— Lori Hogenkamp
Founder, Center for Adaptive Stress
June 2025

Metaphors for Understanding the Impact of the Evolutionary Stress Framework and Emergent Paradigms

To convey the purpose and epistemological stance of the Evolutionary Stress Framework (ESF), it helps to use metaphors that contrast the ESF’s nonlinear, recursive worldview with the prevailing linear paradigms in science. The ESF posits that stress is not a simple cause-effect variable but a framework of communication and adaptation, operating through emergent properties and nested systems. Below, we explore a range of rich metaphors—from history, systems theory, biology, and information science—that illuminate the incompatibility between linear and nonlinear paradigms, emphasize emergent properties and nested systems (with stress as a communication medium), and show how ESF runs parallel to existing science rather than as a mere revision of it. These metaphors provide conceptual bridges for systems thinkers, medical scholars, and interdisciplinary researchers to grasp the “round world” complexity of ESF, as opposed to the “flat world” of linear thinking.

Flat vs. Round World: Competing Maps of Reality

One metaphor already used for ESF is the “flat world” vs. “round world” analogy. Just as medieval maps assumed a flat Earth (adequate for local navigation but fundamentally wrong at scale), a linear scientific paradigm is like a flat map – it can chart simple, close-range cause-and-effect, but it distorts when applied to the full globe of complex phenomena. In contrast, ESF’s nonlinear paradigm is a “round world” perspective, acknowledging curvature, recursion, and interconnectedness much like a globe reflects the true shape of Earth. The two paradigms are incommensurable: a flat map cannot simply be “corrected” into a globe with a minor tweak; it requires a complete change in projection. Historically, shifting from flat-earth to round-earth understanding was a paradigm upheaval – new questions emerged that weren’t even visible on the old map. Similarly, ESF invites entirely new questions about health and behavior (e.g. “How did this pattern emerge?” instead of “What is the root cause?”(evostress.blog) and reveals dynamics (feedback loops, context dependencies) that a linear model would “flatten” or ignore (evostress.blog). The incompatibility is clear: linear models see the world in one dimension at a time, whereas ESF’s round-world model sees multiple dimensions and cycles at once, requiring a different epistemological toolkit (much as navigation needed to shift from flat charts to spherical coordinates). This metaphor highlights that ESF is not a minor course correction to existing science, but a wholesale change in worldview, analogous to moving from a flat-earth map to a globe – a new way to map reality.

Clocks vs. Clouds: Predictable Mechanisms vs. Emergent Complexity

Philosopher Karl Popper’s famous metaphor of “clocks and clouds” provides a vivid distinction between linear and nonlinear paradigms. In this analogy, “clocks” represent systems that are regular, orderly, and predictable, like the tidy gears of a clockwork mechanism (richardschutte.medium.com). Traditional science often treated every phenomenon as a kind of clock – even complex, irregular processes were assumed to be clockwork at heart (“All clouds are clocks – even the most cloudy of clouds,” as Popper quipped, critiquing the Newtonian-era belief that with enough detail, even chaotic-looking systems would obey neat deterministic laws (richardschutte.medium.com). “Clouds,” on the other hand, symbolize systems that are irregular, disorderly, and less predictable – think of the shape-shifting, turbulent nature of a cloud. A cloud-like system has emergent behaviors and inherent uncertainties that cannot be reduced to simple causes. Complexity science (and ESF by extension) aligns with the “cloud” view, acknowledging that many natural and social phenomena are fundamentally non-linear and unpredictable in detail. Weather patterns are a classic “cloud” – tiny differences can amplify into large outcomes (the butterfly effect), defying linear predictability. In the ESF context, a condition like autism or chronic illness is not a clockwork mechanism with a single root gear to adjust, but more like a cloud: a product of many interacting variables with emergent properties (evostress.blog). The linear paradigm tries to force such clouds into clocklike models (flattening complexity to find a single cause), whereas the ESF paradigm accepts the cloud’s complexity and searches for patterns and interactions instead (evostress.blog ; evostress.blog). This metaphor underlines that linear and nonlinear paradigms operate with different assumptions – the clock-model assumes proportional inputs and predictable outputs, while the cloud-model accepts disproportionate, surprising outcomes. Embracing the “cloud” mindset of ESF means valuing emergence, probability, and patterns of relationship over certainty and reductionism. In summary, Popper’s clocks vs. clouds encapsulates the shift from viewing the body or ecosystem as a precise machine to viewing it as a complex, self-organizing system. ESF sits firmly on the “cloudy” side – not to imply vagueness, but to capture those rich, emergent properties that only manifest when parts interact in nonlinear ways (aishwaryadoingthings.com; aishwaryadoingthings.com). It asserts that new holistic behaviors (“more is different”) emerge from complexity that a simple clock metaphor cannot capture (richardschutte.medium.com).

