Tag: economics

strategic foresight

Foresight, Growth and the J-shaped Curve

on May 2, 2019

The business of futures is to see what possibilities lay ahead to better anticipate how to meet them when or if they become reality. When this story line follows a linear path this is a lot easier; when it follows a more complex path reality can bite.

Foresight models look at trends and curves in trajectories of things including those that might disrupt the status quo. Using tools and frameworks (PDF), foresight professionals and futurists seek to better understand the contributors (drivers) and patterns associated with decisions, activities, and circumstances to anticipate what might come and better prepare for it (strategic foresight). Foresight is being used in fields ranging from natural resource management to energy policy to healthcare planning.

A rational look at foresight finds many reasons to embrace it for an organization. Who wouldn’t want to have a better sense of what is coming and prepare for it? The problem foresight poses is that it can lead people to look for the right things in the wrong way and that has everything to do with our human tendencies to see narrative arcs in the stories we tell ourselves instead of seeing either exponential or j-shaped curves.

Both of these models for data have enormous consequences for how we understand some of our greatest challenges as humans and as organizations as we shall see.

Exponential complication

A linear distribution or data structure is what humans see most easily. It’s the maintenance of a status quo, gradual change, or the progressive rise and fall of something over time. It’s what we see when we see in most trends and patterns. This perspective has the tendency to view much of the system in which this change takes place as relatively stable.

Stability is largely a matter of perspective. Everything is in motion to some degree; its the rate of change that we notice. In linear systems, that rate of change is relatively consistent or at a pace we understand while exponential change (or growth curves) are more challenging to see — and potentially more dangerous as the video below illustrates.

Al Bartlett’s lecture and other notes provide just one example exponential growth and how our perception is challenged by these kind of data structures in the world and the systemic effects they can bring.

Without an understanding of the growth dynamics associated with a particular phenomenon, we are at risk of grossing under-estimating the potential implications of what might happen. In these cases we need fixes, but not just any fixes as we shall see.

Deceptive Fixes

Another type of curve that can distort foresight models is the ‘j-shaped curve’. This curve describes situations where there is a long-term trend that is briefly countered in the short-term. An example is the case of alcohol consumption and health. There is evidence that alcohol consumption (e.g., a glass of wine or beer) can have a beneficial effect on a person’s health (at the population level, individual results might vary significantly). However, beyond that certain amount — that varies by person — and alcohol becomes toxic and can substantially contribute to a variety of health problems, injuries, and premature death. The j-shaped curve forms from data showing a mild reduction in health risks associated with modest alcohol intake as illustrated below.

For alcohol use, a single drink can lower your mortality risk before the risk starts rising again. Contrast this against cigarette use where a linear pattern of risk is seen: the more you smoke at any level, the higher your risk. Both patterns have linearity to them, but one is far more deceptive in it’s short and long-term implications.

Where this can fool foresight researchers is that there may be a trend that is showing a certain set of properties assumed to be on the trajectory like that on the left hand side of the graph when it is really similar to the right. Depending on the time horizon you use to inform your decisions based on this data the implications could be markedly different and potentially catastrophic.

Our fixes or strategies to anticipate change based on the wrong model could actually serve to amplify the very problem we sought to solve. A possible example of this is the move to ban single-use plastic bags. While the evidence of the environmental impact of plastic is considerable, a shift from plastic bags has its own negative implications, including the increased manufacturing of (with resulting waste and potential increased consumerism from) reusable tote bags or the increased use of forest products to support paper bag production.

The loss of plastic shopping bags which are often re-used (despite being called single-use) as garbage liners is now resulting in more purchases of plastic-intensive garbage bags. If the systemic implications are not considered in the design of such policies, these well-meaning fixes can profoundly fail. What is needed is a change in the way we consume, store, and buy goods, not just carry them home.

Systems change changes systems

The idea that you could be surrounded by literally thousands of people, connected to most of the planet through a device that fits in the palm of your hand, and still experience profound loneliness would once be considered the most profound oxymoron to anyone born before 1980.

Yet, here we are in a state where the very fixes for connection are failing us. The benefits of social media, social connection, artificial intelligence, and new production methods (e.g. 3D printing) are now starting to show some negative effects on our social and economic systems. Are these linear progressions of technological advancement that are simply generating a few of the inevitable bumps along the way? Are they exponential trends about to explode and profoundly transform the way we live? Or are these j-shaped curved trends that once provided us the benefits of finding connection in the modern world only to entrench our social systems into being online, not off?

