Reflection and Learning

BACKGROUND

It may be helpful to differentiate between CS as the application of mathematical and physics science to large data sets and the use of computer modeling. CASs are special cases of complexity in which some of the basic constructs are used and generally thought of more as complexity theory or a conceptual framework. CS is grounded in theoretical mathematics and physics and is concerned with the interconnection between agents ( Kauffman, 1995 ;  Waldrop, 1992 ). Agents are units or components of the system; they may consist of individual people, birds in a flock, bees in a hive, or a component of a human system such as genes or neurons. An example is the heart and circulatory system, both of which act as agents within a body.

Many of the complexity concepts come from chaos theory, quantum mechanics, and nonlinear mathematics. It is the interconnection between or among these agents that is the focus of CS. Chaos theory signifies a different concept from the common usage of the term chaos. In a chaotic system that conforms to chaos theory, behaviors may appear to be chaotic, random, or incoherent when analyzed in a linear fashion; in contrast, when analyzed using nonlinear approaches, the system exhibits dynamic patterned variation. For this reason, chaos theory is described as patterned complexity rather than being equivalent to the common usage of the terms chaos and random incoherence.

CS provides language, metaphors, conceptual frameworks, models, and theories that can be applied to health care. A metaphor is a figure of speech in which a word or phrase literally denotes one kind of object or idea in place of another to suggest a likeness or analogy between them. Metaphors are useful in describing complicated concepts because they provide a good conceptual analogy, allowing us to communicate and think about abstract concepts. For example, the metaphor of a machine is used to convey the idea of parts that can be identified and understood. This metaphor has been successfully applied to understanding the mechanisms of how things behave or operate.

The use of metaphors in CS also can help communicate abstract information. These metaphors shape our thinking and perspective by connecting an idea to a known concrete entity. In one sense, then, all scientific thinking is metaphorical because the metaphors influence which questions we ask and how we understand phenomena. The machine metaphor has been the predominant metaphor shaping our thinking about physiology and guiding medical research. Indeed, a large amount of medical research is devoted to understanding the mechanisms, whether they are at the genetic, biochemical, structural, or physiological levels. For example, clinical practice and research trials are predicated on understanding the mechanism of a disease or disorder, developing an intervention to repair function, or interrupting a disease process. The expected outcome is better function or a reduction in signs or symptoms of the disease. This linear process approach is a dominating factor in terms of the way we think about clinical research and patient care, with the outcomes being based on efficacy and efficiency.

The machine metaphor not only has been used to make sense of physiological functions of the body, but it has also been applied to social organizations. Although useful, this metaphor has limitations for understanding individual behavior and organizations. CS, by comparison, provides a different metaphor that looks at living systems as complex, adaptive, self-organizing systems. CS is concerned with the relationship among the units, components, or agents rather than just the components themselves. This perspective sheds light on how individuals and organizations behave and how change happens ( Zimmerman, Lindberg, & Plsek, 1998 ). Physicists have identified laws of quantum mechanics on the micro (atomic and subatomic) and cosmic levels, which have been metaphorically applied at the intermediate levels (i.e., the behavior between the atomic and cosmic levels). Currently, new mathematical models are being applied to science at all levels, and the language has begun to penetrate other areas, including health care. Adding this perspective of understanding is most relevant to health care and to the profession of nursing.

Many healthcare disciplines are beginning to acknowledge the limitations of using only reductive approaches and the machine metaphor (Plsek & Greenhalgh, 2001; Sturmberg & Martin, 2009). Consider an example from the field of genetics. Researchers who identified human genes inspired new questions about how these genes are activated. Genetic researchers have become aware of the importance of understanding the gene and its molecular aspects (micro level) as well as the context, the environment, and the ecosystem of behavior (macro level) influencing gene behaviors. Elucidation of both levels is necessary for understanding how genes behave in a human organism related to expressions of health and disease. As noted previously, scholars are not suggesting that CS wholly replace analytical reductive science; rather, CS embraces both the reductive and the complexity perspectives ( Lindberg, Nash, & Lindberg, 2008 ). Using this approach in nursing allows us to begin to address the complex challenges of patient behaviors, work environments, and care environments.

