Before acting in a manner consistent with complexity principles, people need to understand what they are, how they are different from other systems, and what it means for their work. With mainstream education, professional practice so geared to linear forms of learning this bodes poorly for building better systems thinkers.
“Let’s just throw some social media at it” is a variant of an expression I often hear in my work in health communications consulting and training. Organizations seeking to use the new tools and media employed by Facebook, Twitter, and YouTube genuinely want to “get in the game” and use them effectively. Where things get problematic is when I tell them that social media is principally about building relationships and that extends to organizations: you need to relate and therefore act according to how you build relationships.
Just as no one (at least no one I’ve met) would consider drawing up a flowchart and showing a prospective mate the planned trajectory of their dating relationship with milestone targets and deliverables, no organization should think that they can just shovel content to people and expect their audience to relate better to them.
At first one might attribute this to a lack of understanding of social media, but that is only a small part of it. Empathy is another. But the third and perhaps biggest reason is a fundamental lack of understanding of complexity and what it means.
The seductive nature of the “best practice” and the prescription for change in 5,7, 10, 12 or whatever easy steps is something that is endemic in our society. These forms of thought suggest a linear trajectory of events, suggest an ability to control for externalities and parse out their impact, and provide a prescriptive solution that removes much of the worry about unknowns. But H. L. Mencken’s often quoted phrase (which I’ve used often) suggests the folly in this.
Simplicity is another way to get around complexity. It is something sought, but rarely achieved in its application to the lived reality of the human condition, and although much discussed it hasn’t been widely achieved as a means of policy effectiveness. The reason lies with the nature of complexity itself and its resistance to reductionism. Evidence from biology through psychology (see previous links for examples) points to the considerable problem that science has with applying linear modes of thought and inquiry to complex systems.
The problems here are multifold and complicated, if not complex.
1. Our education system is designed for linear, progressive modes of learning not discovery and non-linearity. We sit kids (and adults) in rows, we talk at them, we present material front-to-back. In short, we don’t design education for learning, but for knowledge transmission. Complexity is all about learning. Every situation has a degree of novelty to it that presents new challenges and what happens today might not be the same thing that happens tomorrow even if much is similar. Teaching to discover, adapt, play and risk is something our system doesn’t do well. How can we expect complexity and systems thinking to thrive when the muscles used
2. It’s more convienient to think in dichotomies than spectrums. As I’ve written previously, spectral thinking is something critical to many of the issues we face in complex systems. Good/bad, strong/weak, X/Y lose their meaning in complex environments where there is a. Of all the dichotomies that work, only Ying/Yang comes close. But its a more difficult concept to grasp that maybe things aren’t all one way or the other, that there is use in even something that isn’t well constructed. This problem (and the ones that follow) are tied to the first one: education and learning systems are not set up for this. We are primed for either/or thinking. Think in criminal justice terms how easy it is to demand harsh punishment for criminal acts without considering that the perpetrators are human too, even if their behaviour is unacceptable.
3. Our decision-making tools are ill-equipped to handle ambiguity. Health care is a great example of how badly we do at complexity thinking. Consider the systematic review, often viewed as the gold standard for evidence for adoption into healthcare organizations. If it has a good systematic review, then the chances that we will see that evidence translated into practice is good, right? No. Surprisingly, even systematic reviews of systematic review use shows a mixed bag in adoption. Systematic reviews are designed to reduce ambiguity, but (for those on human social systems at least) they only illustrate how much there is. A systematic review only looks at the evidence created, it doesn’t include all those questions that were never asked, never funded for inquiry, or couldn’t be structured in a manner that fits the criteria for a good review. It is, by its design, reductionistic in its approach to complexity.
4. Our institutions are resistant to complexity. Complexity takes time, nuance, and relationship development; all the things that screw up plans. You can’t plan a relationship, but you can anticipate some things. You might even be able to use scenario tools and strategic foresight methods to anticipate what might happen, but you can’t plan it. John Lennon is right:
Life is what happens when you’re busy making other plans
While we plan, the complex systems move along. We can plan and fail, fail and plan, or plan to fail and work build the strategic foresight to know what to do with these “failures”.
So now what? Being aware of these things is a start, but making systems change is really the key. Making change is about questioning the way we have been taught to learn, and what our assumptions are about the universe are. Learning the difference between a simple, complicated, complex and chaotic system and the means to identify when those systems present themselves (and how they often change) is another. This means finding like minds, sharing stories, and building networks. It means creating space for relationships — even in our linear planning models if we must keep them (or better yet, get rid of most of them) — and considering what kind of returns we get from paying attention, being mindful of our systems, and what kind of things contemplative inquiry might offer that simple, detached data analysis does.
