Britten Coyne Announces New Online Version of their Strategic Risk Governance and Managment Certified Competence Course

Britten Coyne Partners and their affiliate, the Strategic Risk Institute LLC have launched an on-line programme leading to a Certified Competence in Strategic Risk Governance and Management.

This 12 module on-line course draws upon Britten Coyne Partners’ five years’ research and leading edge thinking. It qualifies for 40 hours of CPD.

“Overall I have enjoyed the material enormously and would not hesitate to recommend the course to anybody with a serious interest in this critical subject.” UK Chartered Director

"To undertake this learning whilst my company was hit by the Covid-19 pandemic gave the course an unnerving backdrop. But as well as discomfort I had an increasing array of tools and thinking at my disposal to use in a real life situation. The test of any tool is how it performs under pressure and this course delivered.” UK Chartered Director

"I graduated with a degree in history, and took this course to increase my appeal to employers. I found it to be rigorous, engaging, and deeply relevant to my future." Recent University Graduate

******* Enrollment now open *******

******* 50% “Early Bird” discount *******

A 50% discount on the launch price is offered for the first 100 students.

Go to the Strategic Risk Institute website for more information about the course and the enrollment link. Enter the code 50PERCENTPOUND (for payment in pounds) or 50PERCENTDOLLAR (for payment in dollars) on the payment page.

A Deeper Look at Individual Reactions to the COVID-19 Surprise

Once the immediate challenges posed by the global COVID-19 pandemic have been met, as they surely shall, a great deal of research will focus on this question: Why did it seem to take so many people, companies, and governments by surprise?

In our work with clients, we stress that strategic disasters results from some combination of three failures: to anticipate a threat, to accurately assess its potential impact, and to adapt to it in time.

The root causes of each of these failures lie in a complex mix of interacting individual, group, network, and organizational factors. In this post, we’ll take a closer look at recent research into one of these: the ways human beings react to surprise.

Some researchers distinguish between two types of surprise, which they found to activate different areas of the brain (see “Information Theoretic Characterization of Uncertainty” by Loued-Khenissi and Preuschoff).

“Stimulus Surprise” is triggered by something that (a) is not consistent with our expectations; and (b) whose potential impact on us or on people we care about is substantially negative. This type of surprise is an emotional alarm signal that automatically triggers our “System 1” “fight or flight” response.

In contrast, “Bayesian Surprise” is, at first glance, a conscious, “System 2” cognitive response that triggers reflection and learning to improve the accuracy of our mental models and the expectations they produce.

However, in “Neural Mechanisms of Updating Under Reducible and Irreducible Uncertainty”, Kobayashi and Hsu find that brain regions associated with learning automatically increase their activity only in the presence of reducible uncertainty. So here too there is an automatic aspect to our response to surprise.

In “Evidence for Surprise Minimization and Value Maximization in Choice Behavior”, Schwartenbeck et al note that, “classical economic models are predicated on the idea that the ultimate aim of choice is to maximize utility or reward. In contrast, an alternative perspective highlights the fact that adaptive behavior requires agents’ to model their environment and minimize surprise about the states they frequent.” The authors present evidence that “choice behavior can be more accurately accounted for by surprise minimization compared to reward or utility maximization alone.”

A considerable amount of research has identified numerous obstacles to our ability to accurately update our mental models of the world (e.g., our natural human tendencies toward over-optimism, overconfidence, hindsight bias, and, especially when uncertainty is high, conformity to the views of our group or a dominant leader). New research has added to this list.

In “All Thinking is Wishful Thinking”, Kruglanski begin by succinctly describing the ideal updating process: “Basically, we construct new beliefs from prior beliefs by assimilating new evidence. We do so through an inference process probabilistically modeled by Bayesian principles. According to that portrayal, relevant evidence (to which we are exposed) occasions an updating of our beliefs on the topic."

"In Bayesian belief updating, two components are crucial: (i) the strength of the prior belief; namely, the subjective probability of it being true; and (ii) the cogency of the new evidence; namely, the degree to which it strengthens or weakens prior beliefs. In other words, people update their prior beliefs given new evidence, depending on whether the new evidence is perceived as precise, strong, and relevant (versus imprecise, weak, and irrelevant) and whether their prior belief was held with high (versus low) confidence. The change in prior beliefs, in light of the new evidence, is quantified by the degree of “informational gain” or Bayesian surprise.”

However, the authors go on to present evidence that “the belief updating process is suffused by motivation; people actively seek to obtain, avoid, or create new information about the world to increase the consistency between their [existing] models and the evidence at hand.”

