Challenges of Regenerative Systems Design (Part 1 of 3)
The Illusion of Separation: How Economics and Ecology Shape the Systems We Build
Can we agree that it is reasonable to question the systems shaping our lives? Can we agree to investigate whether the structures we depend on are actually serving our long-term well-being?
In Braiding Sweetgrass, Dr. Robin Wall Kimmerer describes how some indigenous council traditions would begin by first establishing shared ground: gratitude for the living world and recognition of reciprocity. This common perspective creates the foundation of relational engagement from which disagreement, inquiry, and decision-making can responsibly emerge.
This article is written in that spirit. It is not an argument for abandoning economics, nor a claim that one discipline holds all the answers. It is an invitation to examine the assumptions beneath the systems we have normalized, especially those shaping markets, business, and our relationship to the living world.
Over time, many economic ideas have come to be treated less like models and more like natural laws. Yet persistent inequality, ecological degradation, market instability, and rising social fragility suggest that it’s worth asking whether some of our foundational assumptions are incomplete.
One of the central challenges in regenerative systems design is that living systems do not behave like controlled machines with static operations that produce predictable outcomes. They are adaptive, interconnected, and constantly changing. Designing systems that support long-term social, ecological, and economic health therefore requires us to examine whether the models guiding our decisions are capable of perceiving those relationships in the first place.
So before defending conclusions, this article asks something simpler:
Can we agree to question and investigate established systems?
Making the Invisible Visible
Revealing the Relationships that Shape Reality
Once we begin questioning the assumptions beneath a system, comparison becomes useful because it creates friction; the kind that sharpens perception and allows us to see what might otherwise remain invisible. We often only notice what matters when something resists us or breaks from expectation. Everyday tensions, misunderstandings, constraints, or competing perspectives interrupt autopilot and force us to reconsider what we assume to be true. Across psychology, science, and innovation, constructive tension often becomes the catalyst for new understanding. Comparison itself can act as this kind of friction.
In this article, I use comparison to examine the methods of economics and ecology. Across psychology, organizational research, and history, we see the same pattern: where there is constructive tension and difference, there is usually a higher chance of new ideas emerging.Comparison is a helpful mechanism of friction. Their contrasts reveal not only what each model can explain, but also the limits created by how they define and measure systems. This is not an argument that one is right and the other wrong, but an invitation to examine both more clearly.
If we agree it is reasonable to question ideas in theory, why do we so often operate our markets and businesses as though economic models are absolute truth, despite persistent market failures, rising inequality, health crises, and accelerating climate disruption?
Persistent failures across multiple domains suggest not isolated malfunctions, but potentially flawed assumptions in the underlying model. When failures emerge repeatedly across ecological, financial, and social systems, it becomes reasonable to ask whether the issue lies not only in implementation, but in the assumptions embedded within the models guiding decisions.
Having established the need to question our assumptions, can we now turn to consider how the world is a system of interconnections. Markets, technologies, cultures, and ecological systems are not separate; they continuously shape one another and produce consequences that extend far beyond immediate experience.
This interdependence has become increasingly visible through globally entangled financial markets, supply chains, climate disruptions, housing insecurity, layoffs, and rising food and energy costs. Systems once perceived as distant now materialize directly in everyday life.
For example, many people rarely consider the forces that shape the price of food until costs rise sharply. Yet higher prices are not caused by a single factor, but by interacting conditions such as labor, energy, climate, supply chains, subsidies, and policy. Friction draws attention to these hidden relationships, revealing that beneath seemingly simple outcomes lies a network of interconnected systems.
Changing the Questions We Ask
Learning to Think in Systems
Systems theory is a way of understanding how parts interact within the whole, rather than studying each part in isolation, in order to explain complexity. That perspective makes it easier to see where standard economic thinking may be too reductionist, too linear, or too dependent on equilibrium assumptions that do not fit real economies. Systems theory enables us to question the established view that markets naturally self-correct or that individuals always optimize in stable, predictable ways. It also helps explain why some economic models can look elegant on paper but fail in practice when they ignore institutions, power, and adaptation. From a systems lens, an economy may be “not working for us” if it produces instability, ecological damage, insecure livelihoods, or widening inequality even while headline indicators such as GDP improve. Because systems theory highlights emergence, it can reveal harms that are invisible when each sector is studied alone, such as how housing, labor, finance, and health reinforce one another. That makes it especially useful for arguing that current economic arrangements are not neutral outcomes, but system designs with tradeoffs that can be redesigned.