Machine vs. Ecosystem: From Reductionist Engines to Living Systems

A closely related metaphor is the contrast between viewing a system as a machine versus as an ecosystem (or living organism). For centuries, the dominant scientific metaphor was mechanistic: the universe (and the human body) was likened to a clockwork machine, composed of parts that can be individually analyzed and fixed. This machine metaphor, rooted in Newtonian science, assumes linear cause-and-effect, additivity, and centralized control (aishwaryadoingthings.com). It has been tremendously useful in engineering and early biology, but it breaks down for complex, self-organizing phenomena. As one complexity scholar notes, “the machine metaphor has its limitations when it comes to understanding complex systems that are nonlinear, unpredictable, and self-organizing.” It fails to account for spontaneous, emergent properties – for example, why a network of neurons produces consciousness, or how individual ants collectively build an intricate colony . ESF’s paradigm is better captured by the ecosystem (organismic) metaphor. In an ecosystem (or any living system), there is no single central gear – order arises from distributed interactions and feedback loops, and the whole is more than the sum of parts (aishwaryadoingthings.com). Importantly, an ecosystem responds to stress very differently than a machine does. In a machine, stress is often a sign of dysfunction or load – a machine under stress (heat, strain) simply degrades or breaks unless an outside mechanic intervenes. In a living system, however, stress can be informative and adaptive. Biologist Ludwig von Bertalanffy (founder of General Systems Theory) argued that stress is an essential driver of evolutionary change: “stress…creates higher life”, meaning that without stress and perturbation, organisms would never have evolved beyond the simplest forms. An ecosystem facing stress (say a drought or a new predator) doesn’t merely break; it reorganizes, adapts, or finds a new equilibrium. In fact, lack of stress can be harmful – equilibrium is akin to stagnation or death in a biological sense (synthegrate.com). This viewpoint reframes stress as functional information that triggers adaptation, much like an ecosystem uses feedback (predator-prey dynamics, resource scarcity signals) to adjust population balances. ESF adopts this organismic, ecological view: human biology under ESF is seen as an adaptive system of systems, constantly reallocating energy and recalibrating in response to stressors rather than a static machine with a broken part (evostress.blog; evostress.blog). In summary, where the linear/mechanistic paradigm might ask “Which part is broken and how do we fix it?”, the ESF systems paradigm asks “How is this system adapting to its conditions, and what emergent strategy is at work?” (evostress.blog). The machine vs. ecosystem metaphor highlights that ESF is parallel to, not a subset of, the mechanistic paradigm – it uses an entirely different root metaphor (organism, not engine) to guide inquiry (synthegrate.com; aishwaryadoingthings.com).

Food Chain vs. Food Web: Linear Causality vs. Interconnected Networks

Figure: A complex food web (Chesapeake Bay waterbirds) illustrates non-linear, networked interactions, as opposed to a simple linear food chain. A food chain follows a direct linear pathway, whereas a food web depicts multiple interconnecting pathways of energy and influence en.wikipedia.org.

