Complexity 101 by Cynthia Cavalli

Complexity 101 By Cynthia Cavalli Ph.D.

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Title: Complexity 101 for Church Leaders - A New Vocabulary for Transformation and Change

By Cynthia Cavalli, Ph.D.

ABSTRACT

The field of complexity studies is changing our understanding of nature and dynamic systems

much as quantum mechanics changed our understanding of the underlying nature of reality in the

early part of the last century. In complex systems, the interactions and relationships between the

relatively simple parts at one level of the system give rise to new and unpredictable behaviors at

a collective level. Complexity science and the related phenomenon of emergence also shed new

light on evolutionary processes, filling in previous gaps in conceptual understanding of how truly

new possibilities enter the world. Complex behavior is found in many different kinds of systems

involved in a wide diversity of phenomena including population dynamics, weather systems,

epidemiology, and traffic congestion. This paper offers an introduction to this relatively new

field of study, and provides descriptions of basic terms and concepts to facilitate understanding

of complexity as it relates to change in human systems and organizations such as churches and

emerging spiritual movements.

Introduction

Traditional scientific inquiry has long relied on approximations of artificially idealized systems

to predict behavior of real world systems. Such approximations provided sufficiently accurate

insight into the behavior of simple systems, but often had the effect of reducing nature with its

varied manifestation into geometric shapes and replicable mechanical processes. The truth is that

natural processes and life itself are not closed systems, but open systems, which means they are

influenced by outside forces and cannot be separated from them. The discovery of complex

systems and their behavior challenges the notions of linearity, equilibrium, and entropy described

by traditional Newtonian physics while offering an alternate approach to understanding the open

systems found in nature (Miller, 1998).

This paper provides an introduction to the basic concepts of complexity and emergence as they

manifest in a particular kind of complex system known as the complex adaptive system (CAS)

and applies them to organizations. The term “complexity” refers to the dynamic nature of

relationship and interactions between the agents or elements of a system. These dynamic

interactions give rise to collective behaviors that cannot be determined or predicted from that of

the individual constituents themselves.

Other names for this field of study include complexity science, complex systems theory, and

non-equilibrium dynamics.

The field of organizational complexity draws from patterns and dynamics found in complex

systems theory and applies them to human systems, or organizations. Organizational dynamics


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often mirror patterns found in non-human complex systems. Gaining familiarity with some of the

dynamics of complexity can be helpful to understanding how they impact organizational

behavior.

The Vocabulary of Complexity

Whenever a new scientific area of interest captures the imagination of a wider audience, there is

a tendency to extrapolate from the actual findings to sometimes wild, possibly creative, but not

necessarily accurate interpretations. Besides being irritating to many scientists, it can also be

damaging when critical theoretical aspects are extrapolated far beyond what the data support in

ways that can mislead, be misunderstood or misapplied, or even damage credibility.

Thus in instances where direct empirical evidence to support extrapolation is missing, it is

helpful to consider the findings from complexity science as having metaphorical value in

understanding the dynamics of human systems.

Furthermore, these fields are still emerging and developing, and consensus regarding what

exactly constitutes complexity or emergence does not yet exist; this paper therefore focuses on

providing access to basic terms and concepts as they apply to organizational dynamics and does

not address deeper philosophical points about either complexity or emergence. However a list of

references and other resources is provided at the end for the interested reader.

Basic Terms & Concepts

Complex adaptive systems. Complex adaptive systems (CAS) are a special form of non-linear

dynamic systems that includes organizations and other human systems. The term itself was

coined by scientists at the Santa Fe Institute, a think tank of Nobel laureates and professors from

widely varying disciplines, founded specifically to explore complexity and CAS.

A system is a group of objects, elements, or components, also known as agents, which are

interdependent and are organized together to achieve a common objective; it can also be seen as

an interdependent group of objects that forms a unified pattern (Simonovic, 2011).

The term complex was mentioned briefly in the introduction, and refers to the nature of dynamic

interactions between the agents comprising the system elements. Such interactions may arise

because of proximity, or they may arise because of shared information between the agents. Thus

these agents can be seen as forming a part of a network, which is why study of complexity is

synonymous with the study of networks (Johnson, 2007).

