Results for: complexity

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Is Simplicity the Antidote to Complexity?

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.

complexityeducation & learningsystems sciencesystems thinking

Can We Reduce Complexity?

A sketch of social complexity (by Phil Hawksworth)

Is it possible to make the complex simple? That was the subject of a conference call that I was a part of last week involving researchers and organizational change practitioners from around the world. The purpose of the call was to explore the potential for creating a conference that would address this very issue.

The call convenors were Eugen Oetringer and Dave Snowden . Like any topic worth spending time on, there was much debate on the very topic itself (and wide agreement that a conference framing the debate was a good idea).

The question of reducing or simplifying complexity is an important one for those trying to use complexity science methods to address wicked problems. The reasons are many. As a teacher (and always a learner) of complexity science and systems thinking methods and theories I can attest to the difficulty that people have with the subject matter. The reasons are also many.

First, in cognitive terms, the brain has a difficult time with processing multiple things at the same time. Research on cognitive complexity points to “chunking” as a promising means of supporting the necessary parallel processing of information necessary to make sense of complex information. The aforementioned citation by Halford, Wilson and Phillips (1998) nicely points to ways in which cognitive scientists define complexity:

Complexity is defined as the number of related dimensions or sources of variation. A unary relation has one argument and one source of variation; its argument can be instantiated in only one way at a time. A binary relation has two arguments, two sources of variation, and two instantiations, and so on.

In many complex problems, there are multiple arguments in play and no clear sense of what argument (if any) explains the problem in full. Much like Buddhist concepts of skillful and unskillful actions, complexity science deals with arguments that are more or less appropriate, not good or bad.

A second reason is that complexity is inherently mutli-disciplinary in its orientation. That is, the knowledge required to address problems of a complex nature cross many boundaries and it is rare if not impossible that one party will have a complete understanding of the situation. This requires that we problem-solve using not only multiple actors with different backgrounds, but multiple means as well. As the quote attributed to Albert Einstein illustrates:

We can’t solve problems by using the same kind of thinking we used when we created them.
Requiring different perspectives, and a diversity of tools, necessitates that there be some manner of engaging this diversity in a meaningful way. This is where we get social complexity. It is here that things often break down. The means of putting together individuals, ideas, and strategies from different backgrounds with different mental models of the way the problem is structured and about the landscape in which the problem occurs is probably the biggest challenge facing the task of complexity reduction.To reduce complexity, there is some need to get on the same page about what makes a problem complex, what elements exist within it, and how those elements are related before one can reasonably hope to make sense of what those patterns of relations actually mean, let alone devising a strategy for intervening.
Terms like “collaboration” are as commonly used as “innovation” and “networking” without much attention to what they mean at a fundamental level. Who among us in the academy, scientific, business or non-profit community would claim not to be innovative, networked or collaborative? My guess is few. Yet, the nature of what these terms means is critical for understanding the potential for creating strategies for addressing complex problems — let alone implementing and evaluating that strategy.
So: can we reduce complexity? The answer will depend on whether we can hope to organize ourselves in a manner that allows us to answer that question in the first place.
education & learningpsychologyresearchscience & technologysocial media

Complexity, Innovation and Fear

 

“If you can’t get over your fear of the stuff that’s working, then I think you need to give up and do something else” – Seth Godin

Seth, who I’ve been celebrating this week, had it right. Many of us fear the stuff that works, because in a complex world, innovation is what often works to solve problems instead of the same way we’ve always done things. In a period of accelerated change, information abundance and overload, and hyperconnectedness, the fear that one is losing their place is palpable when you speak to those over the age of 40, and many below that age.

Harold Kushner has written much on the concept of fear and the ways it influences our lives. In a recent talk in Toronto, Rabbi Kurshner told the audience a story about how his young nephew taught him how to access a computer file and the implications for an age where the young mentor the old and how the older people in society feel left behind by technology. Being left behind, ignored, or rejected is a primal driver of fear. Another sage (albeit a ficitional one) said it best:

Fear leads to anger, anger leads to hate, hate leads to suffering — Yoda

Indeed, what Kushner was speaking about was how fear leads to anger and hate and the suffering that it causes. My colleague Izzeldin Abuelaish, his work, charity and campaign is all about removing fear and promoting understanding for peace. In an interview with TVO he spoke to this issue how the fear and hate associated with a complex issue like the Middle East relations cannot be made to interfere with our fundamental knowledge of what it means to be human. And being human is increasingly complex.

The Middle East, new technology, and a rapidly changing society all reflect a more complex world. Complexity, by its very nature, produces unpredictability and instability. Yet it is in complexity, the boundaries between systems and ideas, and channeling diversity that we innovate. Innovation, by definition, is doing something new to produce value. New means challenging the status quo by default. Resistance to ‘new’ is so easy to see everywhere and the lesson of Darwin and paleoanthropologist Richard Leakey taught us is that a failure to adapt results in extinction. So if we do the math, complexity leads to fear and fear prevents innovation and that leads to extinction.

It is why people like Seth Godin write “Linchpin” and speak on standing out as the means of survival. It’s why Peter Diamandis had such trouble raising the funds to support the X-Prize from organizations, yet found dozens of teams interested in competing for it(a great set of stories about the Prize and innovation are included in his talk on TVO’s: Big Ideas). In both cases, the audience is the individual and small teams or tribes as Seth Godin puts it.

