Category: complexity

complexitydesign thinkingeducation & learninginnovationresearch

Embracing Complexity / Science

Seeing Complexity for What it Is (CC - Flickr by nerovivo)

SEED magazine recently posted on the concept of early warning signs in complex systems that I found quite provocative and important.

Science is a creative human enterprise. Discoveries are made in the context of our creations: our models and hypotheses about how the world works. Big failures, however, can be a wake-up call about entrenched views, and nothing
produces humility or gains attention faster than an event that blindsides so many so immediately.

There are so many key points in this one phrase that are worth discussing at length.

Science is a creative enterprise . For reasons I’ve discussed elsewhere, I think that science needs to embrace its creative side more than ever and embrace design. This isn’t a universal, but if we (scientists) approached problems from the multidimensional manner in which designers typically approach them, we might create new innovations and discoveries that are different than the ones we’ve made before. Why is this important (beyond the obvious to those whose business it is to discover)? Complexity. The problems we are dealing with now more than ever are likely to be complex ones, which require different ways of approaching them and (some) different science and practice.

And as Albert Einstein famously said (or at least many people have attributed this to him — I can’t verify it, but it works nonetheless):

“We can’t solve problems by using the same kind of thinking we used when we created them.”

Discoveries are made in the context of our creations: our models and hypotheses about how the world works. In public health where I work, the dominant models remain those rooted in reductionist science. We are asked to ‘prove’ the links between certain activity and the outcomes they produce. This works relatively well in areas like sanitation, toxicology, (some) pharmaceutical or vaccination interventions, and injury prevention. It is for these reasons that the top achievements in public health, including massive increases in life expectancy and reductions in premature death took place in the 20th century. But that was then. The challenges we face now are into the realm of complexity, unless we fail to support fundamentals in public health and then we’ll have both simple and complex challenges on our hands. The point here is that our models will only take us so far without some acknowledgement of the complexity of the problems they seek to explain. The context of our creations is complexity.

Big failures, however, can be a wake-up call about entrenched views. The key term here is “entrenched views” . My colleagues Alex Jadad, Murray Enkin, Shalom Glouberman and others once had a group called the Clinamen collaborative that wrote a great piece on the problem of complexity when dealing with entrenched health care practices. Their recommendations are essentially:

1) there are no recipes for universal success,

2) pay attention to local conditions,

3) intervene small and often and then scale,

4) aim for stability first, then change.

In science, we’re failing a lot and rather than see this as a potential positive, I see more conservative approaches to science based on risk aversion. Providing support for smaller, rapid response scientific studies that are encouraged to fail will do more than these big, non-adventurous team projects that provide high-level window dressing for grant funders and avoid making anyone look bad.

and nothing produces humility or gains attention faster than an event that blindsides so many so immediately. Humility is a word that is too often absent from my profession. I’m not talking about the kind of humility that comes from acknowledging the limitations of a scientific study or the recommendations of a report. I am speaking of true humility, where one “seeks to first understand, then to be understood”. Indeed, I would argue that we are lousy at both more often than we’re successful. Our understanding comes from a scientific perspective that holds us up as the experts. Once you’ve labelled someone or yourself an “expert” conversations immediately shift. Watch a classroom where the instructor insists on pure lecturing, being called “Dr.” and where “right” and “wrong” are regular parts of the conversation. Then watch a classroom where students learn from each other, are encouraged to share their experience and challenge the material, where the professor doesn’t push her or his titles and credentials, and where there is interaction between everyone. You’ll see a very different sense of humility from students and teachers alike.

When I encounter others on genuine, authentic and intimate level of learning I never cease to be left in awe. That comes from humility and is something I was fortunate to have modeled to me. I was once told by a retiring professor who was leaving on the day I was convocating from my undergraduate degree:

“When I was in my undergraduate, I new everything. Now that I am a retired professor, I realize I know nothing. Every year of learning serves to teach me that I know less and less.”

The SEED article goes on to point to the current problems in science in dealing with complexity and the imperative towards collaboration and cross-disciplinary engagement:

Examples of catastrophic and systemic changes have been gathering in a variety of fields, typically in specialized contexts with little cross-connection. Only recently have we begun to look for generic patterns in the web of linked causes and effects that puts disparate events into a common framework—a framework that operates on a sufficiently high level to include geologic climate shifts, epileptic seizures, market and fishery crashes, and rapid shifts from healthy ecosystems to biological deserts.

