Brokers have relationships across many groups and are able to bridge silos to generate new insights, they also act as gateways for new ideas.
Connectors have many relationships within their core group and are well positioned to get ideas adopted locally, they are also highly trusted within their primary team.
Energizers are able to create a reputation that spreads quickly across the network, they tend to get the most out of others, and they are more likely to get ideas noticed.
Challengers provoke change in an organization by tapping into external pressures, they entice debates to encourageidea enhancement and moderate network buzz.
The Embodied Mind: Cognitive Science and Human Experience (The MIT Press) – Varela, Thompson, Rosch (1993 )
The radical uncertainties of coronavirus
Radical uncertainty arises when we know something, but not enough to enable us to act with confidence. And that is a situation we all too frequently encounter
by John Kay and Mervyn King / March 30, 2020
When we set out two years ago to write a book on radical uncertainty, and when we delivered it last year and agreed on a publication date of 5th March 2020, we did not know—how could we have known?—that the world would at exactly that time be plunged into radical uncertainty by a radically uncertain event. But as we wrote in that book, “we must expect to be hit by an epidemic of an infectious disease resulting from a virus which does not yet exist.” There is no pleasure in seeing this warning borne out.
Covid-19 has been described as a “black swan.” It is not. The options trader turned sage, Nassim Nicholas Taleb, used this memorable metaphor to describe what the politician turned (less successful) sage Donald Rumsfeld described equally memorably as an “unknown unknown.” Europeans once believed all swans to be white—as all European swans are—until the colonists of Australia observed black swans. The observation of a black swan was not a low probability event; it was an unimaginable event, given European knowledge of swans. As the convict colonists boarded the First Fleet, none of them would plausibly have speculated on the possibility (still less assessed the probability) that there might be non-white swans in Australia. The thought would not have occurred to them.
Likewise, before the wheel was invented no one could talk about the probability of the invention of the wheel, and afterwards there was no uncertainty to discuss. The unknown unknown was, at once, turned into a known known. In this sense, to identify a probability of inventing the wheel is to invent the wheel.
A century ago, a telephone that would fit in your pocket, take photographs, calculate the square root of a number, navigate to an unknown destination, and on which you could read any of a million novels, was not improbable. It was just not within the scope of imagination or bounds of possibility.
True “black swans” are—like these examples—states of the world to which we cannot attach probabilities because we cannot conceive of these states. The dinosaurs fell victim to an unknown unknown—even as they died, they did not know what had happened to them.
But human extinction will more likely come about in another way. Martin Rees, a Cambridge scientist and Astronomer Royal, has founded a Centre for the Study of Existential Risk to identify such potential threats and suggest measures to mitigate them. He warns of the possibility of runaway climate change, robots escaping our control, and—more pertinently just now—pandemics. Although we can and have imagined all of these things, they are still instances of radical uncertainty.
A global pandemic is not a “black swan,” an unknown unknown. Nor is it a low probability event, an extreme observation from a known probability distribution, such as tossing a coin 100 times and getting a head every time. (Incidentally, if you did toss a coin a hundred times and it came up heads every time, you would be wise to consider other explanations before concluding that you had experienced a “once in a lifetime” freak of nature. In August 2007, David Viniar, then CFO of Goldman Sachs, told the Financial Times that the bank had experienced “things that were 25-standard deviation moves, several days in a row.” What he should have said was that the Goldman Sachs models were misleading guides to the real world.)
A global pandemic was a likely event at some point, a known unknown in that sense. But the occurrence of such a pandemic in 2020 was not a very likely event, and we could not in advance do anything more than guess at what form it would take, and even then our guesswork was likely to be limited by mixing and matching between what we know about more familiar pathogens. We could acknowledge the possibility of something new and different, outside the range of past experience, but have only a limited ability to imagine what this might be, still less reckon with the probability of it coming to pass. The question “what was the probability that coronavirus would break out in Wuhan in December 2019?” is not one to which there is any sensible answer.
