and for more people in a KIF than in
other organizations 2 (p. 18).
The disquiet that brought me to the study led me to focus on contestations of mindsets the consequences of
these clashes playing out in my workplace. My interest centres on what lies beneath the surface in terms of
epistemology, ontology, axiology, power relations and cognition within periods known as the Industrial Era,
Information Age and Knowledge Era.
Preamble: Different eras, mindsets and hegemony
The Industrial Era mindset was grounded in scientist standpoints and mechanist metaphors that privileged
rationality and certainty—and for good reason—such understandings were congruent with conditions of their
time. However, today’s knowledge intensive enterprises do not relate to mechanistic metaphors for work or
leadership.
This is because today’s conditions are more likely to be described as volatile, uncertain, complex and uncertain
(VUCA) 60 than certain, stable and linear. In relation to leadership studies many have already questioned
rationalist models underpinning scientist based research methodologies 61,64,105 theories of leadership and
management based on scientific management 72,3,42,33,95 and, power relations behind of economic neo-
liberal ideologies underpinning globalization and governance 27,29. A conflation of these concerns is expressed
in Uhl-Bien’s critique of scientific management, where she reasons:
… science came into the field of management to help legitimize the field. But what has happened is
that the sciences in the field of management are actually outdated. While we think we are doing
science we are very outdated relative to where the real state of science actually is 105 (9min:35sec).
It is timely therefore to pause and reflect upon contested mindsets and make space to “unlearn certain
ingrained habits of representation in order to enact theory differently” 28 (p. 525). The purpose of five
Knowledge Era Leadership Literacies to follow is to capture the essence of shifts towards Knowledge Era
mindsets and how these might be different or similar to leadership literacies that served in the past. The idea of
leadership literacies is a highly contestable term given the strong connections between language and power
and that any language rarely comes into common use without struggle. Given its support in the literature 84,79,
52 and my own experience with expanded notions of literacies, I made these connections visible in my on
research to explicate the relationship between language and literacies, and how metaphorical and literal
meanings of words and actions may be comprehended differently through lenses of Industrial or Knowledge Era
mindsets. Such fluency also surfaces the need for some degree of translation, where for example, displays of
humanity such as vulnerability or concern for others may be viewed as signs of weakness or strength,
depending on our underlying worldview 23.
Emergence: Complexity & Organization
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In the next section, sense seeking frames that heed calls to ‘do leadership differently’ 10 are discussed.
Making sense of the work of leadership as sites of social complexity
Grint’s 48 notion of ‘leadership as social construction’ recognizes the impact of social complexity as context.
Upon this argument Grint theorizes a more expansive standpoint for leadership:
… put another way, we might begin to consider not what is the situation, but how it is situated … [to
argue that leadership] depends upon a persuasive rendition of the context and a persuasive display
of the appropriate authority style…rooted in persuading followers that the problematic situation is
either one of a Critical, Tame or Wicked nature (p. 1477).
Grint’s work, especially when combined with the Kets de Vries 63 idea that “leaders are in the business of
energy management” (p. 111) opens up possibilities to think afresh about the nature of leadership studies at
this time. Emerging themes illuminated by Grint, Kets de Vries, social complexity theories and the five
Knowledge Era leadership literacies, for example, invite discussion about what ‘doing leadership differently’
might look like. I argue that such conversations may elicit more humane leadership practices than those
afforded in the 19th and 20th centuries. This shift is cognisant of more expansive notions of the field, a more
‘worldly’ standpoint which integrates responsibilities for human and ecological sustainability which run counter
to adversarial power relations. These perspectives privilege notions of ‘soft power’ 73, reciprocity and cooperation.
Social complexity and human relations theories are deeply entwined and can be traced back to at least the
beginning of the 20th century. Indeed, many of the ideas emerging today within leadership studies have their
roots in the work of Mary Parker Follett and the Human Relations Movement 70,37,38,4 which emerged to
counter the excesses of Scientific Management 100. While largely unprivileged for most of the 20th century,
their contribution is being reimagined today for leadership studies. Indeed social complexity theories, notions of
‘leadership as energy management’ (which has also been traced back to Follett) and relational leadership
approaches all serve to make these human and social systems visible.