THE SCIENTIFIC ROOTS OF COMPLEXITY SCIENCE

A brief introduction to the mathematical and scientific roots of CS grounds the understanding of the concepts, which are applicable to other systems such as individuals, groups, and organizations. The following descriptions of nonlinearity are adapted from Liebovitz’s (1998) explanations of fractals and chaos, with the concepts being simplified here for the life sciences. Another scientific application of CS lies in coordination dynamics where mathematics is applied to reconcile the polarized world of contradictory pairs for the purpose of understanding the poles and the world between them.

Nonlinear Mathematics

Nonlinear mathematics provides a language to explain complex dynamic changes over time and three-dimensional space (four dimensions); it focuses on the interactions among variables, rather than the variables themselves. Some of the major concepts are presented in this section, although a full discussion of the mathematical and science concepts is beyond the scope of this chapter. Nonlinear approaches can be applied when the data do not follow a normal distribution pattern or when additional data do not fall close to the norm. Given that these concepts apply to the mathematical functions and patterns, one must be careful when applying them directly to other observed phenomena.  Table 6-2  lists some nonlinear dynamics concepts.

Table 6-2 Nonlinear Dynamics Concepts

· Nonlinearity · Focus on simple rules · Coupling · Deterministic · Sensitivity to initial conditions · Fractals and self-similarity · Scaling · Emergence

Focus on Simple Rules

These mathematical functions refer to the rule that describes relationships. For example, in the common equation 1 + 1 = 2, a linear approach deductively examines the dependent variable as 2 and then focuses on the two independent variables of 1. In contrast, the nonlinear approach focuses on the plus (+) symbol, which is the rule or mathematical function that explains the relationship between the variables.

Coupling

Coupling examines the strength of the relationship between functional units. Additionally, the whole may be greater than the sum of the parts.

Deterministic Systems

In a deterministic system, system behavior is not random, but rather has coherence or a pattern. A small number of equations and the understanding of their variables from the past can be used to compute values in the short term. Computer modeling may be used to make these explanatory predictions. The simple rules/functions specify how these variables change over time and space, at least in the short run.

Sensitivity to Initial Conditions and Perturbations

An initial condition is the starting point of a dynamic system, and a small difference in starting values can result in significantly different trajectories. To understand this concept, picture a plot on a graph where a minuscule difference in the starting point could alter the angle of the trajectory so that it points in a different direction if you tracked it over time. These perturbations can disrupt a system or create new emergent behaviors.

Fractals and Self-Similarity

A fractal is a geometrical pattern or structure that is self-similar and repeating. Similar patterns are repeated on different scales or resolutions. The similarity may be apparent, but when used strictly in mathematics and science, it is also mathematically similar so that the scales are proportional (i.e., self-similar). A common classic example from the discovery of fractal geometry is provided by  Mandelbrot (1967 ,  1982 ). He described the coastline of England as a fractal because as it is observed from closer points of view (i.e., by changing the scale of magnification), the patterns appear to be repeated in a self-similar pattern. If you were to use binoculars and take repeatedly closer looks (different scales of resolution) at the entire coastline, you would likely see similar patterns, albeit on an even smaller scale. In CS, one finds self-similar mathematical patterns.

Scaling

The different resolutions of measurement are referred to as scaling. Perhaps more familiar examples of fractals would be the body’s networks of blood vessels or the branches of a tree, in which the patterns are repeated on different scales; in other words, patterns are repeated at increasingly finer and finer resolution. In CS, new information is apparent at finer resolutions. As the object is viewed under continually closer resolutions, the object appears more complex rather than exactly the same. Liebovitz (1998) distinguished nonfractal objects from fractal objects when he stated, “As a non-fractal object is magnified, no new features are revealed. As a fractal object is magnified, ever finer features are revealed. The shapes of the smaller features are kind of like the shapes of the larger features” (p. 4).