Another metaphor from ecology is the difference between a food chain and a food web. A food chain is a simple linear sequence: for example, grass → rabbit → fox → wolf. In early ecology (and in grade-school science) this linear chain was a convenient simplification, much like linear models in medicine trace a single cause to a single effect. However, in real ecosystems, food chains interweave into food webs: rabbits might eat many plants; foxes might eat rabbits and mice and berries, etc. The food web is non-linear, with nested and overlapping loops of causation. As Wikipedia succinctly notes, “A food chain follows a direct linear pathway of consumption… [whereas] natural interconnections between food chains make a food web, which is non-linear and depict interconnecting pathways of consumption and energy transfer.” (en.wikipedia.org) In other words, the “chain” metaphor flattens complexity (much as a linear paradigm tries to isolate one cause), while the “web” metaphor embraces interconnected complexity. This directly parallels ESF’s stance: health outcomes or traits are not the end of a single chain of cause, but the nexus of a web of interactions (genes, environment, stressors, timing, development, etc. all feeding into each other) evostress.blog. For example, instead of saying “Gene X causes condition Y” (a linear chain), ESF would map how Gene X, under certain environmental nutrients and stress signals, leads to a network of metabolic changes that collectively manifest as Y – a web of conditional factors. The food web metaphor also underscores emergent behavior: in a web, you can get cascades (remove one species and the whole network shifts unpredictably), which is akin to how a perturbation in one part of a complex system (say a stress spike or an energy deficit) can lead to non-linear ripple effects in health. Linear science often assumes ceteris paribus (all else equal) and looks for A→B→C chains, but ESF sees the “entangled bank” of factors Darwin spoke of – a dynamic web where stress is like a thread running through multiple connections. In this sense, stress can be seen as the medium that carries influence across the web – analogous to how energy or information flows along a food web’s links. This metaphor helps an academic audience appreciate that ESF isn’t just adding more factors to a linear chain; it’s describing a different architecture of causality altogether (a network with loops and multiple pathways). It also legitimizes why linear methods struggle with problems like multi-factorial diseases: it’s like trying to trace a single chain in what is actually a web. The food web paradigm coexists with but differs from the simplistic chain paradigm in ecology, just as ESF coexists with linear science but offers a more realistic map for complex systemic issues.

Nested Russian Dolls: Multi-Scale, Layered Systems

Figure: Russian nesting dolls symbolize nested systems: each doll sits inside a larger doll, reflecting how ESF views biology, psyche, society, and environment as layers within layers. Changes or “stress” at one scale propagate to others in nested hierarchies (much like Bronfenbrenner’s ecological model of child development used the nesting doll metaphoropen.edu).

Nonlinear paradigms like ESF also emphasize hierarchical organization and nested systems. A useful metaphor here is Matryoshka dolls (Russian nesting dolls) – a set of dolls of increasing size, each containing the smaller ones. Traditional linear science tends to isolate a single level (e.g. molecular biology focuses on the gene, ignoring social context, or sociology looks at society, abstracting away individuals). In reality, systems are nested: an individual neuron is in a brain region, which is in a brain, inside a person, who exists in a family, which exists in a society and environment – each level encapsulated in a larger one. Psychologist Urie Bronfenbrenner’s ecological systems theory explicitly used the metaphor of nested Russian dolls to describe how a child’s development is shaped by concentric layers of environment (micro, meso, exo, macro) brainly.comopen.edu. Likewise, ESF considers nested scales of stress – cellular stress, organ system stress, psychological stress, social stress – each layer influencing the others. The nesting doll metaphor highlights two key ideas: (1) Incompatibility of scale-bound paradigms, and (2) emergent properties through layers. A linear paradigm often sticks to one scale (e.g. “find the gene for X” stays at the molecular level), whereas a nested-systems paradigm like ESF insists we look at inter-level interactions (how does a societal stress like poverty manifest through psychological stress, which impacts physiology, which in turn alters gene expression?). You cannot explain the whole system by a single layer’s logic – the phenomenon “travels” across layers. This is inherently nonlinear and often counter-intuitive to reductionist thinking. For instance, stress hormones released during a social conflict (macro-level event) can alter neural development in a child (micro-level outcome), which then influences behavior and perhaps the social environment in a feedback loop. Each “doll” affects and is affected by the others. Emergent properties often appear at the macro scale that you could not predict by only examining one small doll: e.g., consciousness “emerges” from neuronal networks, or an economic recession emerges from myriad individual actions. The nesting metaphor thus complements the web metaphor: web gives us breadth (multiple factors interacting), nesting gives us depth (multiple levels interacting). Importantly, this stresses why ESF runs parallel to conventional science – it’s not that conventional research on one level (say, genetics) is wrong, but focusing on one doll gives an incomplete picture. ESF adds a new dimension by linking the dolls together. It views stress as a connecting force across levels – much like pressure on the smallest doll will be transmitted to the outer ones, stress in one part of a system can reverberate through the whole hierarchy. In academic terms, this aligns with systems theory’s concept of recursiveness: subsystems within systems, each influencing the otherisaga.com. The Russian doll metaphor provides an intuitive visual for scholars to grasp ESF’s holistic, multi-scale epistemology.