In “Valuation of Knowledge and Ignorance in Mesolimbic Reward Circuitry”, Charpentier et al study the activation of various parts of the brain after positive and negative surprises. They find that humans “pursue opportunities to gain knowledge about favorable outcomes but not unfavorable ones…We choose ignorance about future undesirable outcomes more often than desirable ones.”

In “Evidence Accumulation is Biased by Motivation”, Gesiarz et al reach a similar conclusion: People tend to gather information before making judgments. As information is often unlimited a decision has to be made as to when the data is sufficient to reach a conclusion. Here, we show that the decision to stop gathering data is influenced by whether the data points towards a desirable or undesirable conclusion…The motivation to hold a certain belief decreases the need for supporting evidence.”

At this point, many people reading this will be nodding their head in painful recognition of the authors’ conclusion.

Who has not at some point found themselves in a meeting where a surprising result or new piece of information was either dismissed as an anomaly not worth exploring, or when the potential implications of the surprise created so much cognitive dissonance (and/or political risk for some people in the room) that they were dismissed as implausible (which is not the same as impossible).

These situations always bring to mind the conclusion reached by a 1983 CIA study of failed forecasts: "Each involved historical discontinuity, and, in the early stages…unlikely outcomes. The basic problem was…situations in which trend continuity and precedent were of marginal, if not counterproductive value” (“Report on the Study of Intelligence Judgments Preceding Significant Historical Failures”).

That is why being alert to surprises, and committed to discovering the meaning of the high value information they contain, is one of the hallmarks of high reliability organizations.

These new research findings provide different lenses we can use to better understand the initial reactions we observed to the increasing flow of news about the appearance of a new coronavirus in Wuhan, and then its exponential spread around the globe.

For many (perhaps most) people, their initial assessment of early news items about a novel coronavirus was strongly affected by motivated cognition, and a desire to avoid collecting information about the potential negative consequences of the new virus. This would have particularly been the case if they held strong prior views based on memories that the successful containment of both the 2003 SARS coronavirus outbreak and the 2012 MERS coronavirus outbreak.

But between January 23rd (when Wuhan was locked down) and March 8th (when the quarantine of Lombardy was announced), people around the world experienced a “Stimulus Surprise” which initially produced a sharp spike in uncertainty and anxiety, leading to increased social copying like the panic buying of toilet paper, masks, hand sanitizer, and other supplies.

This was followed by the still ongoing “Bayesian Surprise” phase, in which people have struggled to sort through an exponentially increasing flood of information (of varying value and credibility), to gain some measure of situation awareness as the first step in formulating expectations about possible future scenarios and designing a “go forward” plan.

For some (perhaps most) people, this process was undoubtedly complicated by the cognitive bias Daniel Kahneman has called “What You See Is All There Is” or WYSIATI. This refers to our tendency to reach quick intuitive judgments using a narrative constructed solely on the basis of information that is in front of us, and rather than a more deliberate approach that takes into account a wider range of unresolved uncertainties and the different future scenarios they imply.

For other people, this sensemaking process has been a more systematic struggle to develop a better understanding of the complex trends and uncertainties driving the evolving COVID-19 situation, their relationships to each other, and the potential future outcomes they could create.

Regardless of the approach used, reducing the unprecedented level of uncertainty created by COVID-19 continues to be a slow process, with progress only made at a high mental and emotional cost as we work through the surprises we have experienced.

A Damning New Report on the State of Risk Oversight Before COVID19

The AICPA and the Poole College of Management at North Carolina State University have just released their 2020 "State of Risk Oversight" report, based on data collected from over 500 organizations in the fall of 2019.

It paints a damning picture of the situation that prevailed just before the arrival of the COVID19 pandemic.

Here are some of the report's key findings:
  • "Most respondents believe the volume and complexity of risks is increasing extensively over time."
  • "Respondents noted that a number of external parties were pressuring senior executives for more extensive information about risks."
  • "Strong risk management processes are becoming an expected best practice."
  • "Few executives described their organization's risk management process as mature."
  • "Only about half of respondent organizations engage in formal risk identification and risk assessment processes."
  • "The process used to generate risk reports to the board is often ad hoc."
  • "Only 24% of the organizations’ board of directors substantively discuss top risk exposures in a formal manner when they discuss the organization’s strategic plan."
  • "Most boards of large organizations (84%) or public companies (91%) discuss written reports about top risks at least annually; however, just 60% of those describe the underlying risk management process as systematic or repeatable."
  • "Less than 20% of organizations view their risk management process as providing important strategic advantage."
  • "Many executive teams and boards of directors are now realizing the implications of being ill-prepared to manage the multitude of enterprise-wide risks triggered by such a large scale root cause event of the magnitude of the evolving COVID-19 crisis."


