Changing the rules: The implications of complexity science for leadership research and practice

Complex Systems Leadership Theory

Hazy & Uhl-Bien 2/11/2012 Page 1 of 64

Changing the rules: The implications of complexity science for leadership

research and practice

James K. Hazy Adelphi University

Department of Management, Marketing and Decision Sciences

Mary Uhl-Bien University of Nebraska

Professor and Howard Hawks Chair in Business Ethics and Leadership Department of Management

January 30, 2012

Please do not quote prior to publication. Cite as:

Hazy, J. K. & Uhl-Bien, M. (in press). Changing the Rules: The implications of complexity

science for leadership research and practice. David Day (ed.) The Oxford Handbook of

Leadership and Organizations. Oxford: Oxford University press.



Changing the rules: The implications of complexity science for leadership

research and practice

James K. Hazy Adelphi University

Department of Management, Marketing and Decision Sciences

Mary Uhl-Bien University of Nebraska

Professor and Howard Hawks Chair in Business Ethics and Leadership Department of Management

ABSTRACT

The study of complexity has become an important lens through which to view and

understand the causes and potencies of individual action and interaction in organizations as well

as their meaning for leadership research and practice. This review of key complexity ideas and

their theoretical implications for leadership describes emerging theories in the field, highlights

the growing empirical support for these approaches, and sets an agenda for future research. The

thesis averred is this: Just as complexity has become an overarching theoretical paradigm in the

natural sciences, it is providing the basis for a paradigm shift in the social sciences, particularly

in leadership and organizational studies. Complex systems leadership theory describes the

process whereby the rules governing local interactions are changed in response to and

anticipation of changing circumstances. In shifting the focus from the individual to the

organizing process itself, the complexity leadership perspective has important implications for

both research and practice.

Keywords: Complexity leadership, complex systems leadership theory, human interaction

dynamics, generative leadership, adaptive leadership, unifying leadership, adaptation,

innovation, chaos theory



INTRODUCTION

Complex Systems Leadership Theory (CSLT) is about interactions and emergence. It is

about events and how these shape future action (Lichtenstein et al., 2006), and it is about how

human activity is organized into a system of choices and actions when organizations are

considered to be complex adaptive systems (Marion & Uhl-Bien, 2001). By describing an

overarching dynamic theory of human organizing, CSLT transcends traditional approaches to

leadership research by offering a theoretical framework within which prior results can be better

understood, evaluated, and integrated into a common view of how human agency drives

collective performance and adaptation.

In the complexity approach, “leadership” is not considered to be a person or persons.

Rather, it is the recognizable pattern of organizing activity among autonomous heterogeneous

individuals as they form into a system of action (Lichtenstein et al., 2006; Hazy, Goldstein &

Lichtenstein, 2007a; Uhl-Bien, Marion & McKelvey, 2007). At the same time, for organizing to

occur, leadership must perform certain functions, what Katz and Kahn (1966) called the

“influential increment.”

Interpreting Katz and Kahn (1966), Hazy (2011a) argues that when human interactions

are considered as complex systems, leadership performs three functions as it organizes human

activity. First, it influences human interactions in ways that unify individuals into organized

groups. This includes what might be called the strategic functions, such as setting vision and

strategy (Boal & Schultz, 2007) and establishing identities and ethics (Hazy, in press-a). Second,

leadership changes the rules so as to generate a variety of ideas and plans of action (Hazy, 2006)

as a mechanism for adapting to changing circumstances (Uhl-Bien et al., 2007). Creativity and

problem solving (Guastello, 2007) are important elements of this function, as are the constraints



on action that enable the emergence of novelty (Goldstein, Hazy & Lichtenstein, 2010). Third,

rules are changed in ways that enable the convergence of disparate, sometimes conflicting

individual perspectives, preferences and activities into effective and predictable collective action

(Phelps & Hubler, 2007; Dal Forno & Merlone, 2007; Hazy, 2008b). In sum, leadership is about

changing the rules that guide individual choices and interactions. In complex adaptive systems,

“changing the rules of interaction” locally can also change organizational outcomes more

globally. CSLT studies this process.

Individuals, of course, enact pieces of this functional puzzle, and as such, the complexity

paradigm implies certain things about individuals and their capacity to succeed as they engage in

leadership situations (Hannah, Eggers, & Jennings, 2008; Lord, Hannah, & Jennings, 2011). But

isolated individual behaviors are not leadership per se. Leadership is in the whole; it serves to

form, sustain and grow the system, just as product development or accountability processes are

parts of the whole system. No one person is “governance”; likewise, no one person is

“leadership.” At the same time, individuals must enact the leadership process just as they enact

other organizational processes. In this sense, CSLT offers a systems’ perspective within which

traditional views of leadership that include individual skills and actions can be integrated into a

process perspective (Hazy, 2011b).

For the purposes of the analysis herein, we define complex systems leadership as system

processes that change the rules of interaction and do so in specific ways that form human

interaction dynamics (HID) into a complex adaptive system (Hazy et al., 2007) in a manner

analogous to how physical and biological interactions are understood as systems. Core to

complexity is the realization that the rules governing the individual human interactions of day-to-

day experience are what determine the social structures that emerge (Goldstein, 1989, 2007,



2011; Holland, 1975; McKelvey, 2004). These emergent forms can sometimes be recognized as

stable properties, and when they are, they can be evaluated and managed within a particular

economic, political, social and technological context.

However, interventions cannot directly cause outcomes to change. Emergent forms can

only be affected by judiciously changing the local rules that govern the interactions of others and

from which the relevant outcomes emerge (Goldstein et al., 2010). It is within the nexus

connecting local rules to emergent forms that leadership gathers its potency. This is why

leadership is central to human experience. It is important for both organizing to succeed as a

group, and as a means to enable individual success through others. Both of these have been the

focus of leadership research. CSLT connects these complementary aspects of leadership within a

systems framework.

Leadership guides both performance (i.e., “exploitation,” March 1991) and survival of

the system in the short-term, and adaptation (i.e., “exploration,” March, 1991) and thus its

prospects for survival over the longer-term. It does this by processing and using information

gathered as feedback from the environment and from within the system itself (Gell-Mann, 2002).

It senses this feedback and channels it to individuals who are in a position to use the information

it contains to find new ways of organizing in an effort to acquire, store and allocate resources of

all types. As such, complex system leadership theory (CSLT) transcends and integrates prior

research and offers a platform for understanding leadership in fundamentally new ways.

Thesis and overview

Our thesis is summarized as follows: Just as complexity has become an overarching

theoretical paradigm in the natural sciences, it is serving as the basis for a paradigm shift (Kuhn,

1962) in the social sciences, particularly in the areas of leadership and organizational studies. By



shifting the focus from the individual to the organizing process itself, a key value of complexity

is its strong implications for practice: Individual action must be considered in nonlinear systems

terms. In the fast-changing global ecosystem, approaches to management grounded in linear

assumptions may overly emphasize applying controls on interactions, thus failing to stimulate

information flows, learning and growth. New techniques that exploit nonlinearities and embrace

fast-paced interaction are needed.

To explain this, we begin with some of the challenges a complexity paradigm brings to

the field. We then describe how complexity thinking is applied to theorizing about leadership,

the growing empirical support for this approach, and the new methods that might change the

research process going forward. Because one of the key ideas of complexity is the continual

unfolding of newness, we conclude by looking ahead.

COMPLEXITY BRINGS CHALLENGES

The paradigm shift (Kuhn, 1962) towards complexity in leadership research brings with it

certain challenges, particularly to those who have traditionally seen leadership as something to

be admired in, or executed by, especially gifted or specially trained individuals. In this mindset,

individual “leaders” cause things to happen. As these traditional observers might see it, when

organizing is needed, that is, when one observes that leadership is necessary, this “leadership

vacuum” is translated into the idea that someone, some person, should “step up” to “take charge”

causing something to happen (MacGillivray, 2010).

With complexity, however, the scenario is different. While the need for leadership

remains, causality—at least much of the causality that really matters—is assumed to be indirect

and diffuse (Streatfield, 2002). When circumstances require leadership, individual observations

and experiences, connections and shared values, relative status, and the interaction dynamics



themselves, force the issue until leadership structures that cross levels-of-analysis emerge

through the constructive actions of individuals (Goldstein et al., 2010). These leadership

structures provide downward influence on individual interactions even as they are themselves the

emergent outcome of those same interactions, in a dynamic process that complexity pioneer

Haken (2006) calls circular causality. Certainly people “step up,” but they are not seen as

providing leadership for others. Rather, they are seen as being drawn into the leadership process

along with others. The system properties that begin to emerge are then sustained, evolved or

replaced within the system of interaction, as leadership unfolds dynamically (Panzar, Hazy,

McKelvey & Schwandt, 2007).

One difference in this perspective stems from the fact that complexity science has found

that the order we observe and are able to create in the world manifests itself simultaneously at

multiple levels of scale. Not only do unique dynamics unfold within individual interactions, but

also at the group level, the department level, the firm level and the institutional level. Each of

these levels, provides feedback to all of the other levels, influencing the dynamics of the others.

These changes in turn feed back once again to the other levels, and so on in an ongoing adaptive

spiral.

