Gatrell, 2005; Resnicow & Page, 2008; Graham & Martin, 2012

“ 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)

“Strive to produce a single, one-faceted, and static explanation of human behavior.” Do not account for: 1) Dynamic changes or adaptations occurring within a system. 2) Interactions among elements of the system. 3) Interaction among various systems. (Goodson, 2010)

e.g., Attitudes e.g., Social Pressure (normative beliefs) Behavioral response surface Any phenomenon that displays divergence, bimodality, discontinuity, inaccessibility, or hysteresis can be modeled by Catastrophe Theory (Flay, 1978). Preceded Complexity Theory

 Propose that the linear paradigm is flawed  Key Principles:  Quantum behavior change  Chaotic process, sensitive to initial conditions  Occurs within CASs  Results often greater than sum of parts

 Waves of motivation/inspiration vs. “particles” of pros and cons  Dramatic experiences  Sudden insights  Can occur with little (new) input

 Butterfly Effect  Infinite permutations  Fractal patterns  Identification of fractals suggests intervention points

 Consider why, after years of false starts and failed attempts, a person succeeds at ending an addiction, increasing his or her physical activity, eating healthier, or losing weight.  Or why, after years of success, a person relapses into substance use.  The concept of external stimuli affecting motivation is similar to the cues concept in the health belief model.  Motivation can also arrive as opposed to being planned

 “Particle components of a motivational quantum” = different starting points  Multiple pathways & path dependence  “Lever points” or “tipping points”

“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  Examples:  Weather systems  Ecosystems  Neural connections in the brain  Health or social behavior  Human relationships  Communities

1) Relations and networks 2) Non-linearity 3) Emergence 4) Hybrids

 Large number of elements, interacting dynamically (flows) across networks  Interaction is rich and may involve both human and nonhuman agents (hybrids) or elements  Interactions may be short range but the richness of interactions or relations across networks means that ‘influence’ can be wide ranging  Each element is ‘ignorant’ of the behavior of the system as a whole; therefore, we cannot understand the system by ‘summing’ or ‘averaging’ the behavior of individual components  People influence each others’ health-related behavior  People interact with other agents and organizations (e.g., healthcare providers and facilities)  Interactions tend to be local, but time-space compression means that interactions having health consequences can be ‘at a distance’  One is generally ignorant of the possible system-wide consequences of one’s health- related behavior; the ‘public health’ is more than the sum of individual disease profiles

 Complex structure and system- wide properties emerge from simple unstructured beginnings. System-wide properties emerge  Interactions are non-linear (that implies that small causes have large results) with feedback loops  Complex systems are open systems, interacting with environments  Complex systems are far from equilibrium  Complex systems have a history; their past is ‘co-responsible’ for their present behavior  The ‘health’ of a neighborhood or community emerges from the activities and health profiles of the local population  Disease outbreaks that are highly localized can spawn epidemics or even pandemics  The health system is only closed at a global level, and even then it is open re environmental change  Population growth and movement ensures that the system is never fully stable  Migration, history of inequalities

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) 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.

 School district  Puzzle  Others? Challenges in practice: Allowing a system to self-organize? Recognizing how behavior itself influences the system?

 A simulation technique that has been used to explain complex patterns of behavior at the individual and group level  An agent-based model consists of individual entities— agents—that follow rules.  The agents are situated in place and time and interact with one another according to their behavioral rules.  In many models, the agents evolve their behavioral rules on the basis of feedback from the model.  Agent-based models enable researchers to include inter-individual variability in the pathway to change.

 Many large-scale interventions might work for some of the target audience  because they have spun the balls of motivation in a large group of individuals,  and for a subset of these individuals,  the balls hit the necessary trigger point,  arrived in the proper order,  arrived while the person was in the right state,  and fit and stuck to their motivational receptors.  Interventions need to be offered on a repeating basis, so that individuals have the opportunity to experience the “perfect storm” of a symbiosis of environment and intrapsychic factors.

 Draws attention to the role of human history, ancestral social structures, prehistoric ecology, and enduring strategies used to navigate therein.  Encourages constructive critique of many modern practices and technologic developments, including health behaviors themselves, particularly where they distance people from long-standing aspects of established human existence.  Accommodates human irrationality, emotionality, and behavioral inflexibility because these qualities are the product of evolution and the development of a species that has survived on the basis of quick, heuristic thinking and hot emotionality.

 Networks are only the skeleton of complexity, the highways for the various processes that make our world hum.  Gender is missing – complexity theory is heterarchical (rather than hierarchical) so should appeal to women and feminists  Territory and a sense of identity with particular places still matters.

 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?

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

 Identification of leverage points  Consideration of timing and initial conditions  View behavior as probabilistic  Encourage “wing flapping”?

 Practical implications  Repeated exposures  Understand individual “receptivity” (sounds familiar)  Lower upper limit on variance explained

 Potential areas of research  Qualitative methods  Quantitative methods  Physiological mechanism studies  Agent-based and computational modeling

 If the applied science of [health behavior change and health promotion] is itself to evolve and to empower a transformative rethinking of health behavior,  [We] must accept and adapt to the challenging environment in which it fınds itself—a complex, stochastic system of competing, unlearned, evolved motivations. ▪ Graham & Martin, 2012