The Lessons of a Global Existential Threat

Over the last several years in our research, writing and consulting work on Strategic Risk Governance and Management, we have been seeking to define the measures that organisations can take to improve their competence and capability to anticipate, appropriately assess and adapt to or mitigate the consequences of existential threats rising from uncertainty.

Many of the lessons observations and conclusions that we have written and spoken about are now being demonstrated with brutal effect by the Covid-19 (SARS-CoV-2 coronavirus) pandemic.

The characteristics of complex adaptive systems

The globally connected technological, economic, social and political environment we all inhabit is a vast Complex Adaptive System (CAS). This matters because many of the assumptions that governments, businesses and individuals make about the future assume relative stability with variations about quite stable means, that is a normal distribution of variation. The reality in a CAS world does not confirm to these assumptions. CAS exhibit dramatic shifts from apparent stability to near chaos, caused by physical, informational and emotional feedback loops. CAS exhibit exponential not linear changes or growth patterns. The occurrence of “long-tail” or extreme and (by most) unexpected events, is a feature of this global CAS.

The Covid-19 outbreak has perfectly demonstrated all of these characteristics. The shift from an apparently stable economic order to a shuddering halt to global economic activity has occurred within weeks. Within the business community organizations both very large – e.g. car makers, aerospace and airlines – and very small – local stores, the self-employed - have been affected.

The rates of infection among populations where the virus has spread all show exponential patterns of growth; infection rates typically doubling every three days: 1 infected victim can become 1,000 in a month, a million in two, left unchecked. Such exponential rate of change is beyond the experience or capability of the typical bureaucracy grappling with a response.

The very interconnectedness of the global economy has produced dramatic ripple effects across geographies and sectors even before the virus hit locally. In one example, shortly after the outbreak was announced in China, a retail outlet complex in the UK, Bicester Village reported an 85% fall in revenues. Why? Because it was a favourite for Chinese tourists who were no longer travelling. The knock on effects included the train operator whose station announcements in Mandarin, to help the visitors, became redundant at about the same time as their passenger numbers plummeted on the line between London and Bicester.

The need for purposeful anticipation

The characteristics of CAS make forecasting very difficult, but the consequences in terms of Strategic Risk and existential threat mean that they cannot be ignored. Indeed, a CAS environment increases the need to purposefully anticipate such threats. We often refer to this as a requirement for “Searching the Realm of Ignorance”. That is, trying to answer the question: what are the plausible causal narratives that could lead to our (corporate) extinction?

Another way of looking at this process is to consider how to improve strategic warning and avoid harmful surprise. History shows us that warnings are frequently given and just about as frequently ignored. Paying heed to “credible Cassandras” is one way of avoiding becoming blind to a potential existential threat.

Again, the Covid-19 outbreak is illustrative. The Centre for the Study of Existential Risk, at Cambridge University, which comfortably satisfies the criteria for being a “credible Cassandra”, has, for some years, identified a global pandemic as one of the top five existential threats humanity faces*. But even if this was too generic an alarm to be heeded, other warnings were raised.

In its November 2012 analysis of alternative future scenarios (Global Trends 2030: Alternative Worlds), the US National Intelligence Council wrote: “An easily transmissible novel respiratory pathogen that kills or incapacitates more than one percent of its victims is among the most disruptive events possible. Unlike other disruptive global events, such an outbreak would result in a global pandemic that directly causes suffering and death in every corner of the world, probably in less than six months.”

In the UK, professor of global public health, Devi Sridhar, described a similarly prescient scenario as recently as 2018. Professor Sridhar addressing a conference stated that “The largest threat to the UK population is someone in China being infected from an animal. Then they get on a plane to the UK”.

Health authorities in the US and the UK explicitly recognised the scenario of a global, flu like pandemic. In October 2016, epidemiologists from Imperial College London told Government ministers what Britain would look like seven weeks into a pandemic. “Exercise Cygnus” showed the NHS unable to cope, with a lack of personal protective equipment (PPE) for doctors and nurses, inadequate numbers of ventilators and mortuaries overflowing. (This report was never published and, as far as we know, no action was taken on its findings.)

In the US, “Crimson Contagion”, a classified exercise led by the US Department of Health and Human Services in 2019, tested the capacity of the U.S. federal government and twelve U.S. states to respond to a severe influenza pandemic originating in China. The conclusion of this exercise outlined the US government's limited capacity to respond to a pandemic, with federal agencies lacking the funds, coordination, and resources to facilitate an effective response to the virus. It predicted that in less than two months a virus could infect 110 million Americans, killing more than half a million. There had also been clear warnings in the US from other sources in 2017, 2018 and another in 2019**.