We are creating systems that are changing themselves and having profound effects on the fundamentals around us. Retail conveniences created by online shopping means changing the relationship we have with our local merchants and that changes their viability. Handheld computers like an iPhone are engineered to hold our attention; what happens when we stop paying attention to the world around us?

These are systems questions and ones that foresight — when applied well — holds some promise in allowing us to anticipate and maybe deal with before its too late.

We can’t see these things coming if we hold models of the future that are based on a linear framing of what is happening now and what is to come. We also can’t adapt if we assume that even non-linear change will take place and persist within the same system it started in. Systems change changes systems.

Data models are fundamental to foresight and understanding them is the key to knowing whether your ahead of the curve, behind the curve, or sitting in the middle of the letter J.

Photo Credits: Ricardo Gomez Angel on Unsplash and Cameron Norman

complexityeducation & learningevaluationinnovationknowledge translation

You Want It Darker?

on November 11, 2016

shutterstock_150465248

It is poetic irony on many levels that weeks after Leonard Cohen releases his album about the threat of death that he passes on, mere days after we saw the least poetic, most crass election campaign end in the United States with an equally dramatic outcome. This points to art, but also to the science of complexity and how we choose to approach this problem of understanding– and whether we do at all — will determine whether we choose to have things darker or not. 

A million candles burning for the love that never came
You want it darker
We kill the flame

Canadian-born and citizen-of-the-world poet, literary author, and songwriter Leonard Cohen passed away last night and the words above were part of his final musical contribution to the world. It is fitting that those words were penned at time not only when Cohen was ill and dying, but also as we’ve witnessed the flames of social progress, inclusion, and diversity fall ill.

Donald Trump is the president-elect of the United States, a fact that for many is not only unpalatable, but deeply troubling for what it represents. A Trump presidency and the social ills that have been linked to his campaign are just the latest sign that we are well into a strange, fear-ful, period of history within Western democracies. His was not a win for ideas, policy, but personality and as a vector for many other things that simply cannot be boiled down exclusively to racism, sexism, celebrity, or education — although all of those things played some part. It was about the complexity of it all and the ability for simplicity to serve as a (false) antidote.

No matter what side of the political spectrum you sit, it’s hard to envision someone less suited to the job of President of a diverse, powerful nation like the United States than Donald Trump using any standard measure of leadership, personality, experience, personal integrity or record of public conduct. Yet, he’s in and his election provides another signal that we are living in complex times and, like with Brexit, the polls got it very wrong.

We are seeing global trade shrink at a time when globalization is thought to be at its highest. We are witnessing high-profile acts of hatred, discrimination and abuse at at time when we have more means to be socially connected across contexts than ever before. We are lonely when the world and connection is at our fingertips.  It is a time of paradox and when we have so many means to cast light on the world, we seem to find new ways to kill the flame.

It is for this reason that those who deal with complexity and seek positive social change in the world need to take action lest things get darker.

Complexity just got real

The election of Donald Trump and the Brexit vote are two examples that should serve to wake-up anyone who seeks greater accounting of complexity in the making of social decisions.

This is not about voting for a Republican President or for citizens wanting greater control of Britain, it’s about understanding the premise of which those decisions were based on. The amount of cognitive dissonance required to assume that Donald Trump has the qualities befitting a leader of a country like the United States is truly astounding. And just like Brexit, the theories and models proposed post-event by the same people who predicted the opposite outcome pre-event will be just words, backed with too little understanding of complexity or why things actually happened.

Those who understand complexity know that these simplistic explanations are likely to be problematic. But that doesn’t make us better people, but it does mean we have certain responsibilities.

Complexity rhetoric vs science

For those who rely on complexity science as a means of understanding these kinds of events its now time to start matching the science to our rhetoric so we can back up the talk. In crude, but truth-speaking pop culture parlance: “This shit just got real“.

As complexity and systems thinking has gained attention in social science and policy studies we are seeing much more attention to the idea of complexity. Yet, the level of rhetoric on social complexity has overwhelmed any instances of evidence of how complexity actually is manifest, emergent, harnessed, or accounted for in practical means.

This isn’t to say that the tenets of complexity for understanding social systems aren’t true, but rather we don’t know that it’s true for sure and to what extent in what situations. I write this as a true-believer, but also as one who believes in science. Science is about challenging our beliefs and only if we cannot refute our theories through our best efforts can claim something is true. Thus, if we can’t show consistently how the principles of complexity are employed to make useful choices and inform the documentation of some of the outcomes related to our actions based on those choices, we are simply making fables not flourishing organizations, communities and societies.