The main themes of this framework are twofold: First, they are all complex systems of interconnected and interdependent parts. Second, they are nonlinear, non-equilibrium systems that can undergo rapid and drastic state changes.

Complex systems require the kind of deep attention that science brings, the spirit of engagement and problem solving that designers offer, and a space to bring them together. With their focus on reductionist science and the lack of embrace of design, universities haven’t been the home to this kind of thinking. But things can change because, after all, this is a complex dynamic system we’re talking about.

complexityeducation & learningemergencepublic healthsystems science

Spectrum Thinking and Complex Systems

Among the most frustrating aspects of being a systems thinker/actor/researcher is the “one thing” question: What is the one thing that we can do to solve this problem?

The answer is almost always: there isn’t one thing you can do, the problem requires a complex response*

*That isn’t always the case, but in my line of work, it pretty much is true that the problem and its solution fall within the realm of complexity.

When you work in complex systems, the problems are nearly always multifaceted, convoluted and multi-dimensional in their scope and impact. Yet the “one thing” request comes up all the time.

Dave Snowden, from Cognitive Edge,  in his work with the Cynefin Framework nicely points to the difference between best, good, and emergent practice. In public health and medicine, the term “best practice” has been so dominant that it is hard for people to lose the terminology, even if they acknowledge that it sometimes doesn’t fit (the concept of “better” practice is often snuck in as a way to placate those who subscribe a model of thinking that challenges “best” practice thinking). While this is very useful, the problem that even useful frameworks like Cynefin produce is a tendency for people to put whatever they are doing into boxes.

Looking at Cynefin, you can see the world compartmentalized into four nifty quadrants, making the world simple — or at least complicated, but certainly not complex. Dave Snowden himself has been critical of this tendency in his writings on Cognitive Edge’s blog, yet time and again I see this type of thinking come alive. I currently am teaching a course for graduate students on systems science perspectives in public health and this is one of the pitfalls that I hope the learners in my class can avoid.

It’s not easy. We humans love to compartmentalize things. Charles Darwin, one of the founders of modern science, famously began his career putting things in literal and metaphorical boxes. Classification is something we do from our earliest years and do all the way through school. Indeed, a brilliant and somewhat depressing look at how engrained this thinking is can be seen in Sir Ken Robinson’s animated TED Talk on the history and possible future of education.

Systems thinking requires spectrum thinking. People must be able to see things on a gradient, rather than in absolute compartments. Students can’t be faulted too much for having a hard time with this when they are graded based on letters where a B+ is a 79 and an A- is one percentage point higher, yet the mere presence of a B (anything) on a transcript can mean the difference between an award, admission, or a job and not.

This is in no way a criticism of Cynefin or other frameworks used to explain or assist in the understanding of complexity, but rather a statement of the problems inherent in our quest to teach others about complexity that is intellectually honest, reasonably accurate, yet also effective in helping people understand the gravity and scope of complexity in practice. I don’t have an answer for this, but do find using a spectrum useful.

And on a side note, the multi-coloured palette of the spectrum also allows for an introduction of the concept of diversity and human relations at the same time as it illustrates a way of thinking about complex systems.


complexityemergencepublic healthsocial mediasystems science

Systems Thinking, eHealth and Changing Public Health

Tomorrow is my last class in CHL 5804: Health Behaviour Change for the 2010 year. Like every year, it was filled with the expected, unexpected and everything in between. I love teaching the course and interacting with about 30 graduate students from different disciplines, research backgrounds and educational levels. And while we often don’t admit it to our peers, one of the biggest reasons to teach is that we learn a lot, maybe more, than the students in our courses.

This year I was quite surprised by the interest in two areas: systems thinking and eHealth. Now these are my areas of interest so this is not a surprise on the surface, but then I’ve had these interests for a few years and are about equally passionate today than in past years. Another argument is that I am a better teacher today, which I suppose is possible, but as I look more into my own teaching practice I can’t imagine that the quality of teaching is significantly different than in past years.

I am taking this as a sign of maturity of both fields relative to public health. Both of these fields are relatively new. Depending on your definitions — of which there are many — eHealth has been around for about 15 years, evolving with the World Wide Web. Systems thinking and public health is a little younger, with its rise beginning less than 10 years ago. The publication of the US National Cancer Institute’s monograph on systems thinking and tobacco control, Greater than the Sum, published a couple years ago and the special issue of the American Journal of Preventive Medicine and the American Journal of Public Health, both signaled the rise of systems thinking and public health.