Systems theory enables questioning established economic thought by showing that the economy is not a machine with one simple fix; it is a living network of relationships whose behavior depends on interaction, feedback, and context. Once we accept that, we can more credibly ask whether the theories guiding policy are simplifying reality so much that they are no longer serving people well.
The Collapse of Certainty
When the Formula Stopped Working
These questions became personal for me during the 2008 financial crisis, when I began to realize that instability was not an exception to the system, but a feature of how interconnected systems behave.
At the time, I was living in New York City, studying to be an ecologist while working full time, interning and intensely pushing myself beyond my biological boundaries because I believed the formula for success was clear: work harder than everyone else, perform at the highest level, sacrifice now, and stability would eventually follow. I gave up sleep, health, freedom, and financial security in a disciplined pursuit of a future I believed the system would reward. I accepted student debt, barely afforded food and insulin, and structured every hour of my day around survival and ambition. I believed it would all pay off one day.
Then I watched the financial crisis unfold in real time on the streets of Manhattan. In the subway, I saw men in Armani suits crying. In the Upper East Side restaurant where I bartended, I listened to wealthy families panic as investment portfolios collapsed and lifestyles changed. I overheard women commiserating about how they wanted to divorce their husbands because they could no longer spend $10k per week shopping. Middle-class families lost homes. The people who had seemingly followed the formula for success and benefitted had just discovered how fragile their “security” really was.
What unsettled me most was not simply the crisis itself, but the realization that no one seemed to fully understand what was happening. The assumption had been that if the variables were known and the formula followed correctly, stability could be engineered. But the system was behaving less like a machine and more like something unpredictable, interconnected, and unstable beneath the surface. This is when I began to look for the undercurrents.
The week the Bear Sterns’ collapse was announced, I was sitting in class at Columbia University, listening to Prof. Jeffrey Sachs, the first UN Director of Sustainable Development, as he sidelined the planned lecture to explain how short-sighted loan lending practices had become the mechanism of the financial crisis. I take comfort in understanding the inner workings of things, so I felt privileged and fortified gaining this insight from such a renowned economist. But his closing comments derailed this solace.
He said, if anyone claims to know what's going to happen in the economy, you know that you can't believe them, because we are in unprecedented times with globalization and we don’t have models that predict with certainty. He went on to specify, “any explanation is just rationalization.” This statement shook something loose in me the more I thought about it. This class, Challenges in Sustainable Development, and the newly minted department by the same name, were one of the primary reasons why I gave up almost half my graduating credits to transfer into Columbia. I wanted to study ways we could adapt our economic systems to integrate with our ecological systems.Yet it sounded to me like the economist the United Nations trusted to lead sustainable development strategy was admitting that no one truly understood how the system behaved at scale.
This inspired a new realm of questioning that would expand throughout my life. It also reminded me of another time I heard a similar sentiment from the very first lecture of my community college biology class when the professor proclaimed ironically with a wry smile, “the only thing that is certain is that anyone who says anything with absolute certainty, can not be trusted, because there is no absolute certainty in science.”
It was in the inevitability of uncertainty that a biologist and an economist could agree.
The Inevitability of Uncertainty
So the investigation begins at a point of agreement between a biologist and an economist: uncertainty is not an anomaly; it is the condition of complex systems.
All systems are shaped by interconnection. As relationships multiply, outcomes do not simply add together; they compound, interfere, reinforce, and evolve. This creates a range of possible futures rather than a single predictable path. The more pathways of interaction a system contains, the greater the range of possible outcomes.
In both ecological and economic systems, change is constant. Conditions shift, feedback loops emerge, and relationships continuously reshape one another. As Dr. Spencer Johnson writes in Who Moved My Cheese?, “the cheese is always moving.” Long-term systems cannot be understood as static because the variables themselves continue to change.
Both economists and ecologists recognize that uncertainty is not a failure of measurement, but a defining characteristic of interconnected systems.
Systems Behave Through Relationships
Seeing Beyond Linear Cause and Effect
In complex systems, relationships generate behaviors that cannot be understood in isolation. In a forest, the health of a tree depends not only on the tree itself, but on soil microbes, water cycles, climate, insects, and surrounding species. The system behaves through interaction.
These interactions are nonlinear. The same cause can produce different effects depending on context because relationships influence one another simultaneously through feedback loops. In these loops, each effect becomes part of the next cause, making systems dynamic and unpredictable.
For example:
In an ecosystem, rising temperatures may alter soil composition, which changes water retention, which affects insect populations and eventually crop yields.
In an economy, rising interest rates may slow investment, affecting employment, consumer spending, political sentiment, and regulatory responses.
