Complexity sciences, in plain English, are the sciences of interconnectedness.
Basically, a system consists of a number of parts that are connected to each other. Systems differ depending on the nature of their connectedness. Simple systems have one-to-one relationships and their behaviour is precisely predictable. Complicated systems have one-to-many relationships with mostly predictable behaviours.
This book deals with complex adaptive systems with many-to-many relationships. Their many-to-many relationships make their behaviour emergent, hence their outcomes are unpredictable. Complex adaptive systems have a special characteristic, the members of the system can learn from feedback and experiences. The relationships in complex adaptive systems change constantly allowing the system to evolve over time in light of changing demands. However, a system’s overall behaviour, despite its adaptation to changing circumstances, remains relatively stable within boundaries, but occasionally, its behaviour may change abruptly and dramatically for no apparent reason.
Another important characteristic of complex adaptive systems is its nonlinear behaviour to change, i.e. the magnitude of change in one member of the system shows a disproportional change in that of others. As experience shows, small changes in the behaviour of a system member often show dramatic changes in the behaviour of the whole system, whereas a major change in the behaviour of that member typically results in little or no change.
Studying complex adaptive systems aims to understand the relationships and the dynamics between the members of the systems. This understanding allows for better responses when the system as a whole is challenged by constraints and/or unfamiliar challenges.
A special characteristic of social systems is their “goal-delivering” nature. In organisational terms these are codified by their purpose, goals, and values statements.
Overview. Complexity sciences, in plain English, are the sciences of interconnectedness.
Basically, a system consists of a number of parts that are connected to each other. Systems differ depending on the nature of their connectedness. Simple systems have one-to-one relationships and their behaviour is precisely predictable. Complicated systems have one-to-many relationships with mostly predictable behaviours
This book deals with complex adaptive systems with many-to-many relationships. Their many-to-many relationships make their behaviour emergent, hence their outcomes are unpredictable. Complex adaptive systems have a special characteristic, the members of the system can learn from feedback and experiences. The relationships in complex adaptive systems change constantly allowing the system to evolve over time in light of changing demands. However, a system’s overall behaviour, despite its adaptation to changing circumstances, remains relatively stable within boundaries, but occasionally, its behaviour may change abruptly and dramatically for no apparent reason
Another important characteristic of complex adaptive systems is its nonlinear behaviour to change, i.e. the magnitude of change in one member of the system shows a disproportional change in that of others. As experience shows, small changes in the behaviour of a system member often show dramatic changes in the behaviour of the whole system, whereas a major change in the behaviour of that member typically results in little or no change
Studying complex adaptive systems aims to understand the relationships and the dynamics between the members of the systems. This understanding allows for better responses when the system as a whole is challenged by constraints and/or unfamiliar challenges.
A special characteristic of social systems is their “goal-delivering” nature. In organisational terms these are codified by their purpose, goals and values statements.
Points for Reflection
What do you understand by the terms “complex/complexity”?
What do the terms “complex health system”, “complex disease”, and “complex patient” mean to?
How do you explain the nature of this “complexity”?
How do you suggest to best manage this “complexity”?
Systems thinking is a discipline of seeing whole.
– Peter Senge
Everyone has experienced the complexities of the health system, irrespective of their particular role along the continuum of being a patient, working in grass roots care delivery to having overarching policy and financing responsibilities. We are all part of many different systems within the entire health system. We all have observed and experienced the at times surprising behaviours inside our “immediate working system” and the system as a whole. Most of us would have forwarded hunches why a particular system outcome may have occurred. Some of us may well have been involved in analysing “system failures”, but did we do so from an understanding of the interconnected behaviours of complex adaptive systems?