In short, feedback is all over the place and in all kinds of directions, making it

exceedingly difficult to meaningfully control events or to cause specific outcomes in the

traditional sense (Tobin, 2009). This would seem to account for the common practice in

management to attempt to control events by containing the flow of information and thus limiting

all diverging nonlinear effects that often accompany reinforcing feedback in complex systems.

Because a lot is happening at once, this logic goes, if one doesn’t control events and information

flows there can be unintended consequences, and it is better to stop them before they happen

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rather than risk that they might challenge one’s assumptions about strategy and direction. CSLT

suggests ways for thoughtfully relaxing control and letting constructive deviations build upon

their successes, while identifying and dampening destructive deviations before they threaten to

pull the organization apart.

A note on terminology: To help clarify for the reader where we are focusing discussion in

this paper, we will follow Gell-Mann (2002) and use the term “fine-grained” to refer to

individuals interacting with one another, and “coarse-grained” to refer to the higher scale

properties of organizations that are of interest to leadership researchers, such as profits,

employee turnover, regulatory regimes, or even the leadership capability itself. In truth,

however, the experience of leadership floats between the fine- and the coarse- grained levels,

crossing scale and effectively becoming scale-free (Boisot & McKelvey, 2007). Organizational

life thus challenges people to act both individually and collectively at the same time in the face

of this complexity. We describe some of these challenges next.

Stability and Attractors: Coarse-Grained Prediction with Fine-Grained Uncertainty

One of the key insights from complexity science is that organizing, and thus leadership,

drives change at the fine-grained level of individual human interaction. Leadership does this by

changing the rules that govern the nature of connections and exchanges between individuals.

From these fine-grained interactions, persistent patterns emerge as coarse-grained system

properties that are sometimes quite stable at a higher level of scale. For example, warehouses and

logistics processes that at one point emerged almost by chance may remain at the center of stable

activities for months, years or even decades, attracting behaviors to them (Allen, 2001). Stable

properties like these can be recognized and modeled as organizational capabilities (e.g., the

logistics example we mentioned, but also accounting or customer service) (Dosi, Nelson, &

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Winter, 2000; Helfat et al., 2007). To change these coarse-grained properties of organizations,

however, one must first change the rules governing fine-grained interactions of the people who

are implementing them.

CSLT defines leadership in complex adaptive systems as the social process that changes

the rules of interaction across levels of analysis, i.e., among individuals, work groups,

departments, organizations, and institutions. The leadership meta-capability in particular, is

defined as the routines, knowledge management, and decision making processes (Helfat et al.,

2007) that serve the coarse-grained function of changing the rules of interaction inside the

organization or the broader system (Hazy, 2004, 2006; Hazy, Goldstein & Lichtenstein, 2007b).

Thus, coarse-grained properties are changed only indirectly: they change when the rules of

interaction are changed at a finer-grained scale. This is an intuitive result: one must change the

way that people do what they do in order to change the outcomes they produce. CSLT seeks to

discover and specify the mechanisms, both direct and indirect, that enable this top-down/bottom-

up iterative process in real organizations, as well as how individuals learn to recognize and

become proficient in enacting this capability.

As alluded to above, stable coarse-grained properties are often associated with a

dynamical attractor within the system of HID, like for example, the logistics capability

described previously (Allen, 2001; Hazy & Ashley, 2011). This complexity term means that

there is a subset of all possible coarse-grained states of the system such that the state is “sticky,”

meaning that these attractor states effectively “pull” the system back to their original state if

something disturbs its normal functioning. After a storm that destroys some logistics equipment,

for example, activities are “automatically” enacted to return to the “normal” state. As an even

more general example, firms that maintain their profitability can be stable for a time. When

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profits are threatened, they take action to return to profitability. At the same time, firms that lose

money will eventually implode or disband. They are not dynamically stable.

Patterns of activity that enable stable profitable operations (i.e., “exploitation,” March

1991) form an attractor of coarse-grained properties. These in turn drive what is happening at the

fine-grained interaction level. As individual behavior patterns and choices at the fine-grained

level converge towards a pattern of interaction they reinforce the coarse-grained organizing form

in what amounts to an iterative stabilizing feedback process that extends back and forth across

levels of scale (Hazy & Ashley, 2011). Convergent interactions that enable stability and thus

predictability are achieved through information feedback processes, wherein coarse-grained

structure provides information to individual agents, and their actions in turn influence the

specific characteristics of the coarse-grain structures in order maintain their relative stability

(Hazy, 2011b).

These feedback loops, both positive or reinforcing and negative or stabilizing, can shape

the emergent, dynamically stable coarse-grained state of the system. In turn, coarse-grain

stability can imply fine-grained choices and actions, such that their emergent outcomes become

to some degree predictable in the aggregate even though any particular event remains difficult to

predict, a circumstance that is called statistical complexity (Prokopenko, Boschetti, & Ryan,

2009). For example, one roughly knows what to expect when entering a retail store, a coarse-

grained structure. At Wal-Mart, a greeter will greet you somewhere with high probability. At the

same time, there is no way to predict exactly when or where such an event will occur. Thus,

although there is a level of stability and predictability at the coarse-grained level, there is always

unavoidable uncertainty at the fine-grained level. Individual interactions are neither random nor



completely predictable. They are constrained, but not determined, by attractors (Hazy & Ashley,

2011; Hazy, 2011b).

Emergence: Fine-Grained Prediction with Coarse-Grained Uncertainty

On the flip side of the comfortable stability and general predictability of familiar coarse-

grained aspects of organizational life is the reality that things sometimes change. When this

happens, old coarse-grained institutional structures must change as well. This implies a

conundrum: If coarse-grained properties are recognizable patterns that emerge from within

individual interactions, how does adaptive change actually happen at the coarse-grained level

when human interaction is experienced and predicted at the fine-grained level? In evolutionary

systems, this occurs through the process of variation, selection and retention among genetically

related but distinct entities over many generations. In cognitively-enabled systems, this occurs

through an intra-generational learning process whereby organisms learn to respond to stimuli in

their environment in a single lifetime. CSLT offers a framework that describes how

organizations both evolve through variation, selection and retention over many generations and

also learn to adapt within a single organization in a given generation. How well organizations

learn impacts their ability to survive and thus to contribute to the evolution of organizational

forms (Hannan & Freeman, 1989).

The process wherein organizing forms evolve and learn is the purview of a key area of

complexity research: emergence. Much has been written about emergence as a general matter in

complexity (Goldstein, 2007, 2010; Lichtenstein & McKelvey, 2007) as well as with regard to

leadership (Plowman et al. 2007a,b; Lichtenstein & Plowman, 2009). The idea is that under

certain exogenous constraints, a changing system of fine-grained interactions can cause the

emergent coarse-grained properties that are observed to undergo a qualitative transformation in



their coarse-grained patterns and structure. Examples of this phenomenon that are taken from the

natural sciences—such as the phase transition from liquid to gas—have provided metaphorical

insights for leadership researchers.

One important natural science example of emergence is the appearance of what are called

dissipative structures (Prigogine, 1995; Prigogine & Stengers, 1984; MacIntosh & MacLean,

1999). An occurrence of this phenomenon is described in some detail by Goldstein et al. (2010)

to illustrate how coarse-grained structures emerge in quasi-closed systems of fluids during what

are called far-from-equilibrium conditions. In the case described, this type of emergence happens

when heat is continually applied to the bottom of a closed container of liquid. As the intensity of

heat crosses a certain critical threshold, internal fluctuations interact with one another in the

presence of the exogenous force of gravity to cause an observable qualitative shift in the coarse-

grained behavior of the system.

The system’s dynamic behavior rather suddenly shifts from a relatively calm state where

heat is transferred by the mechanism of conduction to an orderly state of circular flow that

transfers heat through the mechanism of convection. As this occurs, the emergence of persistent,

coarse-grained hexagonal convection cells can be observed. Prigogine (1995) calls these

“dissipative structures” because convection dissipates heat more quickly than conduction does.

The onset or settling down of this qualitative change in structure can be “toggled” by the

experimenter by increasing or decreasing the heat applied to the bottom of the container. Thus,

changing patterns in fine-grained interaction behavior among the molecules can be seen to relate

to a qualitative change in the coarse-grained properties that emerge in the system, and these are

themselves shaped by external constraints on the system, the shape of the container for example.



Another example of emergence relates to what Haken (2006) calls “order parameters”

that emerge through a process of circular causality (Haken, 2006). Using the example of a laser,

Haken shows how under the right conditions the light waves emitted by individual atoms

eventually synchronize their phases as represented by an order parameter. This occurs as

constructive interference reinforces a particular phase determined by interaction with external

constraints while destructive interference dampens others that are not reinforced by the

environment. This means than bottom up processes can lead to emerging order. And emergent

order places downward pressure on bottom up events, “enslaving” them to be in phase with the

order parameter, a process called “entrainment.” This is what is meant by circular causality.

Related to the above, is the subset of systems whose order parameters characterize phase

transitions. This well-studied phenomenon of circular causality in natural systems involves a

change in physical state as energy that is being added to the system modifies the internal

structure of the system to change how the system processes energy and information. Water

freezing, iron becoming magnetized, and the onset of superconductivity are examples of this.

Well-established mathematical models describe how external constrains—for example, ambient

temperature and pressure in the case of water freezing—interact to influence the “order” that

emerges within the system of interactions. The progress of these changing dynamics is described

by mathematical models that relate an order parameter to the change in state (Goldstein et al.,

2010).