These approaches offer ways to not only ‘see’ possibilities for leadership studies and practice but also for
leadership methodology. This suggests that both the leadership-as-subject as well as ways to research
leadership are all amenable to complexity theories as sense seeking frames. The work of Ralph Stacey 98,97,46,
47 is congruent with these aims, particularly his understandings of how knowledge production emanates from
complex responsive processes, that:
aims to move on from systems thinking about learning and knowledge creation in organizations to
argue that knowledge arises in complex responsive processes of relating between human bodies,
that knowledge itself is continuously reproduced and potentially transformed. Knowledge is not a
‘thing’ or a system, but an ephemeral active process of relating 98 (p. 3).
Tim Harle 54 explores the scope of complexity theories well beyond social complexity to make links with
relational aspects of leadership. In an extensive review he argued that complexity generally provides insights
for leadership because of connections to living systems, self-organization and emergence. Harle highlighted
Mandelbrot’s 69 fractals theory of ‘repeating patterns in nature observed at different levels’ as relevant to
leadership studies:
… with our focus on living systems, it is naturally occurring patterns that provide a vivid window into
Emergence: Complexity & Organization
3
the world of leadership. In particular, the idea of repeating patterns at different levels provides a
helpful conceptual framework for study global and local—or worldly—leadership 54 (p. 37-38).
The intent of this paper is to connect leadership studies and social complexity theories that are already
emerging in the literature. For example these explorations already include Complexity Leadership Theory 108,
106,107 evaluation of new paradigms of leadership development 62, Dynamic Complexity Theory, 15 Complex
Adaptive Leadership, 74 and Chia and Holt’s 16 reconceptualizations of strategy.
Social complexity as context allows consideration of the turbulence our times without expecting guaranteed,
certain, or ‘right’ answers. It is possible to work with these conditions, rather than succumb to threat rigidity
[depicted as a “failure to alter responses in face of environmental change” 99(p. 501)], pretend they do not
exist, or think they are someone else’s problem. To make sense of these understandings requires ontological
and cognitive shifts of mindset that more closely match the requisite variety of conditions of our times and that
strive to be “efficaciously adaptive” 9.
One approach that takes up calls to do leadership differently, in terms of both methodology and theory is Fullan
and Scott’s notion of ‘ready, fire, aim’ within the domain of leadership studies in higher education where they
argue that:
… shifting the focus to strategic thinking requires a considerable change in culture. To survive and
thrive in uncertain context of the 21
st
century, universities have to shift from a propensity to engage
in ready, ready, ready (have a subcommittee, conduct a review, etc.) to ready, fire, aim—a process
in which ready is a need to act, fire is to try out a potentially viable response under controlled
conditions, and aim is to articulate what works best and scale this up once it has been tested and
refined 43 (p. 29).
This is one example of many perspectives amenable to working with contemporary leadership challenges that
become visible through lenses of social complexity. This ‘ready, fire, aim’ approach sums up the research
position I took up in my own research. In so doing the construction of speculative typography named as the five
leadership literacies, to be discussed in this paper, represent the ‘ready’ position of their argument, that is the
‘potentially viable response under controlled conditions’ that were then ‘fired’ to explore for meaning and
congruence as theorized and practiced in Australian tertiary education management.
Therefore I position social complexity theories as viable frames with which to view contemporary leadership.
However, great care is needed to ensure that these are not read as a panacea to fix, tame or otherwise apply
certainty to current volatile, complex and ambiguous conditions. Rather social complexity theories provide “an
alternative way of legitimizing the current interest in boundary critique, creativity, and pluralism” 81(p. 17).
Cilliers’s legacy: Modest claims for knowledge
Emergence: Complexity & Organization
4
One particular way of addressing this interest is to explore Paul Cilliers work, in particular his call for modest
claims for the reach of knowledge, one of many contributions he made to the before his untimely death in 2011.
Indeed, his elegant distillation is drawn from many decades working at the interface of complexity theory,
critical theory and ethics saw him argue that we can only ever make modest claims for the ostensibly
unknowable conditions; that scholars need to be careful about the reach of our claims; and, need to be mindful
of the constraints that make these claims possible 18. This is a salient legacy with which to ground leadership
studies for knowledge-intensive enterprises upon. Further, this approach is an eloquent critique of the
continuing dependence upon rationalist ontology when current realities are more likely to framed by
uncertainty, unpredictability and complexity than certainty or order.
Knowledge claims, framed this way, are understood to be declaratively modest in the face of ostensibly
unknowable conditions and we are asked to take care to “describe reflective positions that are careful about the
reach of their claims being made and of the constraints that make these claims possible” 19 (p. 256). Here the
term modest signifies a position of strength when viewed through a social complexity lens and illustrates the
power of language to surface underlying mindsets. Such nuanced arguments for the relevance of mindfully
modest claims are a welco…

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