Measuring Regenerative Vitality: 10 principles and measures undergirding systemic economic health

Applying thermodynamic concepts and methods to economic systems is not a new idea. In the last few decades, however, advances in nonequilibrium thermodynamics (i.e., self-organizing, open, dissipative, far-from-equilibrium systems), and nonlinear dynamics, information theory and other mathematical approaches to the recurrent geometries found throughout the cosmos have produced a new set of concepts and methods, which are vastly more appropriate for socioeconomic systems than the traditional equilibrium ones. In several previous papers, for example, we used research from these new Energy Network Sciences (ENS) to show how and why systemic health requires a balance of efficiency and resilience be maintained within a particular a “window of vitality” (Ulanowicz et al. 2009; Lietaer et al. 2009; Goerner et al., 2009). The current paper outlines the logic behind 10 additional principles of systemic, socioeconomic health and the quantitative measures that go with them. Our particular focus is on “regenerative learning”, i.e., the self-feeding, self-renewal, and adaptive learning processes that natural systems use to nourish their capacity to thrive for long periods of time. In human systems, for example, regenerative vitality requires regular investment in human, social, natural, and physical capital. Long-term vitality, however, also requires effective collective learning. Note: A wide range of related work involving energy and flow-network concepts and methods is emerging under a host of diverse disciplinary titles such as resilience theory, complexity theory, self-organization theory, nonequilibrium thermodynamics, ecological network analysis, and Panarchy. We use the umbrella term Energy Network Science (ENS) to refer to this related array. 1.0 Introduction: Energy and the Transdisciplinary Science of Systems Researchers in ecology and its allied field, ecological economics, have produced many of the key advances in the study of energy flow networks. Yet, even though ecological economists apply flow-network thinking to economics, they often see these economic applications as metaphoric extrapolations from biology and ecology. So, while network methods are well known in ecological economics, their use in understanding systemic health in economic networks In Press; Do Not Distribute © Goerner & Fath, 2018. 2 themselves requires some justification for why this approach is something more than mere biological analogy. Explaining why flow-networks can provide a rigorous, transdisciplinary science requires some historical background. Note: The transdisciplinary nature of this science also requires some adjustments to terminology. For example, where ecologists call their flow-network methods Ecological Network Analysis, to emphasize this work’s broader applicability, we will replace the discipline-specific word "ecological" with the transdisciplinary term, Energy Network Analysis. From resilience and complexity theory to self-organization and ecological network analysis, the disciplines we group under the umbrella term Energy Network Science (ENS) are all offshoots of the original General Systems Science (GSS) impetus. GSS is a transdisciplinary study built around two core pillars: 1) the existence of universal patterns; and 2) energy’s role in organizational emergence, growth and development. The history of this transdisciplinary empirical science starts with the ancient Greek and Egyptian observation of mathematically precise, recurring patterns and principles of growth and development occurring in vastly different types of systems (Figure 1). The ubiquity of Fibonacci growth patterns and Golden spiral organizations are examples of this observation. The study of fractal patterns and nonlinear dynamics is a modern-day expansion of what is now called morphodynamics or the "geometry of behavior" (Abraham, 1985; Abraham & Shaw, 1982). Scientists across the millennia have used these universal patterns and principles to understand biological growth patterns, predict patterns of behavior in extremely complex systems, and build striking structures such as the Parthenon. (Schroeder, 1991) Figure 1. Some universal geometries as examples of “organized complexity” (Weaver, 1946). Thermodynamics – i.e. the study of energy dynamics in all its forms – provides a logical basis for a transdisciplinary “systems” science because energy processes are both universal and amenable to scientific inquiry and measurement. This idea too has ancient roots. In his theory of “flux and fire”, for example, pre-Socratic, Greek philosopher Heraclitus argued that all things were caught up in endless cycles of change, transformation, and rebirth in which “all things were flowing” and the world was “an ever-living fire.” By the 16 century, European scientists such as Robert Flood and Galileo Galilee began measuring heat energy using an early thermometer. In Press; Do Not Distribute © Goerner & Fath, 2018. 