Emergence and Coordination Dynamics

The unpredictability of a complex system that allows for new and unexpected behavior that is generated by the system itself is called emergence. Another scientific application of complexity lies in coordination dynamics—the study of patterns of coordinated behavior in living things.

In addressing coordination dynamics,  Kelso and Engstrom (2006)  noted, “It is a set of context dependent laws or rules that describe, explain, and predict how patterns of coordination form, adapt, persist and change in natural systems” (p. 90). These dynamics exist between two points or between complementary pairs. In their book The Complementary Nature, Kelso and Engstrom explored many of the contraries or oppositional pairs that are ubiquitous in the way humans make sense of the world. Coordination dynamics are a way to begin to understand these dichotomies, dualities, and polar opposites.

Complementary Pairs

By definition, complementary pairs are opposed; they actually are coexistent, linked, and often mutually dependent.  Kelso and Engstrom (2006)  recommended that these dichotomized pairs be written with the tilde symbol (~)—for example, mind~body, random~determined, objective~subjective, local~global, stability~instability, and qualitative~quantitative. In approaching complementary pairs, it is important to understand not only both poles of the pairs, but also the dynamics that occur between them. A scientific example is the wave~particle theory of light. The dynamics of waves and particles, as well as the interaction between them, are needed to describe light comprehensively.

Coordination dynamics offers a way to address the whole~part phenomena. Using coordination dynamics harkens back to the appeal to think in terms of “both/and” rather than “either/or” or “black/white.” Likewise, the study of living things can be studied by reductive/analytical~emergent approaches. One key feature of coordination dynamics is control parameters, which can be either endogenous or exogenous. Control parameters can cause the system to adapt and change; conversely, they can be stabilizing to the system. Thus, the control parameters may be described as stabilizing~destabilizing/transformative. Life is replete with ambiguities and paradoxes.

COMPLEX ADAPTIVE SYSTEMS

The term complex adaptive system (CAS) refers to special cases of complex systems. CASs have been studied for more than 40 years, beginning at the Santa Fe Institute, where interdisciplinary researchers investigated the application of CS in physical, biological, computational, and social sciences. Other groups across the world also have engaged in the application of CS and CASs to real-world problems. The control within a CAS is decentralized and dispersed. Coherent behavior in the system arises from competition and cooperation among the agents ( Waldrop, 1992 ). A CAS is a collection of individual agents with freedom to act in ways that are not always totally predictable and whose actions are interconnected so that the action of one part changes the context for other agents ( Wilson, Holt, & Greenhalgh, 2001 ).

A CAS is a network consisting of many agents that follow simple rules, are in constant dynamic interaction with one another, and can generate complex structures. The CAS has the capacity to adapt and become a good fit within a changing environment. This ability to adapt allows the organism or organization to continually modify itself relative to a changing environment by changing the rules of interaction among component agents. The process of adaptation occurs through learning new rules or behaviors and accumulating new experiences. In this process, the organism/organization evolves through a process of self-organization, which, in turn, allows for creativity and lacks complete predictability. A cardinal aspect of a CAS is that small actions or inputs may have large effects; conversely, large inputs may have small effects.

A CAS has a high degree of adaptive capacity and is characterized by self-similarity, complexity, emergence, and self-organization. The concept of CASs has been used to describe a flock of birds, a school of fish, a hive of bees, a human body, a family, and a community in which each level is considered a system that is different from, and more than, the sum of its parts of components. Each of these CASs is embedded in larger contexts. Moreover, CAS concepts have been applied to human physiology, such as cellular networks, neural networks, and body systems such as the cardiovascular system. It is clear that the application of a CAS perspective is broad and wide, and relevant to health care and nursing.

