In Defense of Synergy
Or, how I learned to stop worrying and love complexity (science).
Oh that word…
Synergy, circa 1965:
The word synergy is a companion to the word ‘energy.’ Energy and synergy. The prefix ‘syn’ of synthesis meaning ‘with, to integrate’ and the ‘en’ of energy means ‘separating out.’ Man is very familiar with energy, he has learned to separate out, or isolate certain behaviors of total nature and thus has become familiar with many of the separate natural behaviors.
Synergy is to energy as integration is to differentiation. The word ‘synergy’ means ‘behavior of whole systems unpredicted by behavior of any of the systems parts.’ Nature is comprehensively synergetic. — R. Buckminster Fuller
Synergy, circa 2023:
In the name of Bucky Fuller, I propose we reclaim “synergy” for the forces of good (design). Here’s why.
A shifting center of gravity
Remember Nicolaus Copernicus? He was the fellow who challenged the prevailing astronomy paradigm of his day to suggest Earth was not the center of the universe. The design world is collectively recognizing in similar fashion that a human centered design paradigm won’t serve us much longer. But, unlike Copernicus, whose model replaced the Earth with the Sun, we cannot simply replace one center of our design universe with another and expect to understand the broader constellation of forces at work.
Copernicus’ contribution was largely one of reevaluating existing data. However, Tycho Brahe (and later Johannes Kepler) came along with a wholly improved model that incorporated new info about not only the objects (planets & stars) but also their relationships (elliptical orbits, who knew?!), that revolutionized our understanding of the solar system and behavior of everything within it.
Thankfully, we’ve got more modern thinking about human dynamics (objects and relationships) than Copernicus did about planetary behavior. Systems thinking, and in particular, the transdisciplinary study of complex systems we call complexity science, offers an approach to the most challenging design endeavors we face today, and will arguably be one of the most critical disciplines for design strategists and other agents of change in the coming decades.
Some (re)assembly required
Systems thinking is a means to engage with our world and its complexity by understanding wholes and relationships rather than analyzing separate components in isolation. It may be counterintuitive for those of us who were taught to understand things by taking them apart (figuratively or literally) to see how they work, (a practice not without value for deep specialist knowledge about those isolated components), but it’s not the deconstructing that gives us deepest insights about our biggest challenges — it’s learning how the pieces function together in conjunction to produce results that can’t be explained by mere sum-of-parts logic.
Reductionism was the driving force behind much of the twentieth century’s scientific research. To comprehend nature, it tells us, we first must decipher its components. The assumption is that once we understand the parts, it will be easy to grasp the whole. Divide and conquer; the devil is in the details. We have been trained to study atoms and superstrings to understand the universe; molecules to comprehend life; individual genes to understand complex human behavior; prophets to see the origins of fads and religions.
Now we are close to knowing just about everything there is to know about the pieces. But we are as far as we have ever been from understanding nature as a whole. Indeed, the reassembly turned out to be much harder than scientists anticipated. The reason is simple: Riding reductionism, we run into the hard wall of complexity. We have learned that nature is not a well-designed puzzle with only one way to put it back together. In complex systems the components can fit in so many different ways that it would take billions of years for us to try them all. Yet nature assembles the pieces with a grace and precision honed over millions of years. It does so by exploiting the all encompassing laws of self-organization, whose roots are still largely a mystery to us.
— Albert-László Barabási
A systems orientation that infuses design strategy with complexity science enables us to engage with ambiguity without reducing it, and to both envision and facilitate desirable futures through more fluid forms of intervention and enablement — where analytically siloed problem solving strategies historically fall short.
What’s old is new again
Don’t let anyone sell you an officially licensed “Systems Thinking process,” (or at least not the same way they sold Design Thinking). It’s neither a prescriptive model nor convergent linear method. It’s simply a lens, an orientation, that helps us build a richer understanding of our surroundings and identify the places in a system where we can intervene with higher likelihood of positive outcomes. That’s all it is… and that’s all it needs to be to radically enhance your design, strategy, and/or innovation practice.