From this it seems that there are a few courses of action that won’t ignore complexity (contributing to what management theorist and systems thinker Russell Ackoff described as ‘doing the wrong thing righter’ ), help spur innovation, reduce fear and hate as a result.

I suggest five things:

1. Teach systems thinking and complexity science in schools, the community, in the media. By understanding how things come together, the unintended consequences and opportunities that emerge from systems, the complexity is reduced or at least made less mysterious in a manner that invokes fear.

2. Provide people with opportunities to develop the analytical skills to make sense of complexity. John Mighton’s work at JUMP Math is a great example. He teaches people to enjoy mathematics and how to learn about it and use it everyday. Math and number fear is (in my opinion) one of the most significant barriers to people understanding complexity. If you fear numbers, you’ll hate math and statistics, and you’ll not want to learn about things like stochasticity (randomness) and risk.

3. This includes working together — experts and non-experts alike — to create the tools necessary to anticipate change. Having a sense of what might reasonably happen (using the aforementioned skills) reduces anxiety. As Kushner recalls, people who are about to die don’t fear death, they lament the life they didn’t live because of fear of the unknown.

4. Nurture individuals and teams because those are where real relationships form. Networking large organizations is fine, but it is in building relationships between people and the small tribes they form that will create the trust and goodwill to allow people to be open and transparent. And this transparency and openness reduces fear.

5. Encourage people to use – and learn from — tools that help people form relationships, maintain them. Social media tools that can’t break are ones that allow people to try and fail and learn. Without a culture that supports relationships and encourage wild attempts that might fail, innovation is unlikely to follow or be sustained.

Anything missing from this? Anything off the mark?

Don’t let fear dissuade you from innovating and making this better and different.

behaviour changecomplexityeducation & learningenvironmenthealth promotion

Complexity and the Information Landscape

 

This morning the newswires are buzzing with a story that alleges Britain’s Climatic Research Unit fudged some of its climate change data and suggesting that a ‘bunker mentality’ took hold in the unit, which led to this kind of skewing of the data and science. One scientist told Doug Saunders from the Globe and Mail that “It wouldn’t be an exaggeration to say that this has set the climate-change debate back 20 years.” Indeed, with the Copenhagen Climate Summit about to start, there is real concern that these allegations – whether proven true or not — will impair the delegates’ ability to reach a deal.

On a different, yet related note, yesterday I went and got my H1N1 shot and was told by the official guiding people through the clinic that about 37 percent of the population of Toronto have had the vaccination. I went to the downtown clinic and waited about 2 minutes to see someone, which is in stark contrast to what we saw a few weeks ago.Why? The threat of H1N1 seems much less in the here and now than it did a few weeks ago when, in the span of one weekend, when U.S. President Obama declared swine flu a national emergency, and two young people in Ottawa died from H1N1. Towards the end of October, H1N1 seemed a lot more scary and that made the issue a lot simpler: get protected or die (or so it seemed)

So what do these two stories have in common? Both illustrate the problem of complexity in the information landscape. H.L. Mencken is quoted as saying: “For every complex problem there is an answer that is clear, simple, and wrong“.

The problem that public health and scientific research faces is that it is in the business of complexity, yet the business of the media is too often in simplicity.  This caused that. That person is bad, this person is a hero and so on. The archetypes and stereotypes come in spades and that is the problem. On the issue of climate change, most scientists worth their salt looking at the data are concerned about what is happening to our climate, not because they know for sure, but because they don’t. In a complex system like the environment, the overlaying causes, consequences and potential confounders of data make it impossible to say for sure that something causes something else in a specific dose. What can be done is that we can observe large scale patterns of behaviour and anticipate changes based on models developed using past, current and possible future (estimated) data and scenario planning.

In public discourse however, this makes for a less compelling story. Many like to think that buying a hybrid car, recycling, and carrying a reusable shopping bag will help solve the problem of climate change, when the truth is an entire system of small changes needs to take place if we really want to make a difference. This speaks to a fundamental lack of understanding of complexity.

With the H1N1 example, complexity is less about the cause and effect relationship of the disease and host and more about the vaccine developed to help prevent it. There are an entire littany of websites, pundits and voices who have turned something that is complicated like a vaccine, with potential complex outcomes in rare events such as allergic reactions, into overly complex issues around patient safety, conspiracy theories and the like. I commented on some of these issues in a previous post. At issue here is a fundamental lack of understanding of statistics and probability.

The problem is that the two are related. For those of us in public health, this is an issue that can lead to sleepless nights. How to both make complex information accessible and interpretable to those without the interest, time or ability to sift through it and make reasoned, informed decisions AND how to enhance people’s understanding of probability? Just yesterday in my course on health behaviour change a student in epidemiology remarked that even something as fundamental as an odds ratio to her field gets debated and misunderstood among her peers. John Sterman at MIT has studied his students — ones that learn about system dynamics — and found that many of them have difficulty grasping the fundamentals of the ‘bathtub problem’ and accumulation, which I discussed in a previous post.

I would argue that this is one of our most fundamental challenges as educators, scientists and members of society.