As we know from work in knowledge translation, it can take a long time to get knowledge into practice and this year I think the knowledge about the potential of tools like social media, mobile technologies and consumer-oriented databases has translated into action. My students do presentations each year pitching a hypothetical version of a Framework Convention similar to the one on tobacco control. This year, to my surprise (and with no coaching, particularly given that my eHealth “lectures” are all delivered electronically) most of the groups included some type of eHealth or mHealth intervention in their plans.

These were not just ideas aimed at impressing the professor, rather they represented some remarkably creative ideas on how to use technology to support health promotion, disease prevention, and public eHealth all around.

Attached to this idea of technology aiding the development of interventions for change was the idea that these eHealth tools exist within a larger system. When you speak of social networks or systems dynamics, you are in the realm of systems thinking. The idea that things are connected and intertwined is an idea that seems to hold a lot of appeal for many students and this is growing, particularly as more of my students have real world experience each year. This is important because once you’ve spent time dealing with problems at anything other than a theoretical level, you begin to see the breakdown in linear approaches to problem solving and the need for thinking in systems.

At the same time, as you spend time in that world you also see the problems and costs associated with relying solely on face-to-face methods of intervening. There simply isn’t enough funds, people or other resources to sustain a model that relies exclusively on physical, one-to-one care and prevention efforts. eHealth provides an avenue to consider ways of doing things at a distance and, for some conditions, this translates into interest in doing things that can reach more people for less money, hence the interest in eHealth.

This makes me quite pleased.

behaviour changecomplexityeHealthinnovationknowledge translation

The Face-to-Face Complexity of eHealth & Knowledge Exchange

The Public Health Agency of Canada‘s 2010 Knowledge Forum on Chronic Disease was held last night today in Ottawa with the focus on social media. The invitation-only affair was designed to bring together a diverse array of researchers, practitioners, policy developers, consultants and administrators who work with social media in some capacity. There were experts and non-experts alike gathered to learn about what the state of the art of social media is and how it can support public health. By state of the art, I refer not to the technological side of things, but rather the true art of public health, much like that discussed earlier this year at the University of Toronto.

Last night began with a presentation from Leanne Labelle that got us all thinking about how social media is radically different in the speed of its adoption and breadth of its social impact drawing inspiration from this video from Eric Qualman’s Socialnomics website.

Today we got down to business and started working through some of the issues that we face as a field when adopting social media. I would probably consider myself among the most experienced users in the audience, yet still gained so much from the day. Although I learned some things about how to use social media in new ways, what I learned most was how others use it and what struggles they have. This is always a useful reminder.

What stuck out was a presentation and related discussion from Christopher Wilson from the University of Ottawa’s Centre on Governance and a consultant on governance issues. In speaking about the challenges of doing collaboration, Christopher pointed to the problems of a ‘one-size fits all’ strategy using a diagram illustrating the fundamental differences between engagement at a small scale (under 25 people) and what is the mass collaboration that folks like Clay Shirky, Don Tapscott, and others write about. His diagram looks like this:

Technology Spectrum of Social Collaboration by Christopher Wilson

What Wilson stressed to the audience was the role that complexity plays in all of this. Specifically, he stated:

The more complex and interdependent things become, the more people need to be aware of the changing context and the changes in shared understanding.

As part of this, groups are required to engage in ways that enable them to deal with this complexity. In his experience, this can’t be done exclusively online. He further stated:

As complexity increases, the need for offline engagement increases.

I couldn’t agree more. In my work with community organizing and eHealth promotion, I’ve found the most effective means of fostering collaboration is to blend the two forms of knowledge generation and exchange together. The model that my research team and I developed is called the CoNEKTR (Complexity, Networks, EHealth, and Knowledge Translation Research Model).

This model combines both face-to-face methods of organizing and ideation, with a social media strategy that connects people together between events. The CoNEKTR model has been applied in many forms, but in each case the need to have ways to use the power of social media and rich media together with in-person dialogue has been front and centre. Using complexity science principles to guide the process and powered by social media and face-to-face engagement, the power to take what we know, contextualize it, and transform it into something we can act on seems to me the best way forward in dealing with problems of chronic disease that are so knotted and pervasive, yet demand rapid responses from public health.

complexityemergenceknowledge translationpublic healthsystems science

Evidence Democratization in Complex Systems

In the social media and marketing world there is a concept called “brand democratization”, which refers to the notion of having your customers contribute to and partly shape a brand’s identity. Marty Neumeier, who has written extensively on branding, asserts that a brand isn’t what the producers of the product say it is, but rather what the customers say it is.