Common to these examples is the idea that the breakdown of order within extant coarse-

grained properties is a prerequisite for emergence. This condition has been called the “edge of

chaos” (Kauffman, 1995; Mitchell, Hraber, & Crutchfield, 1993), “far-from equilibrium”

(Prigogine, 1995; Meyer, Gaba, & Colwell, 2005) and “criticality” (Bak & Paczuski,1995) in

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varying complexity situations. To emphasize the dynamic nature of this condition and of its

potential to enable a qualitative transition from one stability regime to another, we prefer the

Goldstein et al. (2010) term for the onset of these conditions: criticalization.

The term criticalization highlights the potential for change when a certain parameter

crosses a critical threshold, what is called a bifurcation in mathematical modeling. In the case

where complex systems of human interaction dynamics (HID) experience adaptive tension that

pushes the system beyond a critical threshold, Uhl-Bien, Marion, and McKelvey (2007) describe

the requisite complexity of the system as a prerequisite for the onset of emergence. The

interaction between the complexity present in the environment and that which develops within

the system has been codified by Boisot & McKelvey (2010) in what they call “Ashby Space,” to

recognize Ashby’s law of requisite variety (1956).

The challenge when going beyond complexity metaphors to develop a theory of

emergence in human systems is to explicate the emergence phenomenon in the HID case while

taking into account the differences inherent in human interactions when compared to physical

systems. Prietula (2011) describes some of the practical differences in the context of agent-based

modeling. Hazy and Ashley (2011) explore the implications of these differences when

developing a theory of emergence in HID.

According to these researchers, the difference boils down to this: In contrast to physical

systems where many agents of a particular class (like water molecules) interact with one another

in the same way, human interactions are heterogeneous, each being determined by individual

preferences, personal histories, social connections and perceived difference in power and status,

all of which are stored in the individuals’ memories. Prediction becomes problematic since these

memories are largely hidden from the observer.

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Further, human connections are interdependent rather than independent since individuals

incorporate into their choices not only information from their direct experiences, but also

information and knowledge that is received through communication with others. This

interdependent heterogeneity implies that many of the statistical methods used in the natural

sciences are not appropriate in the human case since traditional methods assume independence

and consistency across time. In many cases, neither of these assumptions is valid with human

beings (Hazy & Ashley, 2011). As a result, the development of a well-specified and robust

model describing the mechanisms of emergence in HID remains an ongoing challenge for the

field (Hazy, 2011b).

Complexity: A Journey from Novelty to New Paradigm

In the late 1980s and 1990s, complexity thinking for organizations had not yet come into

sharp focus. Still the application of complexity was all the rage within the social sciences

(Anderson, 1999; Cilliers, 1998; Dooley, 1997; Goldstein, 1989; Levinthal, 1997; McKelvey,

1997; Thietart & Forgues, 1995). Exotic concepts that were discovered in the natural sciences,

like chaos, strange attractors and the possibilities implied by emergence - where entirely new

order springs forth seemingly in whole cloth - led researchers to look for complexity applications

in management and human organizations (Wheatley, 1999). Initial interpretations of complexity

often led to an unfortunate tendency to recommend to practitioners a version of laissez-faire

leadership - arguing that a hands-off style was all that was needed and that employees would

simply “self-organize” to solve business problems.

In contrast, Marion and Uhl-Bien (2001) saw the promise of complexity as beyond

metaphoric and suggested specific areas for theoretical exploration and empirical research. This

article follows in their direction and that of others (Schneider & Somers, 2006) and reviews what

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has happened since these early days, and it explores what these activities might mean for the

future of leadership research and practice.

Ontological and epistemological issues

Relevant to the complexity framing of business and organization has been the distinction

between ontology and epistemology. The philosophic challenge is to determine the extent to

which the complexity mindset is a reflection of what is real in the world (that is identifiable

through observation - ontology) versus the extent to which complexity is just a new or different

way of knowing or understanding what is happening or perceived to be happening in the world

(Boisot & McKelvey, 2010). In this latter way of thinking, complexity is just an analytical “tool-

kit.”

The distinction is not a trivial one. It exposes the question of how human beings deal with

experienced complexity. As this paper explores, some interesting questions this implies include:

Is the world a complex system? Is it essentially a computation that is unfolding (Dooley, 2007;

Richardson, 2010)? If so, what are its algorithms? Are there better, more complex ways to model

and predict the world? How does complexity science inform the study of cognitive

neuroscience? What about the study and practice of psychology? And of course, what can it tell

us about study of leaders and leadership? These concerns can be considered from various

philosophical perspectives, for example, as constructionist, constructivist, objective realism,

critical realism, etc.

From the practical perspective of epistemology, the increased use of nonlinear techniques

and probabilistic prediction models in management are aspects of the complexity revolution in

the social sciences that have already established their value in practice. Monte Carlo analyses,

information theoretic approaches, game theory modeling, and system dynamics approaches are




all now in the mainstream decision support tool-kit in business. All of these are complexity-

informed tools that support managers in their decision-making. The adoption of these tools,

presumably because of their usefulness, supports the notion that ontologically, human

organizations appear to act like complex systems. What is missing is a holistic modeling

approach - something analogous to a statistical mechanics of human interaction dynamics (HID)

- that would represent and study organizations using complexity theories framed in a useful

epistemology. Such an approach would be invaluable to practice as it would allow managers to

forgo their need to apply controls, which can dampen both learning and growth in the service of

a perceived sense of stability and predictability, a practice that can be counterproductive.

One additional point is relevant here. There are particular ontological implications of the

complexity notion of emergence. Does an organization exist ontologically, in other words, does

it have agency? There is an argument that insect colonies do in fact exhibit ontological agency.

Tens of thousands of honeybees swarm as a collective to find a new nest without central control.

Choosing a proper nest is critical for survival of the swarm, and thus for evolutionary adaptation.

This is also true for the individual bees, all of which carry the DNA of the queen (Seeley, 2010).

Can the same argument be made for a firm or a nation? Does the diversity of DNA in human

organizations make the super-organism argument untenable (Nowak, Tarnita, & Wilson, 2010)?

This is a fundamental question for leadership researchers. Stated differently, is

anthropomorphizing the organization wrongheaded, or forward thinking? Do individuals actually

“lead” organizations, or is something else going on, something akin to the emergence of

collective agency?



COMPLEXITY APPROACHES TO LEADERSHIP RESEARCH

With the above background, we now turn to the various applications of complexity to

leadership. Although there are copious studies that apply conceptual ideas to leadership

metaphorically, such as the idea of fractals (Levick & Kuhn, 2007), the onset of chaos (Brown &

Eisenhardt, 1998), emergence and attractors (Shoup & Studer, 2010), and sensitivity to initial

conditions (Peterson & Meckler, 2001), others have cautioned against such metaphorical

applications (Simpson, 2007; Goldstein et al., 2010). Moving beyond metaphor, we focus only

on the approaches that uniquely assume a complexity ontology or that apply one of the

complexity inspired epistemologies: agent-based modeling (ABM), dynamic network analysis,

nonlinear dynamical systems (NDS) modeling, or information theoretic framings such as game

theory, and non-Gaussian statistical methods. (For a description of these methods see, for

example, Hazy, Millhiser & Solow, 2007, Boisot & McKelvey, 2007, and Guastello, 2002).

Because we are for the most part assuming a complex systems perspectivei, presumably the

studies of emergent properties within HID would unfold in a manner analogous to dynamical

systems models of weather patterns, ecological models or epidemiology studies, or in the way

that agent-based models represent conditions of criticalization or uncertainty that signal an

impending phase transition, for example when a system changes its properties qualitatively—as

when water freezes into ice.

As Goldstein et al. (2010) point out, in human systems, situations of “criticalization” occur

when conditions in the environment combine with the state of the organization to create

uncertainty and unpredictability about where things are going. Researchers studying knowledge

generation in organizations describe the unfolding of plans and strategies during everyday

practice (Tsoukas, 2005). Often different individuals are observed espousing multiple different

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plausible futures, and individuals are left to decide which approach is likely to “win” in the end

(Hazy & Ashley, 2011). Other authors have referred to conditions like this as “disequilibrium”

(Lichtenstein & Plowman, 2009) and identified these conditions as a prerequisite for emergence.

Given how little is known about the relationship between fine-grained interactions and the

emergence of coarse-grained properties (Hazy, 2011b), we first explore how leadership can be

relevant at the coarse-grained level. We begin with an overarching model as context for the other

approaches we will describe.

The Leadership and Capabilities Model (LCM)

Although many articles begin with a statement that the authors are assuming organizations

are complex adaptive systems (Boal & Schultz, 2007; Brown & Eisenhardt, 1997; Levinthal,

1997), their use of complexity is usually limited to the assumption that semiautonomous agents

interact, and that organizations somehow emerge from this process. Little has been written about

the course-grained properties that emerge, how they emerge, and what leadership has to do with

this. The Leadership and Capabilities Model (LCM) developed by Hazy (2006; 2011a) addresses

this gap by explicitly describing human organizing as a complex adaptive system of interactions

that performs certain functions.