Also affecting the behavior of these interacting and interrelated objects or agents is the

phenomenon of feedback. Positive feedback serves to reinforce the action or behavior of CAS;

negative feedback serves to correct the behavior or limit the progress of CAS. The term

“adaptive” refers to the ability of a system to learn from its history or experience, and to change

in response. Negative feedback to a system is what enables it to be adaptive. Without negative


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feedback, a system has no mechanism for recognizing that its behavior is no longer helping it

achieve its goals. Adaptation based on negative feedback allows the mechanism to make course

corrections; it is what transforms a complex system into a complex adaptive system (Johnson,

2001, p. 139).

Co-evolution. The feedback loops also occur between levels of organization, with micro-level

interactions providing information to macro-level interactions and vice versa. The result is a kind

of iterative local to global to local positive feedback loop also known as “co-evolution” which

describes how organisms create their environment even as they are shaped by it. This is

somewhat different than the traditional understanding where all potential evolutionary strategies

are eventually pursued. Rather in this view the organism participates in its development and

evolves in response to information from its environment.

Because the process of feedback occurs iteratively, these feedback loops serve to make the CAS

more robust over time. So complex systems can lose a few components or agents and still

survive because they have the ability to adapt to change, something non-complex systems cannot

do without incorporating some features of redundancy (for example, by containing multiple

copies of a part) (Rickles, Hawe, & Shiell, 2007). It is also possible for single events to alter a

complex system in a way that persists for a long time.

Emergence. CAS also possess what are called “emergent” properties, or self-organizing

characteristics that arise in response to environmental conditions (Cambray, 2002). When the

system components compete for some limited resource (such as food, wealth, space, etc.) their

collective action can result in a phenomenon known as emergence, where CAS behavior as a

whole is greater and functions at a higher level than can be anticipated by the behavior of its

individual components, or even the sum of the behaviors of the individual components.

Emergence, then, refers to the behavior observed in CAS where the components at one level of

the system interacting with each other produce behaviors at a level of the system above them.

Steven Johnson describes it as the “movement from low-level rules to higher-level

sophistication” (2001, p. 18).

An example of emergence in a non-biological system is helpful in understanding this concept of

“higher-level sophistication.” At the level of atoms, oxygen and hydrogen do not display the

qualities of a molecule of H2O. Even this molecule does not give any hint of the qualities

displayed when you amass a sizable collection of them into a glass and drink it as water – no

indication of its wetness, or taste, or thirst-quenching properties. And even this glass of water,

representing as it does numerous molecules of H2O, gives no hint of the way water behaves as

an ocean, with its crashing or turbulent waves, and tides influenced by the moon. Each of these

higher level behaviors are emergent properties, and cannot be predicted from the behavior or

understanding of the lower level system out of which they arise.


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Other examples of emergent phenomena include the development of a human being from the

zygote, ant colonies created by ants, and life emerging from physicochemical system

interactions. The city can also be seen as an emergent property of multitudes of human beings,

and organizations as an emergent property of its people, technology, real estate, etc. (Bennett and

Bennett, 2004).

Historical Background

Much of the work on complex systems began in the early part of the last century, out of the

development of systems theory. Other fields whose concurrent development contributed to

complexity theory include information theory, control systems and feedback loops, pattern

recognition, and machine language (near the middle of the last century, approximately between

1940-1960+). Early exploration into complex phenomena eventually reached an impasse because

the mathematics required to describe complex system behavior was itself too complex and

required computing capability not yet developed at that time. Only in the last 30 years or so has

it become possible to deepen inquiry into complex systems phenomena.

In fact, many of the great minds of the last few centuries contributed to the thinking in this field,

including Charles Darwin, Adam Smith, and Alan Turing. However, since there was not yet a

separate area known as complexity studies, and the ideas expressed by these individuals took

place in their own and separate fields of study, it was difficult to recognize at first that they were

all speaking to the same overarching phenomenon (Johnson, 2001, p. 18). Only when results of

studies from across varying disciplines were compared did it become possible to recognize

something greater was at work.

According to Johnson, the first real introduction to complexity theory was written by American

scientist Warren Weaver, and appears in a preface to a treatise on communication titled “The

Mathematical Theory of Communication” in 1949 (2001).