Think you know about stats and complexity? You might be surprised (and entertained) by how randomness creeps into our lives by listening to the recent podcast on recent episode on stochasticity, or randomness, from WNYC’s Radio Lab.

complexityemergencepublic healthscience & technologysocial media

Seeing Simplicity / Seeing Complexity

 

We are on the cusp of what is known in public health circles as ‘flu season’. Unless you don’t get out much, you probably know that this year’s season has special significance because of the presence of a new relatively new, and powerful strain of influenza known as H1N1 (or ‘the swine flu’ to some). This week we saw the first large-scale roll-outs of vaccinations for H1N1 along with the annual drive to provide the public with flu shots. As is to be expected, there has been a lot of coverage of the flu and the efforts to provide a form of preventive medicine (a vaccine) in anticipation of what is expected to be a heavier-than-usual year of the flu. Judging by the waves of people I know reporting they and their loved ones are (or have been) sick in person, on Facebook or Twitter , I’d say we’re already off to a big year.

Vaccines provoke a lot of concern from people. After all, the basic tenets of a vaccine are to inject someone with either a dead or live version of the virus in tiny forms to boost the host’s immune system and it is natural to those without immunology or biology in their educational history to find this odd. Yet, we’ve nearly wiped out diseases like polio and smallpox because of these vaccines. Those successes have not translated into desire for more vaccines (despite their declared importance to public health), rather the opposite is happening. The current issue of Wired magazine focuses on this problem surrounding the link that some have drawn between Autism and vaccines. This link is possible because the diagnosis of autism often is made about the same time that the most common childhood vaccinations are administered. Despite there being considerable evidence to the contrary, a connection between two unrelated activities gets put together. Something simple is made complex.

This same example also illustrates the opposite. Vaccines and drugs are often developed by profit-making companies who hope to make money as well as profit health benefits. This profit motive can easily get translated into callous disregard for the public’s health and the inability to see the harm products cause: greed rules. Something complex is made simple.

The ability to shift between these two levels of abstraction is a critical challenge for public health. Unlike some areas of health practice, public health deals in the public realm, looking at issues that have importance to everyone, not just individual citizens. We are guided by public ethics, not private morals. But public health is messy for this very reason, because at its root are problems that are mostly complex ones — those with multiple causes and overlapping sets of consequences that cannot be fully predicted using simple methods or models. Complicated problems are ones that have a lot of components to them, but their organization and relationship to each other allows us to diagnose and prescribe a solution. Simple ones have few parts and very straightforward relationships. (A great illustration of these problems is here) .

Yet the impact of making problems at one level look like those at another cannot be understated. This is how myths develop and conspiracy theories take hold. In Canada, public health officials are trying to counteract the myths that the H1N1 vaccine (and flu shots in general) are being perpetuated. But in a social media ecology, that is hard to do, particularly when the myth-makers get so much attention and are motivated — by many conflicting reasons — to get their message out. Research has looked at the messaging behind anti-vaccination messages on YouTube and found it to be a source of a lot of contradictory messaging.

For public health, this is complexity in action and perhaps it is complexity — and social media — that might be the lens and tools to address these myths, otherwise they will continue to flourish. This isn’t such a problem when the consequences of doing something or not doing something has little impact on others. Vaccinations on the other hand impact us all – whether we take them or not — because compromised immunity for one, can lead to disease transmission to another. But things aren’t always what they seem. Once we believed that smoking was a simple choice, then we realized that it caused problems to the individual smokers’ health in myriad ways making it much more complicated, and now research has shown that cigarette smoking is having wide-scale complications to the health of others through second-hand smoke.

So is it simple or is it complex?

business

Strategy: Myths, fantasies, and reality

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A defining feature of sustained excellence in any enterprise is a good strategy — a vision and plan linked to the delivery of something of value, consistently. One of the big reasons many organizations fail to thrive is not just that they that have the wrong strategy, but that they don’t have one at all (but think they do). 

Strategy is all about perception.

Whether you think you have one or not is partly perceptive. Whether you are delivering a strategy in practice or not is also a matter of perception. Why? Because strategy is what links what you build your organization for, what you drive it toward, and what you actually achieve. Lots of organizations achieve positive results by happenstance (being at the right place at the right time). That kind of luck can happen to anyone, but it hardly constitutes a strategy.

Also, statements of intent are great for creating the perception of strategy because one can always say they are working toward something in the abstract, but without a clear sense of how intentions are connected to actions and those actions connected to outcomes, there really isn’t a strategy.

Do you have a strategy?

The best example of this is in the entertaining and instructive illustrative book ‘I Have a Strategy (No You Don’t)‘, Howell J. Malham Jr literally illustrates the problems that beset conversations about strategy as it chronicles two characters (Larry and Gary) talking about the subject and busting the myths associated with what strategy is and is not. One exchange between the two goes like this:

Larry: “Hey Gary, I was working a strategy to put a cookie back in a cookie jar but I tripped and fell and the cookie flew into my mouth instead. Good strategy, huh?

Gary: “That’s not a strategy. That’s a happy accident, Larry

The entire book is like this. One misconception after another is clarified through one character using the term strategy to mean something other than what it really is. These misconceptions, misuses, and mistakes with the concept of strategy may be why it is so poorly done in practice.

Malham’s work is my favourite on strategy because it encapsulates so many of the real-world conversations I witness (and have been a part of) for years with colleagues and clients alike. Too much conversation on strategy is about things that are not really about strategy at all like wishes, needs, or opportunities.