Thus, it can be argued that sufficient warning of the Covid-19 pandemic or something very similar to it existed. Yet even without warnings as specific as those above the occurrence of a global pandemic was not something immune to anticipation. Being aware of “base rates” of events that can represent Strategic Risk is high-value information in terms of avoiding damaging strategic surprise. In fact, the “base rate” of occurrence of a major global pandemic is around 20-30 years***. The last major outbreak of a high-contagion, high-mortality rate coronavirus, SARS-1, was at the turn of the century. In 2016 the WHO identified this coronavirus as a likely cause of a future epidemic. We know that viruses are prone to mutation. It should not have been a surprise that a new form of dangerous coronavirus, of animal origins (the virus causing the Covid-19 outbreak is called SARS-CoV-2), represented an existential threat.

Even without this plethora of warning and base rate data, governments around the world might have reflected on the number of viruses in birds and animals that have the potential to cross the species barrier, which may be as high as 800,000. according to one estimate****.

In other words, no one should really be surprised at the Covid-19 pandemic. Yet, palpably governments and businesses around the world have been. Some of the blindness to this threat may become explicable by understanding the paradox of surprise: The greater the risk, the less likely it seems, and the less risky it becomes. Human cognition treats rarer events as less and less realistic, eventually to the point of becoming persuaded that such events are, in practice, impossible.

Countering the cognitive biases that influence decision making by policy makers and business leaders about Strategic Risk and existential threat is not a matter that can be left to happenstance. It requires processes that are explicitly designed and implemented to avoid the worst anticipation failures.

The key issues in assessment

It’s possible that some governments or businesses did accurately anticipate the threat from a global pandemic. If so, it is highly unlikely that they assessed the threat appropriately. The UK government’s panel of scientific advisors decided that the risk from Covid-19 to the UK was “moderate” as recently as 21February (source: The Times). It is not known how the government was supposed to use this information to guide its policy or mitigation actions.

There is a practically universal dogma practiced in relation Strategic Risk and existential threat, which is to treat it as directly analogous to throwing dice. A moment’s reflection should be sufficient to realise that the “standard” risk assessment of probability times impact is wholly inappropriate. Time, not probability, is the critical factor.

Two estimates of time are relevant: the Time to Event Threshold (TE), or how long will it be before calamity strikes and the Time to Implement (or first define and then implement) adaptation or mitigation options (TM). If the first of these is less than the second then the probability of occurrence is of no interest or value to decision makers, because if the threat materialises there is insufficient time to mitigate. Some of the advisors who decided that the risk from Covid-19 was “Moderate”, were the same who predicted that the NHS would be overwhelmed in seven weeks; one wonders at what point they were contemplating raising their assessment, if ever. If TE is genuinely greater than TM then there exists a Safety Margin – so long as nothing changes!

This concept of the Safety Margin is the really critical and hugely overlooked assessment objective for Strategic Risk. Again, the Covid-19 pandemic starkly illustrates the consequences of assessment failure. The current policy of most Western governments in bringing their economies to a dead stop is with the explicit intention of slowing the disease spread to allow time for mitigation measures to be ready, i.e. for health care systems not to be swamped with victims, having sufficient beds, equipment and trained (and healthy) personnel to be able to cope as the number of critically ill victims reaches its peak. In other words, these governments are striving to get TM below TE ; to get the Safety Margin positive.

It is hard to imagine a clearer or more dramatic example of the criticality of understanding the dynamics of Time to Event Threshold compared with Time to Implement Mitigation, in other words, the critical importance of understanding Safety Margin dynamics. It’s worth noting that, however thoroughly analysed (and most are not) assessments of probability done once a year (or less frequently) are of limited value anyway before an existential threat materialises (fancy betting on imminent death anyone?) and no value whatsoever if the threat materialises in fact. The Safety Margin on the other hand, if properly analysed and appropriately used, can be of great value before and after a threat materialises and throughout mitigation activity.

Basic capabilities for adaptation

Even if Covid-19 or a similarly dangerous outbreak had been successfully anticipated and appropriately assessed; failures in adaptation could still have undermined all the good work. The first task is, as a result of properly assessing the Strategic Risk, to be monitoring high-value, leading warning indicators of the potential threat becoming an actual threat. In the case of a global pandemic, the potential threat should have been clear. It is less clear whether the indicators of a new outbreak were being heeded. It might be argued that there were no leading indicators in the case of Covid-19. Yet somehow the government of Taiwan was able to take mitigation action much, much sooner than most other countries. We can speculate as to why, but a possible explanation is that, following the SARS-1 outbreak of 2002-3, Taiwan had both anticipated the threat of a new outbreak in mainland China, and was actively monitoring reports from there. As early as December 2019, Taiwanese health officials were boarding flights from Wuhan to check the passengers for symptoms (source: The Economist). This exercise itself would have served as a key warning indicator.