Systems thinking’s more formal field of study, sometimes called systems science, found its origins in the 1940s (with many seeds planted far earlier), which means it’s had plenty of time to evolve dozens of overlapping subdisciplines like nonlinear dynamical systems, chaos theory, cybernetics, network theory, information theory, social & community based system dynamics, complex adaptive systems, and so on. If these terms sound a bit esoteric, keep in mind a “system” can be anything from a computer network to an ant colony to a backyard garden to an urban neighborhood to a global economy. Donella Meadows describes a system as “an interconnected set of elements that is coherently organized in a way that achieves something.”
Systems thinking and its subdisciplines provide an epistemological rigor enabling us to look at things holistically, while keeping touch with the practical and real. With a systems thinking orientation, we can take on complex design challenges while freeing ourselves from reductionist analytical methods that often risk investment in short sighted point solutions (which, at at best, underperform, and at worst, generate more and greater problems).
Historically, systems scientists sought to identify universal principles about system behavior, and many systems thinking subdisciplines evolved to be more descriptive than prescriptive. The earliest work in General Systems Theory was not unlike the work of physicists in pursuit of a unifying “Theory of Everything,” which could potentially explain a great many things, (especially those with reducible and linear cause-and-effect relationships), but may not necessarily provide a prescriptive instrument for change.
Until, that is, systems thinkers like Buckminster Fuller and Herbert Simon came along and developed what they called “design science,” applying design’s generative instinct for invention to the systems theory of the day. Fuller envisioned design science as “the effective application of the principles of science to the conscious design of our total environment in order to help make the Earth’s finite resources meet the needs of all humanity without disrupting the ecological processes of the planet.” Is that all, Bucky?
Since then, the design and science threads have unraveled from each other a bit, but nature loves symmetry, and these days instead of adding designerly thinking into systems thinking, design practitioners are now learning to (re)integrate systems thinking into their work. The generative — beyond merely descriptive — power of systems thinking is manifest when applied harmoniously through design, which Herbert Simon highlighted in his own definition: “To design is to devise courses of action aimed at changing existing situations into preferred ones.”
There’s a powerful synergy — in the truest meaning of the word — between sense making through systems thinking and decision making through design strategy, the power and depth of which we are just now beginning to realize.
(Watch this lecture, or read the transcript here, and ask yourself whether you’d know it was 30 years old, if not for the production aesthetic.)
Let’s talk about complex-it-ty
(Salt-N-Pepa? Anyone?)
Adopting a systems thinking lens requires a few additional shifts in design practice. To quote Donella Meadows once again,
People who are raised in the industrial world and who get enthused about systems thinking are likely to make a terrible mistake. They are likely to assume that here, in systems analysis, in interconnection and complication, in the power of the computer, here at last, is the key to prediction and control. This mistake is likely because the mindset of the industrial world assumes that there is a key to prediction and control… But self-organizing, nonlinear, feedback systems are inherently unpredictable. They are not controllable. They are understandable only in the most general way. The goal of foreseeing the future exactly and preparing for it perfectly is unrealizable. The idea of making a complex system do just what you want it to do can be achieved only temporarily, at best.
We can’t control systems or figure them out. But we can dance with them.
And learning to dance with complex systems requires that we get to know our dance partners’ moves. The study of complex adaptive systems, known as complexity science, is one of the most relevant systems subdisciplines for experience strategy and design practitioners, because so much of our craft is applied to increasingly complex and interconnected experience contexts.
There are…on this planet, phenomena that are hidden in plain sight. These are the phenomena that we study as complex systems: the convoluted exhibitions of the adaptive world — from cells to societies. Examples of these complex systems include cities, economies, civilizations, the nervous system, the Internet, and ecosystems. — David Krakauer
The behaviors of complex adaptive systems are inherently difficult to predict, thanks to the myriad relationships and interdependencies, visible and invisible, between their numerous components and the flow of information and energy among them. Curious readers can find a brief history of complex systems science here, and an interactive explainer here, but the most helpful thing to know is that complex adaptive systems share a handful of specific characteristics that arise from these dynamics and make them unique from simpler, more predictable systems:
Path dependence: When a system is highly sensitive to initial conditions, and its future behavior depends on both its starting point combined with its history, leading to unpredictably large changes in outcomes.
