Adapting Resilience Science to Community Well-Being, Part 1
Introducing the science of flow networks
Most of what I’ve written over the past year has been about systems, one way or another. My focus has been on the structure and interconnectivity that in many ways define systems, whether the simpler socio-technical systems of my first articles or the socio-ecological ones that have begun more recently to preoccupy me.
That’s not wrong. System structures and connections are indeed vital. But they’re not vital in themselves; they’re vital for what they enable, which is flow – the flow or circulation of money or information or resources or power. That is what the system, and therefore the structure and relationships are for.
That seems obvious, once stated, but I had to be led there. I had been chatting with Dr. Laura Lengnick about how to apply her resilient agriculture framework to building social resilience in communities, the question with which I concluded my recent reflection on her book. After our last conversation she shared a couple academic papers that offer a pretty clear path toward an answer.
The first paper1 applies system concepts and methods from ecology and ecological economics to economics proper. More importantly, the article offers a way to think about and measure the health of systems, including the complex ecosystems that we call communities.
Let’s begin by clarifying just what we’re talking about.
A flow system or 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 by the circulation of energy, water, nutrients, and internal products. Ecosystems are interconnected webs of plants and animals … that add to and draw from flows of oxygen, carbon, nitrogen, etc. Economies are interlinked networks of people, communities, and businesses, which depend on the circulation of information, resources, money, goods, and services.
As a unifying concept, the authors define “energy” as “any kind of flow that is critical to drive the system under study” [italics in original]. The paper then focuses on a collection of principles and methods they call “Energy Network Science” (ENS), a generalization of the ecological network analysis methodologies used by ecologists.
The underlying science has its origins in efforts that began in the 1950s and spans disciplines from biology and ecology to physics and mathematics, with branches out into fields like economics and organizational theory. This general systems science is built around “two core pillars: 1) the existence of universal patterns; and 2) energy’s role in organizational emergence, growth, and development” [italics in original].
I want to pause for a moment on that first pillar.
We are all familiar with the deterministic, mechanistic science that emerged in the 16th and 17th centuries and gave rise to the industrial revolution of the 18th and 19th centuries. I’d venture to say that it’s how most of us think of science even today. It’s at the heart of the chain reasoning that underlies standard performance thinking built on rules and linear laws of causality: if this, then that and if that, then the next thing.
In contrast, systems science grapples with the complex tangle of relationship and causation of the real social world. It takes away our simplistic rules, but gives us powerful principles and patterns in their stead.
One example is the “fractal branching” pattern, seen in everything from river deltas to the human circulatory system, where larger conduits branch into sets of smaller ones, which split in turn into still smaller ones in a scheme that looks similar at different levels of magnification. Why this pattern? Think back to a system’s purpose: to optimize flow to all the parts of a system. This branching pattern is the most efficient way to distribute resources, whether they be nutrients rising from the soil through a tree or blood delivered throughout a human body.
In fact, we saw this pattern quite recently. It is exactly that modular pattern of connection we observed in communities that were effective in distributing resources after the storm.
That’s just one pattern characteristic of healthy flow systems. There are many others, which give rise to the principles that constitute the heart of the paper.
Before we go there, however, I want to call out another key insight of ENS:
Network flow also ties directly to systemic health and development because, if critical resources do not adequately nourish all sectors or levels, then we can expect the undernourished segments of the economy to become necrotic. Like necrosis in living organisms, poor cross-scale circulation erodes the health of large swaths of economic “tissue” …, which in turn undermines the health of the whole.
What a powerful way to characterize the consequences of creating systems that exclude parts of our community from power and resources! It’s an elegant way to see that equity is in the interest of the whole, and might even offer guidance in accomplishing it.
That’s the most exciting thing about this paper for me: it offers far more than a set of powerful analogies for thinking about community ecosystems. It also offers principles and measures that “enable anticipatory action and policy to help guide socio-economic systems,” both by elucidating key principles that characterize a healthy flow network and by proposing some concrete ways to measure whether they hold within a given system.
I’ll dive into those in my next article. But first I want to pause for a final thought about that last sentence.
Whenever I talk about driving positive change, I emphasize the importance of using measures of outcomes and impact, not just of the activities that hopefully lead to them. Recall the Results-Based Accountability (RBA) classification of performance measures:
How much did we do?
How well did we do it?
Is anybody better off?
The first two are important for the learning cycle and for very basic accountability, but it’s the last one that really matters.
The problem with impact and outcome measures is that they come after, sometimes long after the effort has been made. They’re important, but, when combined with the inherent nonlinearity of complex systems, they don’t necessarily tell us why an intervention did or did not lead to improvement.
But the measures that the paper develops are essentially intrinsic measures of the health of a system. “Such measures are vastly more effective than traditional outcome metrics or statistical correlations because they assess root causes, i.e., ones that directly impact systemic health.”
I’ll leave digging into the ten principles and measures for the next article, which you can find here.
This is the first in a three-part series on network flow science and its application to improving community health, based on recent academic papers. The second article is here and the last is here.
Links & Thoughts
Minoritarianism is Everywhere. The anti-democratic behavior of the Republican Party is obviously of grave concern and must be fought. But this article argues that the democratic deficit in this country is far from limited to the right and that we need not just to protect the institutions under attack, but to seriously consider how they should be reformed.
If you want to share thoughts on anything I’ve said here or have ideas about further questions or topics you’d like me to explore, please feel free to reply to the newsletter email or contact me here.
Fath, Brian D., Daniel A. Fiscus, Sally J. Goerner, Anamaria Berea, and Robert E. Ulanowicz. “Measuring Regenerative Economics: 10 Principles and Measures Undergirding Systemic Economic Health.” Global Transitions 1 (January 1, 2019): 15–27.