Showing our work

Without something more than rhetoric to back our claims up we become no better than a politician claiming to make America great again because we’ve got great ideas and will be the greatest president ever because we have great ideas.

This is not about reverting to positivist science to understand the entire world, but about responsible practice in evaluation and research that allows us to document what we do and explore the consequences in context. Powered by complexity theory and the appropriate methods, we can do this. Yet, too often I hear reference to complexity theories in presentations, discussions and papers without any reference to how its been used in real terms (and not just extracted from some other realm of science like bee colonies, natural ecosystems and simulation models) to influence something of value beyond serving as an organizing framework.

Like little kids in math class: we need to show our work.

How did complexity manifest in practice in this case? What methods were used to systematically document the process? How does this fit / challenge the theories we know? These are questions that are what responsible scientists and evaluators ask of their subjects and its time to do this with complexity, regularly and often. No longer can we give it the relatively unchallenged ride it’s been given since first being introduced as a viable contributor to social theory about 20 years ago.

The reasons have to do with what happens when we stop trying to understand complex systems.

Evaluators and social sciences’ new moral imperative

As the US election was unfolding I became aware of some prescient, wise words that were uttered by former US Supreme Court Justice David Souter speaking at a town hall prior to the last election. His words were chilling to anyone paying attention to the world today. In the quote and interview (see link) he says on the matter of government and democracy:

What I worry about is that when problems are not addressed, people will not know who is responsible.

His words are not just about the United States or even politics alone. The further we get from understanding how our social, economic, political and environmental systems work the more we all become vulnerable to the kind of simplistic thinking that leads us to someone that embodies H.L. Mencken’s mis-paraphrased words*:

There is always an easy solution to every human problem — neat, plausible, and wrong

It is our duty as scientists and evaluators to show the world the work of the programs, policies and initiatives that are aimed at changing systems — no matter what that system is. We need to be better at telling the story of programs using data and communicating what we learn to the world. It’s our role to show the work of others and to let others see our work in the process. By doing so we can make a contribution to helping address what Justice Souter meant about people not knowing who is responsible.

And like Mencken’s message, our answer won’t be one that is all that neat, but we if we approach our work with the wisdom and knowledge of how systems work we can avoid Mencken’s trap and avoid presenting the complex as simple, but we will go further and illustrate what complexity means.

It is our moral duty to do this. For if not us, who?

People do understand complexity. Anyone with a child or garden knows that there is no ‘standard practice’ that applies to all kids or any years’ crop of vegetables all the time in all cases. It’s evident all around us. We have the tools, theories and models to help illuminate this in the world and a duty to test them and make this visible to help shed that light on how our increasingly complex world works. Without that we are at risk of demagogues and the darker forces of our nature taking hold.

We have the means for people to see light through the work of those who build programs, policies and communities to illuminate our world. In doing so we not only create the candles as Leonard Cohen speaks of, but the curiosity and love that keeps that flame burning. We can’t kill the flame.

And we could use some love right now.

Thanks Leonard for sharing your gifts with us. I hope your art inspires us to reflect on what world you left to better create a world we move to.

*Mencken’s original quote was: “Explanations exist; they have existed for all time; there is always a well-known solution to every human problem — neat, plausible, and wrong.” Alas, this doesn’t make as pithy, Powerpoint worthy comment. Despite the incorrectness of the paraphrased quote attributed to Mencken, it’s fair to say that in many organizations we see this as a true statement nonetheless.

Image Credit: Shutterstock, used under licence.

complexityinnovation

Of tails, dogs and the wagging of both

on September 23, 2014

Who's wagging whom?

Who’s wagging whom?

Evaluation is supposed to be driven by a program’s needs and activities, but that isn’t always the case. What happens when the need for numbers, metrics, ‘outcomes’ and data shape the very activities programs do and how that changes everything is something that is worth paying some attention to. 

Since the Second World War we’ve seen a gradual shift towards what has been called presence of neo-liberal values across social institutions, companies, government and society. This approach to the world is characterized, among other things, by its focus on personal and economic efficiency, freedom, and policies that support actions that encourage both. At certain levels of analysis, these policies have rather obvious benefits.

Who wouldn’t like to have more choice, more freedom, more perceived control and derive more value from their products, services and outputs? Not many I suspect. Certainly not me.

Yet, when these practices move to different levels and systems they start to produce enormous complications that are at odds with — and produce distortions of — the very values that they espouse. We’ve seen the same happen with other value systems that have produced social situations that are highly beneficial in some contexts and oppressive and toxic in others – capitalism and socialism both fit this bill.