Nonlinearity: When systems react disproportionately to changes based on their current state or context. In physics, nonlinear relationships occur when inputs are not proportional to the output; in other words, small forces on a system can cause surprisingly large changes, and vice versa.
Path dependence and nonlinearity are also related to what physics and math call “chaos” and what is popularly known as the “butterfly effect.”
Emergence: When the internal dynamics of a system produce system behaviors that are not related to the individual behaviors of separate components within. Emergence makes it impossible to predict system behaviors by simply understanding their parts in isolation, because the behavior of the whole will look very different than the sum of those parts.
Self-Organization: When systems are composed of interdependent interactions between its components that self-organize into ordered patterns. These patterns emerge without a central control or guiding external force, as seen with flocks of birds or schools of fish.
Emergence and self-organization are properties of systems that make complexity science particularly valuable to experience strategists working with complex service or product ecosystems, decentralized organizations, or rich information environments.
Adaptability: When systems demonstrate resilience as individual components update their strategies and the system as a whole adapts through learning, feedback loops, and evolutionary processes.
Dynamic Equilibrium: When systems operate somewhere between rigid order and total chaos. Without settling into a static state, an adaptive tension emerges as information and/or energy flows into and out of the system, and the system operates with a form of flexibly stability
Outlier Behavior: When systems produce rare but extreme events more frequently than would be predicted by a normal bell curve model, which can in turn have large impacts on the system. For example: mass extinctions, market crashes, and disease pandemics.
Adaptability, dynamic equilibrium, and outlier behavior are the type of characteristics that render prediction-based analytical decision making strategies largely futile in complex systems.
Identifying these traits helps us know when we’re talking about the specific, rigorous concept of complexity, rather than colloquial use of the word, and also helps us distinguish between systems that are largely mechanical in nature from systems that are naturally adaptive. In mechanical systems, we can often predict what each of the parts will do and thus we usually know how a system will behave in many different circumstances; even complicated mechanical systems rarely exhibit surprising behavior. Complex adaptive systems, however, are composed of “parts” that have more agency and respond to changes in fundamentally unpredictable ways (often because they are living organisms that enjoy more degrees of freedom than mechanical elements), so emergent and surprising behavior is more common. As Murray Gell-Mann put it, “Think how hard physics would be if particles could think.” Understanding what makes a system both complex and adaptive helps us understand how to “dance” with these types of systems when trying to effect positive change.
Is any of this sounding familiar? If you’re anything like most humans, you work, play, and learn within dozens of complex adaptive systems, ranging from healthcare to higher ed to K-12 classrooms to stock markets to whole economies. Chances are, you’ve been frustrated by the (lack of) impact a conventional strategy or solution achieves because the aforementioned characteristics reinforce the futility of outdated predict-and-control frameworks and highlight instead the need for organizational, social, and economic infrastructures that enable more flexible sense-and-respond strategies built on holistic understanding of the system.
(And by the way, to get a little meta, Turner and Baker evaluated and observed these characteristics in the context of creativity and innovation as a complex adaptive system, itself. There’s no escaping complexity — might as well learn to swim, or fly, or dance in it!)
To integrate this understanding of complex adaptive systems in everyday creative practice, it helps to think of complexity science in two ways:
As a source of compelling metaphors and conceptual models that facilitate broader perspectives and shared understanding of the experience ecosystem:
Metaphors are generally more accessible to lay audiences than the more esoteric mathematics or computational tools of complexity science, and metaphorical thinking can facilitate shifts in mental models that underpin certain system dynamics via our brains’ affinity for analogical narrative structures. However, a purely conceptual application of complexity science sometimes constrains its value to high level sense making and potentially limits quantitative applications that could inform decision making.
Or, as a quantitative practice applying specific computational methods and mathematical frameworks (such as agent based modeling, network theory, chaos theory, and information theory).
