Here’s the thing: we live in an era of imbecility. Donald Trump, Boris Johnson, and Scott Morrison have encouraged wilfully ignorant, aggressively stupid people to vigorously push dangerously simple-minded, often bigoted ideas, demanding for them some kind of equivalence with facts, reasoned argument, and rationality.
In the absence of a news media thoroughly questioning this new respectability for nonsense and baleful ideology, there are still methods we can use to double-check our assumptions and interpretations. I will look at principles of knowledge management, ideas about managing ‘wicked’ problems, and a re-worked ‘Cynefin’ analytical framework as practical tools for evaluating what we are all told by traditional news media, politicians, social media campaigns, and powerful interest groups.
The playing field
In itself, the aggressive, facile, often bigoted populist demagoguery of Trumpian leaders in business and government was probably predictable, given the stale and unimaginative alternatives on offer. What should worry anyone with an interest in civics is how this new closet-fascism has side-lined rational thinking and reasoned argument.
Worse, since the late 1990s, our universities have no longer taught critical thinking in the professional disciplines, if not also the humanities. The kind of thinking necessary to analyse our history, politics, philosophy, literature, and other arts. The kind of thinking necessary to maintain liberal democracies free from the depredations ushered in by Trumpian demagogues.
It means we have nominally educated people who actually don’t understand much more than following formulae or applying models straight from ideology or textbooks. That’s OK for dead-end jobs in protected parts of our economy, where competition and innovation are words for lip service, not practice. And it plays to populist deference to neoliberalism.
In the post-truth, fake news era, it often doesn’t help to refute the aggressively stupid if counter-arguments are based on a Google search, or any amount of skewed Wikipedia blather-these just don’t have any credibility as reliably factual, or remotely relevant to any adult consideration of serious issues.
But what other ways are there, for busy people to debunk the casual lies and disinformation that now passes for expertise and insight?
Stumbling on some of my post-graduate work recently, I came across three analytical tools that can be easily applied to questionable contemporary discourses.
- A knowledge management ‘double loop learning’ model adapted to trace whether a claim, policy, or argument is based soundly on fact and rationality, or just hot air.
- A theory of ‘wicked problems’ to highlight that not all issues or problems can be addressed with STEM logics.
- A reworking of the IBM Cynefin management framework to highlight that pretending everything can be known and managed doesn’t make it so.
There’s an academic and professional domain of practice called knowledge management, sometimes also known as information management. It concerns itself with making sense of, and leveraging for commercial or political purposes the information and collective wisdom of any organization, meaning the knowledge of its staff and how they apply it to get things done.
Part of that discipline is about identifying, collecting, and making available to staff the information vital to pursuing an organization’s vision, strategy, goals, and every-day practices. Not a small task in itself, but much easier than leveraging that information to create new knowledge and insights. To do the latter, I turn to an unassuming 1990s academic theory about ‘double-loop’ learning.
Academics Chris Argyris and David Schön came up with the notion of single and double loop learning. They did it by using what, for want of a better term, is the data-information-knowledge-wisdom (DIKW) model.
The model proposes a progression from raw data to sophisticated human thinking. The origin of the DIKW model is uncertain, except it seems to be American, arising sometime in the 1940s or ‘50s.
As shown in the illustration, a hierarchy is proposed by which human activities transform raw data into information, and then knowledge and wisdom. Wisdom in this context is probably a high level of professional and social competence in pursuing self-directed goals and outcomes. The model is based on Maslow’s hierarchy of needs.
What’s significant for us that is the reminder that what is presented as knowledge or wisdom can and should be double-checked against the underlying information and data. For example, if a politicians claims that some set of economic numbers justifies a particular policy, we can question the data set, the inbuilt biases of any survey or metric, and the sample size, or other methods used to generate the claimed meaning. It is quite common for politicians to imply meanings and justifications not justified by the data or information on which they base their claims.
What Argyris and Schön did with the pyramid DIKW model was read into it an orderly succession of turning data into wisdom, and emphasizing the potential for double loop learning, whereby someone at a more advanced stage could generate new insights by returning to source data or information to generate a new perspective, knowledge, or expertise.
The re-working of that model in the ‘organizational layer’ is my own, arising on an occasion where I tried to explain creative or innovative thinking to a group of IT support people.
Taking it a step at a time:
- Raw data is just unordered signal. Like cosmic radiation background noise on a radio.
- Anyone can impose order on raw data to make it information. Like selecting sequences of raw data that are dated and timed to imply relationships.