Another feature of Taiwan’s approach was to have developed strategic options or possible mitigation actions in advance – e.g. knowing how to rapidly increase availability of both ITU beds and have reserves of trained personnel; having put in place alternative sources of supply of ventilators before being overwhelmed by demand. Taiwan was able to very rapidly ramp up testing because they were ready to do just that. They had also put in place an organisational response, the Central Epidemic Command Centre (CECC), usually dormant, which was ready to be activated when the warning signals were flashing red. Contrast this with the scramble of the UK government (among others across Europe) to substantially increase the supply of critical equipment several months after the outbreak was undeniable.

Once a Strategic Risk has materialised as actual threat (or the assessed Safety Margin approaches zero), the ability to rapidly and effectively implement mitigation is key to survival – whether corporate or citizen. Taiwan, a country of 24m people has reported just 2 deaths from Covid-19. Contrast this with London, a city of c.9m on the other side of the world from the source of the outbreak which has to date ***** reported 246 deaths.

Successful adaptation in the face of the threat from Strategic Risk may rely on several factors but one of the most important is resilience: how fast can an organization or society respond to a negative event. This will be of acute relevance for national economies following the halting of activity in response to Covid-19. Resilience is a function of several interacting factors, including maintenance of resource reserves, organizational problem detection and problem solving capabilities, structural choices (e.g. incident management teams). For example, Taiwan’s effective response owes something to the establishment of the CECC.

Clearly, building resilience takes resources. For example, maintaining reserves of ventilators in anticipation of an outbreak of a deadly, flu-like respiratory disease would have required investment. Investing in adaptation in the face of Strategic Risk always will be seen as diverting resources from investment in success. Yet it is relevant to ask whether the cost of maintaining a national (or international) security stock of ventilators have been greater than the havoc now being wrought on economies around the world?

Such decisions are not trivial. Yet they are not even available as an option unless decision makers have successfully and appropriately anticipated, assessed and adapted to Strategic Risk. That will remain the harsh lesson from Covid-19. Let us hope that, this time, it will be learned.

* see for the other four!
** “Before Trump’s inauguration, a warning”, Politico 16Mar20; Foreign Policy, 28Sep18, Lisa Monaco; “Event201” run by the Johns Hopkins Center for Health Security, the Bill and Melinda Gates Foundation, and the World Economic Forum
*** Since 1900 there have been six flu-like global pandemics, including Covid-19, excluding H5N1 Bird Flu and other non-flu contagious and mortal diseases such as Ebola.
**** Ed Yong, The Atlantic, July-August 2018
***** 27th March 2020.

Strategic Risk Failure: Why Did Markets Take So Long to React to COVID-19?

This blog post was originally published on The Index Investor, the macro research and forecasting affiliate of Britten Coyne Partners.

On 31 December, 2019, the S&P 500 closed at 3,230.78. That same day, the government in Wuhan, China, confirmed that health authorities were treating multiple cases of pneumonia.

On 6 January 2020, the Wall Street Journal reported that, in China, “medical authorities are racing to identify the cause of a mystery viral pneumonia that has infected 59 people in central China, seven of whom are in critical condition, and triggered health alerts in Hong Kong and Singapore.” The next day, the Financial Times reported that “health authorities are working to identify the outbreak of viral pneumonia that has infected at least 59 people in Wuhan [China]. Officials have ruled out Severe Acute Respiratory Syndrome, Middle East Respiratory Syndrome and certain types of flu.”

On 8 January, the Financial Times (FT) reported that, “The world is already grappling with its first emerging disease of the decade. Dozens of people in Wuhan, a city in central China, have been hit by an unexplained pneumonia. There are no recorded deaths but, among 59 who have fallen sick, seven are reported to be in a critical condition with breathing difficulties. The authorities have ruled out seasonal flu, bird flu, severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS). Singapore and Hong Kong are now screening air passengers for fever. The outbreak, which began in December, has been traced back to a market selling seafood and live animals such as bats and marmots. It has since been closed and disinfected. The possibility that yet another malign microorganism has hurdled the species barrier to infect humans is likely to boost calls for a global catalogue of animal pathogens.”

