Complexity and the Senseless Marketing of the Future
Posted: July 16, 2012 Filed under: complexity, systems science, systems thinking | Tags: climate change, complex adaptive systems, complexity, environment, FastCoExist, foresight, futurists, innovation, marketing, marketing complexity, mindfulness, networks, Pareto distribution, resilience, simplicity, social networks, systems thinking Comments Off
Futurists take what we know now and project into the future ideas about things will be like years from today using the models that have worked consistently up to now. Those models applied to human systems are changing quickly making marketing the future based on them senseless and potentially dangerous.
Earlier this past week a post on FastCoExist caught my attention and brought to mind why I have such an uneasy relationship with futurists and futures as a field. The post, 8 Ways the World Will Change in 2052, is look at the next 40 years written by Jorgen Randers, a professor of climate strategy at the BI Norwegian Business School and written with all the confident swagger that typifies futurists making statements about what is to come. After all, it’s hard to draw an audience (and the benefits that comes with that) when you don’t have a confident answer on your subject matter — even if that answer is wrong. In this latest post in the series on marketing complexity I look at futurists and their predictions and what it could mean for making sense of the threats and opportunities we will face in the years to come.
The Mathematical Problem of Futures and Complexity
The FastCoExist article paints a picture of a world that looks a lot like the one we have today, just with some shifts in economic and social structures. It suggests that much will remain the same even though a few key things will change, but our general relations will remain constant. It is that consistency that raises my concerns about futurist thinking (not all, to be sure) and its use of the data today to make predictions tomorrow. There is an assumption of linearity that weaves its way through the narratives spun by futurists that do not fit with how complex systems behave, nor does it account for the network effects created by interconnected systems.
Where I live now (Toronto), we have seen an almost uninterrupted heat wave for more than three weeks and that is forecast to continue for the week to come. This is the hottest year in recorded history (video), and as this short news clip shows the implications are many. At our current level of focus the implications may seem slight: changing growing conditions for gardens, better cottage swimming weather, brown lawns etc.. But at another scale and perspective, the interconnections between these things will start to reveal themselves if the pattern continues.
It is here where I see futurists getting it wrong as their predicts rest on largely linear trajectories of change and scientific knowledge that uses linear models to create predictions. The mistake is taking linear phenomenon and grafting that knowledge on to complex cases, while another mistake is taking science that works for static things and applying it to dynamic objects.
Complexity often produces change curves that follow a Pareto distribution, which is a way of accounting for things like ‘tipping points’, and is rarely linear in its effects for long periods of time. As the news report mentions, Toronto has an average temperature of 3.5 degrees higher than normal in a single year. It could be an aberration, but when we see record-breaking temperatures for years on end that looks like a pattern forming.
Climate change is not just about things getting warmer, cooler, wetter or dryer. From a human standpoint, how we adapt to these changes is what counts and in a networked world is that adaptations happen simultaneously and in a dynamic, interconnected manner. That means that many things change at the same time and that the relationship between dynamic objects means that the overall quantity and rate of change in the system is likely to be logarithmic (exponential) not additive.
Reframing change models: the language of complex systems.
If we are to create models that are more useful to us, we need to develop them with complexity in mind, think in systems and act as designers. To do this requires a change in the thinking models we use and the ways we communicate these models to the wider world. Yet, it isn’t as alien as it seems; we do it all the time with ourselves in explaining our social lives.
- A child goes from being peaceful and quiet to a tantrum in a matter of seconds.
- A calm, composed individual bursts into tears at a seemingly random event.
- A polite, warm conversation quickly turns cold at the slightest mention of a particular phenomenon
In many of these cases the ’cause’ might not be obvious. An example I use with my students is this:
Imagine a couple in their bedroom and one partner sees a wayward sock that has been left on floor and gets intensely angry at the other partner upon discovery of the sock. Why? Is is that the sock on the floor is so problematic that it reduces an otherwise peaceful environment into a space of conflict? Is the sock really that bad? Or is the sock a catalyst for something else? Does it represent something (or many things) that are embodied in the sock being left carelessly on the floor? Does the sock serve as a vessel for accumulated grievances and stressors only loosely related to its position on the floor?
This example of the sock illustrates how a Pareto distribution of social tensions in a relationship could be expressed. It points to how the most ‘obvious’ linear answer might not always be the case even if initial appearance suggest a relationship.
Explaining the reasons for problems opens a door to solving them. But we can do more.
The power of weak signals
The way to interject into a complex system is not to pay attention to everything all of the time, but to small things that show patterns. Eric Berlow has a remarkable 3 minute TED talk that illustrates how signals can be extracted from networks to reveal simplicity in complexity. A 2008 paper in the journal Physical Review shows the ways in which weak signals can be detected by reducing the overall volume of information or nodes in a network.
But what to pay attention to? This is where mindful evaluation and attention comes in. Mindfulness is not just a way to connect to one’s inner life, but also the outer world around us. A mindful approach to monitoring and evaluation means watching what happens around us and positioning tools, metrics and data gathering processes to give us the necessary feedback on our systems around us. To take the example of the couple’s conflict over the sock, paying attention within the relationship to minor conflicts, areas of tention, and moments of release earlier could have diffused energy enough to mean the sock was just a sock.
In social systems, this means paying attention to areas of intersection where natural tensions occur due to difference. These differences could be perspective, attitude, knowledge, beliefs or capabilities. These points of intersection are often where novelty emerges and innovation takes place, but they are also where deeper problems can begin. Constant, evolving and dynamic methods of data collection that recognizes change in non-linear and linear forms is more likely to enable the sorts of weak signal detection that can help us see the future more clearly.