To address the cross-level definitional issue with regards to the term “leadership,” Hazy

(2006, 2011a) builds upon Katz and Kahn (1966) to define “complex systems leadership” to be a

special kind of organizational capability (Dosi et al., 2000; Helfat, et al. 2007) that performs

particular system functions wherein the human organization is formed and evolved as a complex

adaptive system. In particular, complex systems leadership is the organizational capability that

iterates changes to the system’s configuration (by changing local rules of interaction) to test its

performance in the environment. As such, complex systems leadership is not what individuals

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do; it represents an organizational capability in the sense described in business strategy (Helfat et

al., 2007): it is a set of routines, knowledge management, and decision making processes that

perform leadership functions in the same way that marketing and accounting are capabilities that

perform more instrumental system functions. All of these capabilities, including leadership, are

enacted by individuals and collectives in furtherance of functional requirements as the system

gathers and processes information, resources and energy to create order. Because complex

systems leadership reconfigures other capabilities, Hazy (2006) calls it a meta-capability.

Organizations with well-developed complex systems leadership meta-capabilities are able

to iteratively act on the system itself, changing the rules governing fine-grained interactions

within the system in response to success or failure (Hazy et al., 2007; Hazy 2011b). The coarse-

grained properties that emerge enable subsequent success of the system in changing and adapting

as the situation develops. The complex systems leadership meta-capability guides this process.

The idea of complex systems leadership as a meta-capability extends the work on

organizational capabilities, both dynamic and operational (Barney, 1991; Helfat et al., 2007;

Nelson & Winters, 1982; Teece, Pisano, & Shuen, 1997). However, in contrast to the capabilities

literature, individuals in the organization aren’t actualizing these capabilities; rather they are

enacting the leadership meta-capability of the organization that is acting on the system to test

configurations of other capabilities, and to identify those that work better than others.

Using the complex systems agent-based epistemology of system dynamics, Hazy (2004,

2006) built a system dynamics model of the leadership meta-capability that is called the

leadership and capabilities model (LCM). When the model is run, the leadership meta-capability

performs an iterated operation on the coarse-grained properties within the system that: i)

implements the exploitation (March 1991) of current capabilities, ii) promotes the exploration


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and generative process of new capability creation (March, 1991), and iii) unifies the system to

maintain it as an entity with regard to local and global criticalization conditions as required by

the environment. It changes the properties or capabilities that have previously emerged, and

presumably it does this by changing local rules of interaction among individuals. By changing

the rules, the properties characterizing the system, including its capabilities, also change.

Depending upon the context, the complex systems leadership operation acts on the system

to perform three functions: The convergent operation within the system adjusts the properties of

the system to make them more predictable (Hazy & Ashley, 2011) and thus improve

exploitation. Rules are changed to dampen deviations by increasing individual productivity and

leveraging cooperative activities with technology and other assets. This is called the exploitation

value-gathering loop (Hazy, 2011a).

The generative operation responds to changing constraints in the environment and promotes

exploration, collaboration, creativity and innovation in system properties (Hazy, 2004, 2006;

Surie & Hazy, 2006; Uhl-Bien et al., 2007). If changing constraints on the system implies that a

qualitative change in coarse-grained properties is needed, the system often passes through a

period of criticalization as requisite complexity is engendered. For this to occur, fine-grained

rules of interaction are changed to promote experimentation and to reduce or eliminate premature

convergence. Because a variety of possible futures coexist, this is a manifestation of Ashby’s

(1956) notion of requisite variety. The exploration value-identifying loop enables adaptation

(Hazy, 2011a).

The unifying operation uses communication and symbolic activities, such as policies and

boundary rules for the proprietary use of information, to more clearly specify acceptable and

expected rules for system properties by promoting locally stable collective identities and systems






of ethics (Hazy, in press-a). This tunes the level of criticalization and requisite complexity (Uhl-

Bien & Marion, 2009) both locally and globally within and across the organization.

One implication of the LCM is that, as is the case for other organizational capabilities,

individuals learn to implement aspects of the leadership meta-capability in the same way they

learn to implement manufacturing, customer service, business planning, or any other capability

(Hazy, 2007a, 2008b). The acquisition of leadership skills by individuals is the result of social

learning of the meta-capability within organizations, just as the acquisition of marketing skills

results from social learning in marketing organizations. Individuals are not born to leadership

competency; they learn how to exercise this capability by being involved in organizing efforts

that exhibit the coarse-grained property of an effective leadership meta-capability.

Leadership and Emergence

Given how little is known at present about the emergence of coarse-grained properties in

the unique manifestation of the distinctly human social context, it is at present difficult to offer

specific suggestions about how any given individual should behave. The process of emergence

has been explored, however. Lichtenstein and Plowman (2009) draw on case studies of

emergent leadership in organizational settings, the case of a mission church (Plowman et al.,

2007 a, b) for example, to argue that there are four phases in the emergence process. First there

must be disequilibrium, unstable conditions that were described in the earlier section as

criticalization. During these periods of uncertainty, reinforcing feedback is offered to certain

process fluctuations through amplifying actions that seem to offer promising new ways to bring

back stability. Hazy (2011a) calls these experiments in novelty “constructive deviations” because

they deviate from what had previously been the norm, but they do so intentionally and

constructivelyii The constructive deviations that are working are then combined with other



fragments of collective activities that are at work in other parts of the organization in a

recombination process. In this way, new structures grow through the gradual accretion of

constructive deviations that work. Eventually, stabilizing feedback that operates on this new way

of doing things—for example, lack of funds or time limitations, or a saturated market—bring the

organization back into a stable, albeit qualitatively different, approach to organizing. In the end,

one can observe that distinctly new properties (at the coarse-grained level) have emerged.

As further support for this classification, Beck & Chong (2009) identified these stages in

social entrepreneurship ventures they studied in Indonesia. Aspects of these phases were also

apparent to Butterfield, Shepherd and Woods (2011) in a social enterprise that was studied in

New Zealand, and Baker, Onyx and Edwards (2011) found evidence of recombination of

network components in a developing community of social enterprises in Australia. The next

stage of CSLT research in this area is to uncover the specific mechanisms at work in each of

these stages, that is, the mechanisms of emergence at work in HID (Hazy, 2011b).

A series of research projects in Sweden have provided some hints about these mechanisms.

These studies explored how first-line managers provide the pre-conditions to influence both the

interactions between individuals and the collective organizing which emerges from them

(Backström, 2009; Backström, Hagström, & Göransson, 2011; Backström, Wilhelmson, Olsson,

Åteg, & Åberg, 2011). Backström and his colleagues refer to this aspect of management as the

“directing task” which focuses on enabling a dynamical balance between the autonomy of

individuals and their integration into the emerging organizing structures. The communicative

competence of the employees and the reasons, places and times for them to meet and to

communicate were found to be important conditions for the emergence of structures like



collective culture and identity, institutionalized collective behavior, and patterns of work

relations (Backström, Wilhelmson et al., 2011).

As the above discussion highlights, at present it may be theoretical overreach to posit that

any one person is in control of the emergence process whereby the coarse-grained patterns that

characterize organizational capabilities come into being, at least as regards what is currently

known about complexity (Hazy, 2008b). The assertion that complexity implies a good deal of

ambiguity when linking fine-grained action to coarse-grained properties has found support in the

literature (Siggelkow & Rivkin, 2005). Morrison (2010) showed how the process of gathering

information, analyzing it, recommending approaches, and implementing projects can be a very

challenging undertaking for managers. When this occurs within a network of positive and

negative feedback loops, as is often the case, the challenge is even more daunting.

There is a point where predicting the resulting outcomes in the face of this nonlinearity is

problematic (Siggelkow & Rivkin, 2005; Morrison, 2010). Interdependent, heterogeneous

connections between individuals as they interact seem to make each case unique. This is not to

say these dynamics are not understandable in a general way, only that they are not yet

understood. We next describe work that focuses on discovering additional coarse-grained and

fine-grained leadership activities needed in organizations when they are considered to be

complex adaptive systems.

Complexity Leadership Theory

An approach that is compatible with, but varies somewhat, from the LCM model just

described is Complexity Leadership Theory (CLT) (Uhl-Bien et al., 2007; Uhl-Bien & Marion,

2009, 2010). Complexity leadership theory draws from concepts of complex adaptive systems

(CAS) to offer an organizational theory (OT) framework of leadership.

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Consistent with OT (Barnard, 1938; March, 1991; Selznick, 1948), complexity leadership

theory identifies two primary functions in organizations: an administrative function and an

adaptive function. The administrative function is associated with the bureaucratic structures of

the organization, and focuses on productivity and efficiency through choice, execution and

variance reduction to enhance efficiency and effectiveness (i.e., “exploitation,” March, 1991).

The adaptive function is associated with CAS structures and dynamics in organization, and

engages individuals and organizations in search, experimentation, and variation to enhance

creativity and learning (i.e., “exploration,” March, 1991).

CLT also identifies leadership forms associated with these functions. Administrative

leadership manages in the formal systems and structures (i.e., the administrative function) to

produce results through alignment (e.g., standardization) and control (e.g., visioning, motivating,

inspiring, directing, controlling). Adaptive leadership is a distributed form of leadership

generated in the informal structures and systems of the organization (i.e., the adaptive function).