“Complexity sciences” still is an emerging field of scientific endeavour (Addendum 1) and entails a number of different methodological approaches like system dynamics, agent-based modelling, or network analysis
The colloquial meaning of complex/complexity needs to be distinguished from its scientific meaning. The colloquial meaning of complex/complexity as “difficult to understand” or “complicated” must be distinguished from the scientific meaning of “the property arising from the interconnected behaviour of agents”
“Complexity sciences” defines a worldview that no longer sees the world as mechanistic, linear, and predictable. Rather it sees the world as interconnected. The interactions between elements being nonlinear make the behaviour of complex systems unpredictable (Fig. 2.1)
Paul Cilliers outlined the philosophical foundations of complexity sciences, parts of which are quoted in more detail in Addendum 2
The “complexity science framework”, like any other scientific framework, provides a mental mind model ABOUT the world, i.e. The truth of a theory is in your mind, not in your eyes—Albert Einstein 
Mental models (or worldviews) necessarily have to reduce the real complexity of any phenomenon being described [2, 3]. Useful models, as Box  stated,1 are those that describe the observed causal relationships in the real world2 
“Complexity” in its scientific understanding refers to “the nature of the problem not [emphasis added] the degree of difficulty” . The systems theorist David Krakauer illustrates this aspect in relation to Ebola and is quoted in detail in Addendum 3
“Complexity” exists at every scale, be it at the laboratory or the whole of society level
The way we look at “things” determines what we see and how we understand. Understanding “things” at the small scale results in greater certainty BUT loss of context, whereas understanding “things” at the large scale results in greater uncertainty AND loss of detail (Fig. 2.2)
2.1 Complex Systems Are …
2.2 The Essence of Systems Thinking
As Gene Bellinger put it so succinctly: the Essence of Systems Thinking is Understanding Relationships and Their Implications.3
2.3 Complex Systems Theory: An Overview
Complex systems theory has arisen from two main schools of thought—general systems theory and cybernetics. As a theory it provides a model of reality NOT reality itself . However, models provide a useful frame to solve many common problems.
Result of a long day at work
In simple systems, elements of the system interact in one-to-one relationships producing predictable outcomes. Simple systems can be engineered and controlled. They are closed to and therefore not influenced by their external environment.
Complicated systems display some of the same characteristics of simple systems in that interactions between elements in the systems are predictable, although any one element of the system may interact with multiple other elements of the system. Relationships are still linear and outcomes remain predictable. Generally speaking, “complicated” refers to systems with sophisticated configurations but highly predictable behaviours (e.g. a car or a plane)—the whole can be decomposed into its parts and when reassembled will look and behave again exactly like the whole. They are also closed to and therefore not influenced by the external environment.
Complex dynamic systems have two key characteristics, they self-organise without external control and exhibit feedback resulting in newly created, i.e. emergent (at times unforeseen), behaviours. Complexity is the dynamic property of the system; it results from the interactions between its parts. The more parts interact in a nonlinear way in a system the more complex it will be. Complex systems are also open, loosely bounded, and influenced by their environment. Such fuzzy boundaries entail some arbitrariness in defining a system.
While any one system as a whole may be defined as a complex system, inevitably subunits are also complex systems in their own right. Thus any defined complex system has to be thought of as being simultaneously a subsystem of a larger system (or a supra-system) and a supra-system constituted by a number of subsystems (defining the nested structure of systems).
Complex adaptive systems (CAS) are complex dynamic systems whose elements (agents) learn and adapt their behaviours to changing environments. In the complex adaptive systems literature the elements of the system are referred to as agents. Complex dynamic and complex adaptive system behaviour is influenced by the system’s history, i.e. influences that have resulted in the current state of a system have ongoing effects on future states.
The make-up of the complex and complex adaptive systems presents certain problems in terms of being able to understand, describe, and analyse them. While simple and complicated systems lend themselves to cause-and-effect analysis, complex and complex adaptive systems require a mapping of relationships and drawing of inferences that may be theory based or drawn from multiple sources of knowledge. The Cynefin Framework  provides an excellent way to understand the different degrees of complexity in CAS and is discussed in detail in the next chapter.
Understanding the differences between types of systems is often the clearest way to differentiate the various types of systems. Table 2.1 summarises features of simple, complicated, and complex systems and the language used in the literature to describe them.
2.4 A Detailed Description of “Complex Adaptive Systems”
CAS are systems whose components/agents can change in their characteristics and behaviours over time as they are able to learn and adapt. Characteristics and behaviours of individual components/agents are often well understood; however, when components/agents interact in nonlinear ways and provide feedback to each other, the outcomes of the system’s behaviour have a level of unpredictability. While the underlying “cause and effect relationships” resulting in the observed system’s behaviour are understandable in retrospect, their behaviour cannot be precisely predicted looking forward .