These are starting points, but not all of them. Addressing the challenge of complexity is, ironically or perhaps appropriately, complex. But the challenge of dealing with the negative outcomes resulting from overly simple approaches to dealing with complexity will ultimately be far more so.
Over the past week I’ve been writing about the issue of simplicity and its relationship to complexity. At the focus of this has been the work of John Maeda and his Laws of Simplicity. Today I wrap up my critique looking at the 7th Law: Emotion.
In this law, Maeda states:
More emotions are better than less
The idea is that emotions help us frame the context in which things exist and could be used and thus, the more emotions we apply to an object or phenomenon the more we are able to see the simple.
Emotions animate interest in things, but they also obscure the phenomenon that one views. So perhaps there is greater simplicity, but the clarity that comes from simplicity is removed.
Today continues the discussion about the role of simplicity in relation to complexity with my look at the work of John Maeda and his Laws of Simplicity. I this Maeda’s on to something, but I also disagree with some of his Laws and today I look at the 5th Law: Differences.
Differences: Simplicity and complexity need each other.
Some have argued that differences create. Keith Sawyer addressed this issue in his recent blog post looking at the various commentaries published over the years on ideas around innovation, self-organization and diversity and particularly the recent work of Matt Ridley and his work on the Rational Optimist. In his review of a review, Sawyer writes:
the new portion is Ridley’s emphasis on archeology and the fossil record, to support his claim that human advancement always happens where trade brings together more ideas from more people. (That reminds me of another recent similar book, The Medici Effect, where Johansson calls it “the intersection”.) Ridley argues that the key innovation in history was trade, and when humans started trading about 45,000 years ago, history and cultural change suddenly accelerated. He rejects previous explanations of this sudden burst that appeal to individual-focused explanations, like a sudden genetic mutation that resulted in greater individual creativity, and argues that individuals didn’t change at all–what changed was social organization.
I agree completely, but that idea isn’t really new either. It’s long been a fundamental tenet of economics that trade makes everyone better off and accelerates innovation.
The above quote might be a long way of getting to the point that differences matter and exposure and interaction of diversity is what creates innovation and complexity in complex systems. Maeda’s comments about simplicity and complexity needing each other might be partly true, but like my previous critique, it is problematic enough to be questioned as a Law and explored more fully.
In The Laws of Simplicity, Maeda deftly illustrates that:
The more complexity there is in a market, the more something simpler stands out.
While I agree, the idea that simplicity is gained by adding more complexity tells me that we have more complexity — and that’s problematic when you’re trying to make sense of something. True, it makes those efforts to simply things more noticed, but those efforts must be affixed to the most useful things (which is no guarantee) otherwise one has a lot of simple things that are less useful and complex things that are confusing.
It also somewhat reduces the potential benefit that diversity brings, despite the challenges it also brings. For a great analysis of the role of diversity in complex systems, I suggest you look at my Library Section to find the reference for Scott Page’s excellent work The Difference.
Today I continue to look at the concept of simplicity and its relationship to complexity by focusing on the work of John Maeda, designer, artist and president of the Rhode Island School of Design. Maeda has devoted much of his career to understanding the role of simplicity in art, design, business, technology and everyday living and his book, The Laws of Simplicity, may be the most cogent analysis of simplicity in a manner that adheres to the very laws it espouses. As a designer, academic, and innovator, Maeda’s interest in simplicity reaches to the core of his craft and because of this, his work on the subject is worth paying attention to.
The Laws of Simplicity outline 10 laws, of which most I agree with. However, there are three that I see as problematic and, in some cases, actually inspire greater complexity rather than reveal or produce simplicity. I begin with Law #4: “knowledge makes everything simpler”.
In the fourth law, Maeda argues that simple things often require knowledge to fully unlock their potential. One of the examples he gives is the screwdriver and the screw. Two simple things, but it requires knowledge of how they fit together and which way to use them through such mnemonic devices like “righty tighty, lefty loosy” to make the simplicity work (p.33).
Using the examples of learners tackling new and difficult problems, Maeda discusses how the development and application of knowledge creates opportunities to create simple solutions by understanding the basics relative to the more complex parts — something systems thinkers might consider relating the entire system to the components within it. Using the screwdriver example, this law becomes quite evident and could easily be supported. However, to use tools like screwdrivers as the metaphor, there are problems that require many tools working at the same time to solving them. It is here that a little information helps to a point, but then as starts to fall back on itself because the volume of knowledge required to fully understand things gets too much. In complexity terms, this is where interactions and feedback enter and the previously independent points of knowledge converge, requiring someone to attend to multiple things at the same time. As the metaphor goes, the vise, the saw, the planer, the drill and the screwdriver all need to be thought of at the same time in order to solve the problem. New mnemonics or “simple rules” need to be found.