Simple systems. Weaver divides the previous few centuries of scientific inquiry into three general

categories. The first is the study of simple systems, containing two to three variables that may

describe for example the relationship between electric current and voltage and resistance, or of

the rotation of planets.

Disorganized complexity. The second category covered the study of disorganized complexity,

characterized by millions of variables or more and that required the mathematics of statistical

mechanics and probability theory to apprehend. Much of popular (current) understanding of

probability and statistics falls into this group and in fact, inquiry in this area led to development

of mortality tables used by insurance companies, among other things.

Organized complexity. The third category is one Weaver recognized as largely unexplored at the

time, the middle region between the two previously named categories, which he called organized


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complexity. In contrast to the multitude of variables in disorganized complexity, the variables in

organized complexity were all interrelated.

While it is possible to predict behavior of the few variables involved in simple systems, behavior

prediction becomes extremely difficult when numerous heterogeneous variables are involved. It

is, however possible to make accurate predictions about the behavior of the overall system being

studied. For problems falling in the category of disorganized complexity, such as the behavior of

gases under various conditions, probability and statistics enabled understanding of overall

behavior, even if it was not possible to learn what each individual atom of gas was doing at any

given moment.

The whole is greater than the sum of its parts. Meanwhile, the questions of organized complexity

were of a completely different nature: How and why did flowers open when they did? What was

responsible for the mechanism of aging? How did adult human beings develop out of the original

genetic material? These were problems that recognized the interrelationship and interaction of

the many variables with each other, and as part of a larger organic whole. The phrase which

captures such relationship is “the whole is greater than the sum of its parts.”

While statistical methods can be used for problems of disorganized complexity, the problems of

organized complexity required computational capability that would not exist for some time to

come. Fortunately various contributions from different disciplines over the course of the next

few years facilitated development of computing capability that furthered the study of complex

processes: Several breakthroughs were made in biological science in the areas of pattern

recognition and feedback; Norbert Wiener published his book on cybernetics, launching a new

field in the study of feedback loops and control theory; and a student of Wiener’s, Oliver

Selfridge, began teaching computers to learn using distributed, bottom-up intelligence rather than

the traditional top-down approach. One of the inventers of the digital computer, Alan Turing

studied the capacity of life forms to develop intricate bodies from very simple beginnings known

as “morphogenesis” and was able to demonstrate mathematically how complex organisms could

assemble without a “master planner” to control the design (Johnson, 2001, p. 14). This is one of

the hallmarks of CAS.

Equilibrium structures. This brings us to the early study of self-organization and how order

arises in the universe. There are two ways scientists recognize that this happens. The first

involves low-energy equilibrium systems. An example of this is a ball rolling down the sloped

sides of a bowl to eventually rest at the bottom, where its position minimizes its potential energy

(Kauffman, 1995, p. 20). Once the ball is at rest at the bottom of the bowl, no further energy is

required to keep it there at the bottom.

Non-equilibrium structures. The second way that order arises is through nonequilibrium

structures. An example of this is the Great Red Spot vortex, which appears to be a giant

whirlpool storm system in the upper atmosphere of Jupiter. In fact, another quality of even non-


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biological complex systems becomes apparent when considering examples like the Red Spot of

Jupiter, which is that its persistent existence makes it seem alive. It has been present for many

centuries, which means that its lifetime is far longer than the average life of any single gas

molecule lingering within it. It is a stable organization, an ordered system sustained by persistent

dissipation of matter and energy, meaning that matter and energy continually flow through it.

Such systems were named dissipative structures by the Belgian chemist Ilya Prigogine, whose

research in this area won him the Nobel Prize.

In contrast to equilibrium thermodynamic systems, where equilibrium is attained when the

system collapses to the least energetic (and therefore most probable, least ordered) state, it is the

flux of matter and energy in dissipative systems that generates order. All free-living systems are

nonequilibrium systems, including human systems. The universe is a nonequilibrium system.

The mathematics of statistics and probability used to describe equilibrium thermodynamics is not

here sufficient or applicable (Kauffman, 1995). The same problems of calculation described for

organized complexity earlier are again encountered. As was mentioned previously, advances in

computing capability were required before the behavior of nonequilibrium systems could be

described mathematically and understood.

