In healthcare, we often default to linear modes of thinking: a symptom leads to a diagnosis, which leads to an intervention, which produces an outcome. While this reductionist approach may be efficient for some acute cases, it falls short when we’re dealing with complex, chronic, or emergent conditions.
Recovering from trauma, managing persistent pain, or addressing organizational burnout rarely follow a straight line. Instead, these processes unfold within dynamic webs of interaction—shaped by biology, behavior, relationships, environments, and systems.
To make sense of these patterns, we turn to the theory of Complex Adaptive Systems (CAS). CAS provide a valuable framework for understanding how change unfolds in living systems—across all levels, from cells to communities.
These systems are made up of diverse agents—such as cells, individuals, or teams—that interact in nonlinear, context-dependent ways. Their behavior isn’t dictated by a central command but instead emerges from the interactions between parts. Over time, CAS evolve through processes of self-organization, learning, and adaptation.
This post kicks off a multi-part series designed for clinicians, educators, and healthcare changemakers. In Part 1, we’ll lay the groundwork: What are Complex Adaptive Systems? Why does the level of selection—the biological or social unit that’s being shaped—matter so much? And how do forces like cooperation and competition drive both functional and dysfunctional outcomes in health and care?
At the heart of the human experience, whether we’re talking about physical healing, mental health, or organizational behavior, there are systems that are more complex than the sum of their parts.
These are Complex Adaptive Systems (CAS): systems composed of many diverse, interacting components (or “agents”) that respond to their environment and to each other in nonlinear, often unpredictable ways.
Unlike simple systems (like a light switch) or complicated ones (like a jet engine), CAS don’t operate by rigid rules or pre-defined pathways. They learn, self-organize, and evolve over time. Small changes can produce massive effects, and stable patterns can emerge from apparent chaos.
What’s most important is that the whole system exhibits properties that cannot be fully explained by analyzing any single part in isolation.
We encounter Complex Adaptive Systems across both biological and societal domains. These systems don’t just exist in theory—they’re embedded in the living structures and organizations we engage with every day. For example:
Of course, not all complex systems behave the same way. Some are designed for collaboration and coherence, while others are shaped by conflict and competition. A useful way to distinguish them is by thinking in terms of two types:
This distinction is crucial. It’s easy to romanticize complexity—as if every adaptive system naturally trends toward balance or wellness. But that’s not always true. Not all adaptation is healthy.
In fact, when individual agents act in self-interest without regulation, the system can spiral into dysfunction. Cancer cells aren’t malicious—they’re just doing what they’re designed to do. But without regulatory boundaries, that local adaptation leads to systemic breakdown.
The same principle applies to our healthcare system. When incentives prioritize the goals of departments over patient care, or individual clinicians over team-based collaboration, dysfunction doesn’t just arise—it becomes inevitable.
To really understand how CAS evolve, we need to talk about multilevel selection theory.
In traditional evolutionary thinking, natural selection acts on individuals. But multilevel selection asks: What happens when selection operates on groups too?
A powerful metaphor for multilevel selection comes from evolutionary biologist David Sloan Wilson, who uses the game of Monopoly to illustrate how cooperation and competition play out at different levels.
In the standard game, players win by bankrupting others—an example of pure individual competition. But Wilson asks us to imagine a tournament, where teams compete against other teams across multiple rounds. In that setup, the teams that cooperate internally—sharing resources and supporting one another—are more likely to win over time than teams composed of self-serving individuals.
The key insight? The unit of selection has changed. When success is measured at the group level rather than the individual level, cooperation—not competition—becomes the most adaptive strategy.
This concept isn’t just a clever analogy. Wilson’s work on multilevel selection theory shows that evolution operates across multiple layers—from genes to individuals to groups—and that groups who cooperate effectively tend to outcompete those that don’t, even when individual sacrifices are required for the greater good.
We see this dynamic play out in healthcare all the time. Ant colonies thrive not because each ant is powerful, but because they function as a coordinated whole. Interdisciplinary care teams that share information and align their goals consistently outperform siloed practitioners.
And healthcare systems that prioritize collective well-being over isolated performance metrics tend to be more sustainable and humane—for both patients and providers.
Ultimately, where we place the selection pressure matters. Whether we’re talking about immune responses, organizational design, or public health policy, the central question remains: What level are we optimizing for—and what kind of behavior does that structure incentivize?
One of the most dangerous assumptions we make about complex systems is that they are naturally self-correcting. In reality, not all systems are adaptive in ways that promote health or well-being.