This isn’t to suggest that all outcomes are planned or connected to a strategy, but the absence of a strategy means you’re operating at the whim of chance, circumstance, and opportunism. This is hardly the stuff of inspiration and isn’t sustainable. Strategy is about connecting purpose, plans, execution, and delivery. Malham defines a strategy as having the following properties:

1. It has an intended purpose;
2. There is a plan;
3. There is a sequence of actions (interdependent events);
4. It leads toward a distinct, measurable goal

When combined with evaluation, organizations build a narrative and understanding of not only whether a strategy leads toward a goal, but what actions make a difference (and to what degree), what aspects of a plan fit and didn’t fit, and what outcomes emerge from the efforts (including those that were unintended).

A look at much of the discourse on strategy finds that many organizations not only don’t have strategic plans, they don’t even have plans.

Words and action

One of the biggest problems with “capital ‘S’ Strategy” (the kind espoused in management science) is that it is filled with jargon and, ironically, contributes greatly to the very lack of strategic thinking that it seeks to inspire. It’s one of the reasons I like Malham’s book: it cuts through the jargon. I used to work with a senior leader who used all the language of strategy in talks, presentations, and writing but was wholly incapable or unwilling to commit to a strategic direction when it came to discussing plans and actions for their organization.

Furthermore, it is only marginally useful if you develop a strategy and then don’t bother to evaluate it to see what happened, how, and to what effect. Without the action tied to strategy, it is no better than a wish list and probably no more useful than a New Years Resolution.

Those plans and linking them to action is why design is such an important — and sadly, highly neglected — part of strategy development. Design is that process of shifting how we see problems, explore possibilities, and create pathways that lead to solutions. Design is not theoretical, it is practical and without design doing design thinking is impotent.

Two A’s of Strategy: Adaptation vs Arbitrary

The mistake for organizations working in zones of high complexity (which is increasingly most of those working with human services) is assuming that strategy needs to be locked in place and executed blindly to be effective. Strategy is developed in and for a context and if that situation changes, the strategy needs to change, too. This isn’t about throwing it out but adapting.

Adaptive strategy is a means of innovating responsibly, but can also be a trap if those adaptations need to be built on data and experience, not spurious conclusions. Arbitrary decisions is what often is at the root of bad (or no) strategy.

Roger Martin is one of the brightest minds on strategy and has called out what he sees as sloppy use of the term adaptive strategy as a stand-in for arbitrary decision-making going so far as to call it a ‘cop-out’. One of the biggest problems is that strategy is often not viewed in systems terms, as part of an interconnected set of plans, actions, and evaluations made simultaneously, not sequentially.

Good strategy is not a set of steps, but a set of cascading choices that influence the operations and outcomes simultaneously. Strategy is also about being active, not passive, about what it means to design and create an organization.

Grasping strategy for what it is, not what we imagine it to be, can be a key factor in shaping not only what you do, but how well you do it. Having the kind of conversations like those in Howell J. Malham’s book is a means to get things moving. Taking action on those things is another.

 

Image credit: Photo by Paul Skorupskas on Unsplash

complexityevaluationsocial innovation

Developmental Evaluation’s Traps

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Developmental evaluation holds promise for product and service designers looking to understand the process, outcomes, and strategies of innovation and link them to effects. It’s the great promise of DE that is also the reason to be most wary of it and beware the traps that are set for those unaware.  

Developmental evaluation (DE), when used to support innovation, is about weaving design with data and strategy. It’s about taking a systematic, structured approach to paying attention to what you’re doing, what is being produced (and how), and anchoring it to why you’re doing it by using monitoring and evaluation data. DE helps to identify potentially promising practices or products and guide the strategic decision-making process that comes with innovation. When embedded within a design process, DE provides evidence to support the innovation process from ideation through to business model execution and product delivery.

This evidence might include the kind of information that helps an organization know when to scale up effort, change direction (“pivot”), or abandon a strategy altogether.

Powerful stuff.

Except, it can also be a trap.

It’s a Trap!

Star Wars fans will recognize the phrase “It’s a Trap!” as one of special — and much parodied — significance. Much like the Rebel fleet’s jeopardized quest to destroy the Death Star in Return of the Jedi, embarking on a DE is no easy or simple task.

DE was developed by Michael Quinn Patton and others working in the social innovation sector in response to the needs of programs operating in areas of high volatility, uncertainty, complexity, and ambiguity in helping them function better within this environment through evaluation. This meant providing the kind of useful data that recognized the context, allowed for strategic decision making with rigorous evaluation and not using tools that are ill-suited for complexity to simply do the ‘wrong thing righter‘.

The following are some of ‘traps’ that I’ve seen organizations fall into when approaching DE. A parallel set of posts exploring the practicalities of these traps are going up on the Cense site along with tips and tools to use to avoid and navigate them.

A trap is something that is usually camouflaged and employs some type of lure to draw people into it. It is, by its nature, deceptive and intended to ensnare those that come into it. By knowing what the traps are and what to look for, you might just avoid falling into them.

A different approach, same resourcing

A major trap is going into a DE is thinking that it is just another type of evaluation and thus requires the same resources as one might put toward a standard evaluation. Wrong.

DE most often requires more resources to design and manage than a standard program evaluation for many reasons. One the most important is that DE is about evaluation + strategy + design (the emphasis is on the ‘+’s). In a DE budget, one needs to account for the fact that three activities that were normally treated separately are now coming together. It may not mean that the costs are necessarily more (they often are), but that the work required will span multiple budget lines.

This also means that operationally one cannot simply have an evaluator, a strategist, and a program designer work separately. There must be some collaboration and time spent interacting for DE to be useful. That requires coordination costs.