The notion that the meaning and identity of a brand is outside of the control of those that develop a product is unsettling for many in business. Social media only amplifies this feeling. A social media consultant I work with once relayed a story about how a client (a big company) once asked how they could use social media to control their message more effectively and how unsettled they were to hear that “control” is not what social media is about and frustrated at the thought that they were no longer in the driver’s seat.

To some extent, we are seeing the same thing take place in the health sector, particularly in areas of great complexity. As complex chronic conditions become more prevalent and the number of people with multiple chronic conditions represent a larger proportion of the population (a near inevitability in societies with aging demographics and better health care), complexity will gain a greater place in our health care policy discourse.

Evidence-based medicine holds very clearly a sense of what is good and not-so-good quality content. Hierarchies abound and medical students are taught early on about what the best evidence is and how it is generated. The problem with the “best evidence” in health care is that it typically comes from studies focused within a rather narrow contextual band of activity using designs that aim for simplicity, not complexity. In standard research programs the aim is to reduce variation, not embrace it.

This is nearly the opposite of complex systems, where variation is, to a point, the key to learning and adaptation. Making sense within a complex system requires deep appreciation of context and an understanding of one’s position within the system. However, because such positions are relative, the ability to hold “expertise” in a system is limited. Indeed, there are many who occupy positions where they have great knowledge and the ability to re-interpret and generate evidence as opposed to only a few.

Does this create evidence democratization? When there is so much information from many sources, a need for contextualized feedback and many actors with useful experience and knowledge occupying different positions related to a problem, it seems that the answer is “yes”.

Social media operates within an environment of complexity due to the widescale involvement of diverse actors working in a coordinated, yet sell-organized manner, across many boundaries within multiple overlapping contexts. What is “true” within a social media sphere is only made so by the application of that knowledge within a particular context. A blog only has meaning to its creator and her or his audience, not the entire system. Yet, the actors engaged with that content may take it and rework it, retweet it, or post it in a new environment, where it is transformed into something else. If that “something else” has value, it lives onward.

In health contexts, that value might be very different depending on the condition and the manner in which that knowledge is applied. From a traditional evidence-based health approach, this is utterly terrifying. How can evidence be re-interpreted? What are the harms associated with taking something from one context and applying them to another for which it wasn’t designed? These are concerns, yet it is hard to imagine that there isn’t also some potential value in exploring ways in which this plays out in chronic and complex conditions given that the evidence is rarely that “hard” going in.

Are we on the cusp of a new wave of evidence democratization? And if so, are we going to be healthier as a result of it? More innovative? Or in deep trouble?

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Systems Thinking, Logic Models and Evaluation

San Antonio at Night, by Corey Leopold (CC License)

The American Evaluation Association conference is on right now in San Antonio and with hundreds of sessions spread over four days it is hard to focus on just one thing. For those interested in systems approaches to evaluation, the conference has had a wealth of learning opportunities.

The highlight was a session on systems approaches to understanding one of evaluation’s staples: the program logic model.

The speakers, Patricia Rogers from the Royal Melbourne Institute of Technology, and consultants Richard Hummelbrunner and Bob Williams spoke to the challenges posed with the traditional forms of logic models by looking at the concepts of beauty, truth and justice. These model forms tend to take the shape of the box model (the approach most common in North America), the outcome hierarchy model, and the logic framework, which is popular in international development work.

The latter model was the focus of Hummelbrunner’s talk, which critiqued the ‘log frame’ approach and showed how its highly structured approach to conceptualizing programs tends to lead to a preoccupation with the wrong things and a rigidity in the way programs are approached. They work well in environments that are linear, straightforward, and in situations where funders need simple, rapid overviews of programs. But as Hummelbrunner says:

Logframes fail in messy environments

The reason is often that people make assumptions of simplicity when really such programs are complicated or complex. Patricia Rogers illustrated ways of conceptualizing programs using the traditional box models, but showing how different program outcomes could emerge from one program, or that there may be the need to have multiple programs working simultaneously to achieve a particular outcome.

What Rogers emphasized was the need for logic models to have a sense of beauty to it.

Logic models need to be beautiful, to energize people. It’s can’t just be the equivalent of a wiring diagram for a program.

According to Rogers, the process of developing a logic model is most effective when it maintains harmony between the program and the people within it. Too often such model development processes are dispiriting events rather than exciting ones.

