Complex Adaptive Systems

1. H671 Advanced Theory Emily Whiteman Complex Adaptive Systems

2. What are Complex Adaptive Systems? “Complex adaptive systems (CASs) consist of a set of interacting elements that are able to change and adapt in multiple ways” • Zimmerman, Lindberg, & Plsek, 1998

3. Examples of CASs • Weather systems • Ecosystems • Neural connections in the brain • Human relationships • Communities

4. Limitations of Linear Theories “Strive to produce a single, one-faceted, and static explanation of human behavior.” Do not account for: • Dynamic changes or adaptations occurring within a system. • Interactions among elements of the system. • Interaction among various systems. (Goodson, 2010)

5. Tenets of Complex Adaptive Systems 1) Whole is much more than the sum of its parts. 2) CASs consist of other CASs: Each individual agent in a CAS is itself a CAS. 3) The agents in a CAS evolve with the CAS to which they belong 4) Diversity is necessary for the sustainability of a CAS. A decrease in diversity reduces the potential for future adaptations; diversity is key to innovation and long term viability of a CAS 5) CASs exhibit distributed control rather than centralized control (i.e., control is distributed throughout the system vs. a “command center”). Outcomes emerge from a process of self-organization rather than being designed and controlled externally.

6. Tenets of Complex Adaptive Systems cont. 6) CASs are nonlinear systems: The size of the outcome may not be correlated to the size of the input. 7) CASs exhibit sensitive dependence to initial conditions (also known as “the butterfly effect”); they are history dependent. 8) CASs are naturally drawn to attractors. The attractor is a pattern or area that draws the energy of the system to it. 9) CASs manifest unpredictable behavior. 10) In a CAS, order underlies even what appears to be disordered or chaotic.

7. Data Analysis Non-linear analysis: Realizes that the association may be better fit with a curved line (quadratic equation) Accounts for complexity and multiple interactions More mathematically complex Linear analysis: • An increase in X= an equal increase in Y • A decrease in X= an equal decrease in Y • Preferred, easy applicable, intuitive answers

8. Gatrell, 2005 Complexity Theory & Health Geography

9. Justification for Complexity Theory “ If one lesson has emerged from the spectacular failure of Western medicine to eradicate certain diseases, it is that diseases cannot be reduced to a single cause or explained within a prevailing linear scientific method: complexity is their hallmark.” (Harvard Working Group on New and Resurgent Diseases, 1996)

10. Four Key Concepts Of CT • Relations and networks • Non-linearity • Emergence • Hybrids

11. Discussion • What are some examples of properties that emerge at a collective level, that do not exist or are lessened when reduced to the individual level? • What is meant by “mutually reinforcing nexus”? Examples?

12. Implications for Public Health Utilization of both linear and non-linear theories in design and data analysis provides the strongest approach for prevention. Complexity theories require the inclusion of other disciplines to fully understand behavior: • Law enforcement • Elected officials • Teachers • Civil engineers Increased collaboration between social and natural sciences