But adequate computing capability alone was not enough to recognize emergence as a real

phenomenon. Only when researchers from widely divergent areas of study began comparing

notes and noticing similar themes across disciplines did the true nature of emergence begin to

take shape.

Organization of slime mold. In the 1960’s, a physicist named Evelyn Keller was working at

Sloan-Kettering in New York. She had spent some time exploring the field of nonequilibrium

thermodynamics and became interested in applying mathematics to biological problems. She

began collaborating with an applied mathematician named Lee Segel investigating the behavior

of slime mold. They found that although it spends much of its life as a distinct, single-celled

organism, it could under certain conditions coalesce into a much larger organism that could

spread across the forest or garden floor consuming rotting leaves and other organic material as it

goes. None of the individual slime mold cells is any different from any of the others. There is no

leader/follower. It turns out that their behavior is triggered by their chemical response to

pheromone trails found in their environment that create positive feedback loops encouraging

other cells to clump together (Johnson, 2001).

Organization of neighborhoods. In the field of urban planning, social scientist Jane Jacobs

recognized her attempts to save a creative neighborhood from being razed as essentially a

problem of organized complexity. The city comprised a complex order, a “whole” all its own. If

some streets had lost their equilibrium, rather than destroying those parts of the city that were

causing problems, she recommended learning from the parts of the city that were working and

improving the problem areas from the ground up. She envisioned cities as much more than the

sum of its residents, more like a living organism capable of adaptive change (Johnson, 2001)


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In fact, the aggregated behavior of the slime mold cells was not so different than some of the

behavior Jacobs observed in her study of the formation of city neighborhoods. Similar patterns of

self-organization were also encountered in studies of ant colonies and their foraging and nest

building behavior, in beehives, and in the distributed networks of the human brain.

Bottom-up intelligence. The theme common to these complex adaptive systems is how their

emergent behavior springs from intelligence located not in some single executive position, but

distributed equally across the individual units, in a bottom-up manner.

This is important for organizations to recognize because emergence is a way for truly new ideas

and innovations to manifest. An organization that facilitates and fosters conditions for emergence

possesses a connection to new sources of solutions to system challenges, which could even lead

to an advantage over competitors. So to summarize, conditions that facilitate emergence include:

Self-organization (in response to some sort of competition for resources); and bottom-up,

distributed intelligence across a relatively flat hierarchy of agents – in other words, a democratic

organization.

Chaos and Complexity

Chaos is a concept frequently used in connection with complexity. There are also several other

terms related to chaos and complexity that are have made their way into popular culture, such as

the idea of a strange attractor, which could be useful to understand more clearly. But in order to

discuss these concepts, a brief introduction to some foundational concepts and how scientists

work with them will be helpful.

Variables, system state, and phase space. The properties of a system are represented by

variables, with a range of possible values. Examples of potential system variables include the

amount of time a cycle of behavior requires, number of people in the organization, level of blood

pressure, etc. The values of a system’s variables at a moment in time describe the system’s state.

This can be represented on a graph using axes that correspond to the variables. The geometrical

space used to represent this state is known as the phase space. The number of variables required

defines both the dimension of the space and the system (Rickles, et. al. 2007).

Non-linearity. The solutions (or equations describing how a system could evolve) for linear

systems satisfy what is known as the superposition principle. For our purposes, the result of this

principle can be summarized by saying that inputs to the system are proportional to its outputs.

Thus a small input results in a small output, while a large input results in a large output (Johnson,

2007).

The equations for nonlinear systems however involve nonlinear terms and thus a small input can

have a large, unexpected output, while a large input may have a small output.

The way the outputs change over time for both chaotic and complex systems are categorized as

non-linear dynamics. Because of this, both chaotic and complex systems exhibit sensitivity to

initial conditions. This means that as time passes, two states that may have been very close


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together initially, even operating under the same rules, will later follow very different

trajectories. This makes it impossible to predict the evolution of the system which would require

a perfect description of its initial state, something which cannot happen since any margin of error

will itself be compounded exponentially over time (Rickles, et. al. 2007).

Deterministic and indeterministic. If it is possible to determine all the points a system passed

through and will pass through from its initial state, a system is said to be deterministic. If it is

only possible to determine the system’s future trajectory but not its past trajectory, the system is

semi-deterministic. When a system does not have a unique future trajectory, the evolution is

random and the system is said to be indeterministic.