Components of Complex Adaptive Systems

A CAS is composed of agents that interact within the system. These agents also may be considered a CAS embedded in a larger CAS. For example, an individual in a family is a CAS as well as an agent in the family. These agents act within the system according to patterned behavior. That behavior may self-organize at the CAS level, leading to new emergent behavior. The components of these networks are agents and patterns.

Agents

Agents are units or components of the system, which, as previously mentioned, can be individuals, birds in a flock, bees in a hive, or a component of a human system such as genes or neurons. These agents interact in a particular way, and their interactions and the patterns of interaction are the focus of CS, rather than understanding the agent in isolation. The interaction enables the system to function in a way that could not be understood from the examination of only the components.

Each agent also may be a CAS and, in turn, is also part of a larger CAS. For example, a nurse in a clinic is a CAS as well as an agent in the CAS of the clinic. The nurse coevolves with the unit as the system emerges from the patterns of interactions. In this way, the nurse also contributes to and is affected by the organization of the unit.

Control of a CAS is highly dispersed and decentralized, and the overall behavior of the system is the result of multiple decisions by individual agents ( Waldrop, 1992 ). The agents, as well as the system, are adaptive. Agents have fuzzy boundaries and simultaneously may be members of several systems. Examples include the immune system, a financial market, and a hospital unit.

Patterns

Patterns are formed by agents acting from a set of internalized rules. In a biochemical system, these patterns can be a series of chemical reactions; in an organization, they comprise the behavior of individuals or groups. Patterns and behaviors are the focus of understanding the CAS. Again, the emphasis is the relationships among the agents, rather than the agents themselves. Agents within a CAS have patterns of behavior that evolve. They absorb their past history and experiences, as well as respond to endogenous and exogenous changes. The CAS can develop rules that shape the interaction of the agents. Such systems are capable of emergent patterns that may be incorporated into the CAS’s future behavior.

Characteristics of Complex Adaptive Systems

The following characteristics are part of the CAS. Many of these constructs are similar to some of the features of the mathematical and science disciplines (see  Table 6-3  for a summary of CAS characteristics). Here, however, they are applied in a more conceptual or theoretical framework, which allows them to be applied to clinical situations, thereby demonstrating both a patient and an organizational focus.

Table 6-3 Complex Adaptive Systems Concepts

Components · Agents · Patterns

Characteristics · Connectivity · CASs are dynamic and adaptive · Simple rules allow CASs to function · Emergence is a property of a CAS · CASs are self-organizing · Control is distributed rather than centralized · Diversity maximizes self-organization · CASs are deterministic · CASs are exemplified by embeddedness (i.e., a CAS may be nested within a larger CAS) · Coordination dynamics occur within a CAS · CASs are sensitive to initial conditions · Coevolution occurs with a CAS · CASs are robust and adaptable

Connectivity

Agents or components of a system are connected to other components both within the system and within larger systems.

Complex Adaptive Systems Are Dynamic and Adaptive

A key characteristic of a CAS is that it is dynamic; that is, the system can adapt to changes in both its internal and external environments. The organism or organization demonstrates transformations of behavior through multiple modes of behavior. According to general systems theory, a system that loses its ability to maintain its equilibrium may cease to exist. Lindberg and colleagues (2008) note that nurses, physicians, and other healthcare professionals often have an orientation toward homeostasis and strive to maintain the status quo both in physiology and in organizations. A healthy system often features processes that keep the system in a balanced or dynamic state of equilibrium so that the system can adapt to both internal and external stimuli. Complexity exists in the dynamic balance between stability and instability.

Goldberger (1996)  provided a physiological illustration of this balance through a discussion of heart rate, rhythm, and cardiac output. At one extreme is a patient who is stable, but whose electrocardiographic tracing strip reflects heart failure with minimum variability. This lack of variability indicates a system that is unable to respond or adapt to changes or demands on the system. This is a most unhealthy, nonadaptive state. Conversely, fibrillation, in which the heart demonstrates chaotic, highly irregular, and unpatterned rhythms, is both dysfunctional and life threatening. These examples stand in contrast to a healthy cardiac system, which operates within the range of complexity so that there are continual alterations in the heart rate in response to small and large stimuli. Thus, the heart is capable of adapting to its changing environment by adjusting output.