These methods, when applied carefully with the right kind of data, can provide deeper insights about system behaviors that appear counterintuitive and reveal novel opportunity spaces. At the same time, the skills, data, and cooperation needed to engage in more expert modeling may not be available and may not be relevant to the objectives of a given team or organization.
Fortunately, practitioners can operate in the gray area between these two extremes, flexing to match the needs, circumstances, and resources at hand. More often than not, we can enrich the standard practices of experience strategy and design with a knowledge of complexity science that provides deeper insights through generative system mapping, and identifies leverage points for design interventions, without needing to build computational models or run in depth simulations. (Though the latter is quite valuable when we have the team and data to support it!)
Getting lost to find the way
So how does a synergetic integration of complexity science show up in the everyday work of experience strategy? This kind of systems orientation helps experience strategists answer questions that conventional human centered design efforts are not always equipped to address, such as:
What are the patterns (visible and invisible) that define this system?
What state does this system tend to stay in, or return to, despite everyone’s best intentions?
What feedback loops, incentives, and intangible forces make change difficult?
Where is the best place to intervene, and what consequences is that intervention capable of initiating (up close, far away, now, later)?
Typically, (with or without complexity science), experience strategy practitioners convene around a challenge or question, immerse in the landscape of the problem space via secondary research such as literature reviews, market scanning, and STEEPLE analysis. We perform primary research with subject matter experts, stakeholders, and others with lived experience, insight, or potential to be impacted by our design interventions, who inform and ideally co-design the ideation, iteration, and implementation of those potential interventions. Social scientists from sociology to anthropology to economics engage in similar practices, albeit with different terminology and lenses on the work, as do educators engaged in action research and, for that matter, plenty of other people without formal training in experience innovation or design of any kind.
Integrating complexity science essentially leads us to diverge more deliberately in the “fuzzy front end” of the process, immersing in more exploratory and perhaps counterintuitively disparate aspects of the problem space early on, so we can dive deeper and look farther afield than might seem relevant at first blush. We do this in order to gain system-wide perspective and to identify invisible relationships that may span quite far over time and space, but hold the keys to meaningful outcomes, and to better understand the dynamics at play so we can devise interventions that work with the energy of a system instead of against it. This early immersion buys down risk and reduces the energy required to subsequently assess potential consequences of an intervention during pilot, or to mitigate their impact after launch.
To wit, even a practitioner with highly specific subject matter expertise or deep industry experience is unlikely to jump prematurely to recommendations or solutions when they are practicing through a complexity lens. The nonlinear, emergent nature of complex system behaviors renders the notion of universal “best practices” far less useful, and in many cases, irrelevant to the unique dynamics of that particular system. It becomes far more valuable to build familiarity with the patterns of systems archetypes and to understand the nature of how complex systems behave in their complexity, probing to understand a system’s unique patterns rather than trying to project specific qualities of one particular system onto another because it exists in the same industry or domain.
Sometimes, this complex systems orientation can manifest as simply as an earlier-than-usual workshop or a few additional collaborative sessions with more varied stakeholders. It often involves generating familiar types of visualizations, such as experience flows, service blueprints, causal loop diagrams, ecosystem mapping, (or other “rich pictures” in the words of systems thinkers), but earlier in the process than folks may be accustomed to. Earlier, because we create these artifacts to guide sense making and help us think more deliberately about the boundaries of the system(s) we study, rather than merely as deliverables to convey findings or recommendations in conclusion. Other times, the process might indeed include more esoteric but valuable frameworks such as C.S. Holling’s adaptive cycle and panarchy model, Dave Snowden’s Cynefin sense making framework, or Nassim Taleb’s concept of antifragility.
Complexity science also teaches us that all-encompassing “solutions,” (as in, one-and-done moves to achieve an ideal final state), don’t really exist. And even if they did, we couldn’t truly predict the impact of a single intervention anyway, nor do static states (ideal or otherwise) ever persist. Instead, we’re better off thinking about each intervention as a wiggling or nudging of select parts of the system in a way that can ripple outward, hopefully with more desirable than undesirable effects.