It is associated with the bottom-up emergent dynamics within organizations. Adaptive leadership

is defined as a dynamic, interactive influence process among individuals and collectives for

which the objective is to lead one another to adaptability, innovation and learning (Uhl-Bien &

Marion, 2009; 2011).

Recognizing that the administrative and adaptive functions often operate in tension with

one another (Boisot & McKelvey, 2010; Heifetz & Laurie, 2001), and that organizations operate

in states of dynamic equilibrium (Boisot & Child, 1999; Davis, Eisenhardt & Bingham, 2009;

Galunic & Eisenhardt, 2001; Meyer, Gaba & Colwell, 2005; Stacey, 1995), CLT adds a third

leadership function called complexity leadership. Complexity leadership (formerly referred to as

enabling leadership) works to achieve requisite complexity through a dynamic combination of






loosening-tightening the administrative function and enabling-inhibiting the adaptive function

(Havermans, Den Hartog, Keegan, & Uhl-Bien, 2010; cf. McKelvey’s 2010 discussion of

complexity leadership action disciplines).

Complexity leadership differs from administrative leadership in that administrative

leadership is only able to affect the former (loosening-tightening the administrative function)—it

is not associated with the adaptive function. It is because of this that current frameworks of

leadership often fall short for practitioners in complex environments (IBM CEO Report, 2010).

Traditional leadership theories are grounded in assumptions of hierarchy and authority (i.e., the

administrative function) that do not recognize complexity dynamics or adaptive leadership

associated with the adaptive function. Therefore they are inadequate with respect to how to

engage the adaptive function, manage in and for emergence, or enable the requisite complexity

required for firm fitness and adaptability (Uhl-Bien & Marion, 2008).

In including both organizational-level (administrative) and micro-level (adaptive)

leadership behaviors, CLT addresses both coarse-grained and fine-grained leadership activities.

Coarse-grained leadership is considered in the administrative function and administrative

leadership roles that occur in organizational structures and systems. Fine-grained is considered in

the adaptive function and adaptive leadership roles and dynamics that are associated with

leadership and emergence. Complexity leadership helps enable transition between fine-grained

and coarse-grained by both operating in the interface between the adaptive and administrative

functions (Uhl-Bien et al., 2007; Uhl-Bien & Marion, 2009).

Leadership and the Implementation of Strategy: The Unifying Process

One of the anomalies that confronts organization and leadership researchers is the

perception that individuals, and in particular executive managers, are in charge of




organizations—they set the directions, they make things happen. As we can see from the above

discussion, this does not square with a complex system viewpoint that organizing patterns

emerge through interactions. One point of synthesis, however, is the realization that there are

variations in the roles and the impact that individuals have in the system—i.e., the influence of

all individuals is not equal. Status and reputation differences have significant impacts on

outcomes (Axelrod, 1984; Simon, 1997). For example, executives typically set the strategy and

direction for the organization; they define the organizations boundaries, its identity, and its

ethics, and they hold it together (Hazy, in press-a). In short, they drive the unifying process at the

highest relevant level of coarse-graining. Thus, contrary to the complex adaptive systems most

often studied in the natural sciences, it does appear that in the case of human interactions,

through intentional action that recognizes and seeks to exploit emergent patterns, individual

agents can make a difference even in properties that are coarse-grained.

Strategy, “Tags,” and the Unifying Process

For example, Boal and Shultz (2007) focus on the strategic aspects of leadership at the top

of the organization. Amid their discussion about how communication strategies and story-telling

are core to business strategy-making, they assert that individuals performing leadership functions

use the complexity idea of “tags,” as defined by Holland (1975), to compartmentalize large

organizations into differentiated sub-structures. They argue that this process is useful because, as

Holland shows, the use of tags in interactions enables individuals who share the same

assumptions and aspirations to identify one another. Tags thus promote higher levels of quality

interaction within same-tagged identity groups. The use of tags also results in lower levels of

interaction across groups with different tags, which can make communication more efficient.



In a more general sense, the implications of Boal and Schultz (2007) suggest that tags are

useful in furthering the unifying process identified by Hazy (2011a). Tags can be thought of as a

unifying mechanism which clarifies and helps resolve identity tensions locally and globally, and

tunes criticalization conditions locally in the face of further tension between adaptive versus

performance concerns. In complexity leadership theory terms (Uhl-Bien et al., 2007),

administrative leadership exploits innovations generated by their adaptive leadership to benefit

the firm, which also seems to be relevant to the strategic unifying process Boal and Schultz

describe. Perhaps, the use of tags is a mechanism of administrative leadership that serves to

further the unifying process within the organization.

Unifying leadership across scale

One might note, however, that the unifying process requires that these activities occur

across the organization and are not limited to the actions of top management. They occur at all

levels of the organization. To further explore the mechanisms of individual influence within

HID, particularly when organizing large-scale efforts, Hunt, Osborn and Boal (2009) describe

the application of complexity to what they call Level VI leadership (Jaques, 1989), the level

below strategic leadership. For Jaques, Level VI implies individual cognitive capacity that

enables a task horizon and job focus of three to five years for mid-career executives.

Citing the Siggelkow and Rivkin (2005) results, Hazy (2007b) argues that at the senior

management level, successful leadership is more about picking strong subordinates and advising

and supporting them with resources than it is about choosing actual projects. The process

whereby these decisions are taken has recently been explored by Anneloes et al. (2011) in the

context of the nature and quality of the interactions between top managers and their middle

management subordinates who are responsible for directly delivering operating results. At this


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level, it may be more appropriate to worry about picking the right individuals, i.e., the right

champions, who have proven they can pick and drive winners, than to try to identify winning

projects oneself.

Extending this line of thinking, Hazy (in press-b) argues that when reputation and status

accrue to individuals who are successful, and when these changes are publicized to the

organization’s members, the organization gets smarter as a system. In his model, relative status

and reputation determine relative influence among individuals in a manner analogous to the

synaptic weights connecting neurons in the brain. These weightings determine which neurons are

activated in response to stimuli in the environment. Similarly, relative status and reputations play

a role in determining who is involved and to what degree when an organization is forming a

response to events in the environment. In this way, the organization “learns” in a manner

analogous to the learning algorithm in the brain’s neural network.

As an example, a successful merchant bank in Sweden, with a record of success even

during the financial crises, illustrates a complexity-aware structure of this type. Each department

operates as its own company, and each employee has full responsibility for a group of customers.

The company’s culture unifies these activities as a major mechanism for integrating different

groups into the company. Activities such as appointing managers with values that closely match

those of the company, and ensuring high quality communication among individuals were shown

to be central for integration into a unified firm with a common identity (Backström, Hagström et

al., 2011). The bank uses a very simple feedback process at the departmental level and uses this

to clarify the relative success, and thus the relative status and reputations of departments (and

thus of individuals), by publishing a monthly ranking of all departments.



Based upon these complexity ideas, Hazy (in press-b) argues that one of the most important

aspects of effective management is to focus on structuring projects so as to receive clear and

unbiased feedback about success or failure and to act on the feedback by adjusting the status and

reputations of those involved. Thus, an important imperative of leadership is to hold people to

account. This would be an element of administrative leadership (Uhl-Bien et al., 2007).

We turn next to the question of what it might take for an individual, a “leader,” to

differentiate his or her actions in order to successfully engage a system that has complex

characteristics.

The Individual in Complex Organizing

Core to the challenge of developing complex systems leadership theory is the unique role

played by individual human beings, particularly high status ones, or powerful ones, in complex

organizations (Denis, Langley & Pineault, 2000). Does complexity suggest what it takes to be a

successful individual “leader” in this context? Building on the work of Zaccaro (1999) and

Hooijberg (1996) this challenge has been taken up by Hannah, Lord, Jennings and others

(Hannah, Jennings & Nobel, 2010; Hannah, Woolfolk & Lord, 2009; Hannah, Eggers and

Jennings, 2008; Lord, Hannah, & Jennings, 2011) to explore the mental characteristics that

support success, as well as the potential for bias, for example, gender bias (Hogue & Lord,

2007). These issues become important to researchers for a number of reasons, one being their

implications to leadership development programs (Boyatzis, 2008).

As described earlier, Uhl-Bien et al. (2007) extend Ashby’s (1956) idea of requisite variety,

which says that in a cybernetic system the regulator must be at least as complex as the

environment it hopes to regulate, to argue that organizations must likewise match the complexity

of the environment, a condition called requisite complexity (Boisot & McKelvey, 2010). In



particular, they argue that complexity leaders must structure the organization to enable requisite

complexity. To further define this concept, Hannah, Lord, Jennings and others (Hannah et al.,

2010; Hannah et al., 2009; Hannah et al., 2008) have worked to define the characteristics of

individuals that enable requisite complexity in action. Most recently, Lord, Hannah, and Jennings

(2011) identified two dimensions of complexity, static and dynamic, the latter taking into

account the unfolding of complex conditions over time. By analogy, they extend the idea of

requisite complexity to groups (Lord, Hannah & Pearce, 2011). The processes of creativity and

innovation they describe require different skills, and support the system’s functional demand that

new information be gathered and synthesized in an ongoing generative process.

Innovation and Adaptation: The Generative Process

To sustain itself as an open system (von Bertalanffy, 1950), a complex system needs

continuing access to resources within its ecosystem (Hazy, Moskalev & Torras, 2009, 2010).