Another big issue is that DE data can be ‘fuzzy’ or ambiguous — even if collected with a strong design and method — because the innovation activity usually has to be contextualized. Further complicating things is that the DE datastream is bidirectional. DE data comes from the program products and process as well as the strategic decision-making and design choices. This mutually influencing process generates more data, but also requires sensemaking to sort through and understand what the data means in the context of its use.

The biggest resource that gets missed? Time. This means not giving enough time to have the conversations about the data to make sense of its meaning. Setting aside regular time at intervals appropriate to the problem context is a must and too often organizations don’t budget this in.

The second? Focus. While a DE approach can capture an enormous wealth of data about the process, outcomes, strategic choices, and design innovations there is a need to temper the amount collected. More is not always better. More can be a sign of a lack of focus and lead organizations to collect data for data’s sake, not for a strategic purpose. If you don’t have a strategic intent, more data isn’t going to help.

The pivot problem

The term pivot comes from the Lean Startup approach and is found in Agile and other product development systems that rely on short-burst, iterative cycles with accompanying feedback. A pivot is a change of direction based on feedback. Collect the data, see the results, and if the results don’t yield what you want, make a change and adapt. Sounds good, right?

It is, except when the results aren’t well-grounded in data. DE has given cover to organizations for making arbitrary decisions based on the idea of pivoting when they really haven’t executed well or given things enough time to determine if a change of direction is warranted. I once heard the explanation given by an educator about how his team was so good at pivoting their strategy for how they were training their clients and students. They were taking a developmental approach to the course (because it was on complexity and social innovation). Yet, I knew that the team — a group of highly skilled educators — hadn’t spent nearly enough time coordinating and planning the course.

There are times when a presenter is putting things last minute into a presentation to capitalize on something that emerged from the situation to add to the quality of the presentation and then there is someone who has not put the time and thought into what they are doing and rushing at the last minute. One is about a pivot to contribute to excellence, the other is not executing properly. The trap is confusing the two.

Fearing success

“If you can’t get over your fear of the stuff that’s working, then I think you need to give up and do something else” – Seth Godin

A truly successful innovation changes things — mindsets, workflows, systems, and outcomes. Innovation affects the things it touches in ways that might not be foreseen. It also means recognizing that things will have to change in order to accommodate the success of whatever innovation you develop. But change can be hard to adjust to even when it is what you wanted.

It’s a strange truth that many non-profits are designed to put themselves out of business. If there were no more political injustices or human rights violations around the world there would be no Amnesty International. The World Wildlife Fund or Greenpeace wouldn’t exist if the natural world were deemed safe and protected. Conversely, there are no prominent NGO’s developed to eradicate polio anymore because pretty much have….or did we?

Self-sabotage exists for many reasons including a discomfort with change (staying the same is easier than changing), preservation of status, and a variety of inter-personal, relational reasons as psychologist Ellen Hendrikson explains.

Seth Godin suggests you need to find something else if you’re afraid of success and that might work. I’d prefer that organizations do the kind of innovation therapy with themselves, engage in organizational mindfulness, and do the emotional, strategic, and reflective work to ensure they are prepared for success — as well as failure, which is a big part of the innovation journey.

DE is a strong tool for capturing success (in whatever form that takes) within the complexity of a situation and the trap is when the focus is on too many parts or ones that aren’t providing useful information. It’s not always possible to know this at the start, but there are things that can be done to hone things over time. As the saying goes: when everything is in focus, nothing is in focus.

Keeping the parking brake on

And you may win this war that’s coming
But would you tolerate the peace? – “This War” by Sting

You can’t drive far or well with your parking brake on. However, if innovation is meant to change the systems. You can’t keep the same thinking and structures in place and still expect to move forward. Developmental evaluation is not just for understanding your product or service, it’s also meant to inform the ways in which that entire process influences your organization. They are symbiotic: one affects the other.

Just as we might fear success, we may also not prepare (or tolerate) it when it comes. Success with one goal means having to set new goals. It changes the goal posts. It also means that one needs to reframe what success means going ahead. Sports teams face this problem in reframing their mission after winning a championship. The same thing is true for organizations.

This is why building a culture of innovation is so important with DE embedded within that culture. Innovation can’t be considered a ‘one-off’, rather it needs to be part of the fabric of the organization. If you set yourself up for change, real change, as a developmental organization, you’re more likely to be ready for the peace after the war is over as the lyric above asks.

Sealing the trap door

Learning — which is at the heart of DE — fails in bad systems. Preventing the traps discussed above requires building a developmental mindset within an organization along with doing a DE. Without the mindset, its unlikely anyone will avoid falling through the traps described above. Change your mind, and you can change the world.

It’s a reminder of the needs to put in the work to make change real and that DE is not just plug-and-play. To quote Martin Luther King Jr:

“Change does not roll in on the wheels of inevitability, but comes through continuous struggle. And so we must straighten our backs and work for our freedom. A man can’t ride you unless your back is bent.”

 

For more on how Developmental Evaluation can help you to innovate, contact Cense Ltd and let them show you what’s possible.  

Image credit: Author

complexityeducation & learningpsychologysystems thinking

Complex problems and social learning

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Adaptation, evolution, innovation, and growth all require that we gain new knowledge and apply it to our circumstances, or learn. While much focus in education is on how individuals attend, process and integrate information to create knowledge, it is the social part of learning that may best determine whether we simply add information to our brains or truly learn. 

