This document discusses complexity science and complex adaptive systems (CAS). It defines complexity science as emerging from simple rules and focusing on relationships among variables. CAS are made up of agents that interact according to patterns and simple rules, which can result in complex and unpredictable behaviors. Healthcare organizations are examples of CAS, as they consist of interconnected systems that are dynamic and adaptive. Viewing healthcare through the lens of complexity science and CAS fits with nursing's holistic approach and supports flexibility, creativity and decentralized leadership in organizations.

1. Chapter 6
Complexity
Science and
Complex Adaptive
Systems

2. Introduction
• Complexity science is a new approach based
on the idea that complexity emerges from
simple rules.
• This paradigm allows us to view health care in
the context of complex adaptive systems
(CAS).
• Complexity science and CAS fit well with
nursing, because both promote adaptability
and employ a holistic point of view.

3. Introduction to Complexity Science
• Complexity science is based in physics and
mathematics, and it utilizes simple rules to
explain relationships among variables.
• This approach allows for variations and outcomes
that aren’t fully predictable.
• Complexity represents an attempt to overcome
the limitations of the analytic/ reductionist
approach to understanding nature.

4. Background
• Complexity science is concerned with the
interconnection between “agents,” or units or
components of a larger system.
• Many concepts of complexity science come from
chaos theory, quantum mechanics, and nonlinear
mathematics.
• According to chaos theory, even though the
behaviors with a system may appear random, when
analyzed using nonlinear approaches, they exhibit
dynamic, patterned variation.
• Away from machine metaphor.

5. Roots of Complexity Science (1 of 3)
• Complexity science is rooted in both nonlinear
mathematics and coordination dynamics.
• Nonlinear mathematics focuses on interactions
among variables (rather than the variables
themselves) to explain complex changes over
time and space.
• Nonlinear approaches can be applied when data
do not exhibit a normal distribution or fall close
to the norm.

6. Roots of Complexity Science (2 of 3)
• Major concepts of nonlinear mathematics
include:
– A focus on simple rules
– Coupling: the strength of relationships among
functional units
– The view that system behavior is deterministic
rather than random
– Sensitivity to initial conditions
– Fractals and Self-Similarity
– Scaling
– Emergence

7. Roots of Complexity Science (3 of 3)
• Coordination dynamics is the study of
patterns of coordinated behavior in living
things.
• Major concepts of coordination dynamics
include:
– Pattern dynamics
– Complementary pairs
• Offers a way to address the whole-part
phenomena.

8. Complex Adaptive Systems (1 of 2)
• A complex adaptive system (CAS) is a
collection of individual agents:
– With the freedom to act in ways that aren’t
always predictable
– Whose actions are interconnected so that the
action of one agent changes the context for other
agents
• CAS is a network.

9. Complex Adaptive Systems (2 of 2)
• Within a CAS:
– Control is decentralized and dispersed
– Coherent behavior arises from competition and
cooperation among the agents
– The agents follow simple rules, are in constant
dynamic interaction, and can generate complex
structure.
• A CAS has a high degree of adaptive capacity and
is characterized by self-similarity, complexity,
emergence, and self-organization.

10. Components of CAS (1 of 2)
• A CAS consists of agents that interact within the
system according to patterned behavior.
• Agents are units or components of the system:
– Agents interact in a particular way
– These patterns of interaction enable the system to
function in a way that cannot be understood by
examining the system components separately
– An individual agent may also be a CAS and/or be part
of multiple systems

11. Components of CAS (2 of 2)
• Patterns are formed by agents acting from a
set of internalized rules:
– Agents have patterns of behavior that evolve over
time
– A CAS can develop rules that shape the interaction
among agents and therefore affect the agents’
patterns of behavior

12. Characteristics of CAS (1 of 2)
• Complex adaptive systems:
– Connected to other components in a system
– Are dynamic and adaptive
– Are supported by simple rules
– Exhibit the property of emergence
– Are self-organizing
– Are marked by distributed rather than centralized
control
– Exhibit diversity

13. Characteristics of CAS (2 of 2)
• Complex adaptive systems:
– Are deterministic
– Are marked by multiple layers of embeddedness
– Involve coordination dynamics
– Are sensitive to initial conditions
– Are sites of co-evolution
– A robust system can respond to external and internal
changes

14. CAS, Complex Responsive Processes,
and Organizations (1 of 2)
• Some scholars view organizations as CASs:
– Here, traditional management theory is seen as
too structured and hierarchical
– According to this view, leaders can promote
flexibility, creativity, emergence, and innovation
by providing employees with only general
direction and a few basic rules and allowing for
innovation and rewards

15. CAS, Complex Responsive Processes,
and Organizations (2 of 2)
• Other scholars argue that organizations are not
CASs but rather groups that exhibit complex
responsive processes (CRPs):
– According to this view, organizational knowledge is found
in the relationships and conversations between the people
in an organization
– These everyday conversations raise the potential for both
continuity and change
– Within nursing, CRP approaches are an important part of
relationship-centered care (RCC)

16. Implications for Practice (1 of 2)
• Practice has various size systems that are related and
connected through relationships.
• Health care organizations (HCOs) are examples of
higher-order complexity systems, because they
usually consist of several systems embedded in other
systems.
• The CAS approach is especially useful for modern
HCOs because rapid system changes make creativity,
autonomy, and flexibility more important than ever
before.

17. Implications for Practice (2 of 2)
• Viewing an HCO as a CAS promotes the emergent
model of leadership, in which an organization’s
leaders are collaborative co-participants with the
people they supervise.
• This approach also opens up new models for
research, such as action research, studies of
positive deviance, and appreciative inquiry.

18. Application to Health Care
and Nursing
• Within the clinical setting, the CAS approach
has led to equipment and methods that offer
more variability to individual patients and are
more attuned to pattern identification.
• The CAS approach also fits well with the
nursing’s holistic view of the individual.

19. Conclusion
• Complexity science focuses on relationships
among variables and allows for emergent
behaviors.
• Complex adaptive systems consist of agents
whose behaviors aren’t always predictable yet
always affect the context for other agents.
• Both complexity science and CASs represent an
alternative to the reductionist view and fit well
with modern health care and nursing.