Time-series. These characteristics can be determined by plotting the states of the system at

different times, which results in generation of a time series; it may be possible to infer the kind

of system involved, as well as whether the system is chaotic or complex based on the way the

points are spread (Rickles, et. al. 2007).

Fractal, self-similar, power laws. The properties of time-series for chaotic systems are fractal.

This means they appear the same regardless of the scale used to view them; this is also known as

self-similarity. The ability to determine complexity from time-series requires finding power law

correlations in the data, which means the system does not possess a characteristic scale, and

includes events at all levels of magnitude. Human height is an example of a phenomenon with a

characteristic scale: most humans fall within a range that averages around 5’ 8”, with a few taller

or shorter outliers (Rickles, et. al. 2007). Earthquakes are an example of phenomena that do not

have a characteristic scale). Other examples include traffic patterns, language, symbolic systems,

even healthcare systems, and other human systems. These are features which help identify a

system as complex or chaotic.

Strange attractors. When a system has been impacted by an outside force, thereby changing the

value of some variable, it requires some time to get back to its previous (or normal) behavior.

The path the system makes in phase space during this time is called transient. The state of the

system corresponding to its “normal behavior” is known as the system’s attractor. This is

generally the system behavior after passing the transient stage. If the attractor is a point that does

not move, it is called a fixed point attractor, often found in dissipative systems, which lose

energy (due to friction for example). If the attractor describes a periodic cycling over the same

set of states, it is known as a limit cycle. A system’s phase space can have multiple attractors,

whose “strength of attractiveness” depends on its initial conditions. The set of points that are

drawn towards an attractor are called the basin of attraction. Initial points that are close to each

other in fixed point and limit cycle attractors will remain close as each evolves according to the

same rules. However, points that were initially close and diverge exponentially over time under

the influence of strange attractors. Chaotic systems have strange attractors while complex

systems have a range of possible attractors, including strange attractors.

Thus, although chaotic systems and complex systems share common features, the two are very

different. The term “chaotic” is a precise mathematical determination referring to the generation

of seemingly random behavior from the iteration of a simple rule (Rickles, et. al. 2007). Chaos in

human systems is necessary to good health. For example, the rhythm of a heart beat possesses

chaotic fractals, which serve to make it more robust, while the lack of such chaotic patterns can

mean death.


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Chaotic or complex. The key differences then between chaotic and complex systems concern the

number of interacting parts and the effect those interactions have on the properties and behavior

of the whole system. The elements or agents of complex systems are coherent in ways that

chaotic systems are not (Rickles, et. al, 2007).

Critical phase transitions. Also important to complex systems is the property of criticality.

Complexity occurs in the region between order and chaos, and complex systems shift between

ordered and chaotic phases. But there is also a region known as a critical phase that occurs at the

edge of chaos which must be reached before the system can transition to another state. For

example, as water changes from gas to liquid or from liquid to solid, it reaches a critical point

just prior to transition from one phase to another where the system undergoes a radical and

instantaneous transformation in its qualitative features. The control parameter is an external

input to the system that can be used to shift the system between these phases (Rickles, et. al.

2007). The phenomenon of emergence constitutes a radical reorganization of the system to a new

state. For organizations, as well as other kinds of systems, this happens spontaneously but often

without conscious recognition from the organization members. Consequently organizations may

react by trying to control the process, or even by intervening and interrupting a natural process of

transformation trying to take place.

Applications for Organizations

With access to these few basic concepts, it becomes possible to apply the perspective of

complexity to organizational contexts, beginning with creating conditions for emergence of new

possibilities to take place. As is seen in the examples of slime mold and ant hills, collective

behavior is more likely to exhibit emergence in systems without central control or top-down

hierarchical structures and more likely to occur when individuals are encouraged to participate in

their environment and collaborate with each other as equals.