Bob Williams concluded the session by furthering the discussion of beauty, truth and justice, by expanding the definitions of these terms within the context of logic models. Beauty is the essence of relationships, which is what logic models show. Truth is about providing opportunities for multiple perspectives on a program. And a boundary critique is a an opportunity for ethical decision making.

On that last point, Williams made some important arguments about how, in systems related research and evaluation, the act of choosing a boundary is a profound ethical decision. Who is in, who is out, what counts and what does not are all critical questions to the issue of justice.

To conclude, Williams also challenged us to look at models in new ways, asking:

Why should models be the servant of data, rather than have data serve the models?

In this last point, Williams highlights the current debates within the knowledge management community, which is dealing with a decade where trillions of points of data have been generated to make policy and programming decisions, yet better decisions still elude us. Is more data, better?

The session was a wonderful puctuation to the day and really advanced the discussion on something so fundamental as logic models, yet took us to a new set of places by considering them as things of artful design, beauty, ethical decision making tools, and vehicles for exploring the truths that we live. Pretty profound stuff for a session on something seemingly benign as a planning tool.

The session ended with a great question from Bob Williams to the audience that speaks to why systems are also about the people within them and emplored evaluators to consider:

Why don’t we start with the people first instead of the intervention, rather than the other way around like we normally do?

complexityemergencepublic healthsystems sciencesystems thinking

Recombination: The Missing Link Between Linear and Non-Linear Views of Change

I teach a course in health behaviour change and one in systems thinking perspectives on public health. Both courses complement each other and both deal with change. However, most of the major theories of behaviour change deal with the subject in a straightforward, linear manner. Models and theories like the Health Belief Model, Theory of Reasoned Action, and Social Cognitive Theory all have elements explicit or implicit to them that suggest change occurs in a largely linear manner from problem state to desired state.

One of the more popular models of change is the Transtheoretical Model, which included the concept of Stages of Change. Developed by James Prochaska and colleagues at the University of Rhode Island (and others), the model has become widely popular and used all over the world to guide change efforts. The problem is that the evidence for its effectiveness, despite the logic it brings with it, is weak.

Robert West, the editor of the journal Addiction, and others, issued a rather stinging set of criticisms against the Transtheoretical Model’s Stages of Change concept, pointing to the evidence that suggests that as many (if not more) people quit smoking or behaviors like that with no apparent plan in place. “It just happened” .

Indeed, the data suggests that Stages of Change is not that strong as a predictor of eventual change, yet its popularity suggests something that goes beyond evidence. At its root is the idea of “ready, set, go” and taps into our deep-seated interests in making plans and moving ahead in a straightforward manner. In short, it fits linear thinking to a tee.

Over time, proponents of the Stages of Change theory and related models and theories have asserted that people do move forwards and backwards through the stages and that it is not simply a one-way view of change, but in both cases the end is still some form of linear trajectory.

What makes behaviour change theories like the TTM and others problematic from the perspective of complexity is that they are linear. Yet, linearity is the way we define the problems in the first place. These theories are all based on some form of cognitive-rational foundation that take at its core the idea that information is the starting point for change and that the way information is perceived and worked through will serve as a touchpoint for further motivational activities.

What is embedded within this assumption is the idea that, once configured, information is organized in a relatively stable, consistent manner. What it does not do is account for the ways in which our memories, circumstance, situation, and the addition of new information can only only change what we know, but also the way in which we know it. Thus, recombination of information leads to new insights and activities, not all of which are necessarily in support of the trajectory that was initiated.

Richard Resincow and Scott Page start to probe some of this terrain in their article published a couple of years ago looking at quantum change. The article, which was widely discussed, challenges the very notion that the approach we take to behaviour change is misaligned with much of what we know about complex adaptive systems. And to this end, the human mind and body is indeed a complex adaptive system in many respects. Certainly our social worlds fit this description.

If this is the case, and we take this idea that recombination of information can and does occur, it has profound implications for how we develop social institutions and the way in which we support individuals looking to make changes. It means not expecting that changes will stay in place, but rather always anticipating the possibility that something might shift and dramatic transformations could occur.

Flexible strategies, adaptive strategies and those that attend to context and the constant, dynamic flow of information are those that will provide more useful models for change in this worldview. It might now repudiate the models we use now, but it certainly casts new light on the directionality of change that they invoke. And in simply shifting those arrows around, we open possibility for understanding change in a wider way that might eventually lead us to one that takes complexity into account more fully, and learning.