By learning everything we can about the system around the experience, service, or product in question, we can identify specific places (elements or relationships) within the system that are realistically influenceable, then map a theory of change and establish the feedback channels and infrastructure needed to learn from the process, before we implement. This might imply more energy spent assessing distant implications of a potential intervention, (beyond the usual questions about investment, feasibility, and success metrics), often using methods informed by futures thinking. Then we implement, learn, rinse, repeat.
In essence, this process looks familiar to most design practitioners, but is enriched and extended by a systems orientation that illuminates:
Hidden interrelationships that influence the behavior of the whole system.
Broader dynamics that link discrete elements separated by time and space.
Tacit assumptions that influence perceptions and structural barriers.
In turn, the outcomes are more transformational.
A vision without systems thinking ends up painting lovely pictures of the future with no deep understanding of the forces that must be mastered to move from here to there. ― Peter Senge
Meet the new maps, same as the old maps
Information visualization has long been an essential craft for experience strategy and design practitioners, with its own rich history and wide application. But, somewhere along the way, specific artifacts like experience journey maps were singled out and reduced to project “outputs” and final “deliverables” intended to conclude an increasingly formulaic process. Still, humans have been creating maps for millennia: to make sense of our surroundings, communicate knowledge to others, and devise courses of action, and a systems oriented practice navigates complexity by returning to mapping’s origins as a generative, sense making activity. As such, mapping deserves special attention for its ability to synergetically combine and convey knowledge about complex systems where patterns matter more than parts. As Fritjof Capra says, “Patterns cannot be weighed or measured. Patterns must be mapped.”
System maps, causal loop diagrams, and other rich pictures help us understand complex systems by capturing multiple perspectives, components, and relationships within the system, including social, economic, cultural, and environmental factors. They’re most effective as living documents employed initially to build shared understanding, and updated periodically as new information emerges. They can illustrate organizational and technological infrastructure, data flows, decision points, incentive structures, feedback loops. They can illuminate complex relationships to increase the quality of decisions and the efficacy of internal initiatives. They can identify root causes, explore potential consequences of proposed interventions, and highlight areas for further investigation. They can communicate complex ideas to wider audiences and, yes, they can serve as final deliverables to summarize key information. The visual design of these artifacts will vary widely to accommodate the different use cases.
When mapping is leveraged as a sense making activity instead of minimized to a communication output, we can integrate an almost limitless diversity of thought to connect the dots between far ranging insights, (like, say, combining Bronfenbrenner’s Ecological Systems Theory with double loop learning, or layering behavior change frameworks on top of experience models).
Birger Sevaldson describes this this type of mapping process as “gigamapping,” and highlights its particular value in supporting the so what? of experience strategy through what he calls ZIP analysis. ZIP stands for zoom, innovation (idea, intervention, innovation) and potential (problem or pain point). Others call it opportunity mapping or leverage point analysis. Regardless, the shift from a purely descriptive visualization to a generative meaning-making tool happens naturally when opportunities and interventions become visible in the maps that teams build collaboratively.
The map is the not the territory
Mapping is critical for sense making; however, it also provides a launchpad for what comes after sense making: decision making (and, hopefully, change making). Mapping is necessary but not sufficient; the other nuance that a systems orientation brings to experience strategy is in the more conceptual shift from nouns to verbs: we can extend our focus from the stuff of a system, (often well understood), such as humans, other organisms and objects, data, and infrastructure, toward the directional flow of things between the stuff, (often poorly understood), such as information, energy, money, power, and value. This in turn means the potential interventions that emerge from our analysis won’t always take the form of products or services, but may also include organizational experiments, learning programs, decision frameworks, feedback cycles, and other less tangible ideas, as well as ideas that seem strange in a close-up view but make great sense from a linked systems perspective.
Two other concepts from systems science can help us think more deeply about the verbs — how intangibles flow in the spaces between the stuff and about the ways a system changes over time:
The first is a mathematical concept known as an attractor. Dynamic systems often evolve towards a state of equilibrium that can be hard to change. Once in place, any further evolution of the system is constrained to remain similar to the “attractor state,“ as a form of mutual dependency or coordination between the system’s components or subsystems.