Without functional organization—without clear structures that support cooperation, feedback, and regulation—systems can spiral into chaos or become actively harmful.
Take cancer, for example. Cancerous cells follow local strategies that help them survive and proliferate, yet in doing so, they ultimately destroy the body they depend on.
Or consider chronic inflammation: the immune system, once a powerful defender, can become trapped in a loop of overactivation, continuing to respond long after the original threat is gone.
In our healthcare system, we see this pattern mirrored in clinician burnout. When an organization prioritizes productivity over presence, metrics over meaning, it begins to degrade the very individuals it relies on to function.
These are all examples of maladaptation—situations where adaptation at one level undermines the integrity of the broader system. What supports survival or success at the level of a single cell, person, or department may ultimately harm the tissue, individual, team, or organization it’s embedded within.
“What’s adaptive for one level may be destructive for another. True resilience requires alignment—where cells, individuals, and systems evolve together.”
As multilevel selection theory reminds us, adaptation must be evaluated in context. Sometimes what is good for one level—a cell, a person, or a department—may be harmful to the levels above or below it. And when those levels fall out of sync, the whole system suffers.
Health depends not on one level winning, but on all levels cooperating.
To make sense of complex systems, we must begin by identifying the unit of selection—the level at which adaptive pressure is being applied.
Are we optimizing for the individual patient? The clinician? The department? Or the financial bottom line? Without clarity on this point, we risk misinterpreting what the system is actually doing.
When we fail to identify the correct unit of selection, we end up analyzing the wrong variables, addressing symptoms instead of root causes, and building systems that are efficient on the surface—but ineffective in practice.
A healthcare system designed to maximize throughput might look productive in metrics, but leave both patients and providers depleted.
The same principle applies to biology. You can’t fully understand inflammation by studying a single immune cell; the behavior emerges from how the entire immune network interacts.
Similarly, recovering from trauma can’t be achieved by focusing solely on surface symptoms. True recovery requires engagement across biological, psychological, and social domains—each one a layer where adaptation and selection take place.
In 1977, psychiatrist George Engel proposed what would become one of the most transformative frameworks in modern medicine: the Biopsychosocial (BPS) model.
At a time when the biomedical model dominated clinical thinking, Engel challenged the idea that illness could be fully understood—or treated—by looking only at biology. He argued that health emerges from the interplay between biological, psychological, and social domains.
But Engel went even further. In his original depiction of the model, he laid out a systems hierarchy—a nested structure of levels that range from subatomic particles all the way up to the biosphere.
Each level, from molecules to families to nations, represents a functional unit that interacts with—and is influenced by—both smaller and larger scales. At the center of this hierarchy lies the person, whose behavior and experience are shaped by layers both internal and external.
This diagram is more than a conceptual framework; it aligns directly with what multilevel selection theory tells us: that adaptive pressure operates at multiple levels simultaneously.
What benefits a single cell might harm the tissue. What helps an individual cope might destabilize a relationship. What advances one family’s well-being might challenge the larger community.
Each level in Engel’s hierarchy is a site of adaptation, constantly responding to challenges and demands. But when adaptation at one level is prioritized—whether it’s higher up the chain, like social expectations, or deeper down, like cellular stress responses—without cooperation from the levels above and below, the result can be imbalance or dysfunction.
A social role that demands constant productivity may override internal signals of exhaustion. Likewise, cellular inflammation may persist in ways that disrupt emotional or relational stability.
When adaptation serves only one layer of the system, it risks fragmenting the whole.
But when the levels above and below the person—families, communities, nervous systems, cells—are aligned and working in harmony, the system becomes resilient, flexible, and capable of peak performance.
Coherence across levels doesn’t just prevent illness—it creates the conditions for healing, growth, and sustained well-being.
This first post laid the foundation for a different way of seeing. We’ve explored what makes a system complex and adaptive, how cooperation and competition shape behavior, and why identifying the appropriate unit of selection is essential to understanding outcomes—whether at the level of a cell, a person, a care team, or an entire healthcare system.
But this is just the beginning.
In Part 2, we’ll zoom in to examine how the human body, brain, and behavior function as complex adaptive systems themselves—and what happens when they become stuck in maladaptive patterns.
From trauma to chronic pain to burnout, we’ll explore how these conditions are not isolated failures, but the consequence of systems losing their ability to self-organize in healthy, flexible ways.
Stay with us as we continue this journey—toward a model of healthcare rooted not in control and correction, but in complexity, coherence, and the possibility for transformation.
Co-Founder
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