Radical uncertainty arises when we know something, but not enough to enable us to act with confidence. And that is a situation we all too frequently encounter.
Hankering for more certainty is a natural enough response, and one that is keenly felt in Downing Street. Dominic Cummings recently put Philip Tetlock’s book Superforecasting into the news, when his pursuit of “weirdos” introduced a “superforecaster” to No 10 before deciding after some controversy that a superforecaster—or at least that particular superforecaster—was perhaps not needed after all. The latter may have been the wiser decision, whether or not Andrew Sabisky himself could see it coming.
“Superforecasters” are good at answering puzzles, questions that are well defined and that will have objectively correct answers, such as “will the number of confirmed coronavirus cases in the UK exceed 100,000 by 15th May 2020?” But the questions to which we really want answers are less well defined.
How serious will the outbreak be before it peaks? What will be the effect on the economy? Not puzzles but mysteries, questions to which the answer will not necessarily be clear even after the outbreak is long over.
The language and mathematics of probability is a compelling way of analysing games of chance. And similar models have proved useful in some branches of physics. Probabilities can also be used to describe overall mortality risk just as they also form the basis of short-term weather forecasting and expectations about the likely incidence of motor accidents. But these uses of probability are possible because they are in the domain of stationary processes. The determinants of the motion of particles in liquids, or overall (as distinct from pandemic-driven) human mortality, do not change over time, or do so only slowly.
But most of the problems we face in politics, business (including finance) and society are not like that. We do not have, and never will have, the kind of understanding of human behaviour which emulates the understanding of physical behaviour which yields equations of planetary motion. Worse, human behaviour changes over time in a way that the equations of planetary motion do not. And Venus continues in its orbit unaffected by our opinions about it, while human beliefs about viruses and anything else, whether true or false, will often have a major influence on human behaviour.
Discourse about uncertainty has fallen victim to a pseudo-science. When no meaningful quantification is possible,
algebra can provide only spurious precision, while at the same time the language becomes casual and sloppy. The terms risk, uncertainty and volatility are treated as equivalent; the words likelihood, confidence and probability are also used as if they had the same meaning. But risk is not the same as uncertainty, although it arises from it, and the confidence with which a statement is made is at best weakly related to the probability that it is true.
The mistake that Viniar of Goldman Sachs exemplified as the credit crunch bit was to believe that a number derived from a “small world” model—a simplification based on a historic data set—is directly applicable to the “large world,” complex and constantly evolving, in which we live. We are both strongly committed to the construction and use of models—we have spent much of our careers in academia and in the financial and business world doing exactly those things. But that has left us aware of the limitations of models as well as their uses.
In a previous pandemic—the Aids virus—the WHO designed a complex model informed by the latest country-by-country demographic data. That model substantially underestimated the extent of the damage the virus would impose. A much simpler model created by the British scientists Robert May and Roy Anderson recognised that what mattered to the spread of Aids was not so much the frequency of sexual encounters as the number of sexual partners—someone who slept with 10 different people would do far more to spread the disease than someone who slept with the same person 10 times. Their model, incorporating this simple insight, was a better guide to both the spread and the incidence of the disease than the more elaborate calculations that missed this one basic point.
A key function of a good model is to direct attention to the usually small number of parameters that really matter. Epidemiological models have taught us that serious pandemics are likely to be inherently self-limiting—an evolutionarily successful virus, like the cold viruses from which humans endlessly suffer, is one that leaves its carriers sufficiently fit and well to spread it. The critical parameters are the numbers of uninfected people to whom each infected person passes the disease, and the mortality or serious complication rate of those infected. From what we know so far—and the information that has been publicly disclosed is patchy—with coronavirus, the first of these parameters is relatively large, and the second relatively low.
Models from epidemiology can help us understand other contagious processes—stock market panics, runs on banks and on supplies of toilet paper, and the competition between political leaders to be at least as vigorous as others in announcing responses to the pandemic.