Think of an attractor as the state a system naturally evolves toward, like a comfortable (or frustrating) status quo or a motivating (or fear inducing) future vision. What state does your system seem to be naturally inclined to operate in? What incentives toward, or immunity against, change do you see in your organization? Understanding a system’s attractor state can be hugely illuminating for transformation.
The second is what Donella Meadows calls a leverage point: “a small shift in one thing can produce big changes in everything.” Leverage points are places where we can nudge a system in a direction that achieves the outcomes we seek — often as subtle shifts that can change the flow of energy or information. She identified several examples of leverage points as places to intervene in a system, in increasing order of their potential for change:
- Constants and parameters, such as adopted standards, regulatory environments, and legal constraints
- Buffers and stabilizers, such as redundancy, savings, or inventory that provides a cushion against disruption
- Physical structures, such as supply chain networks, population age structures, and geographic arrangements
- System delays, relative to the rate of system change, such as reducing or extending information, energy, or financial transfer times that in turn increase or decrease volatility
- Feedback loops, including corrective loops that reduce negative outcomes and enhancing loops that reinforce desirable outcomes
- Information flows, such as who has access to information and how readily they can act on it
- System rules, such as incentives, punishments, and norms, all of which can be tangible or intangible
- Autonomy and flexibility to add, change, evolve, or self-organize system structures
- Goals of the system, such as to engulf other systems, dominate a territory, or achieve a community objective
- Mindsets or paradigms out of which the system — its goals, structure, rules, delays, parameters — arises
- The power to transcend paradigms, as Meadows says, “There is yet one leverage point that is even higher than changing a paradigm. That is to keep oneself unattached in the arena of paradigms, to stay flexible, to realize that no paradigm is ‘true,’ that every one, including the one that sweetly shapes your own worldview, is a tremendously limited understanding of an immense and amazing universe that is far beyond human comprehension. It is to ‘get,’ at a gut level, the paradigm that there are paradigms, and to see that that itself is a paradigm, and to regard that whole realization as devastatingly funny.
Though, its worth noting that Meadows also says:
Magical leverage points are not easily accessible, even if we know where they are and which direction to push on them. There are no cheap tickets to mastery. You have to work hard at it, whether that means rigorously analyzing a system or rigorously casting off your own paradigms and throwing yourself into the humility of Not Knowing.
In the end, it seems that mastery has less to do with pushing leverage points than it does with strategically, profoundly, madly letting go.
Reclaiming synergy
Letting go of the parts, to be part of the whole. In essence, a systems orientation to anything, from experience strategies to organizational transformations to innovation programs to design interventions, is better enabled to succeed in the messiest problem spaces and most complex environments, precisely because zooming out (spatially, chronologically, semantically) and appreciating the synergetic whole enables us to identify emergent dynamics and opportunities that escape more reductionist analysis-by-parts. Systems thinking helps us build robust conceptual models instead of isolated point solutions, and complexity science in particular helps infuse our models with adaptability and resilience so they can continue guiding future decision making as interventions are piloted and changes unfold over time.
As Bucky Fuller said,
“Universe is synergetic. Life is synergetic.”
Your strategy practice should be too.
For further exploration:
Thinking in Systems: A Primer, by Donella Meadows
The Fifth Discipline: The Art and Practice of the Learning Organization, by Peter Senge
Systems Thinking For Social Change, by David Peter Stroh
The Systems View of Life, by Fritjof Capra
Linked: How Everything Is Connected to Everything Else and What It Means for Business, Science, and Everyday Life, by Albert-László Barabási
The Quark and the Jaguar: Adventures of the Simple and the Complex, by Murray Gell-Mann
Designing Complexity, by Birger Sevaldson
Complexity: A Guided Tour, by Melanie Mitchell
Synergetics, by Buckminster Fuller
The Santa Fe Institute, a transdisciplinary research center focused on complex systems science