Organizations are scrambling to convert what they do and what they are exposed to into tangible value. In other words: learn. A 2016 report from the Association for Talent Development (ATD) found that “organizations spent an average of $1,252 per employee on training and development initiatives in 2015”, which works out to an average cost per learning hour of $82 based on an average of 33 hours spent in training programs per year. Learning and innovation are expensive.

The massive marketplace for seminars, keynote addresses, TED talks, conferences, and workshops points to a deep pool of opportunities for exposure to content, yet when we look past these events to where and how such learning is converted into changes at the organizational level we see far fewer examples.

Instead of building more educational offerings like seminars, webinars, retreats, and courses, what might happen if they devoted resources to creating cultures for learning to take place? Consider how often you may have been sent off to some learning event, perhaps taken in some workshops or seen an engaging keynote speaker, been momentarily inspired and then returned home to find that yourself no better off in the long run. The reason is that you have no system — time, resources, organizational support, social opportunities in which to discuss and process the new information — and thus, turn a potential learning opportunity into neural ephemera.

Or consider how you may have read an article on something interesting, relevant and important to what you do, only to find that you have no avenue to apply or explore it further. Where do the ideas go? Do they get logged in your head with all the other content that you’re exposed to every day from various sources, lost?

Technical vs. Social

My colleague and friend John Wenger recently wrote about what we need to learn, stating that our quest for technical knowledge to serve as learning might be missing a bigger point: what we need at this moment. Wenger suggests shifting our focus from mere knowledge to capability and states:

What is the #1 capability we should be learning?  Answer: the one (or ones) that WE most need; right now in our lives, taking account of what we already know and know how to do and our current situations in life.

Wenger argues that, while technical knowledge is necessary to improve our work, it’s our personal capabilities that require attention to be sufficient for learning to take hold. These capabilities are always contingent as we humans exist in situated lives and thus our learning must further be applied to what we, in our situation, require. It’s not about what the best practice is in the abstract, but what is best for us, now, at this moment. The usual ‘stuff’ we are exposed to is decontextualized and presented to us without that sense of what our situation is.

The usual ‘stuff’ we are exposed to under the guise of learning is so often decontextualized and presented to us without that sense of how, whether, or why it matters to us in our present situation.

To illustrate, I teach a course on program evaluation for public health students. No matter how many examples, references, anecdotes, or colourful illustrations I provide them, most of my students struggle to integrate what they are exposed to into anything substantive from a practical standpoint. At least, not at first. Without the ability to apply what they are learning, expose the method to the realities of a client, colleague, or context’s situation, they are left abstracting from the classroom to a hypothetical situation.

But, as Mike Tyson said so truthfully and brutally: “Every fighter has a plan until they get punched in the mouth.”

In a reflection on that quote years later, Tyson elaborated saying:

“Everybody has a plan until they get hit. Then, like a rat, they stop in fear and freeze.”

Tyson’s quote applies to much more than boxing and complements Wenger’s assertions around learning for capability. If you develop a plan knowing that it will fail the moment you get hit (and you know you’re going to get hit), then you learn for the capability to adapt. You build on another quote attributed to Dwight D. Eisenhower, who said:

“I have always found that plans are useless but planning is indispensable.”

Better social, better learning

Plans don’t exist in a vacuum, which is why they don’t always turn out. While sometimes a failed plan is due to poor planning, it is more likely due to complexity when dealing with human systems. Complexity requires learning strategies that are different than those typically employed in so many educational settings: social connection.

When information is technical, it may be simple or complicated, but it has a degree of linearity to it where one can connect different pieces together through logic and persistence to arrive a particular set of knowledge outcomes. Thus, didactic classroom learning or many online course modules that require reading, viewing or listening to a lesson work well to this effect. However, human systems require attention to changing conditions that are created largely in social situations. Thus, learning itself requires some form of ‘social’ to fully integrate information and to know what information is worth attending to in the first place. This is the kind of capabilities that Wenger was talking about.

My capabilities within my context may look very much like that of my colleagues, but the kind of relationships I have with others, the experiences I bring and the way I scaffold what I’ve learned in the past with what I require in the present is going to be completely different. The better organizations can create the social contexts for people to explore this, learn together, verify what they learn and apply it the more likely they can reap far greater benefits from the investment of time and money they spend on education.

Design for learning, not just education

We need a means to support learning and support the intentional integration of what we learn into what we do: it fails in bad systems.

It also means getting serious about learning, meaning we need to invest in it from a social, leadership and financial standpoint. Most importantly, we need to emotionally invest in it. Emotional investment is the kind of attractor that motivates people to act. It’s why we often attend to the small, yet insignificant, ‘goals’ of every day like responding to email or attending meetings at the expense of larger, substantial, yet long-term goals.

As an organization, you need to set yourself up to support learning. This means creating and encouraging social connections, time to dialogue and explore ideas, the organizational space to integrate, share and test out lessons learned from things like conferences or workshops (even if they may not be as useful as first thought), and to structurally build moments of reflection and attention to ongoing data to serve as developmental lessons and feedback.

If learning is meant to take place at retreats, conferences or discrete events, you’re not learning for human systems. By designing systems that foster real learning focused on the needs and capabilities of those in that system, you’re more likely to reap the true benefit of education and grow accordingly. That is an enormous return on investment.

Learning requires a plan and one that recognizes you’re going to get punched in the mouth (and do just fine).