Indeed, there is a point where more information helps, but my experience as an educator and health researcher suggests that there is a threshold in which knowledge sews confusion rather than yields insight. Below is a schematic drawn from my experience paired with insights from cognitive and information science that illustrates what happens when there is too much information. However, before reading this consider the following assumptions in which this model was based:
If we surmise that complex information is more difficult to fully comprehend than something simple, then the likelihood of a message being understood goes up if it has greater simplicity than complexity.
If we consider knowledge as being the understanding of information, then we can conclude that more information equals more knowledge.
In the diagram, there is a steady increase in the amount of clarity that knowledge provides up to a point where it levels off and then, as information increases, the complexity rises and the confusion grows. At some point, the information and knowledge load becomes too large for the problem and the simplicity starts becoming complex. This I describe as a law unto itself, because I have yet to find an issue where this doesn’t apply.
Edmunds and Morris (2000) looked at this phenomenon in a review of the literature published in the International Journal of Information Management, concluding that information overload is a serious problem for organizations and the individuals within them.
To illustrate this problem of knowledge and simplicity, consider a socially conscious trip to your average North American grocery store. I love food and want to eat in a manner that is healthy, ethical and environmentally and economically sustainable. As a result, I devote a lot of time to researching food to find out what options are available to me. This knowledge has transformed something simple like buying groceries into an event of uncommon complexity (or joy into angst on some days). My knowledge of healthy eating means that foods with trans-fats, excess sodium and sugar, and high levels of carbohydrates, fats and calories are out. Add to that what I know about socially responsible farming and the environment, and I’ll try to choose products with less packaging, organically (and sustainably) grown, local (when appropriate), and those that use little harmful chemicals that unnecessarily damage the environment and the creatures within it. I also want my food to be of good quality (fresh) and good value (which often means low cost). Each one of these issues — healthy vs. not, organic vs. not, expensive vs. cheap — are issues where some more information can lead to making the decision simpler. Multiplied together, and this becomes complex.
As author Neil Johnson puts it:
Two’s company, three’s complexity.
Perhaps it should be:
Two’s simple, three’s complex.
So with regards to the Law, I agree that it is correct for certain problems, but not all. Rather, I suggest amending Maeda’s 4th Law to read:
Some Knowledge Makes Some Things Simpler, While Lots of Knowledge Makes a Lot of Things More Complex
One of the questions on my mind lately has been “can we reduce complexity?”.
I’m not alone.
Indeed, almost anyone working in information sciences, media, healthcare, public policy, or any information-driven sector (which is more and more of us these days) wrestles with complexity in their work. Complexity’s problem is simple: it’s very nature requires intense concentration, knowledge, and consideration, which requires mutliple faculties and scales.
In the recent issue of Explore magazine, journalist J.B. MacKinnon (who, with Alisa Smith, wrote the 100 Mile Diet) commented on the practical challenges facing someone trying to live sustainably. One hypothetical example he uses is the hiker, who plans a low-impact, ecologically responsible trip (which heightens his passion for conservation) only to be told that his brand of boots contribute to the death of sea turtles in Mexico. Despite the best efforts, there are too many things to attend to to make a decision that satisfies every demand: it’s too complex.
John Maeda, President of the Rhode Island School of Design and visual artist, has tried to address this issue over his career. In 2006 he compiled his meditations over many years into a book called “The Laws of Simplicity“ In a (simple?) slender volume, Maeda outlines the following ten laws:
1. Reduce: The simplest way to achieve simplicity is through thoughtful reduction.
2. Organize: Organization makes a system of many appear fewer.
3. Time: Savings in time feel like simplicity.
4. Learn: Knowledge makes everything simpler.
5. Differences: Simplicity and complexity need each other.
6. Context: What lies in the periphery of simplicity is deﬁnitely not peripheral.
7. Emotion: More emotions are better than less.
8. Trust: In simplicity we trust.
9. Failure: Some things can never be made simple.
10. The One: Simplicity is about subtracting the obvious, and adding the meaningful.
Rarely has a book been so highly anticipated a read (it’s been on my bookshelf for two years waiting for the right moment) and left me so perplexed. Why? The ideas are certainly simple, the text and argument are simple (but not simplistic), and some are right on the mark, yet others are not.
For me, 4 (Learn), 5 (Differences), and 7 (Emotion) are all problematic, although ironically, I think they are critical to complexity and simplicity, but for reasons that differ from Maeda’s argument.
Humans are complex and each of these three laws deals with phenomena that add information over time and thus, increase complexity, not introduce simplicity. In my next few posts, I’ll be exploring each of these in detail.