Since opportunities in an ecosystem ebb and flow, a complex system needs mechanisms to

explore the environment, identify resource-gathering opportunities and construct structures

within the system to begin to exploit these resources (Garud, Gehman, Kumaraswamy, 2011;

Garud, Kumaraswamy, & Sambamurthy, 2006). In short, the organization must balance

exploration and exploitation (March, 1991). Since ecologies change, this is not a once-and-done

process. This is why Hazy (2011a) calls the value-identifying loop a requisite function for

complex systems of human interaction dynamics (HID). This system requirement places a

functional demand upon the leadership meta-capability: to establish, evolve and regulate this

generative operation for the system by changing the interaction rules among individuals.

Garud et al. (2011) describe the generative environment at 3M as what they call

“complexity arrangements”:

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We conceptualize the different combinations of practices – manifest structure (e.g.,

products, patents and platforms), relational processes (e.g., interactions between people

within and across platforms and businesses), temporal dynamics (e.g., moments of

serendipity enabled by the 15% option) and regulative guidelines (e.g., 30% stretch

objective) – that are activated at various stages of an innovation journey at 3M as

representing complexity arrangements (p.347).

These “complexity arrangements” enable a process of invention, but also innovation, and they

are enacted at the individual interaction level. CSLT explores the mechanisms that evolve the

rules that govern local interactions to enable the generative process of innovation and adaptation.

Divergence through discovery

A literal example of this might be the value-identifying loop associated with an as yet

undiscovered oil field in the Gulf of Mexico. Resources are allocated to exploration (March

1991), albeit with no a priori knowledge of the likelihood that a new field will be discovered at

any particular place. Once indications of potential are identified and the probability of success

increases (after a geological study, for example), locally specific capabilities must be constructed

to explore this possibility further. With additional positive indications, more resources are

allocated to take advantage of this potential, and so on, as long as the opportunity remains viable

(meaning the probability of success relative to risk remains high).

Complex system leadership evolves local rules of interaction to enact this process. As

experiments to acquire resources produce information, feedback (under promising conditions)

leads to significant expected value with regards the resources that could be discovered. This

positive feedback loop is generative of possible future ecological niches for the system.

However, if risks and rewards are not properly recognized and modeled, this feedback doesn’t



have to be beneficial for the organization. The unfolding impact of the BP Gulf Oil Spill in 2010

is a value-destroying example of the same positive feedback affect. Thus proper assessment of

information about risks and benefits is an important aspect of the interaction rules that are

evolved by complex systems leadership.

Events caught up in positive feedback loops can build upon themselves rapidly and their

effects can come to dominate the HID within an organization, as happened at Intel after the

discovery of the microprocessor (Hazy 2008a). Diverging value-identifying loops that build upon

themselves exhibit divergence through discovery because information about the opportunity

grows rapidly after discovery, often feeding upon itself. This information helps individuals to

organize the system to deal with rapid growth and expansion. In the above example, this means

that the petroleum industry ecosystem began as a dynamically stable system exploiting known

resources. Initially the system had no information about this particular oil field (or about the

implications of the Deep Water Horizon’s blowout in the negative case).

Upon discovery, and assuming continued investment of resources, change unfolds. For

example, researchers (Aasen & Berg, 2008; Aasen & Johannessen, 2007; Johannessen & Aasen,

2007) used the complex responsive processes framework to explore the innovation process at a

subsea oil recovery case at Statoil ASA a Norwegian oil company. They observed that

“innovation emerges from the experiences of everyday social interaction, where patterns

gradually perceived as meaningful are created and adopted” (p. 44).

Using systems dynamic modeling, the divergence of discovery as a complexity

phenomenon was shown to describe organizational transformations at NCR (Hazy, 2006) and the

rise of the microprocessor at Intel (Hazy, 2008a). It was also described in for a technology

company (Hazy, 2008c), and in a case study reported by Surie and Hazy (2006). In the latter




case, an Indian manufacturing company was working with foreign partners who were seeking to

access production capacity to outsource their manufacturing. During the negotiation process, the

Indian company discovered and exploited entirely new global markets for its products. New

information about global markets gradually changed the Indian firm’s perception of their

potential customer sets. Their visible world diverged from a narrow, traditional, domestic-only

market, into the wider visibility of a truly world market.

Ecology of Innovation

What these examples have in common, according to Goldstein et al. (2010), is that they

occur in organizations wherein complex systems leadership has managed to position the

organization within an “ecology of innovation.” This means that the firm is well situated within

its network of suppliers, customers and partnerships and maintains excellent communication

across its connections so as to engender knowledge generation, discovery and thus a level of

divergence as new opportunities are discovered. Their point is that leadership occurs at the

nexus of interactions, where generative human dynamics lead to creativity and innovation

(Andriani, 2011; Beck & Chong, 2009; Garud et al. 2011; Garud et al. 2006; Tapsell & Woods,

2009). The key to this is in the quality of interactions, what Goldstein et al. (2010) call

interaction resonance, and what Garud et al. (2011) call relational processes.

In the Complex Responsive Processes (CRP) perspective, Stacey (1993, 1995) argues

that certain conditions are necessary for impactful interactions. These include: trust, the holding

of anxiety, power relationships that are both cooperative and competitive, and conversational

practices that don’t block explorations (Simpson, 2007, p.475). These interaction-level tensions

must be navigated to achieve a high level of interaction resonance. This in turn implies certain

leadership activities that are necessary for adaptation (cf. adaptive leadership in complexity



leadership theory, Uhl-Bien et al. 2007; Uhl-Bien & Marion, 2009). Complex systems leadership

evolves rules of interaction to enable interaction resonance.

Goldstein et al. (2010) go on to argue that conditions of highly resonant interactions,

bounded in the right way, can lead the system to criticalization in the complexity sense, where

the system is poised for a phase transition between two possible dynamic states: one less ordered

and thus more symmetric, and one more ordered or organized into specialties and thus less

symmetric. The transition from one state to the other can be modeled mathematically if an order

parameter can be identified to describe the change (Haken, 2006). Identifying the order

parameter for phase transitions in HID remains an open unanswered question in CSLT.

More specifically, on one side of the phase transition, the more ordered case, individuals

are oriented in certain roles and tasks, and are thus less interchangeable with others. Some are

marketers doing marketing things and are only interchangeable with other marketers; some are

accountants doing accounting things and can only be replaced by accountants, and so forth.

Because fine-grained elements of the system can be differentiated, there is ordering in the system

and thus there are fewer ways the system is self-similar. It is thus less symmetric. On the other

side of the phase transition, everyone does everything and thus, in a certain sense, everyone is

interchangeable with everyone else (ignoring for a moment individual heterogeneity). Such a

disordered system is thus more symmetric.

This is why qualitative change from one state to the other, called a “phase transition,”

represents a discontinuous change in the in the level of order in the system and this change is

often referred to as “symmetry breaking” (Haken, 2006; Guastello, 2002). Ecologies of

innovation enable symmetry-breaking qualitative change. Generative rules of interaction at the

fine-grained level are an enabling prerequisite to innovation reordering of this type (Hazy, 2009).



Performance and Efficiency

In addition to unifying HID into complex adaptive systems, and the exploration and the

generative activities that enable organizing in response to opportunities, the system must also

converge to stable operations that effectively and efficiently exploit the resources to which it has

access. Thus convergence is the third functional demand placed on the leadership meta-

capability as the complex adaptive system (as enacted by its agents) seeks to acquire its requisite

resources and to conserve the resources it has accumulated as slack (Cyert & March, 1963).

Convergence for preservation

This functional demand is needed to encourage efficiency and to preserve slack resources

(Cyert & March, 1963). In the process, however, potentially productive activities and

information about alternative approaches and opportunities are lost (Haken, 2006). The human

relations or “consideration” aspects of the process of convergence that brings people on-board,

and the “initiating structure” elements that enable action-in-concert have long been associated

with leadership (Fleishman, 1953; Stogdill, 1974). Individuals who exhibited either or both of

these behaviors are called “leaders” by others even though leadership is actually emerging from

within the interactions. The tendency to attribute leadership to individuals is a strong one that has

also been observed in the complex system leadership context (MacGillivray, 2010).

Complexity ideas and methods have added a perspective on the “how” behind the “what”

of these behaviors. Consideration and initiating structure by individual actions enable

convergence of a disparate group toward a single, common objective. Convergence of action

satisfies a functional demand of the complex system on the leadership meta-capability as defined

by Hazy (2011a). More specifically, Phelps and Hubler (2007) showed how groups could set and

choose direction when a single individual was sufficiently motivated to move in a particular

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direction, essentially shutting out and forgoing other possibilities, including preferences of

others. In several experimental settings, the role of individuals in catalyzing convergence toward

a particular outcome has also been shown to follow distinctively nonlinear patterns of behavior

by Guastello and his colleagues (Guastello, 2002; Guastello, 2007; Guastello & Bond, 2004,

2007a; 2007b; Guastello, Craven, Zygowicz, & Bock, 2005; Guastello & Guastello, 1998).

Complexity researchers have also studied the impact of leadership on group member self-

selection across groups. Dal Forno and Merlone (2007) demonstrated both experimentally and

computationally that the complex dynamics associated with the distribution of rewards and

punishment by individuals on teams had a significant effect on which teams individuals chose to

join, and this in turn impacted the projects that were ultimately completed.