- Anyone can concatenate and/or contextualize information to use it in practical applications, like rules of grammar, principles of philosophy, or formulae for rocket fuel. This is knowledge. Still neutral about why it should be used, but usable. At this stage of the DIKW hierarchy there can be re-checking with data or information (single loop learning), or with both (double loop learning) to update or amend knowledge.
- In this series, anyone can integrate or develop knowledge into a wider understanding of one or more domains of knowledge, to develop an ‘expert’ theory, or principles of practice. Discipline at this level involves double and triple checking all that underpins the knowledge being turned to wisdom, and perhaps also innovating, by seeing new patterns in the process of revisiting the entire chain of data-information-knowledge.
- Transposed onto a typology of everyday experiences, raw data could be seen instead as cultural messages bombarding a blank intellect, probably best managed by sorting them into specific sources: where did we hear that, and who said what?
- In all cultures there are people or sources of arbitration or interpretation about what information means: experts, academics, public intellectuals, journalists, public servants, and so on. Always beware of their interpretations because they almost always serve interests they will not willingly disclose.
- Sitting above the interpreters are gatekeepers, usually self-appointed, opinionated people in leadership positions, like politicians, business executives, or other ‘influencers’. They act to endorse or dismiss what the interpreters have to say, and often seek to ‘fix’ the boundaries of acceptable knowledge and its meanings, usually as the basis for rejecting alternatives, even if they make more sense.
- Anyone can double and triple-check normalized ‘knowledge’ by tracing back the information and knowledge on which a proposition rests.
In the Argyris and Schön version, at the top of the diagram, the triple loop process is just checking that data and information actually supports the proposed meanings of knowledge. In my adaptation, the triple loop must include the constant question ‘cui bono’-who benefits from structuring data and information into the meanings fixed by authority figures. That can also reveal for whom a message is intended, by whom, and why.
In most cases this will come back to money. To the political economy of how a society’s wealth is distributed, and how such a distribution is justified or criticized.
One of the most common, dishonest ways people try to use information, knowledge, and claimed wisdom, is to propose that simple-minded solutions can address complex issues or problems. When people make such claims, they usually have ulterior motives, and no real intention of improving on existing policy, status quo conditions, or issues management.
To simplify: problems relating to human purposes, motivations, or a sense of self are always ‘wicked’, not because they misbehave, but because they have no fixed solution, no single method for their resolution, no repeatable formula for addressing similar circumstances, and no ‘fix’ that may not also be regarded as a success or failure at different times, and sometimes even both at the same time.
One point that deserves to be highlighted is how misguided it is to apply the perennially popular framework of binary oppositions. You know the saying: ‘There are two sides to every story.’ Nonsense. Sometimes there is only one, but mostly there are many.
Multilateralism allows for many ideas to coexist comfortably. Instead of choosing one or another option, we can choose parts of many different options we judge to be best aligned with what’s desirable, rather than being stuck in the artificial binary oppositions of Western party-political systems, and their aligned interests.
The power in Rittel and Webber’s formulation of wicked problems is that it moves beyond the instinctive reach for technocratic STEM ‘solutioneering’, which doesn’t work too well for managing human, social wants and needs. Not only do the STEM disciplines lack the tools to fathom human motivations, but they simply cannot deal with problems that have no defined parameters at all, including, especially, no indication of where to start or finish.
Managing wicked problems is a bit like trying to herd a bunch of cats. Never easy, and frequently appearing to be pointless, but done every day in workplaces, legislatures, and families – by negotiation, compromise, and temporary accommodations we all know will need to be revised on the fly, and revisited.
The first step is always in recognizing that the ‘solutions’ on offer aren’t necessarily the only or best ones. Even if they come with detailed execution plans to be supervised by technocrat experts.
If not political or STEM logics, what can we rely on instead? I’m not sure there is ever one correct answer at any given moment, but we might be able to think about such problems using the Cynefin model.
Developed in 1999 by IBM’s David Snowden, this management paradigm was named after the Welsh word for ‘habitat’ (pronounced something like kuh-nay-f’n), and was probably driven by the rising tide of alarmism about what might happen when two-digit date fields in computer systems were forced to tick over from 99 to 00. No one really knew what would happen. I still remember the terror instilled in all of us working in IT by the notion that all sorts of embedded software and systems would cease to function.
The model seems to me to owe something to a re-working of the old SWOT paradigm, but emulation and adaptation is no sin.
Snowden made disorder a central component of his Cynefin perspective, implying there is no way of managing much of anything under such circumstances. But the model does highlight four types of manageable environments, in increasing order of difficulty.
- Simple environments are what most people think they live in. Simple and repeatable solutions frameworks are available for people to learn and use.