On 9 January, the FT reported that, “A pneumonia outbreak that has infected more than 50 people in the Chinese city of Wuhan was caused by a coronavirus, which is the same kind of pathogen involved in the deadly SARS outbreak in 2003, Chinese state media said on Thursday. The outbreak, which comes ahead of the lunar new year holidays in late January when millions of Chinese will be travelling to see their families, has caused alarm in the region. The virus has prompted widespread concern on Chinese social media and triggered memories of the 2003 outbreak of severe acute respiratory syndrome, or SARS, that infected more than 8,000 people worldwide and killed more than 700, including almost 300 in Hong Kong. The World Health Organization said, in a statement issued on Thursday, the Chinese authorities believed the disease ‘does not transmit readily between people’, but noted that it could cause severe illness in some patients.”

On 15 January, the FT ran a story headlined, “How Dangerous is China’s Latest Viral Outbreak?” This was its first paragraph: “An outbreak of a new kind of viral disease in China has led to widespread concern about the risks involved and fears of an official cover-up. A 2002-03 outbreak of severe acute respiratory syndrome (SARS) killed more than 800 people after Chinese officials covered up new cases for months, greatly worsening its spread. That has raised questions over Beijing’s handling of the latest outbreak that began in Wuhan, capital of Hubei province.”

On 14 January, there was a Democratic Candidate Debate.

On 16 January, Donald Trump’s impeachment trial began.

On 18 January, in a story headlined “Scientists Warn Over China Virus Outbreak”, the FT wrote that, “Concerns are rising over an outbreak of a virus that originated in China as leading scientists suggested that more than 1,700 people may already have been infected, far more than had been thought. Chinese health authorities said this weekend that they had discovered 21 more suspected cases in the central city of Wuhan, bringing the total number of suspected cases of the pneumonia illness in the city up to 62. But experts warn that there is significant uncertainty about the severity and spread of the illness, which has already killed two people and evoked memories of the SARS outbreak that killed hundreds of people more than 15 years ago. A study by the respected MRC Centre for Global Infectious Disease Analysis concluded that a total of 1,723 people in Wuhan City would have had onset of symptoms by January 12, the last reported onset date of any case. Neil Ferguson, a public health expert from Imperial College London, who founded the centre, told the BBC he was “substantially more concerned than I was a week ago”.

Later in the same story, it was noted that, “on Sunday, Li Gang, director of the Wuhan Center for Disease Control and Prevention, told state broadcaster CCTV that the information available ‘does not rule out the possibility of limited human-to-human transmission.’ ‘The infectivity of the new coronavirus is not strong,’ he added, referring to how rapidly the virus may spread between individuals. ‘The risk of continuous human-to-human transmission is low’. Most patients have presented relatively mild symptoms, Mr. Li said, and no cases had been found in more than 700 people who came into close contact with infected patients.”

On 20 January, the FT reported that, “China Confirms Human-to-Human Transmission of SARS-like Virus”.

On 21 January, the World Health Organization issued its first Situation Report on the “Novel Coronavirus”. The same day, the FT reported that Asian stocks had fallen after Beijing confirmed human-to-human transmission.

On 22 January, the US confirmed its first case, a patent in Washington State who had returned from Wuhan. On the same day, the FT ran a story headlined, “How China’s Slow Response Aided Coronavirus Outbreak.”

On 23 January, Chinese authorities began their quarantine of Wuhan.

On 27 January, The Index Investor posted its first multipart tweet (Twitter: @indexllc) about the new coronavirus, including initial estimates of its Case Fatality Rate, and key uncertainties for investors to monitor. Between then and today (16 March) Index has posted 11 more (often multipart) tweets, covering new high value information about the virus focused on reducing the range of outcomes for critical uncertainties to improve forecast accuracy.

On 30 January, the WHO declared a global health emergency.

On 31 January, the US restricted travel to China. That evening, the UK officially left the European Union.

3 February: Iowa Democratic Caucuses. That same day, The Index Investor tweeted, "Until the uncertainty surrounding asymptomatic transmission of coronavirus is resolved with more evidence, expect travel bans and other isolation measures to continue, as a prudent policy reaction at this stage."

On 4 February, The Index Investor tweeted the key conclusion from a new Lancet article: "Independent and self-sustaining outbreaks in major cities globally could become inevitable because of substantial exportation of presymptomatic cases and the absence of large-scale public health interventions."

On 5 February, Donald Trump was acquitted at the end of his impeachment trial. That same day, the Diamond Princess cruise ship was quarantined in Japan with 3,600 passengers on board.