Information flow is a critical enabler of project selection and execution. Schreiber and

Carley (2006) used data from field observations and dynamic network analysis to study how the

flow of information within teams impacted performance. They found that multiple network hubs

in the flow of information, rather than a single one, that is, a single “leader,” led to better

performance when complex functioning was required. Similarly, Solow and Leenawong (2003)

used Kauffman’s (1995) NK model to show that too much complexity within teams can lead to

situations of overload that greatly reduced performance. Insight on the efficacy of a centralized

control model versus more distributed decision-making was also shown using complexity

modeling (Solow & Szmerekovsky, 2006).

Hazy (2007a; 2008b) summarized many of these studies into the theory of complex

systems leadership. Synthesizing the studies above as well as others completed at the time, he

observed that five distinct aspects of convergent leadership had been identified in these

complexity inspired studies. These five mechanisms comprise the leadership response to the



requisite system demand for collective convergence within HID toward action in concert. These

distinct mechanisms include actions or communications by individual agents which:

1. Espouse an approach or cooperation strategy for working towards a common objective, a “program of action,” such that choosing to participate in the program is an attractor for the individual choices of two or more agents (Phelps & Hubler, 2007).

2. Catalyze social influence conditions such that at least one other agent chooses to participate in the program being espoused rather than continuing to act for its own account or according to an alternative program (Phelps & Hubler, 2007).

3. Catalyze choices and action in other agents intended to navigate complexity and overcome the limits to cognitive capacity in an effort to avoid an interaction catastrophe. This is when there is too much interaction and confusion causing performance to drop precipitously (Solow & Leenawong, 2003)

4. Form a distinct output layer (the Executive Office) that expresses learning and action for the system as a unity in the environment; to do so, the agents disambiguate learning and enable the unambiguous expression of action by the system in the environment (Hazy, 2007b).

5. Process feedback information regarding success or failure of enactments in the environment or internal to the system, and translate this information into structural changes in the influence network among agents. This is done by changing the reputation and status of participating individuals and thus changing their relative influence (Hazy, 2007b).

Hazy argues that all of these aspects of leadership are facilitated either directly or

indirectly by changing the rules of interaction. A theoretical framework for how these leadership

mechanisms are actualized within a group of individuals in an organizational setting is discussed

next.

Microenactments within a CAS

By analogy with insect swarming behavior, an emergent process that has been studied

intensely (Seeley, 2010), Hazy and Silberstang (2009a) suggest that individual interaction events

in groups likewise can be understood as signaling behavior among individuals that culminates in

a specific collective action - programs of action (PoAs) - independent of the specific content of

the action being considered. Whereas bees swarm in search of a new nest (Seeley, 2010), humans

“swarm” in an effort to act in concert with regards to any particular project being considered.



Examples of PoAs around which people might “swarm” include the decision to disband a

meeting or any social gathering, to initiate a project, or just to meet again the following week. Or

they might involve taking a single step in a larger program: to launch as new product

development effort, for example. When the relative status and thus influence of the various

individuals involved is taken into account the authors posit that this dance of interaction is

actually how decisions are taken when the authority to make a decision is “in the room.”

To explore this proposition, the authors go on to describe individual signaling behaviors,

called “microenactments” (Hazy & Silberstang, 2009a, 2009b; Silberstang & Hazy, 2008). These

include signals that initiate a PoA, reject it, accept/imitate it, negotiate to modify it, or synthesize

it with others to form a qualitatively new project. Together these microenactments constitute a

language of interaction that can enable action in concert. For efficiency, this language unfolds as

separate enactments made by individuals, but they are experienced by others in the context of a

shared grammar that enables action-in-concert. Aspects of the grammar might include such rules

as when and how to propose new ideas (Beck & Chong, 2009), what constitutes a quorum that

enables decisions that are binding on the group (Phelps & Hubler, 2006), and when to accept the

decision of a quorum (Guastello, 2007; Goldstein et al., 2010).

Detailed research on the precise nature of the microenactments that individuals display as

well as the grammars that enable shared meaning within this communication system might lead

to a deeper understanding of human collective behavior. Although the palette might be distinct

for each class of problem, there may also be commonalities, and these might shine additional

light on the precise nature of leadership in daily practice. In the spirit of the interdisciplinary

nature of complexity thinking, this same problem has also been addressed using an agent-based

modeling framework by Panzar, Hazy, McKelvey and Schwandt (2007).



Developing fields, like the leadership-as-practice research where the focus is on the

dynamic activities of leadership rather than heroic individuals (Raelin, 2011), are linked to the

complexity way of thinking. Here we see mutual influence among individuals with common

objectives forming into collective action. Research into the precise nature of this process for

different classes of problems might lead to a deeper understanding of human collective behavior.

Independently, Mangiofico and Feyerherm (2011) have qualitatively identified many of the

activities described by Hazy and Silberstang in a non-profit organization. Likewise, research in

Shared Leadership can be viewed in this way (Lord et al., 2011).

A Contrarian Challenge to Systems Thinking - Complex Responsive Processes

The focus of the complex responsive processes approach to complexity is in

understanding the interpretive experience of the individual as human beings interact within

complex human organizing activity (Stacey, Griffin & Shaw, 2000; Griffin, 2001). This

approach distinguishes human complexity from the “systems approach” (von Bertalanffy, 1950;

Katz & Kahn, 1966) arguing, in effect, that human organizing is not appropriately studied using

systems models (Stacey, 1995). Instead, as Johannessen (2009) states:

Taking a complexity approach means that the focus of research attention is drawn towards

the exploration of the phenomenon of human interaction and emergence. Human

interaction is the cause of emergence, and human interaction only creates further

interaction and emergence. This is what is meant by a radical process view of reality (p.

217).

The implication of this framing is that the focus of research is on the unfolding of experience, on

the narratives themselves, rather than on structures or artifacts that can be modeled and studied

using systems approaches.

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Although coming from a different theoretical framing, the implications of this research

stream are not that different from the system-based approaches described in previous sections.

For example, in a case study Mangiofico and Feyerherm (2011) have integrated the notions of

complex response processes with the systems perspective of Goldstein et al. (2010) to identify

the four leadership skills that are implied by complexity thinking: i) perpetually scanning of the

ecology to identify flows of information and resources, ii) weaving webs of interaction among

actors within and across boundaries, iii) creating coherence among the signals that flow through

these networks, what Goldstein, Hazy and Lichtenstein call “interaction resonance”, and iv)

support for expanding innovation by offering stabilizing feedback to converge activity toward a

kind of dynamic stability that represents a new way of doing things. In a certain sense, these four

principles frame the practical implications of the new perspective that is offered by complexity

science.

The Emerging Complexity Paradigm

Figure 1 shows a new paradigm that is emerging. Complexity science provides

conceptual tools for thinking about organizing (left box in the figure) and identifies key system

demands that must be met if a CAS is to be effective at both performance and adaptation (right

box). Three organizing mechanisms and their relationship to the leadership process were

described in this chapter (center box). These can be used by managers to drive organizational

outcomes. At the bottom of the figure we identify the challenges that all of this complexity

brings forward for individuals who are asked to manage or to lead. These include complexity

within a given situation such as during a large-scale project, as well as complexity that results

from changes over time, as might be the case as technology advances more rapidly than ever.

Each of these areas has implications for practice and begs further research.



This new paradigm is a significant contribution to leadership research because it begins

to connect individual behaviors and actions to system processes, functions and outcomes. To do

so it explores the complexity dynamics within fine-grained HID in areas such as criticalization,

and through the study of emergence, it also sheds light on how these might result in beneficial

changes to the organization’s coarse-grained properties. This perspective is new to leadership

research, and it represents a significant advance in understanding which is only beginning to

show its promise. A summary of representative empirical studies is shown in Table 1.

Functional Demands On System

ComplexityTool-kit

Convergence Stability & Predictability

Enables EffectivePerformance

GenerativeInnovation Enables

Variety & ProvidesFuture Options for

Adaptive Opportunities

UnifyingCommon Models, Ethics, & Identities

Engender Unity within

Boundaries & Enable Collective Action

Coarse-GrainingAttractors, Statistical

Properties, Stabilizing Feedback, Phase Transitions,

Order Parameters

Scale-CrossingCriticalization, Fluctuation,

Amplifying Feedback,

Divergence, Recombination,Self-Similarity/Fractals,

Networks, Dissipative Structures

Fine-GrainingAutonomous agents

Heterogeneity

Interdependence

Boundaries & Tagging

Individual Requisite ComplexityStatic Complexity Dynamic Complexity

Cognitive & emotional traits Situational awareness, action, & feedback

Ch

an

gin

g R

ule

s o

f Inte

ractio

n

En

actin

g C

om

ple

xity

Me

ch

an

ism

s

OrganizingMechanisms

Entrainment Leadership Reinforces

Structures for Day-to-DayEffectiveness

EmergenceLeadership Promotes

High-Bandwidth

Information Sharing,

Experimentation & Synthesis

Identity TensionLeadership Orchestrates

Individual, Group &

Intergroup Connections & Synthesizes

Overlapping Models &

Identities

Figure 1 - A 2012 view of the emerging Complex Systems Leadership Paradigm includes a complexity tool-kit of ideas and the organizing mechanisms that can be used to satisfy the functional demands of a complex adaptive system.