Can this be done for real? Yes, it can. For more information on how to create a true learning culture in your organization and what kind of data and strategy can support that, contact Cense and they’ll show you what’s possible. 

Image credit: Social by JD Hancock used under Creative Commons license.

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A mindset for developmental evaluation

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Developmental evaluation requires different ways of thinking about programs, people, contexts and the data that comes from all three. Without a change in how we think about these things, no method, tool, or approach will make an evaluation developmental or its results helpful to organizations seeking to innovate, adapt, grow, and sustain themselves. 

There is nothing particularly challenging about developmental evaluation (DE) from a technical standpoint: for the most part, a DE can be performed using the same methods for data collection as other evaluations. What stands DE apart from those other evaluations is less the methods and tools, but the thinking that goes into how those methods and tools are used. This includes the need to ensure that sensemaking is a part of the data analysis plan because it is almost certain that some if not the majority of the data collected will not have an obvious meaning or interpretation.

Without developmental thinking and sensemaking, a DE is just an evaluation with a different name

This is not a moot point, yet the failure of organizations to adopt a developmental mindset toward its programs and operations is (likely) the single-most reason for why DE often fails to live up to its promise in practice.

No child’s play

If you were to ask a five-year old what they want to be when they grow up you might hear answers like a firefighter, princess, train engineer, chef, zookeeper, or astronaut. Some kids will grow up and become such things (or marry accordingly for those few seeking to become princesses or they’ll work for Disney), but most will not. They will become things like sales account managers, marketing directors, restaurant servers, software programmers, accountants, groundskeepers and more. While this is partly about having the opportunity to pursue a career in a certain field, it’s also about changing interests.

A five-year old that wants to be a train engineer might seem pretty normal, but one that wants to be an accountant specializing in risk management in the environmental sector would be considered odd. Yet, it’s perfectly reasonable to speak to a 35-year-old and find them excited about being in such a role.

Did the 35-year-old that wanted to be a firefighter when they were five but became an accountant, fail? Are they a failed firefighter? Is the degree to which they fight fires in their present day occupation a reasonable indicator of career success?

It’s perfectly reasonable to plan to be a princess when you’re five, but not if you’re 35 or 45 or 55 years old unless you’re currently dating a prince or in reasonable proximity to one. What is developmentally appropriate for a five-year-old is not for someone seven times that age.

Further, is a 35-year-old a seven-times better five-year-old? When you’re ten are you twice the person you were when you were five? Why is it OK to praise a toddler for sharing, not biting or slapping their peers, and eating all their vegetables and weird to do it with someone in good mental health in their forties or fifties? It has to do with developmental thinking.

It has to do with a developmental mindset.

Charting evolutionary pathways

We know that as people develop through stages, ages and situations the knowledge, interests, and capacities that a person has will change. We might be the same person and also a different person than the one we were ten years ago. The reason is that we evolve and develop as a person based on a set of experiences, genetics, interests, and opportunities that we encounter. While there are forces that constrain these adaptations (e.g., economics, education, social mobility, availability of and access to local resources), we still evolve over time.

DE is about creating the data structures and processes to understand this evolution as it pertains to programs and services and help to guide meaningful designs for evolution. DE is a tool for charting evolutionary pathways and for documenting the changes over time. Just as putting marks on the wall to chart a child’s growth, taking pictures at school, or writing in a journal, a DE does much of the same thing (even with similar tools).

As anyone with kids will tell you, there are a handful of decisions that a parent can make that will have sure-fire, predictable outcomes when implemented. Many of them are created through trial-and-error and some that work when a child is four won’t work when the child is four and five months. Some decisions will yield outcomes that approximate an expected outcome and some will generate entirely unexpected outcomes (positive and negative). A good parent is one who pays attention to the rhythms, flows, and contexts that surround their child and themselves with the effort to be mindful, caring and compassionate along the way.

This results in no clear, specific prototype for a good parent that can reliably be matched to any kid, nor any highly specific, predictable means of determining who is going to be a successful, healthy person. Still, many of us manage to have kids we can proud of, careers we like, friendships we cherish and intimate relationships that bring joy despite no means of predicting how any of those will go with consistency. We do this all the time because we approach our lives and those of our kids with a developmental mindset.

Programs as living systems

DE is at its best a tool for designing for living systems. It is about discerning what is evolving (and at what rate/s) and what is static within a system and recognizing that the two conditions can co-exist. It’s the reason why many conventional evaluation methods still work within a DE context. It’s also the reason why conventional thinking about those methods often fails to support DE.

Living systems, particularly human systems, are often complex in their nature. They have multiple, overlapping streams of information that interact at different levels, time scales and to different effects inconsistently or at least to a pattern that is only partly ever knowable. This complexity may include simple relationships and more complicated ones, too. Just as a conservation biologist might see a landscape that changes, they can understand what changes are happening quickly, what isn’t, what certain relationships are made and what ones are less discernible.

As evaluators and innovators, we need to consider how our programs and services are living systems. Even something as straightforward as the restaurant industry where food is sought and ordered, prepared, delivered and consumed, then finished has elements of complexity to it. The dynamics of real-time ordering and tracking, delivery, shifting consumer demand, the presence of mobile competitors (e.g., food trucks), changing regulatory environment, novelty concepts (e.g., pop-ups!), and seasonality of food demand and supply has changed how the food preparation business is run.

A restaurant might not just be a bricks-and-mortar operation now, but a multi-faceted, dynamic food creation environment. The reason could be that even if they are good at what they did if everything around them is changing they could still deliver consistently great food and service and fail. They may need to change to stay the same.