On 7 February, there was another Democratic Candidate Debate. Earlier that day, in China, Li Wenliang, the Wuhan doctor who tried to raise the alarm about the new coronavirus (and was accused by the police of “rumormongering”) died after contracting it.

11 February: New Hampshire Democratic Primary.

On 12 February, Dr. Nancy Messonnier, Director of the US Center for Disease Control’s Respiratory Disease program, noted on a press conference call that, “the goal of the measures we have taken to date are to slow the introduction and impact of this disease in the United States but at some point, we are likely to see community spread in the U.S.”

On 18 February, in our new issue of The Index Investor, we wrote, “The Wuhan coronavirus will almost certainly depress global economic growth, by an amount that is highly uncertain at this point. Global aggregate demand has already been weakening. A worsening slowdown (or growth turning negative) will very likely be reinforced by mounting debt servicing problems in our highly leveraged global economy.”

On 19 February, the S&P 500 closed at 3,386.15, thus far the 2020 high. That night there was a Democratic Candidate Debate, the first one to include Michael Bloomberg.

On 20 February, the FT’s Gillian Tett titled her column, “Share Prices Look Sky High Amid Coronavirus Fears.”

On 23 February Italian authorities limited travel to 10 towns in the Lombardy region after a sudden increase in coronavirus cases.

On 25 February, Larry Kudlow, Director of the National Economic Council, said, “We have contained this. I won’t say [it’s] airtight, but it’s pretty close to airtight”. He added that, while the outbreak is a “human tragedy,” it will likely not be an “economic tragedy.” That night, there was another Democratic Candidate Debate.

At a 26 February press conference, president Trump said that, the current number of COVID-19 cases in the U.S. is “going very substantially down, not up.” He also claimed that “the U.S. is “rapidly developing a vaccine” for COVID-19 and “will essentially have a flu shot for this in a fairly quick manner.”

3 March: Super Tuesday Democratic Primaries.

On 15 March, there was another Democratic Candidate Debate, this time just between Joe Biden and Bernie Sanders.

On 16 March, after multiple trading stops, the S&P 500 closed at 2,386.13, down 29.5% from its February peak.

In the future, many people will ask the same question the Queen asked in the aftermath of the 2008 global financial crisis: “Why didn’t anyone see this coming?”

Our preliminary answer to that question is that it is very likely that multiple interacting factors were at work, including the following:

• We naturally resist the cognitive dissonance that is produced by evidence that severely contradicts our current system of interrelated and mutually supporting beliefs. One aspect of this is our tendency to avoid information that challenges our existing beliefs in a negative way, and to give more attention to evidence that supports our existing views (see “How People Decide What They Want to Know” by Sharot and Sunstein). Another is the way we subconsciously try to fit new discordant evidence into our existing world view by subtly adjusting our beliefs to incorporate it. As Daniel Kahneman noted in his book, “Thinking Fast and Slow”, it is only when this automatic adjustment fails that we note our feeling of surprise at a new piece of information and consciously reason about its potential significance.

• But as Tali Sharot’s research has repeatedly found, even our conscious reasoning is flawed because we often fail to fully incorporate negative information into our beliefs and our memories. Sharot finds that this is the root cause of our natural over-optimism bias.

• Another factor is that as uncertainty increases, so too does our natural human desire to conform to the views and behavior of our group, and to engage in more social learning (i.e., copying). In our evolutionary past this was undoubtedly adaptive; today it is not. When uncertainty increases, the number of diversity of narratives about the future by different members of a population tends to decline, making the system of beliefs more fragile, and susceptible to changes that are both sudden and large.

• As uncertainty increases, people are also more willing to discount conclusions based on their private information when those conclusions disagree with the dominant view in their group. This can allow an increasingly dangerous state to persist for long periods of time, until a strong public signal that is consistent with group members’ private information causes them to quickly and substantially update their beliefs all at once. However, if public signals are confusing or contradictory, the dominant narrative can remain in place, despite accumulating evidence that it is wrong.

• Another relevant shortcoming of human reasoning is our tendency to form beliefs by unconsciously matching the features of a current situation to similar ones that are stored in our memory (this is sometimes called “Retrieved Context Theory”). Our strongest memories are those formed by events that triggered high levels of emotional arousal and negative feelings (or “valence”). In this case, many people may have initially associated early reports about COVID-19 with vague memories of the relatively benign way the SARS epidemic played out in 2003. In the future, early reports of new respiratory viruses are almost certain to be associated with people’s much more negative COVID-19 memories.

• Another factor that may have contributed to assessment failure is that most people struggle to grasp the future implications of processes characterized by time delays and non-linearity, such as those that underlie infectious disease dynamics.