Understanding the features of chaos and complexity is also helpful when considering cycles of

transformation and change in organizations. Since complexity comprises the region between

order and chaos, the human experience of these shifts sometimes involves extended periods of

uncertainty, which is often what the phase known as “the edge of chaos” or “critical phase

transition” feels like for humans. Although humans typically prefer phases of order to phases of

chaos, understanding that the movement between order and chaos is necessary for complex

systems to transition and transform makes it possible to resist the temptation to intervene in what

feels like a disaster or even death of a system, but may in fact be the process of new possibilities

being born. Wheatley and others suggest (2006; Peat, 1987) that the phenomenon of meaning

may behave as a strange attractor, around whose influence the human agents in a system can be

aligned. This is related to the practice of using vision and mission statements, but this kind of

meaning can become a powerful vehicle for transformation and change when it manifests in a

bottoms-up rather than a top-down manner.


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Conversation as emergent behavior in humans. There are many human activities that are

emergent in nature: relationship, conversation, and learning are just a few. There are also many

examples of activities that can be leveraged within an organization to foster emergent behavior.

Conversation is a very familiar practice that possesses highly emergent potential: when engaging

conversation with another individual, even if you have an idea of the subject that will be

discussed, it is impossible to know exactly what will be said. Especially if the conversation is

unscripted and unrehearsed, what ensues has the potential to be truly emergent, and to take the

participants to new levels of insight and understanding.

Three examples of conversation as an emergent phenomenon are offered in this paper. The first

example is drawn from a case study on conversation as intervention in healthcare. The second

example involves a deep form of conversation in a small group, David Bohm’s concept of

Dialogue. The third is an example of a large group conversation, the World Café. These practices

can foster familiarity and regular participation with emergence.

Conversation in healthcare. Utilizing concepts from sociolinguistics, Jordan et. al. (2009)

explored how conversation enhanced interventions by improving sensemaking and learning, and

also reduced intervention success by inhibiting sensemaking and learning. They first

distinguished conversation from tasks such as instruction-giving and information-exchange, and

then identified sensemaking and learning as two organizational actions important for successful

intervention in the healthcare industry.

The study found that staff and clinicians produced a shared vision of how a given intervention

could improve their patients’ care through conversations that greatly enhanced real adoption of

change. Conversations among practice members provided a chance to learn about their own

thoughts and ideas while they collectively generated new ideas. It was through conversation that

people organized group thinking around problems, and jointly developed possibilities for

coordinated action. Collective sensemaking was accomplished through narrative storytelling

which was used to interpret surprising events. Sensemaking narratives tended to be nonlinear,

with multiple story tellers/creators contradicting and interrupting, offering justifications,

presenting multiple possibilities, and delineating dilemmas. Thus the physicians' willingness to

let staff speak up and voice disagreement facilitated sensemaking through multi-voiced

storytelling, which in turn led to action.

The more stable the environment, the more scripted the dialogue could be without detriment;

however for organizations wanting change, the study found that it was conversational

improvisation that facilitated learning, questioning of beliefs and practices, and building new

knowledge.

Existing routines and status relationships could also block learning, and decrease the chance of

success of intervention initiatives. The study concluded that it was less important for change

agents and other leaders to dictate actions to others than to create an organizational culture where


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learning is valued, and where diverse insights and understandings are respected (Jordon, et. al.

2009).

Dialogue. The practice of ‘dialogue’, as described by David Bohm (1996), is a regularly

occurring, deepening conversation between dedicated individuals where trust facilitates the

ability to address subjects otherwise too sensitive to discuss fully. This is an ongoing, long term

activity whose benefit builds the more it is utilized. This practice can occur within a structured

time frame, occurring regularly over a longer period of time. So for example a strategic foresight

committee choosing to engage in Dialogue might meet every week for a year or more, and each

meeting would allow each member opportunity to speak and be heard, and to listen.

Alternatively, a group of individuals may choose to meet in a secluded space over the course of a

few days, every few months or so.

Over time a level of trust develops that fosters the opportunity to share deeper levels of truth and

insight. Eventually a group that meets with the same members over a long period of time can

find that trust within the relationships deepen, providing a safe space for hard truths to surface.

The level of truth and insight that is revealed as a result of this trust is an emergent outcome of

the process of dialogue.

World Café. The World Café (Brown, 2005) is another example of emergent conversation but

generally occurs in single instances, or as needed, rather than recurring periodically. It is

especially useful in cases where a controversial issue is causing division in a community. It can

be used with fairly large groups of people, even several thousand. The Café is called around a

particular question such as, “what are the current challenges to clean transportation (or adequate

housing, or affordable healthcare, etc.) facing our community and how can we address them?”