Key properties of complex adaptive systems (CAS)
• Results not proportional to stimulus
• Can lead to sudden massive and stochastic changes of the system
• Sensitive to initial conditions
• Accumulations, delays, and feedbacks
• Allergic responses and anaphylaxis
• More intensive glucose control increase mortality 
• Response to coumadin-therapy
• Increasing the dose of chemotherapy does not improve therapeutic response or survival 
• Chemotherapy initially not only reduces tumour size but also induces the promotion of secondary tumours 
• Large investment in health services has not been matched by a similar magnitude of improvement in inequity between social classes 
Open to environment
• A system continuously interacts with its environment, e.g. exchanging material, energy, people, capital, and information
• Nonlinear responses to the external environment can lead to sudden massive and stochastic changes
• Physiological function
– Immune system
– Respiratory tract
– Gastrointestinal tract
– Semi-permeable membranes
• Pathological function
– Food poisoning
• Strategies to train and maintain more health professionals need to account for competing individual, organisational and social factors in motivation, and other markets 
• An epidemic like SARS arises from the global openness to fluidity, flows, mobility, and networks 
• Relies on four basic principles
– Recursive feedback (positive and negative)
– Balance of exploitation and exploration
– Multiple interactions
• “Homeostasis” in health, e.g.
– Blood glucose levels
– Thyroxin levels
– Water balance and creatinine levels
• And disease, e.g.
– Stable heart failure
– Intermittent claudication
• DRG (Diagnostic Related Group) payment mechanisms leads to
– Category creep
– Shift of emphasis 
• The natural formation of viable high performing teams is based on multiple interactions and feedback 
Emergence relies on four
• Occurs when a number of simple entities (agents) operate in an environment, forming more complex behaviours as a collective
• Arises from intricate causal relations across different scales and feedback—interconnectivity
• The emergent behaviour or properties are not a property of any single such entity, nor can they easily be predicted or deduced from behaviour in the lower-level entities: they are irreducible
• Appearance of superbugs in response to antibiotic therapies
• Appearance of previously unknown infectious disease epidemics like SARS 
• Emergence of drug side effects in particular individuals
• Emergence of new patterns of morbidity, gene expression, as the population ages
• Brain function from complex cellular self-organisation
• Prevention paradox—inequities emerge when “innovative” health promotion guidelines are put into place without considering social and cultural assumptions between public health practitioners and target groups as is seen in
– Screening programmes
– Well baby checks
– Teenage pregnancy education
– Smoking cessation programmes 
• The addition of nurse practitioners to primary care
– Did not alter costs or efficiencies
– Did address considerable other unmet needs 
Pattern of interaction
• Different combinations of agents lead to the same outcome, or
• The same combination of agents leads to different outcomes
• Sinus-rhythm heart-rate variability in patients with severe congestive heart failure 
• Loss of beat-to-beat variability in autonomic neuropathy 
• Cheyne–Stokes breathing 
• Most patients with cancer display drastically different patterns of genetic aberrations 
• Many biological factors (genetic and epigenetic variations, metabolic processes) and environmental influences can increase the probability of cancer formation, depending on the given circumstances 
• Patterns of maternity provider interaction appropriate for the local context influence the emotional well-being of rural mothers 
• International comparison shows that many diverse multifaceted health services lead to remarkably similar outcomes
– Smoking cessation successes 
– Obesity challenges exist across diverse cultures and levels of development despite evidence-based national dietary guidelines 
Adaptation and evolution
• In the clinical context, numerous diseases develop over many years, during which time the “whole body system” has adapted to function in the altered environment
• Changes involve the whole system and are not restricted to a few clinically measurable factors
• Adaptation leads to a new homeostasis with new dynamic interactions [28 ]
• Coronary artery disease due to stable plaques
• “Burnt-out” rheumatoid arthritis
• Stable chronic obstructive airways disease
• Coeliac disease
• Hearing impairment
• Adjustments to the health care system due to challenges in
– Health care delivery
– The rate of development of new health technologies
– Rising community expectations [29 ]
• Stable ritual of clinical care delivery despite ongoing reforms, research, and interventions [30 ]
• Healing tradition moves from mainstream health care to alternative health care [31 ]
• Each agent in the exchange is changed