• It is also the case that when our mental energy has been depleted, we exhibit poor control over the allocation of our attention, and are therefore likely to miss important signals. In this regard, the early development of the Wuhan coronavirus crisis coincided with the Trump impeachment trial, multiple Democratic Candidate Debates, the Iowa Democratic primary caucuses, and the New Hampshire Democratic primary.

• Finally, in October 2019, the Economist estimated that 35% of public equities are now managed using quantitative processes. This presents a number of fundamental challenges when disruptive changes like the arrival of COVID-19 roil the financial markets. Most of these processes cannot detect, early on, changes that are not contained in the set of historical data on which they were trained, and/or which have not yet manifested in the signals they track (e.g., changes in sentiment or momentum). Because they are far better at causal and counterfactual reasoning (which quantitative techniques still can’t handle), human beings are still far more effective at making sense of the uncertainties that are inherent in highly complex, evolving systems like global macro.

Since 1997, the mission of The Index Investor has been to help investors, corporate, and government leaders to better anticipate, more accurately assess, and adapt in time to emerging macro threats. This mission provides a framework for answering the question, “Why didn’t anyone see this coming?”

It wasn’t due to a failure of anticipation. Over the years, multiple risk analyses and simulations have considered the potential impact of pandemics. For example, in its most recent analysis of alternative future scenarios (“Global Trends 2030: Alternative Worlds”), the US National Intelligence Council wrote this: “An easily transmissible novel respiratory pathogen that kills or incapacitates more than one percent of its victims is among the most disruptive events possible. Unlike other disruptive global events, such an outbreak would result in a global pandemic that directly causes suffering and death in every corner of the world, probably in less than six months.” We also included the possibility of a global pandemic in our January 2020 feature article, “Global Macro Risk Dynamics in the 2020s and Beyond.”

At The Index Investor, we have written about the risks posed by pandemic influenza many times since 1997, and have a substantial amount of information about this threat in the free research library on our website.

In sum, with respect to anticipation failure, COVID-19 was not a black swan.

Failure to accurately assess the threat posed by the Wuhan coronavirus after it appeared is a far better explanation for the human, economic, and financial pain and losses that many individuals and organizations have suffered.

Many of the factors that contributed to this failure are listed above. In the future, more will be discovered and discussed. As we noted, many of them are deeply rooted in human nature.

One of the most important lessons learned over the years at Britten Coyne Partners (The Index Investor’s strategic risk governance and management consulting affiliate) is that simply increasing awareness of them usually doesn’t reduce their impact. A far more effective approach is the consistent use of organizational processes that are designed to offset their potential negative impact. Such processes include, for example, the use of pre-mortem analyses, warning indicators, reference cases, and the systematic search for evidence that contradicts current beliefs.

In some cases, failure to adapt in time to a threat that was anticipated and accurately assessed must also have played a role, as it has so many times in history.

In some instances, this failure might have been rooted in system design (e.g., quantitative strategies and robo-advisors that were constrained to remain fully invested, and unable to shift into cash, or exceed certain allocation limits to less risky asset classes).

However, we suspect that the incentives faced by decision makers played a far more important role, as it always does in cases of strategic failure. When managers are evaluated and compensated on the basis of beating the performance of a benchmark index, there is a very strong incentive to avoid selling too soon, even as they see downside risks increasing. This was famously summed up in former Citibank CEO Chuck Prince’s quote from July 2007: “As long as the music is playing, you've got to get up and dance. We're still dancing.

Moreover, as organizations grow larger and become more concerned with efficiently scaling a successful business model, their culture usually evolves to one that penalizes errors of commission (“false alarms”) more heavily than errors of omission (“missed alarms”). In the face of disruptive changes in their external environment, this can be deadly.

A final cause of failure to adapt to emerging threats lies in the frameworks we usually use to think about and discuss them. Modern risk assessment has its roots in quantitative tools used by actuaries to estimate the probability and potential impact of discrete events (e.g., prices falling below a put option’s strike price, or the number and severity of hurricane related insurance losses in 2020).

What this framework leaves out is often critical. In a world of evolving uncertainty and novel threats, time dynamics are critical. The key concept is the changing relationship between how much time remains before an emerging risk reaches a critical threshold (e.g., when does it become a dangerous threat), and how much time is still needed before an adequate response to it can be developed and implemented. Boards, management teams, and individuals that closely monitor changes in gap (what Britten Coyne calls the “safety margin”) have a much better chance of adapting in time.

Strategic failure is a complex phenomenon that is always rooted in some combination of interacting individual and organizational failures to anticipate, assess, and adapt to emerging threats. COVID-19 is just the latest example of this process.