The group is broken out into smaller groups of 4-6 individuals who spend 20-30 minutes around

small tables with markers brainstorming their responses to the question before moving to another

table where the conversation continues for another 20 minutes. This is repeated one more time.

One individual from each group remains at the table to record ideas and summarize the points

made for the next group. This conversational approach allows a serious and deep treatment of

difficult material to take place between strangers fairly quickly. It has been even been used to

negotiate union agreements between employers and employees and demonstrates how large

groups of people can quite quickly come to an emerging consensus around divisive issues.

Conclusion

Whether you find yourself in a corporation, a university, or a church community, your

organization will exhibit behaviors that are complex in nature. Thus complex perspectives hold

value for organizations through the insight they provide into group dynamics.

Conventional wisdom held that a wise individual or hero was necessary to lead a group

successfully or help an organization achieve its goals. Although democracy is cherished in

principle, organizations typically operate using traditional power and reporting structures and


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hierarchies. But complex systems reveal that top-down control and rigid hierarchical structures

actually may suppress a hidden dimension of potential genius residing at the collective level of

the organization. The actions required to unlock this potential align well with employee

engagement initiatives: encouragement for each individual to participate, and for every voice to

be heard; the recognition that each perspective offers value and that all of us together are smarter

than the smartest of us alone, or any non-representative subset of us.

Another helpful insight from complex systems concerns how they develop and change over time,

moving back and forth between order and disorder. This occurs even as the individuals in the

group themselves are moving between order and disorder, from phases of relative equilibrium

and stability to phases far from equilibrium. These dynamics are further complicated by network

interactions and relationships between individuals.

Numerous attractors and strange attractors may draw the organization towards periods of certain

behaviors. Qualities such as “fear,” “hope,” or “meaning” may operate as strange attractors,

taking over and possibly influencing the direction of the organization. Small events may have

large impact, moving the organization far from equilibrium and towards critical thresholds. This

may offer a possibility to consciously engage and participate in the process of change by

understanding what elements could serve as the control parameters, which might then be used to

shift the system dynamics, tipping it past critical thresholds towards change. This should be not

so much an attempt to manipulate the natural processes as to participate in them and be proactive

rather than reactive.

It is at this point of being “far from equilibrium” that the greatest potential for radical change

appears. During this time, the system or organization may even appear to be breaking down, in

order to make way for the new thing. Organizations are often unable or unwilling to stomach

such periods of uncertainty, but interventions at this critical time can interrupt the natural process

of change and possibly even abort the system’s attempt to birth a new order.

Thus the organization has potential to exhibit emergent phenomena which are surprising and

may even be extreme (Johnson, 2007). This can be good or bad! But when members of any

group including ministries and other spiritual organizations, come together around a shared goal

and collaborate without concern for power and control, it is not known what they can

accomplish, or what will be unleashed. But it will be something new, something created out of

their collective efforts and intentions. Perhaps churches and spiritual ministries in particular

appreciate how in this way, with each of us in conversation with the larger whole both

individually and together collectively, participate in an ongoing process of development, change,

and co-evolution.


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References

Bennett, A., Bennett D. (2004). Organizational survival in the new world: the intelligent complex

adaptive system. Oxford: Butterworth-Heinemann.

Bohm, D., (1996), On dialogue, New York NY: Routledge.

Brown, J., (2005), The World Café: Shaping our Futures by Conversations that Matter, San

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Briggs, J. & Peat, F. D. (1989). Turbulent mirror: an illustrated guide to chaos theory and the

science of wholeness. New York, NY: Harper & Row Publishers.


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Kauffman, Stuart. (2008). Reinventing the sacred: a new view of science, reason, and religion.

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Kauffman, Stuart. 2006. Retrieved March 20, 2011 from

http://www.ucalgary.ca/files/ibi/BeyondReductionism9.pdf

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change. The Academy of Management Executive, 14-4, 128-141.

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The Santa Fe Institute: http://www.santafe.edu/

SFI’s complexity explorer: http://www.complexityexplorer.org/

http://www.ucalgary.ca/files/ibi/BeyondReductionism9.pdf

http://www.santafe.edu/

http://www.complexityexplorer.org/

Complexity 101 by Cynthia Cavalli

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