Posted by Adam Gordon on Mar 16, 2011 in all, decision-making, forecast filtering, leadership, management, managing uncertainty, risk management, strategic foresight
Fukushima plant, Japan. Picture: digitalglobe.com
At the time of writing, Japan is battling a nuclear meltdown and radiation emergency, and Fukushima could become a word suddenly the whole world knows, like Chernobyl.
Bloomberg News has called the whole tsunami crisis Naoto Kan’s “Katrina moment,” and one can only hope and pray for all concerned that the Japanese prime minister is a more competent leader than Bush was at this moment of human catastrophe.
As to the nuclear meltdown: If ever we have been warned about anything in the future, we have been warned about nuclear plant catastrophes. Not only have there been, as it were, verbal warnings going all the way back to the 1950s, but real-world events such as Three-Mile-Island and Chernobyl have fully fleshed out the scenario of nuclear reactor failure or near failure in populated areas.
If nuclear-generated electricity makes sense anywhere, it makes sense in Japan, which famously has no coal or gas reserves. But these are nuclear plants … built right on the Pacific Ring of Fire? Japan is a small island with 125 million people densely packed into urban areas. As we face the possibility of this many people put at risk, however the next few days play out it’s clear the risk and reward of nuclear energy here is out of alignment.
This is hardly news. The question is, why are the plants are there? And the answer is not a simple one of collusion or corruption of government, or shenanigans of power companies, although there may be some of that. It comes down to a misapprehension of probability and risk among leaders and decision-makers such that it appears that risk and reward are in balance, when in fact they are not.
Year 869AD
To think about this, consider yesterday’s BBC Story: Japan tsunami ‘could be 1,000-year event,” saying last week’s tidal wave was equivalent to a giant wave that hit the Sendai coast in 869AD. The report says: ”It is not unusual for undersea earthquakes to generate tsunamis in this part of Japan. Offshore quakes in the 19th and 20th centuries also caused large walls of water to hit this area of coastline. But previous research by a Japanese team shows that (only) in the 869 ‘Jogan’ disaster, tsunami waters moved some 4km inland, causing widespread flooding.”
The point is, tsunamis are common, but “the big one” is a one-in-thousand year event — an extremely low probability outcome.
Here I’m strongly reminded of the days following the depth of the Credit Crunch, Bear Stearns’ collapse, and general world financial system meltdown of 2008. If bankers said one thing sensible through the whole period it was: “this was a one-in-ten-(hundred, etc.)-thousand probability outcome, and extreme ‘outlier’ event!”
A low-probability event means we can relax, right? Wrong. The problem is probability says zilch about impact. “Wild Cards,” or now more famously in Nassim Taleb’s terms, “Black Swan” events are low probability but of game-changing impact.
Taleb’s point, made repeatedly across his various books and articles, is that standard probability theory and Gaussian statistics lull analysts into thinking that because an event is low probability – an outlier in a normal bell-curve distribution – it is of low or lower consequence.
Ignoring the tail of the Bell Curve is okay if events are genuinely assessed as low impact. If they are high-impact aka “fat-tailed” events, they are the most important events we face in the future, in building or maintaining any system or organization.
A probabilistic framework misleads decision-makers because it degrades their attention to crucial events (by tagging them low-probability,) which means next thing they are betting banks on mortgage-backed securities, or building nuclear plants on earthquake fault lines.
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Posted by Adam Gordon on Jul 22, 2010 in all, decision-making, economy & finance, management, risk management, scenario planning
Preliminary results of the European banking stress test are to be published by the Committee of European Banking Supervisors tomorrow (July 23.) Although the exact nature of the tests have remained under wraps — not without controversy — the essence is clear. Regulators are simulating various forms of adverse financial conditions (GNP performances, interest rates, currency values and flows, and other money metrics) to see if important banks have the resources to withstand these conditions.
Controversy has resulted from lack of transparency in the tests, leading to speculation that they are designed to have most banks “pass” in order to boost confidence — as clear an example of mixing up judgment and advocacy as one is likely to get.
The key measure for determining which of the 91 banks fail the test — and need to raise capital — is whether their Tier 1 capital ratio would fall below 6% under the “loss assumptions” imposed by the test. This is the same level that was required in the stress tests of U.S. banks in its similar May 2010 test.
Model worlds
Anyhow, what is particularly interesting to this author is that the concept “scenario planning” has not been used through the bank test process, but these tests are fundamentally future scenarios, this is what scenarios are all about: creating model future worlds that express the evolution of important uncertainties towards somewhere at the limits (but not beyond) of plausibility, with the specific intent to use these worlds to stress test current decisions as to what a company is and does — from its business model to its resource base to product line to marketing, and so on.