Stress as Signal vs. Stress as Load: Communication Medium in Complex Systems

A critical aspect of ESF is reconceptualizing stress itself. In conventional models, stress is often treated as a negative input or a “load” on the system – something to be minimized or eliminated, much like noise in a communication channel or strain on a structure. In the ESF, however, stress is viewed as a medium of information and communication, analogous to a signal that coordinates a system’s response (evostress.blog). A useful metaphor is to think of stress as a language or signaling medium that the body (or any adaptive system) uses to convey messages across different parts. For example, when you exercise, the “stress” on muscles leads to biochemical signals that communicate the need for growth and repair – in effect, stress carries meaning (here, “adapt to handle more load”). If we extend this metaphor, stress hormones (like cortisol) are words in this language, travelling through the bloodstream to tell various organs to adjust metabolism, immune function, etc., in response to a challenge. In ecology, a drought (environmental stress) sends signals through the ecosystem – trees might close stomata, animals migrate or curtail breeding; it’s a form of system-wide communication that something is changing. Rather than viewing stress as mere damage, ESF aligns with the idea that “stress is not a virus to eliminate, but a message to decode.” Brent Duncan (2022) notes that systems theory exposes how attempts to eliminate all stress are misguided; instead, stress is a “vital catalyst for growth and survival” (synthegrate.com). In fact, the absence of stress (total equilibrium) is death for a dynamic system (synthegrate.com )– just as a musical instrument string produces no music if completely slack. This suggests a metaphor: stress as tension that allows communication, much like tension in a violin string allows it to resonate and produce sound. With no tension, there’s silence (no signal); with moderate tension, the string can convey complex vibrations (information/music); with excessive tension, it might snap (damage). ESF is concerned with that healthy range of stress that enables inter-system communication and adaptation, rather than zero stress or overwhelming stress. The linear paradigm often saw stress only as a risk factor to reduce (treating it like external noise obscuring the true signal of health), whereas the ESF paradigm sees stress itself as part of the signal – a core part of the framework by which systems achieve allostasis (active, dynamic stability) evostress.blog. In scholarly terms, this aligns with viewing organisms as communication networks: stress is one of the key channels of feedback (negative or positive) that regulate the network (pmc.ncbi.nlm.nih.gov) (evostress.blog). Thus, metaphors like “stress as the messenger” or “stress as the body’s Morse code” can be invoked. It runs parallel to existing science by suggesting that instead of simply fighting or blocking stress (as in a purely linear intervention), we listen to it and interpret its pattern in context. This metaphor enriches the narrative for medical and systems researchers: for instance, rather than asking “How do we eliminate stress to cure this disorder?”, an ESF-informed question would be, “What is the pattern of stress telling us about the system’s needs and adaptations?” (evostress.blog) (evostress.blog). By treating stress as information, ESF deviates from the linear approach without invalidating it – much like switching from seeing stress as an independent variable to seeing it as the medium on which the whole system’s state is encoded (evostress.blog). This is a fundamentally different epistemology, not merely a new variable in old equations.

