Complex Adaptive Systems

We often hear that “The health care field is complex, perhaps the most complex of any area of the economy (Morrison, 200, cited in Begun et al, 2003). And yet despite the number of times I’ve heard this, I often see it treated as if it were not complex and/or am not convinced that we are talking about the same thing when we use the word “complexity”. As a refresher on what “complexity” means, I did a bit of reading on complexity science. Here are my notes:

Often the metaphor of a machine is used when people think about organizations – where workers are like cogs in the machine, everything operates based on simple linear cause and effect, and management’s job is to control (e.g., make sure the cogs are in place and do their job as a cog in the machine); or where the organization receives inputs, transforms them, produce outputs (in the case of healthcare, the output might be “improved health”)

  • healthcare organizations 1and, truthfully, probably any organization are much more complex than this machine metaphor suggests
  • but it’s important to remember that the models people use will “shape the way [they] believe the system works, and hence, constrain the possible ways people thing” (Begun et al, 2003, p. 253)
  • complex adaptive system (CAS) = “a collection of individual agents who have the freedom to act in way that are not always totally predictable, and whose actions are interconnected such that one agent’s actions change the context for other agents (Plsek, 2003, cited on Sibthorpe et al, 2004, p. 2.)
    • complex: “diversity – a wide variety of elements”
    • adaptive: “capacity to alter or change […] to learn from experience”
    • system: “a set of connected or interdependent things” (Begun et al, 2004, p. 255)
  • “CAS defined in terms of:
    • component parts
    • behaviour of those parts
    • relationships between the parts
    • behaviours (or properties) of the whole” (Sibthorpe et al, 2004, p. 2)
  • agents are “information processors” – “they can process information and adjust their behaviour accordingly” (Sibthorpe et al, 2004, p. 2). All agents have some “information about the system but none understands it in its entirety” (Sibthorpe et al, 2004, p. 2)
    • in a health care system, agents can include:
      • people (e.g., clinicians, patients, administrations)
      • processes (e.g., nursing processes, medical processes)
      • functional units (e.g., nursing, communications, accounting)
      • small organizations (e.g., a medical practice)
      • large organizations (e.g., hospitals, insurance companies)
  • agents are connected (and share information) through a web of relationships
    • can be described as “massively entangled” as the “parts of the system and the variables describing those parts are large in number and interrelated in complex ways” (Sibthorpe et al, 2004, p. 2)
    • agents “both alter other agents and are altered by other agents, in their interactions” (Begun et al, 2003, p. 256)
    • “the diversity, extent, intricacy, and strength of the relationships influence the system’s ability to adapt” (Sibthorpe et al, 2004, p. 2)
  • agents respond to their environment using “simple rules” – which “need not be shared, explicit, or even logical”, but they “contribute to patterns and bring coherence to behaviours in complex systems” (Sibthorpe et al, 2004, p. 2)
  • CAS-defining properties:
    • dynamic: “the continual presence of multiple interactions and their accompanying surprises, challenges and responses both within the system and between the system and its environment” (Miller et al; cited in Sibthorpe et al, 2004, p. 3)
      • change is “discontinuous” – “periods of stability and periods of change”, with change occurring at different times/paces
    • self-organizing and emergent: “new structures and forms of behaviour emerge that cannot be obtained by summing the behaviours of the constituent parts, because new system properties emerge from the nonlinear interactions between agents” (Sibthorpe et al, 2004, p. 3) – you cannot control or predict what will happen
      • “the behaviour of the resulting whole is more than the sum of individual behaviours” (Begun et al, 2003, p. 256)
      • “one agent’s actions change the environment for other agents […and…] and surprising and innovative ideas can emerge from unpredictable corners of a complex system. ” (Sibthorpe et al, 2004, p. 3
    • CAS can be sensitive to initial conditions – “an apparently trivial different in the beginning state of the system may result in enormously different outcomes” (the “butterfly effect”)
    • CASs are “embedded within and bounded by other CAS with which they co-evolve” (Sibthorpe et al, 2004, p. 3) – they change and they cause the world around them to change too
    • CAS operate at multiple levels/scales
    • they have “fuzzy” boundaries
    • the above properties are “dependent on feedback loops – the movement of information between agents and between systems” (Sibthorpe et al, 2004, p. 3) – these loops can generate change or stability as they “fuel the interdependence of the system by keeping the parts synchronised, and simultaneously support evolution of the system by providing impetus and resources for adaptation” (Sibthorpe et al, 2004, p. 3)
Complexity Science Established Science
Holism Reductionism
Indeterminism Determinism
Relationships among entities Discrete entities
Nonlinear relationships – critical mass thresholds Linear relationships – marginal increases
Quantum physics – influence through iterative nonlinear feedback – expect novel and probabilistic world Newtonian physics – influence as direct result of force from one object to another – expect predictable world
Understanding; sensitivity analysis Prediction
Focus on variation Focus on averages
Local control Global control
Behavior emerges from bottom up Behavior specified from top down
Metaphor of morphogenesis Metaphor of assembly

Source: In Begun et al, 2004, p. 260 – adapted from Dent, 1999, Table 1.

  • “Traditional systems thinking has created a vicious cycle of (1) design a system, and (2) when the system does not act as predicted, redesign the system. The assumption is that leaders can control the evolution of complex systems by intentions and clear thinking. Complexity science leads one to ask different questions. For example, when an intended design does not play out as predicted, how do things continue to function?” (p. 286-87)
Begun, J. W., Zimmerman, B., & Dooley, K. (2003). Health care organizations as complex adaptive systems. In Advances in Health Care Organization Theory. Eds. S.M. Mick & M. Wyttenbach. San Francisco: Jossey-Bass, pp. 253-288.
Sibthorpe, B, Glasgow, N., & Longstaff, D. (2004). Complex Adaptive Systems: A Different Way of Thinking about Health Care Systems A Brief Synopsis Of Selected Literature For Initial Work Program – Stream 1. Canberra. Australian Primary Healthcare Research Institute, Australian National University.


1 and, truthfully, probably any organization
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