Ecological example:
temperature → soil → water → species → ecosystem shift
Economic example:
interest rates → employment → spending → policy → market response
These are not isolated chains of events but interacting loops. Outcomes emerge through relationships, not through single variables acting independently.
As interconnection increases, predictability decreases. Small disturbances can cascade into larger systemic changes because interactions generate behaviors that cannot be understood by examining components separately.
The Map is Not the Territory
How Boundaries Shape Perception
Linear models simplify systems by narrowing the number of variables under consideration. This creates analytical clarity, but also constrains what the model can perceive.
Because tightly bounded models account for only a limited range of measurable variables, they often struggle to capture long-term systemic change. When short-term assumptions are treated as stable realities, models can reinforce the very patterns they are attempting to predict.
Consider the U.S. housing market before the 2008 financial crisis. Financial models assumed housing prices would continue rising because historical data suggested they generally had. As confidence in those models grew, lenders issued more mortgages, investors purchased more mortgage-backed securities, and more capital flowed into housing. These behaviors helped drive prices even higher, seemingly confirming the model's assumptions. The prediction became part of the system itself. What the model failed to adequately account for were the growing interdependencies, feedback loops, and risks accumulating beneath the surface. By the time those relationships became visible, the system had already become fragile.
This illustrates how models can make short-term assumptions appear true by reinforcing the behaviors they measure, even as important relationships and risks accumulate beyond their boundaries.
Models with a more open design attempt to account for changing relationships, feedback loops, and uncertainty. Rather than assuming static conditions, they recognize that systems adapt continuously through interaction.
Every model creates a boundary around what it can see. What lies outside that boundary may still shape the system, even when excluded from measurement.
The Challenge for Regenerative Systems Design
Designing for Adaption Rather Than Control
If regenerative systems design seeks to work with living systems rather than impose control upon them, then uncertainty is not a problem to eliminate, but a condition that must be understood. Complex systems are uncertain, interconnected, and cannot be understood through overly linear models.
This creates one of the defining challenges of regenerative systems design:
How do we build systems for long-term viability within conditions that are inherently dynamic, interconnected, and uncertain?
Long-term system health is not a static equilibrium but a dynamic one. Just like a sailboat does not remain upright because the wind and waves stop changing; it is resilient because it continuously adjusts. The boat keel & sails counterbalance the wind and sea while the sailor points the helm to respond to shifting conditions in an ongoing process of dynamic equilibrium. Regenerative systems operate similarly. Their resilience emerges not from constant stability, but from the capacity to adapt while remaining viable. This models living systems that achieve viability through adaptation rather than control.
Regenerative systems design begins with a deceptively simple realization: uncertainty is not a flaw in living systems. It is a consequence of interdependence. The more connected a system becomes, the more its future emerges through relationships, feedback loops, and adaptation rather than through linear prediction and control.
This creates a profound design challenge. If uncertainty is inherent to complex systems, then the question is no longer how to eliminate it. The question becomes whether our models are capable of perceiving the relationships that generate it.
Every model draws boundaries around what it includes and excludes, therefore, the way we define a system determines what we can see, what we can measure, and ultimately what we believe is real. Some models prioritize stability, prediction, and control. Others prioritize relationships, feedback, and adaptation. These differences are not merely technical choices. They shape how success is defined, how decisions are made, and what kinds of futures become possible. This is the foundation of regenerative systems design.
The boundaries of our models shape the boundaries of our possibilities. Before we redesign our systems, we must first question how we have learned to see them.
In Part 2, we will examine how economics and ecology define systems differently, how those assumptions influence what each discipline can perceive, and why the boundaries of a model may ultimately determine the boundaries of what a society believes is possible.
Primary Sources:
Bertalanffy, Ludwig von. General System Theory: Foundations, Development, Applications. George Braziller, 1968.
Kimmerer, Robin Wall. Braiding Sweetgrass: Indigenous Wisdom, Scientific Knowledge, and the Teachings of Plants. Milkweed Editions, 2013.
Kimmerer, Robin Wall. The Serviceberry: Abundance and Reciprocity in the Natural World. Scribner, 2024.
Korzybski, Alfred. Science and Sanity: An Introduction to Non-Aristotelian Systems and General Semantics. International Non-Aristotelian Library, 1933.
Meadows, Donella H. Thinking in Systems: A Primer. Chelsea Green Publishing, 2008.
Sachs, Jeffrey D. The Age of Sustainable Development. SDG Academy / Columbia University, 2015.
Stiglitz, Joseph E. Freefall: America, Free Markets, and the Sinking of the World Economy. W. W. Norton & Company, 2010.