2. Developmental Factors
B. Cycles, Curves, Models, and Laws
Cycles, also known as pendulums, are a particularly simple and ubiquitous relationship between two variables. Think of the seasonal cycle, the business cycle, the hype cycle, the drama cycle, the Kuznets cycle, the plutocratic-democratic cycle, and the materialism-idealism-conflict (MIC) social cycle. Many of these are discussed in Chapter 8. Whenever we can find them, cycles are important predictable constraints on the future. Some cycles are chaotic, or irregularly irregular. That means we really can’t tell when the cycle will reverse itself. Others are only partly chaotic, meaning that the longer a system is at one extreme of the cycle (for example, plutocracy), the higher the probability that conditions will soon conspire to move it back toward the other extreme (hello democracy!). Groups like the Cycles Research Institute, the Foundation for the Study of Cycles, and many others are dedicated to a better understanding of this classic developmental factor.
Curves are a more complex set of possible relationships that emerge in forecasting. Once we have some data relating two or more variables over time, we can ask if that relationship fits any of the classic families of curves that we find in complex systems. We discussed several of those in Chapter 8. Could it be an S-curve? A power law (performance) curve? A Kuznets curve? A Life Cycle curve? A J-curve? A U curve? Something else? If it looks like it may fit a certain type of curve, we can then ask why that might be, and how long the curve might continue to apply. The categorization of a curve can gives us some indication of its causal factors, and help us with another developmental factor, models.
Models are another classic way to conceptually constrain and predict the behavior of a system. We saw several models in Chapter 8. We also introduced a few universal models, including exponential foresight (Chapter 2) and evo devo (this chapter), and some related submodels. All models are incomplete and wrong in part, but the better ones uncover causal variables and relationships that help us better understand and simulate the system in question.
Futurist’s Pierre Wack’s predictable “dominant tendencies” (“tendances lourdes”, in French), are models that trying to be candidates for forces, constraints, or laws that affect classes of complex systems. Just as there are laws of physics, chemistry, and biology, we know there are laws, or at least, statistically dominant tendencies, of societies, economies, technologies. But until they are accepted by the scientific community as laws, they are just models.
Well-characterized and widely-accepted systems laws, persistent relationships, rules, or laws that apply to classes of complex systems, are particularly rare. Systems theory is that branch of philosophy, which we have tried to practice in this chapter, that studies systems in general, and looks for common patterns and principles that apply to all systems of a particular class or type. Many laws can be guessed at for any system, with varying levels of evidence. It’s always worth investigating the systems literature for these, and asking how they relate to systems laws that have been recognized, for the universe as a system.
Many of our scientific laws of physics, and a few of chemistry and biology can be derived from the known forces, but most of our laws are empirically (experimentally) observed. As we climb further up the systems hierarchy to human society and economy, and later to self-improving technology, we generally ignore forces, and talk instead of systems laws that act in broad ways across the system as a whole. The higher we go up the hierarchy, the less these laws are theoretically derived, and the more they are experimentally observed. Our scientific and practical knowledge becomes less deductive and mathematically precise, and more inductive and descriptive. Yet the more developmental relationships we can uncover, the more prescriptive our science can become.
Besides physics and chemistry, all other academic disciplines, like ecology (“Cope’s rule”, “Bergmann’s rule,” “Foster’s rule”), sociology (“law of least effort” and “law of time-minimization”), economics (“law of supply and demand”), statistics (“law of large numbers”, “regression to the mean”), and many others have collected their own starter lists of apparent laws. A good systems thinker will try to understand as many of these as possible, and to study examples of how they interact, to understand the “dominant tendencies” one might expect to constrain the nature and future of any system, in any environment. This kind of foresight can be incredibly powerful, as it has such generality of application, but it is today more art than science.
We have argued that the growth of adaptive collective complexity, computation, or intelligence in the universe, using both evolutionary and developmental processes, is at least a fundamental “dominant tendency” or rule of our local environment, and is likely a universal law. I look forward to seeing this hypothesis better critiqued and tested in coming years. The evo devo universe model may or may not eventually produce a series of widely accepted laws. I am hopeful that it will. But even if it does not, that doesn’t mean that there are not universal (or if you like, cosmological) systems laws out there, waiting patiently for futurists to understand. Systems theorists believe that the better we understand laws in any complex system, the better we can appreciate them in the universe as a system.