If the organization’s key decisions would hold up (produce profitability or however success is defined) in different, alternative tests, this tells managers theirs are probably good decisions for the future. If they would flop in any test, this points to what needs to be urgently addressed. In this way an organization explores and becomes robust to its unknowable and unpredictable future.
Notably, it is precisely the stress-test purpose of scenarios that stops this foresight technique becoming (as it does all-too-often in the wrong hands) a “wishing well” for better times. When scenarios cease to be direct stress tests of present decisions, they become floaty indeed.
Full scenarios
Having said all this, the difference between the US and European banking stress tests and full scenario work is the bank tests are considering only economic factors, only adverse (risk) conditions, and only “known unknowns.” Full scenarios would include the full range of important drivers of change — and potential surprises — outside of economics or finance in their construction. In operating as stress tests, they would look at threats to the status quo as the bank tests do, but also provide a testbed for exploring opportunities in change.
read morePosted by Adam Gordon on Jul 16, 2010 in all, policy, risk management
I was interested to see FEMA’s (U.S. Federal Emergency Management Agency) launch of its “Getting Urgent About the Future” Strategic Foresight Initiative, not only in itself unfashionably embracing deeper, longer-term thinking about key policy & security issues, but also making an excellent fist of defining its benefits (a definition that is in all essentials equally valid for business-industry foresight):
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“The world around us is changing in ways that may have profound effects on the emergency management enterprise. Collectively, we must begin to think more broadly and over a longer timeframe if we are to understand these changes and their potential impacts. To this end, FEMA has launched a Strategic Foresight initiative (SFI), the objective of which is straightforward: to seek to understand how the world around us is changing and how those changes may affect the future of emergency management and our community…
“The SFI can serve as one important tool in the development of both strategy and plans. By understanding the potential future environment, organizations will better understand and anticipate risk while ensuring opportunities can be fully capitalized. For example, the SFI may identify new or increasing capability requirements as well as emerging capabilities that do not exist today. Such identifications could support decisions about future investments as well as planning activities and exercises. In a more indirect manner, the SFI can help establish a research agenda for the emergency management field by highlighting areas of emerging relevance and the key questions that remain unanswered.”
[On March 1, 2003, FEMA became part of the U.S. Department of Homeland Security.]
read morePosted by Adam Gordon on Nov 24, 2009 in all, failed predictions, managing uncertainty, Perils of Prediction, risk management, scenario planning, strategic foresight, trend tracking, wildcards & black swans
We owe a debt to Nassim Taleb for memorably encapsulating the demerits of predicting by extrapolating trends as “The Turkey Problem,” and now seems the moment to reiterate it:
Imagine you are a turkey. Every day someone comes to feed you. Every day you get bigger. Your portion sizes get bigger too, brought by a nice man at regular intervals. You extrapolate the trend and you confidently predict a bigger you, with more to eat. Regularly too.
But what happens is … Thanksgiving. Or Christmas
Taleb, N., The Fourth Quadrant: a Map of the Limits of Statistics, Edge Foundation, September 2008
The hard reality for those who predict the future by extrapolating trends (and those gullible enough to believe them) is that even if our turkey had excellent data points (carefully observed and accurately recorded in, for example, a time series analysis) and, moreover, even if our turkey was a mathematically sophisticated — not merely simply projecting trends, but applying all the latest modeling techniques, from moving averages to compound regression — he is still going to be wrong about the future. Dead wrong.
All the data analysis in the world, all the fancy computer software, all the consulting time paid for, and he is still a dead duck.
Ouch. The lesson: there may be (or, vexingly, may not be) something outside the trend, a framing condition, which where it does exist is invisible within the trend projector’s mental model. The only way to get a view of the future that is “robust to Thanksgiving” is (a) to question assumed framing conditions, for example through properly done scenarios, and (b) to hold a view of the future which assumes fundamental ‘game-changing’ surprises can and will occur.
If, as they say, “the trend is your friend” it is assuredly only your fair-weather friend.
read morePosted by Adam Gordon on Nov 12, 2009 in all, innovation, management, managing uncertainty, risk management, strategic foresight, technology change
I’m pleased to have been invited to be one of a dozen or so regular contributors to the blog ‘Risk Matters,’ because, well, risk matters. It’s a key part of the reason why anyone or any group would look to the future… which of course also conditions how we look, what we look for, and what we find or miss.
So this stimulates me to put down a few thoughts about risk assessment and its relationship with industry and strategic foresight as a whole. This is a big topic of course, but seeing as the categories are confused a lot, it’s worth tackling even if just in summary terms.