3 Work growing around the pillars of energy and universal patterns, especially of growth and development, began to come together in the early 1900s. In his 1917 book On Growth and Form, Scottish mathematical-biologist, D’Arcy Thompson outlined the mathematical and scientific basis for morphogenesis, the universal processes of growth and development that give rise to the recurring shapes, patterns and forms found in plants and animals. In 1922, mathematicalbiologist Alfred Lotka expanded the study of energetics from biology to ecology and evolution, arguing that the selective principal operating in evolution was a physical law favoring “maximum useful energy flow transformation.” Lotka’s 1925 book, Elements of Physical Biology, even extended the energetics of evolution to suggest the physical (i.e., energy) nature of consciousness. General Systems ecologist, Howard Odum (2007) used Lotka’s research as the centerpiece of his work in Systems Ecology, and redefined Lotka's energy law of evolution into a Maximum Power Principle. Writing in the 1940s through 60s, American scientist and mathematician Warren Weaver (1948) then gave a proper name to the complexly organized systems that emerged from morphodynamic processes. In contrast to the simple, unidirectional causality that defined classical physics and the highly disconnected interactions that are the basis of statistics, Weaver explained that the “organized complexity” that fills our world is a natural product of the subtle relationships that connect diverse elements into profoundly organized, interdependent wholes (Figure 1). This mathematically-precise “organization” allows us to do empirical science on the extremely complex systems we care about most: living systems, human systems and ecosystems. Consequently, in 1961 urban anthropologist Jane Jacobs used Weaver’s work to define “the kind of problem a city is.” In the 1950s, and 60s, biologist Ludwig von Bertalanffy (1968) sought to connect energy dynamics and pattern formation as the basis of a unified scientific research program studying the behavior of complex systems in general, including the dynamics governing their formation, selfmaintenance, and increasing complexity. A “system” was initially defined as ‘any assembly of parts whose relationships make them interdependent.’ The goal of this General Systems Science was a coherent, transdisciplinary, empirical science of “systems,” including living, non-living and supra-living organizations such as ecosystems and economies. The probing, seeping, circulatory nature of energy dynamics even explains the subtly interconnected nature of all organization and flow. Hence, as Heraclitus put it, “listening not to me but to Logos, it is wise to agree that all things are One” (Bryan, 2014). In the 1970s, Belgian chemist Illya Prigogine unified this work (and won a Nobel Prize) by explaining how an energy-flow process called self-organization drives the emergence of new configurations, and creates pressures which drive the ongoing cyclical development of existing ones (Prigogine, 1972; Jantsch, 1980). Apropos of an energy-flow process, every round of emergence and development follows a similar process, which is found in a vast array of different systems. Energy buildups create pressures that drive change. Naturally-occurring diversity (inhomogeneity) provides the seed crystals that open new paths and catalyze new forms of organization. Meanwhile, the matrix of internal and external constraints determines the degree of flexibility or rigidity, which in turn shapes the outcome and whether flow moves toward constructive or destructive ends. For example, a tornado’s funnel and a hurricane’s Golden spiral In Press; Do Not Distribute © Goerner & Fath, 2018. 4 (organization) both emerge from the confluence of: 1) heat, i.e. a temperature gradient that creates pressure; 2) naturally occurring variations, i.e. small gusts, twists of geography, etc.; and 3) pressure or geographical constraints that block more gradual dissipative flow. Prigogine’s work, however, produced a distinct disjuncture from classical thermodynamics. Where classical Thermo is built around the study of systems which are at or near equilibrium, the complexly organized systems that emerge from self-organizing processes are specifically designed to maintain their organization far-from-equilibrium. They do this by autocatalytic or autopoietic arrangements (i.e. self-feeding, self-renewing, “regenerative” ones), meaning they are designed to channel critical flows back into maintaining their organization on an ongoing basis. The energy network research we do today is a natural continuation of this far-from-equilibrium work. Here, self-organizing processes naturally give rise to what researchers call flow systems or flow networks. A flow network is any system whose existence arises from and depends on circulating energy, resources, or information throughout the entirety of their being. Your body, for example, is an integrated network of cells kept healthy