Parallel Paradigms: Different Operating Systems Running Side by Side

Finally, it can be helpful to use a computing and historical metaphor to emphasize how ESF runs in parallel to mainstream science rather than simply revising it. Thomas Kuhn’s insight about paradigm shifts is relevant: a new paradigm often isn’t immediately commensurable with the old – it asks different questions and uses different rules (evostress.blog) (evostress.blog). We might say a paradigm is like a operating system for doing science. Just as Windows and macOS can run on the same hardware but speak different languages (software from one doesn’t run on the other without adaptation), a linear paradigm and a complexity paradigm are like two operating systems. ESF uses the “complexity OS”, which means its models and terminology might not execute properly under the assumptions of the “linear OS.” This metaphor reassures us that ESF isn’t throwing out science – just as you don’t throw away a computer when you install a new OS – but it is reformatting how we process and prioritize information. In practical terms, ESF develops in parallel to existing research: for example, traditional biomedicine might continue on its linear OS to find specific gene targets for a disease, while ESF’s complexity OS looks at patterns of stress adaptation across multiple systems. Both can run simultaneously, but their outputs may differ and need translation (just as files or data have to be converted between operating systems). A historical example of parallel paradigms can be drawn from physics: Newtonian physics vs. Einstein’s relativity. Einstein’s relativistic framework wasn’t a direct incremental improvement to Newton’s in the sense of tweaking one variable; it was a new conceptual OS (space-time curvature, nonlinearity in gravity). For a while, both paradigms ran in parallel – Newtonian mechanics is still perfectly useful for everyday speeds and served as a special case within Einstein’s broader theory (einstein-online.info). But one could not simply use Newton’s linear time and absolute space concepts to derive relativistic effects; you had to shift the frame entirely. Likewise, ESF runs alongside conventional science, often translating findings into a new context (e.g. reframing a “cause” as a contributing factor in an emergent pattern). This metaphor stresses flexibility: just as computers can dual-boot two operating systems, scientists can be bilingual – understanding that when you “boot into” ESF mode, you operate with complexity rules (feedback loops, nonlinearity, context dependence), and when you boot into linear mode, you operate with reductionist rules. Trying to solve complex adaptive problems on the wrong OS leads to frustration (much as incompatible software yields errors). This reinforces the point that ESF isn’t a replacement that falsifies all prior science; rather, it’s a parallel framework that explains phenomena the old framework couldn’t, and it positions old knowledge as a subset or special case (just as Newtonian physics is a special case of relativity for low velocities (einstein-online.info; einstein-online.info). By using this metaphor, we convey an epistemological humility and complementarity: ESF can incorporate linear findings (as approximations or ingredients), but it is fundamentally a different language or operating system for understanding reality. This helps interdisciplinary researchers see that adopting ESF doesn’t mean abandoning evidence-based science, but expanding to a dual paradigm literacy, where one can decide which set of assumptions – linear or nonlinear – is appropriate to the problem at hand (evostress.blog) (evostress.blog). In academic writing, this metaphor can be explicitly referenced (e.g., “Paradoxically, the Evolutionary Stress Framework runs parallel to mainstream models, more akin to a new operating system than a patch – it asks not ‘Which part is wrong?’ but ‘Are we in the wrong framework entirely?’”). Such phrasing underscores that ESF is introducing a round-world view that exists alongside the flat-world maps, gradually drawing converts as its utility is demonstrated, much as historically the new paradigms eventually gained traction without literally revising all the old data, but by reinterpreting it.

Conclusion: Each metaphor above – flat vs. round worlds, clocks vs. clouds, machines vs. ecosystems, chains vs. webs, nesting dolls, stress-as-signal, and parallel operating systems – shines light on different facets of the ESF paradigm shift. They collectively highlight that linear and nonlinear paradigms are incompatible in their pure forms (you must choose the right lens), yet they can coexist with the new enriching rather than erasing the old. They underscore the importance of emergent properties, feedback loops, and multi-level integration in ESF, with stress serving as a crucial channel of communication within and between systems. By employing these metaphors in scholarly discourse, one can make the conceptual leap more accessible: inviting medical and systems science audiences to step into a “round world” of thought where complexity is not an obstacle to be flattened, but the very medium through which life evolves and communicates (evostress.blog) (synthegrate.com). Such metaphors are not merely illustrative; they carry epistemological weight, helping to position the Evolutionary Stress Framework as a holistic, paradigm-level framework that offers a profoundly different, and arguably richer, map for navigating the health and behavioral sciences.

Sources:

The metaphors and concepts above draw upon insights from complexity science, systems theory, and history of science.

For instance, Popper’s “clouds and clocks” metaphor delineates deterministic vs. indeterminate systems (richardschutte.medium.com) (richardschutte.medium.com).

Systems theorists like Bertalanffy and Capra emphasize the active, adaptive role of stress in driving evolution (synthegrate.com) (synthegrate.com).

Ecologists contrast linear food chains with non-linear food webs to show interdependency (en.wikipedia.org).

Psychologists like Bronfenbrenner use nesting doll analogies for ecological systems (open.edu).

The ESF’s own literature stresses paradigm shifts (asking new questions) and treating “stress as a medium, not just a variable”(evostress.blog)(evostress.blog).

By synthesizing these sources, we see that metaphors are not just rhetoric but serve as “conceptual scaffolding” for paradigm change, helping scholars re-imagine what scientific inquiry looks like in a nonlinear, evolutionary-stress-informed world.