This only can happen if we view our programs as living systems and create evaluation mechanisms and strategies that view them in that manner. That means adopting a developmental mindset within an organization because DE can’t exist without it.

If a developmental evaluation is what you need or you want to learn more about how it can serve your needs, contact Cense and inquire about how they can help you. 

Image Credit: Thinkstock used under license.

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Reframing change

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Change is one of the few universal constants as things — people, planet, galaxy — are always in some state of movement, even if it’s imperceptible. Change is also widely discussed and desired, but often never realized in part because we’ve treated something nuanced as over-simplified; it’s time to change. 

For something so omnipresent in our universe, change is remarkably mysterious.

Despite the enormous amount of attention paid to the concept of change, innovation, creation, creativity, and such we have relatively little knowledge of change itself. A look at the academic literature on change would suggest that most of human change is premeditated, planned and rational. Much of this body of literature is focused on health behaviours and individual-level change and draws on a narrow band of ‘issues’ and an over-reliance on linear thinking. At the organization level, evidence on the initiation, success, and management of change is scattered, contradictory and generally bereft of clear, specific recommendations on how to deal with change. Social and systems change are even more elusive, with much written on concepts like complexity and system dynamics without much evidence to guide how those concepts are to be practically applied.

Arguments can be made that some of the traditional research designs don’t work for understanding complex change and the need to match the appropriate research and intervention design to the type of system in order to be effective.  These are fair, useful points. However, anyone engaged in change work at the level where the work is being done, managed and led might also argue that the fit between change interest, even intention, and delivery is far lower than many would care to admit.

The issue is that without the language to describe what it is we are doing, seeing and seeking to influence (change) it’s easy to do nothing — and that’s not an option when everything around us is changing.

Taking the plunge

“The only way to make sense out of change is to plunge into it, move with it, and join the dance.” – Alan Watts

Dogs, unlike humans, never take swim lessons. Yet, a dog can jump into a lake for the first time and start swimming by instinct. Humans don’t fare as well and it is perhaps a good reason why we tend to pause when a massive change (like hopping in a pool or a lake) presents itself and rely both on contemplation and action — praxis — to do many things for the first time. Still, spend any time up near a cottage or pool in the summer and you’ll see people swimming in droves.

The threat of water, change of fear of the unknown doesn’t prevent humans from swimming or riding a bike or playing a sport or starting a new relationship despite the real threats (emotional, physical, and otherwise) that come with all of them.

Funny that we have such a hard time drawing praxis, patience, and sensemaking into our everyday work in a manner that supports positive change, rather than just reactive change. The more we can learn about what really supports intentional change and create the conditions that support that, the more likely we’ll be swimming and not just stuck on the shore.

Whatever it takes

“If you don’t like change, you’re going to like irrelevance even less.”—General Eric
Shinseki, retired Chief of Staff, U. S. Army

“It’s just not a good time right now”

“We’re really busy”

“I’m just waiting on (one thing)”

“We need more information”

These are some of the excuses that individuals and organizations give for not taking action that supports positive change, whatever that might be. Consultants have a litany of stories about clients who hired them to support change, develop plans, even set out things like SMART goals, only to see little concrete action take place; horses are led to water, but nothing is consumed.

One of the problems with change is that it is lumped into one large category and treated as if it is all the same thing: to make or become different (verb) or the act or instance of making or becoming different (noun). It’s not. Just as so many things like waves, moods, or decision-making strategies are different, so too is change. Perhaps it is because we continue to view change as a monolithic ‘thing’ without the nuance that we afford other similarly important topics that we have such trouble with it. It’s why surfers have a language for waves and the conditions around the wave: they want to be better at riding them, living with them and knowing when to fear and embrace them.

What is similar to the various forms that change might take is the threat of not taking it seriously. As the above quote articulates, the threat of not changing is real even if won’t be realized right away. Irrelevance might be because you are no longer doing what’s needed, offering value, or you’re simply not effective. Unfortunately, by the time most realize they are becoming irrelevant they already are.

Whatever it takes requires knowing whatever it takes and that involves a better sense of what the ‘it’ (change) is.

Surfing waves of change

To most of us, waves on the beach are classified as largely ‘big’ or ‘small’ or something simple like that. To a surfer, the conversation about a wave is far more delicate, nuanced and far less simplistic. A surfer looks at things like wind speed, water temperature, the location of the ‘break’ and the length of the break, the vertical and horizontal position of the wave and the things like the length of time it takes to form. Surfers might have different names for these waves or even no words at all, just feelings, but they can discern differences and make adjustments based on these distinctions.

When change is discussed in our strategic planning or organizational change initiatives, it’s often described in terms of what it does, rather than what it is. Change is described as ‘catastrophic‘ or ‘disruptive‘ or simply hard, but rarely much more and that is a problem for something so pervasive, important, and influential on our collective lives. It is time to articulate a taxonomy of change as a place to give change agents, planners, and everyone a better vocabulary for articulating what it is they are doing, what they are experiencing and what they perceive.

By creating language better suited to the actual problem we are one step further toward being better at addressing change-related problems, adapting, and preventing them than simply avoiding them as we do now.

Time to take the plunge, get into the surf and swim around.

 

 

Image credit: June 17, 2017 by Mike Sutherland used under Creative Commons License via Flickr. Thanks for sharing Mike!