- Complicated environments are what most professionals think they live in. Scientists, engineers, mathematicians, politicians, business executives, and some others actually do live there, and skills honed by experience seem to work pretty well for most things that need managing. Managing things is easy compared to managing people and their diverse needs and wants. Managing people as if they were things is big clue about the quality of the ideas offered in support for such ideas.
- Complex environments are the norm in liberal democracies. This is where creative thinkers and intuitive professionals excel. Education, experience, and applied expertise all count.
- Chaotic is what Snowden thought of as the year 2000 panic, when system clocks turned over from 99 to 00. His idea of rapid response is a bit like sending out emergency services teams. Not very practical on a large scale: chaos is emergencies normalized, and we’d all have to be first responders.
Phobia of disorder highlights one of the greatest tensions in the modern era: that between the sciences and humanities. In recent decades, the rise of IT-related economic drivers has eclipsed the humanities and often devalued their importance to civic engagement. From my perspective (with both humanities and STEM qualifications), this ‘tilt’ in Enlightenment values too far towards only one side has led to a generation of private and public sector leaders who are unable or unwilling to think critically and independently to develop innovative solutions not based on ideology or simple-minded prescriptions. They mask this failing by pretending that following the rules of some textbook method or prescriptive ideology is good enough. Even as they are busy trying to cover up the wreckage left behind by their ignorance. Climate change policy is the most extreme example of this failure.
From my perspective, in the West we mostly live in complex environments, in which the idea of known, ‘best-practice’ approaches is often laughable for being completely disconnected with the daily realities experienced by most people.
Nevertheless, with a little tweaking, the Cynefin model can at least suggest what we ought to be looking for in expert or professional opinion, and the pronouncements of our nominal leaders.
In my tweaked version of the model, I move away from paranoia about chaos, and embrace instead the notion of ‘wicked’ problems, needing ‘wicked’ management.
At the core of my amended model is disruption rather than chaos, implying a state in which norms are re-defined, new methods are developed, and new approaches become valid, while older methods and approaches become increasingly less useful.
- In my tweaked Cynefin model, ‘the simple’ is just that: an uncomplicated view of the world as possessed by children, in which they observe and emulate behaviours to manage their uncomplicated tasks. Many politicians and public servants think and act as if the entire ‘public’ lives in this world.
- The complicated becomes a more adolescent conception of the world in which you might turn to textbooks, social media, and other sources of ‘authority’ for guidance.
- The complex is pretty close to the reality of the Western world. Advanced industrial, economic, and social systems come with complexity that requires skilled, experienced, and creative managers. Unfortunately, a lot of mediocre ones exist in key public and private sector positions. Worse, silly people are given credibility as nominal experts or ‘influencers’, and millions of social media addicts uncritically emulate or parrot the nonsense that passes for wisdom in that domain.
- The Transformational dimension is not really chaos. It could be that people just fear change and resist it, causing consternation among public servants, politicians, and other authority figures in times of regular disruptions, often because they seek to hold on to existing privilege and advantage.
I propose that the Western world probably straddles the complex and the transformational in the diagram, and that the higher order complexities associated with transformational change are likely to be comprised of wicked problems, requiring wicked management, and an expectation that such problems can never really be solved, just managed along.
The Cynefin model serves to remind us that most society-wide problems or issues have no simple solutions or accommodations, and that we might question any proposed, simple solutions for their real purpose, while imagining our own, multilateral, innovative approaches.
Putting it all together
The ‘shortcuts’ to critical thinking explained above will suffice as tool for analysing and verifying the credibility of many preposterous claims given oxygen by unthinking people in politics, business, news, and social media.
The analytical toolkit described here can be used quite easily to debunk even aggressively pursued nonsense.
Qualitative claims (not based on metricated evidence) can nevertheless be evaluated by checking their origins in the double and triple loop learning models, and aligning them with a dimension of the adjusted Cynefin model, discovering whether such ideas are insightful, simply private, or contain an ambition to impose control on others.
For example, a well-written film review can be insightful, while a claim it is ‘good’ or ‘bad’ is merely a private response. Does the reviewer point to any examples in the film, or outside authorities, to back a value judgement? Beyond the review itself, any demand to have the film banned, boycotted, or otherwise discriminated against, deserves to be validated against evidence of ‘badness’, including a sound definition of what constitutes bad, by what criteria, and evaluated in the permanently transitional continuum of public standards on freedom of speech, expression, and access to information.
A note in parting: this toolkit shouldn’t be applied too literally or prescriptively. If it were to become an iron rule, it would defeat its own purpose.
My hope is that this perspective is useful. Drop me a line if you think I missed anything.