That can help us make sense of future possibilities, rather than make empty predictions that guide what we do now at the expense of paying attention to what might come (and what is really happening).
The Complexity Challenge
Posted: June 30, 2011 Filed under: complexity, systems thinking | Tags: complexity, contemplative inquiry, education, learning, mindfulness, organizational change, simplicity, systems thinking 2 Comments »
Is Learning Falling Down When it Comes to Complexity?
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.
The only dichotomy that works in complex systems?
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.
A Complex View of New Year’s Resolutions
Posted: December 28, 2010 Filed under: behaviour change, complexity, design thinking, health promotion, psychology, social systems, systems thinking | Tags: behaviour change, complex adaptive systems, complexity, design, design thinking, health, new year's resolutions, simplicity, systems thinking 5 Comments »
A Happy, Simple New Year (CC- WilliamCho)
The end of the year is coming and, despite good advice and the warning about how they don’t work, you’re still determined to come up with a really good New Year’s Resolution and this year, dammit, you’re going to stick with it.
It’s simple, right? Make a commitment, come up with a plan to stick to it, and you’re ready to go.
Firstly, change in human systems is rarely a matter of simplicity, which is why New Year’s resolutions tend to benefit the diet industry and fitness clubs, but few others.
Another reason lays in the meaning of the term simple. Simplicity implies that there are relatively straightforward mechanisms that underlie a cause and consequence, that these can be predicted with reasonable certainty and consistency, and that we can derive “best practices” from such events given their reliability and efficiency. When we see something as simple, we usually have a high level of control.
Yet, it is the very nature of human systems that makes control such an elusive concept when wish to change something. Complexity science provides us with a different way to handle these problems. It provides a means of understanding complex situations — those where there are multiple causes and consequences that interact and change dynamically — that represent the lives of human beings. Rather than predict what is going to happen based on flawed assumptions of control, complexity science helps anticipate change and prepares people to adapt to these changes wisely.
Diet and exercise tend to be near the top of New Year’s Resolutions. Typically, people will make a resolution to start an exercise plan and reform their diet all in one swoop. The thinking is akin to “go hard or go home”. The problem with this is that what we eat, how we eat, and the activities that we do on any given day are part of a complex weave of activities that shape our lives. Few of us have jobs or lifestyles where everything is the same day to day. If you have children, you’ll know firsthand that even with the most regimented schedule for them and you, every day brings new surprises. But for the most part, these are little surprises that happen consistently and, consistent with a complex system, you adapt.
If your diet consists of a lot of take-out food, pre-prepared foods like frozen dinners or canned goods, the idea that you will suddenly start cooking at home, eating healthy meals and changing the portion sizes right away is setting yourself up for failure. This change alone requires shifts in your time (now you need to shop, cook, clean, and plan in advance), which suddenly changes how you use the rest of your time as it might impact upon work, play, social activities and so on. This isn’t to suggest that such investments in this new lifestyle are not worth it, but that simple shift will drastically change not just your diet, but your lifestyle as a whole all at the same time. That’s a lot of stress to put on the system that is your life.
An alternative is to make small shifts, ones that don’t upset things too much like perhaps making one meal on the weekends. Once that is in place, perhaps change the meal to allow for leftovers so that one day or two you pack a lunch instead of eating out. Maybe then shift towards changing the lunch options you choose when you do eat out one or two days per week. The key is to take one thing, do it and do it well and then build upon it by introducing another thing. Over time, your schedule will adapt and you’ll find the ways to make the changes without them feeling so big.
Exercise is the same way. Rather than sign up for a year’s membership at the gym and workout 2 hours a day for the first week only to find yourself so sore and tired that you can’t imagine going back, try upping the activity level you engage in with different strategies. If you don’t go to the gym at all, starting there might not be the best option. Try walking a little more around your neighbourhood or take the stairs when there is an escalator. Maybe get off the bus one or two stops early and walk the rest of the way home. Once you start doing that, try a day pass a gym and do some very light weights or some simple cardio workouts like walking on a treadmill. As you build up over time, you will find what works and doesn’t work in terms of your likes and dislikes and what seems to be effective. This is called feedback, another critical component of complex systems.
By paying attention — being mindful — of what you’re doing and how it is working, you can start to build a longer-term strategy or pattern of activity that moves you along to where you want to go. It also prevents you from the let down at having not achieved your goals, but setting yourself up for success rather than failure. In doing so, you work with the complexity of human systems and our daily lives rather than against them.
Emotional Complexity
Posted: May 29, 2010 Filed under: complexity, design thinking, psychology | Tags: complexity, emotion, psychology, simplicity Comments OffOver 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.
My early training in psychology and a lifetime of experience suggests that, like my previous critiques , this law overstates things somewhat and requires an asterisk.
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.
Complexity is to Difference as Simplicity is to…?
Posted: May 24, 2010 Filed under: complexity, design thinking | Tags: complexity, design, designer, diversity, John Maeda, simplicity 1 Comment »
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.
Rethinking the Relationship Between Simplicity, Complexity and Knowledge
Posted: May 23, 2010 Filed under: complexity, systems thinking | Tags: book, complexity, design, design thinking, John Maeda, simplicity, systems thinking 5 Comments »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.
Limits to Knowledge in Complex Systems
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
Is Simplicity the Antidote to Complexity?
Posted: May 22, 2010 Filed under: design thinking, systems thinking | Tags: book, complexity, design, simplicity, systems thinking 2 Comments »
Taken by by TheAlieness GiselaGiardino²³ and posted to Flickr. Used under Creative Commons Licence
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.
