The Generative Paradigm

In its simplest sense, the generative paradigm is a meta-model of how and why events occur in complex living systems. Events that are generative embody the fundamental principles of holism, in that they result from the phenomena of emergence, a tendency for complex interactions to produce unanticipated, or even counterintuitive, results. Elements of the generative meta-model involve the use of exemplars with the ability to predict, acknowledge, align and harness emergent events.

The basic unit in the generative paradigm is the complex system, which can be defined as a system composed of interconnected parts that as a whole exhibit one or more properties (behaviors) not obvious from the known characteristics of the individual parts. The complex system usually features a large number of interacting components, such as agents, variables and processes. The aggregate activity of a complex system is almost always nonlinear: its actions cannot be imputed from the summation of the activity of its individual components. The complex system typically exhibits hierarchical self-organization under selective pressures.

The generative paradigm is typically associated the analysis of the behavior of complex adaptive systems, which are one type of complex system. Complex adaptive systems are ‘complex’ in that they are diverse and made up of multiple interconnected elements and ‘adaptive’ in that they have the capacity to change and learn from experience. Examples of complex adaptive systems include the stock market and Internet, the colony creation capabilities of social insects, the biosphere and the ecosystem, human social group-based endeavors in a cultural and social system, the brain, the immune system, the cell and the developing embryo.

From its earliest beginnings naturopathic philosophy has simultaneously struggled and embraced the dualities of reductionism and holism, linear and non-linear determinism and the reality of complex adaptive systems, because it held to the tenets of vis medicatrix naturae (Vis) long after most other medical disciplines had rejected its precepts as simple vitalism. The generative paradigm attempts to fit the doctrines of holism, complexity and the Vis into a framework that is buttressed by the recent advances in bioinformatics, molecular genomics and network combinatorics. The result provides a new, exciting and helpful way of predictive modeling that combines traditional naturopathic healing wisdom with a strong and robust conceptual framework.

The generative paradigm provides a complete epistemological and methodological system for the investigation of the vis medicatrix naturae, perhaps the most basic tenet of naturopathic philosophy. It affords robust modeling opportunities and enhances our ability to understand the emergent healing properties of the complex adaptive systems we call ‘patients’.

Like the vis, generative behavior can be difficult to discern, and it is understandable that at times the busy practitioner will undoubtedly find themselves resorting to reductionist processes. However, it should be kept in mind that virtually every process, parameter, agent and variable described in this article cannot be modeled by reductionist empirical reasoning. It is well worth the effort, for unlike reductionism, which strives to define and determine the outcome from the clash of opposing forces, the generative paradigm strives to understand the unity of opposites as the basic workspace of life’s complex nature:

Consonance in dissonance,
dissimilarity in similarity,
regularity in irregularity,
identity in change,
innovation in conservation,
generation in stagnation,
autonomy in dependency,
isolation in intimacy,
integrity in disparity,
order in chaos,
simplicity in intricacy and
unity in diversity. 
Modeling the vis will be elusive. A writer once described the difference between a linear and non-linear equation as a comparison between two different ways of walking through a forest. In the linear differential forest one computes the location of the first tree and from that result locates the next, and so on. In the non-linear differential forest one computes the location of the first tree and then must wait until all the trees in the forest assume their next location. Generative, complex, non-linear behaviors are not easy to identify with the unaided eye and often require specialized bioinformatic tools. Happily these new tools can be more easily produced by the increasingly high-speed, high-throughput computational technologies now readily at our disposal and the abundance of extant algorithms and agent-based models. The generative paradigm holds the promise that soon enough we will begin to see ‘the vis for the trees.’