Simple Rules Support Complex Adaptive Systems

It is posited in CS that a CAS operates by an adherence to a few simple rules that allow the system to function. These rules are not overly specific, written, or overt; rather, they are part of the function of the components of the system. A frequently cited example is used to illustrate some of the rules that allow a flock of birds to fly in a group. The following rules have been suggested by computer modeling: (1) Maintain a minimum specific distance from other birds; (2) match velocity with other birds in the flock; and (3) move toward the center of the mass of birds in the neighborhood. Imagine what it would take to give specific instructions to each bird, depending on the bird’s location within the flock.

A different set of rules operate in a bee hive, where residents of the hive have different specific roles and each bee (the agent) adapts to certain external and internal environmental stimuli based on simple rules. When agents follow these rules, a form of collective behavior, a structure or a process that is more complex than the rules that produced it, emerges ( Paley, 2007 ). These simple rules guide the CAS and allow it to act as a system. For example, an application of simple rules allows social insects such as bees to form a collective unit that enables them to behave like a single organism or system ( Kauffman, 1995 ;  Waldrop, 1992 ). Scientists and mathematicians have begun to discover some of these rules and are seeking to construct mathematical models to understand the behavior of the CAS—namely, its ability to behave as a single organism with multiple agents.

Emergence Is a Property of Complex Adaptive Systems

Emergence has been compared to novelty, flexibility, and the creative advancement of a system ( Capra, 2002 ). Novel or new structures, patterns, properties, or processes can emerge during the process of self-organization in a CAS. Examples of emergence include the ways in which termites and ants build large complex structures that appear to have had an architect, designer, or structural engineer guiding the process. The human brain may operate in a similar fashion, as individual neurons (i.e., agents) operate locally and in networks through some simple patterns of reciprocal activation. In both cases, the collective structure or activity cannot be deduced from individual behaviors. These emergent behaviors can be innovative and creative. It is also a possibility that some emergent patterns will not be sustainable or adaptive over the long run.

Complex Adaptive Systems Are Self-Organizing

A cardinal property of a CAS is that it is self-organizing. Most CASs operate on a stability~instability dynamic. Self-organization requires appropriate conditions, often described as far from equilibrium or on the edge of chaos. This terminology indicates a zone that is closer to the instability pole where change can occur. Given its ability to exhibit emergent collective behavior, the CAS can self-organize into novel or new patterns. In this way, it moves away from a simple equilibrium or stable state and activates the nonlinearity inherent in the system. This evolution may lead to a new pattern and change the dynamics of the system. The important concept here is that these activities, behaviors, or patterns come from the CAS without the imposition of a central grand plan or control, or an externally imposed plan.

Control Is Distributed Rather Than Centralized

A characteristic of self-organization in a CAS is distributed control. This term implies that the agents or components do not act through a central agent or blueprint within or outside of the system. There is no central control that is responsible for the behavior or structures that emerge; rather, the CAS is a network of disparate agents exhibiting coherence and the ability to change without central direction or a single intelligent executive function. The commonality in the examples of emergence is that the activity or structure is produced by local interactions, without any central command center. This principle is often referred to as distributed control ( Lindberg et al., 2008 ), previously addressed.

Unexpected complex results from the decentralized local interaction of agents often appear to mimic more centralized organized and planned activities that are evident in a growing number of disciplines ( Paley, 2007 ). For example, the brain has approximately 100 billion individual neurons—agents interacting with one another through hormonal and other mediators, which are themselves often localized ( Holt, 2004 ). The individual neurons have no cognitive capacity, but nonlinear interactions between them may generate higher-level cognitive functions. Distributed control is an important characteristic of self-organization. Nevertheless, the self-organization is not always independent of the environment because often it is the environment to which the CAS adapts.