When I reach the topic of Risk Assessment in my ‘Industry Foresight and Business Future Strategy’ MBA elective, I use the ‘Adidas-Salomon: Incorporating Risk into Corporate Strategy’ mini-case [Ref: ICFAI 304-141-1; sourced via Cranfield’s Case Clearing house.]
The case is a useful baseline in risk assessment because it describes the various risks a multinational company typically faces: marketing risks (market change, brand image); operations risks (quality; reliability of processes and suppliers); social & environmental risks (workforce & natural resources compliance); legal (liability, regulation, patent); information technology (compromise or disruption); and financial risks (currency, interest rate, credit).
Business disruptors
In sum these are the things that could damage or disrupt the business. Isolating such factors, keeping vigilance over them, and having thought through or enacted counter-measures in advance, allows the organization to better control or reduce the impact should risk become reality.
All risks are future events, so a risk assessment is undoubtedly a future study, but assuming a company looks diligently across all these categories for potential and emerging hazards, how prepared is it for a changing world? What kind of industry foresight does this give managers? Is a risk assessment a futures assessment?
The obvious first answer is that a risk assessment is only half the equation. It’s oriented to the downside potential of changes not the upside; looking for threats not opportunities. Obviously that means that opportunities are less likely to be identified.
The second thing is that a standard risk assessment operates in the realm of known risks, in known categories, that may cause disruption and damage in a known way. It doesn’t have the mechanism to expand conceptions of what could go wrong, or how it could go wrong, or what the full knock-on effects will be. The types of mental-model-expanding techniques that fuller foresight offers are not built into a typical risk assessment.
Strategy questions
Third, risk assessments never really broach the question: is the business idea or business model good and will it keep on being good? That is, what products or services will be appropriate going forward, or how will models of supply or manufacture or marketing or fulfillment need to change, due to technology change or shifting consumer preferences.
In other words, risk assessment doesn’t ask strategic questions of managers. It is part of the day-to-day management vigilance necessary with reference to the future – the hygiene factors in running an organization. It is about keeping the business going as is, not about changing it for a changing word.
There’s nothing wrong with this. The point is, it’s just ‘first base’ in building a quality view of the future, and therein a robust point-of-view about what to do next. Although no doubt companies such as Google or Apple or Virgin, etc., assess and mitigate their risks, they didn’t become successful in their future by doing risk assessment and saying ‘That’s it, were done. We’re ready for the future.”
read morePosted by Adam Gordon on Oct 29, 2009 in all, economy & finance, failed predictions, forecast filtering, foresight tools & methods, managing uncertainty, Perils of Prediction, risk management, strategic foresight
Continuing the theme of financial types talking to each other about predictions and predictability, this ‘Tea with the Economist’ interview of Stephen Roach, Chairman, Morgan Stanley Asia by Economist New York Bureau Chief Mathew Birk, carries interesting lessons about the limits of prediction.
Birk commends Roach for being one of the few to have predicted the Credit Crunch problems, to which Roach demurs in saying he was “too early”. He then furthers his modesty in saying that the “breakage” in the financial system was “in excess of anything I envisioned.”
Self-deprecation in assessing one’s predictive abilities will endear anyone to me. Even Roach, who later in the interview burns this hard-won credibility by laying the blame for the credit crunch at the door of regulators, forgetting how hard financial institutions lobbied regulators for greater freedoms in the 1990s.
But I digress. The predictive issues the interview raises are as follows. Issue one: it’s not enough (as any stock short-seller will confirm) to get the direction of a future change right. One must get the timing right too. Issue two: it’s not enough to anticipate a change. One must be able to judge it’s impact. Getting either timing or impact wrong is effectively to have missed the future.
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Probability
On the latter topic — the problem of impact — Nassim Taleb is unrelenting, and he is right. Analysts routinely mix up probability and impact. They think that because an event has a low probability (‘it would be a 10-sigma event!’) it can be marginalized in the predictive number crunching. Of course, it can’t. The low-probability of a wildcard or black swan event is irrelevant because when it happens it will change the game, and that’s why, in every predictive situation of reasonable complexity and uncertainty, using statistical extrapolations (regressions and so on) to predict, is to dangerously paper over the cracks. It is precisely the cracks that businesses and policy makers need to worry about.
Determining the direction of change is hard enough. Assessing timing or extent of impact — a ‘total future impact index’ — is wickedly difficult. It’s a task not to be underestimated, and to simply extrapolate current trends (= assuming the trend’s timeline and impact stay the same as in the past) is the royal road to underestimating it.
This is the reason foresight for complex, uncertain, changing situations can only be grasped by NOT predicting (quantitatively or otherwise) but by exploring the limit-conditions of the plausible (What would happen if the timing of the change accelerated, or was significantly delayed? What if the impact was 10x or one tenth of what we expect? And so on.)
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