Models should be treated not as forecasting tools but as ways of organising our thinking. Their construction and interpretation require judgment. Their value depends on our understanding of the processes that give rise to the data we observe, and the quality of that data. We will never really know either the infection rate or the mortality rate from coronavirus because many people will catch the disease but never be tested, and very many of those who die will be people with underlying health issues (which may or may not have killed them anyway) who then test positive for the virus in the course of treatment.
Few people—even actuaries and statisticians—use probabilities to run their own lives. We cope with a world that contains mysteries rather than puzzles by telling stories, constructing a “reference narrative” that incorporates our realistic expectations. When uncertainty encroaches on that narrative, it may be good or bad—the frisson of uncertainty that attracts punters to gambling venues and the uncertainty attached to visiting new places, meeting new people, and enjoying new experiences that adds much to the pleasure of life. And it is uncertainty that creates opportunities for entrepreneurship and profit, and is the dynamic of a market economy. But for human beings to thrive in a world of unknowns, you need to develop the capacity to manage uncertainty, and even embrace it. That is easier in a world of universal healthcare, and in an economy that is not too reliant on self-employment and the gig economy, but instead demands a more supportive relationship from employers. That sounds a lot more like Europe than America, and hence Europe may ultimately be better placed to handle the current epidemiological emergency, and the economic dislocation in its wake.
Charting a happy course through a world where much is unknown means ensuring that one’s reference narrative—personal, financial, commercial or political—is robust and resilient to events we cannot fully anticipate. The establishment in 2017 of the international Coalition for Epidemic Prevention and Innovation was an attempt to promote such robustness and resilience, and its existence may accelerate the quest for a coronavirus vaccine, which Philip Ball discusses in detail in this month’s issue of Prospect.
Robustness and resilience in complex systems are achieved by ensuring that the system is organised in a way that ensures a failure of part of it need not jeopardise the whole. In business and finance over the last 50 years we have viewed the protection and capacity that this involves as evidence of inefficiency, as when Northern Rock announced plans to return “surplus” capital to shareholders shortly before the drying up of wholesale markets for short-term funds put the bank out of business. Northern Rock fell victim to radical uncertainty, the credit crunch being an event that was possible though not likely.
But as the economy is convulsed by the coronavirus-induced lockouts, shutdowns and panic purchases, other (non-financial) modern business fashions—such as lean production and just-in-time inventory management—are likewise exposed as dangerous devices for flattering short-term profits at the expense of long-term business resilience. The vicissitudes of our uncertain world have not only subjected our society to a brief if nasty disease, but also exposed our economy’s susceptibility to, in the parlance of the hour, a serious underlying condition.
A series of pieces on coevolving.com from January-March of this year, which I’ll be linking out one per week (but all are on David Ing’s blog already). Here is 2/5
The Systems Changes Learning Circle has met at least every 3 weeks over the past year. As part of an hour+ lecture to introduce systems thinking, students in the Systemic Design course in the Master’s program in Strategic Foresight and Innovation at OCAD Universitywere immersed in questions where we’ve focused our attention, complemented by background into traditional foundational materials. An audio recording has now been matched up with presentation slides, so that learners outside the classroom can partially share in the experience.
This lecture begins with the rising interest in “systems change”, that is related to “theory of change” from funders of social innovation programs. From there, the lecture aims to recast (speak in a different way) and reify (make some specified ideas more prominent) an understanding of systems thinking.
The presentation was overprepared — we can’t predict how engaged students will be on the ideas, before their brains are full. Of 55 slides, we stopped on slide 37. For streaming, the video is accessible on Youtube. (with a 6-minute excerpt on the Luoyang Bay abalone farmsfrom the documentary Watermark, by Edward Burtynsky, removed).
continues in source Are Systems Changes Different from System + Change? – Coevolving Innovations
slides etc also at http://coevolving.com/commons/20200115-ocadu-systems-changes-different-from