THE ROAD AHEAD

In 2007, Jennings and Dooley used textual analysis to identify the emergence of a new

paradigm in complex systems leadership research. Five years later the synthetic analysis



described herein has brought this paradigm into clearer focus. A review of the literature shows

that a new theory has evolved in what can best be described as the abductive theorizing approach

advocated in recent years by Max Boisot (2010). The new paradigm that has emerged both

transcends and challenges prior research approaches. It transcends prior approaches in that it

represents a general theory of human interaction dynamics (HID) consistent with analytical

approaches used in the natural sciences. It challenges them in that its generality implies

additional criteria and analytical techniques through which prior results may be evaluated.

Implications for Research

As this theory comes into focus, additional empirical research is needed to inform future

iterations of CSLT. In this next phase of abductive theory development, it is increasingly

important that quantitative methods be added to the mix so that constructs can be validated and

relationships between them identified and tested with statistical methods. However, these

methods must be implemented while cognizant of the limitations inherent in traditional methods

when events are not independent and where individuals have different preferences, histories and

values.

To do this, a new way of thinking, new methods, and a new set of skills are needed. Tools

like agent-based modeling, the mathematical treatment of dynamical systems, dynamic network

analysis, and power-law analysis can all inform these new techniques. However, because the

HID approach is qualitatively different from the interaction dynamics typically modeled in the

natural sciences, many new technological advances are also needed (Prietula, 2011).

As a result, quantitative research in CSLT will require advances on many levels, and

many of these will need to be built fresh, from the ground up. It will involve changing the rules

of interaction among all of those who are engaged in leadership research and practice. This is



perhaps the greatest challenge of all: The new paradigm is not just another way of thinking about

leadership; it is also a new way of thinking about social science more generally. The magnitude

of this challenge is daunting, certainly. But the potential benefits of action—particularly given

the many challenges that face the world in the coming decades—make a focused effort toward a

new and better way forward not merely a choice to be taken. It is an ethical imperative.

Implications for Practice

These developments also have significant implications for practice and thus for

leadership development. One of these is the realization that leadership development must go far

beyond the current focus on individual self-understanding and communications skills. In the fast-

moving economy of the 21st century, individuals will need tools to help them better understand

the nonlinear effects in their ecosystems. Increasingly, these are being enabled by social media

and socio-technical networks, for example. It would be beneficial to be able to anticipate how

various possible interventions might influence emergent outcomes, and then learn to respond

quickly and thoughtfully to unfolding events in real-time.

We expect that leadership development programs will increasingly be characterized by

computer assisted simulations which express real life organizations as complex adaptive systems

in a manner analogous to flight simulators in the airline industry, or battlefield simulations in the

military. One of the advantages that CSLT provides is a theoretical framework within which one

could identify opportunities for management interventions. The potential exists that in the not too

distant future, computer simulations could be used to evaluate actions of individuals and groups

and then present to the user the likely outcomes at the coarse-grained levels under various

scenarios. Not only would this be useful in training, it could also be used to pretest actions to



explore possible unintended consequences. With better information, more informed action could

be contemplated, and possible contingencies could be better evaluated.

Table 1. Empirical studies using various methods Author(s) Description Method Complexity

Paradigm

Aasen & Johannessen (2007) Aasen,& Berg (2008).

Innovation in the subsea technology development

Longitudinal industrial case ethnographic

Complex Responsive Processes

Backström, 2009 Resource generation in pharmacies

Case study CSLT/Directing leadership

Backström, Hagström, & Göransson, 2011

Integration of employees in the company culture of a bank

Network analysis & survey

CSLT/Directing leadership

Backström, Wilhelmson, Olsson, Åteg, & Åberg, 2011

Training of first line managers Action learning network, survey and interview

CSLT/Directing leadership

Baker, Onyx & Edwards (2011) Community service projects Network analysis & qualitative case study

CSLT/Generative Leadership

Beck & Chong (2009) Community groups solving local problems

Action learning &participant observer

CSLT/Leadership of emergence

Buckle Henning & Dugan (2007)

Self-organized patterns in the workplace

Grounded Theory CSLT

Butterfield, Shepherd & Woods (2011)

Development Entrepreneurial business model

Case study CSLT/Generative leadership

Dal Forno & Merlone (2007) Project tem formation and success

Laboratory study & Agent-model

CSLT/Convergent Leadership

Garud, Gehman, & Kumaraswamy, (2011).

Innovation process at NCR Case study Complexity and innovation

Groot (2009) Performance improvement at Dutch Railways

Case Study/ personal reflection

Complex Responsive Processes

Guastello et al. (2004, 2005, 2007a,b)

Emergence of individuals as team leaders in simulation games

Laboratory Study Nonlinear dynamical systems/ Game theory

Havermans, Den Hartog, Keegan & Uhl-Bien (2010)

Leadership practices in project-based organizations

Qualitative/Interviews Complexity Leadership Theory

Hazy (2008a) 18 month study of 50 person, technology firm

Survey/case study Leadership Meta-capability

Hazy (2004, 2008a) Organizational Transformations: NCR & Intel

System dynamics modeling of case study

Leadership Meta-capability

MacGillivray (2010) Leadership in community group

Phenomenographic study

Complexity Leadership Theory

Moerschell (2010) Leadership emergence in recreational groups

Grounded theory Complexity and punctuated equilibria

Phelps & Hubler (2007) Emergent leadership in youth groups

Multi-Case study and agent-based model

Dynamical Systems & Bifurcation Theory

Plowman et al (2007) Transformation of a mission church

Case study Leadership of emergence

Schreiber & Carley (2006) Workgroups at NASA Network Analysis Dynamic Network Analysis

Surie & Hazy (2006) Indian Manufacturing Companies

Case studies CSLT/Generative Leadership

Tapsell & Woods, (2009) Maori of New Zealand Case Study CSLT/Generative Leadership

Tobin, J. H. (2009) US Hospital merger Case Study/ personal reflection

Complex esponsive Processes



The CSLT approach is a useful and important advance in any case. By making managers

aware of the conditions that imply different types of leadership action, and by clarifying what

actions lead to which outcomes in a given situation, the process of enacting leadership becomes

more understandable as a capability to be learned. This is distinct from more cultish approaches

to leadership development that treat “leadership” as a “mysterious” or “authentic” attribute of

certain special individuals, one that is hidden deep within a person’s soul and can never be fully

understood. Rather than leaving us to trust that certain special people will reach inside

themselves to lead us where they believe we should go, CSLT exposes the human interaction

dynamics that each of us can influence to construct the world in which we choose to live.

FUTURE DIRECTIONS

The advancement of complex systems leadership theory has brought to the fore several

important research questions:

1. How should the ubiquity of interdependent heterogeneity in HID be treated rigorously

when modeling complex systems leadership events and when analyzing empirical data?

New statistical techniques are needed that take into account individual learning and

memory and path dependence with regards human interactions which limit the validity of

many traditional methods (Hazy & Ashley, 2010).

2. What are the precise mechanisms of emergence in HID? What is the role of leadership in

this process? Can the number and duration of experiments be quantified to determine if

potential success is sustained through reinforcing feedback?

3. By what mechanisms do constraints on human interaction dynamics imply the particulars

of emergent coarse-grained properties? How are constraints and their impacts measured?

For example, how are constraints to resources, such as financial, human, temporal, and



technological, linked to leadership actions and outcomes? How do changes to the

constraints translate into qualitatively different properties? What is the role of leadership

in this process?

4. How do individuals who are seeking to have an impact recognize coarse-grain properties,

determine the need to change them, and then translate this into complex systems

leadership actions that locally change the rules of interaction in ways that alter these

emergent properties as intended?

5. By what mechanisms do individual agents influence the local rules of interactions of

others? What is the role of identity? Of ethics?

6. By what mechanisms do fine-grained interactions imply specific coarse-grained

properties? How do changes in local rules become manifest in qualitative changes in the

emergent properties? Is the analogy with phase transitions informative for this?

7. By what mechanisms do coarse-grained properties entrain fine-grained rules of

interaction in HID? How is this related to leadership as well as to cultural norms,

institutional effects, etc.?

8. When focusing on the rules that govern local interactions, how are these rules recognized,

developed, shared, remembered, adapted, and replicated? What role do identities play in

storing, sharing and evolving rules of interaction? How do ethics play into this question?

9. Can examples of collective agency with regard coarse-grained organizing forms in HID

be identified and shown to be ontologically distinct from individual intention and action?

What would this mean for leadership research?

10. What are the implications of this new paradigm for leadership development programs

going forward?



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i In a later section we describe a competing approach, Complex Responsive Processes (CRP) put forth by Stacey (1993), which challenges the systems approach entirely. ii Constructive deviations are ad hoc experiments performed with the intention of achieving some purpose; this idea is distinct from the notion of “positive deviance” which is a post hoc analysis and intervention technique used to identify positively performing subgroups (outliers) in populations facing many of the same challenges. The positively deviant solutions are then analyzed and understood before being replicated more broadly across the population (Pascale, Sternin, & Sternin, 2010). This technique could presumably be applied with positive affect to evaluate the relative success of the constructive deviations described herein as they are occurring within organizations that are experiencing criticalization.

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