• Parallel development of a subsystem with new characteristics and dynamics
• The physician learns from the patient and the patient learns from the physician 
• A person becomes blind AND develops superb hearing
• Microorganisms succumb to antibiotic therapies AND some develop drug resistance
• Local systems function well in response to local need in spite of or in parallel to top-down health initiatives
– User driven health care 
– Self-help groups 
– Health 2.0 
Agents (or components) are connected within loosely defined or fuzzy boundaries; each CAS is simultaneously a subsystem of a larger system (or a supra-system) and is itself constituted by a number of subsystems (the nested structure of systems)
Agents (e.g. humans) in a CAS can change in terms of their structural position in the system as in their relational behaviour
- The interactions between agents within a CAS define the systems typically nonlinear dynamic. Interactions are:
Sensitive to initial condition, i.e. bound by their historical and contextual conditions
“Path dependent”, i.e. prior decisions result in bifurcation (branching) of the systems behaviour
Are stable to many interventions, but change suddenly when reaching a tipping point
Result in feedback loops, i.e. an output becomes a new input, which modifies agents future behaviour (reinforcing or self-stabilising/balancing feedback)
Emergent, thus self-organising, as a result of the above
Agreed purpose, goals, and values statements are the basis for defining the driver of the system; together they give rise to the “operational instructions” that coherently direct the interactions within a CAS. These are termed “simple (or operating) rules”, usually 3 but never more than 5, and must not be contradictory
“Simple rules” reflect the core values of a social systems. Core values are those that remain unchanged in a changing world.5 If internalised and adhered to by all agents it results in the “smooth running” of the system (e.g. the flocking birds) [43, 47, 54, 55]
“Simple rules” provide the necessary “safe space/freedom” to adapt an agent’s behaviour under changing conditions. Adaptation is desirable; it fosters creativity and provides flexibility; it is the prerequisite for the emergence of the system and the achievement of its goals (learning) [43, 47, 54, 55]
In CAS “control” tends to be highly dispersed and decentralised . CAS activity results in patterned outcomes, based on purpose, goals, and values within the constraints of the local context. These outcomes, while not necessarily intuitively obvious, are the result of the emergent and self-organising behaviour of the system. Local outcome patterns, while different, are “mutually agreeable”.
Of note, system solutions—often termed innovations—are unique; they cannot be transferred from one place to another as the local conditions that resulted in the system’s outcome will be different, the reason why even proven innovations fail when transferred into a different context .
2.5 Consequences of Complex Adaptive System Behaviour
Nonlinearity means disproportional outcome responses to rising inputs, very small inputs may result in very large (“chaotic”) responses and vice versa large inputs may result in no change whatsoever
Nonlinear behaviour makes outcomes less predictable
The “same” intervention in different location often results in a number of outcome patterns as the initial conditions vary somewhat between locations. These patterns describe mutually agreeable outcomes
Feedback loops contribute to the robustness of a system
Core values define a system’s driver and “determine” the direction the system takes. Different core values within a system’s subsystems can result in very different system behaviours which may or may not lead to conflict, e.g. the “cure-focus” of an oncologist may lead to desperate interventions whereas the “care-focus” of a palliative care physician may lead to ceasing treatments in favour of improving the patient’s remaining quality of life
In an integrated system, subsystems may have a set of unique purpose, goals, and values; however, in overall terms they need to align themselves with the main purpose, goals, and values of the system to contribute seamlessly to its overall function
Essentially, all models are wrong, but some are useful. Box, George E. P.; Norman R. Draper (1987). Empirical Model-Building and Response Surfaces [3, p. 424].
However, there are also many unobserved causal relationships (latent variables).
To avoid confusion: from a systems theoretical perspective (and design thinking approach) purpose, goals, and values are defined a priori, when exploring existing systems they can be deduced a posteriori. The analysis of systems will be explored in Part III.
The significance of “constraints” is discussed in the chapter.
These characteristics were formulated in collaboration with Fred Boogerd and Frank Bruggemans at the Department of Molecular Cell Physiology at the Free University, Amsterdam, based on the arguments in Cilliers (1998), and used in Cilliers (2005).
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