Invisible tails and wags

What makes ‘isms’ so powerful is that they can become so prevalent that their purpose, value and opportunity stop being questioned at all. It is here that the tail starts to wag the dog.

Take our economy (or THE economy as it is somewhat referred to). An economy is intended to be a facilitator and product of activities used to create certain types of value in a society. We work and produce goods (or ideas), exchange and trade them for different things, and these allow us to fulfill certain human goals. It can take various shapes, be regulated more or less, and can operate at multiple scales, but it is a human construction — we invented it. Sometimes this gets forgotten and in times when we use the economy to justify behaviour we forget that it is our behaviour that is the economy.

We see over and again with neoliberalism (which is among the most dominant societal ‘ism’ of the past 50 years in the West and more reflected globally all the time) taken at the broadest level, the economy becomes the central feature of our social systems rather than a byproduct of what we do as social beings. Thus, things like goods, experiences, relations and so on we used to consider as having some type of inherent value suddenly become transformed into objects that judgements can be made.

The role of systems

This can make sense where there are purpose-driven reasons to assign particular value scores to something, but the nature of value is tied to the systems that surround what is valued. If we are dealing with simple systems, those where there are clear cause-and-effect connections between the product or service under scrutiny and its ability to achieve its purpose, then valuation measurement makes sense. We can assert that X brand of laundry detergent is better than Y on the basis of Z. We can conduct experiments, trials and repeated measures that can compare across conditions.

It is also safe to make an assumption of value based on the product’s purpose that can be generalized. In other words, our reason for using the product is clear and relatively unambiguous (e.g., to clean clothes using the above example). There may be additional reasons for choosing X brand over Y, but most of those reasons can be also controlled for and understood discretely (e.g., scent, price, size, bottle shape etc..).

This kind of thinking breaks down in complex systems. And to make it even more complex, it breaks down imperfectly so we have simple systems interwoven within complex ones. We have humans using simple products and services that operate in new, innovative and complex conditions. Unfortunately, what comes with simple systems is simple thinking. Because they are — by their nature — simple, these system dynamics are easy to understand. Returning to our example of the economy, classical micro-economic models of supply and demand as illustrated below.

Relationships and the systems that surround them

supply_and_demand

Using this model, we can do a reasonable job of predicting influence, ascertaining value and hypothesizing relationships between both.

In complex systems, the value links are often in flux, dynamic, and relative requiring a form of adaptive evaluation like developmental evaluation. But that doesn’t happen as much as it should, mostly because of a failure to question the systems and their influence. Without questioning the values and value that systems create — the isms that were mentioned earlier — and their supposed connection to outcomes, we risk measuring things that have no clear connection to value and worse, we create systems that get designed around these ineffective measures.

What this manifests itself in is mindless bureaucracy, useless meetings, pompous and intelligible titles, and innovation-squashing regulations that get divorced from the purpose that they are meant to solve. And in doing so, this undermines the potential benefit that the original purpose of a bureaucracy (to document and create an organizational memory to guide decisions), meetings (to discuss and share ideas and solve problems), titles (to denote role and responsibility — although these aren’t nearly as useful as people think in the modern organization), and regulations (to provide a systems lens to constrain uncoordinated individual actions from creating systems problems like the Tragedy of the Commons).

More importantly, this line of thinking also focuses us on measuring the things that don’t count. And as often quoted and misquoted, the phrase that is apt is:

Not everything that counts can be counted, and not everything that can be counted counts.

Counting what counts

It is critical to be mindful of the purpose — or to reconnect, rediscover, reinvent and reflect upon the purposes we create lest we allow our work to be driven by isms. Evaluators and their program clients and partners need to stand back and ask themselves: What is the purpose of this system I am dealing with?

What do we measure and is that important enough to matter? 

Perhaps the most useful way of thinking about this is to ask yourself: what is this system being hired to do? 

Regular mindful check-ins as part of reflective practice at the individual, organizational and, where possible, systems level are a way to remind ourselves to check our values and practices and align and realign them with our goals. Just as a car’s wheels go out of alignment every so often and need re-balancing, so too do our systems.

In engaging in reflective practice and contemplating what we measure and what we mean by it we can better determine what part of what we do is the dog, what is the tail and what is being wagged and by whom.

Photo credit: Wagging tail by Quinn Dombrowski used under Creative Commons License via Flickr. Thanks Quinn for making your great work available to the world.

Economic model image credit from Resources for Teachers used under Creative Commons License. Check out their great stuff for helping teachers teach better.