The Postnormal Perfect Storm Part 2

Jordi Serra del Pino, Christopher Jones, & Liam Mayo


05 May, 2020
Micrograph of SARS-Cov-2 Virus
SARS-CoV-2 in all her glory!

The Covid Chronicles:

The Postnormal Perfect Storm - In Three Parts

Part 2: Navigating the Crisis

What do we know about Covid-19? Well, we know where it originated, how it spread, and have a rough idea of its contagion levels. We know that the spread of the virus has been matched by the spread of information – and misinformation – through social media networks, which have exacerbated levels of anxiety and clouded clarity in decision making at every level. We know that governments have made decisions to lock down communities to enact social distancing to mitigate against viral spread, whilst the business sector have stood down masses of workers and large sectors of industries have shut their doors. The global economy is in decline as markets plummet with shutdowns in manufacturing and a decline in the service industry. Healthcare workers, across the globe, are now on the front line of the gravest existential threat to humanity in decades. Suddenly global warming and social inequity are eclipsed by the global pandemic.

How do we better navigate the emergent global Covid-19 crisis? First of all, we need to assess two essential questions:

  • How much do we not know about the Covid-19 pandemic and its consequences at this point in time? And depending on the answer,
  • How are going to deal with it?
The Postnormal Creep
Postnormal Creep

As discussed in Part One, the PNT theory states that the greater the influence of the Cs (chaos, complexity, contradiction) within a phenomenon, the greater the uncertainty. Yet, this uncertainty is not one-dimensional which simply increases in size. Uncertainty grows as the Cs overlap each other in a sort of phase change, a progression that we call postnormal creep. The creep is the specific process any event follows when developing its postnormal potential, as Covid-19 is doing right now, and it has a material and a cognitive side. The more the creep advances the greater uncertainty becomes; however, we do not consider uncertainty as merely the number of questions of which we are unsure.

Surface Uncertainty
Surface Uncertainty

Uncertainty becomes a measure of our capacity to realise what is going on, not just quantitatively, but qualitatively as well – uncertainty also evolves along the creep. For instance, at the beginning we knew little of SARS-CoV-2, but many assumed it would be like any other coronavirus and did not feel particularly concerned about it. We were in surface uncertainty and most people assumed that our accumulated knowledge would carry us through the outbreak. Very soon though, we realised that the novel coronavirus was far more aggressive and lethal than we thought.

Shallow Uncertainty
Shallow Uncertainty

It behaved in unfamiliar ways and it took time for us to uncover mysteries and quirks of the SARS-CoV-2: by then we were already in shallow uncertainty. Soon enough though, some pundits began to wonder if the present crisis could truly shake the foundations of basic assumptions such as globalization or capitalism; or of institutions like the EU.

Deep Uncertainty
Deep Uncertainty

In moments like this we need to ponder if we are already in deep uncertainty. Assessing the kind of uncertainty we may be facing is just half of the task. PNT Theory also demands that we evaluate how we process that uncertainty and how we try to overcome it.

In PNT Theory, growing uncertainty mirrors ignorance. And, as with uncertainty, this concept here goes further than the mere delimitation of what it is that we do not know. Ignorance is a measure of the cognitive system we use to process uncertainty and, as such, it encompasses both what we know, and what we ignore. It is the cognitive side of the creep and it has to adjust and adapt adequately to each uncertainty. However, the point is that each area of uncertainty relates quite naturally with a particular kind of ignorance. Take surface uncertainty: although we are unsure how this will evolve,  we  have quite a good idea of the  direction it may take and what kind of impact it could have. Under this uncertainty, previous experience really helps to anticipate what may come next.

Plain Ignorance
Plain Ignorance

We can learn from past pandemics or earlier crises and gather relevant data, process useful information, and distill the knowledge to get us through the current crisis. This is plain ignorance and it is the cognitive approach we are best at: mechanisms like linear thinking, dichotomy, induction and specialization work beautifully here and give us reassurance.

However, this crisis cannot be really managed by business as usual or, more to the point, by standard procedures. For instance, many Western cities had no contingency plans for a pandemic simply because they have no memory of one. At the beginning of the pandemic, cities and provincial governments may have believed they suffered from surface uncertainty, but in fact were in shallow uncertainty territory and could not rely on past experience to be useful. The mayor of New Orleans, for example,  knew how to respond to a hurricane threat, but not to Covid-19. Shallow uncertainty requires a new approach: vincible Ignorance.

Vincible Ignorance
Vincible Ignorance

Vincible ignorance demands that we explicitly address what is unknown: nothing can be taken for granted, it is essential to weigh what we know and what we do not. Accepting the assumption that Covid-19 was like a mild flu has surely cost tens of thousands of lives. In vincible ignorance we are forced not only to acknowledge our cognitive shortcomings but to expand our awareness by integrating all accessible and available knowledge. And this is why we now realise that to fight a crisis like Covid-19 we cannot resort to medicine alone, we need to add health systems management, logistics, psychology, network management, and engineering, and, in time, we will need other things that we are not cognisant of right now. Because this is the main characteristic of vincible ignorance, we have to accept that we may lack the perspective of sufficient time to properly address the current situation. This would seem to be precisely the spot we are in with Covid-19.

Invincible Ignorance
Invincible Ignorance

We are beginning to see that the current pandemic may precipitate a deep systemic crisis that could potentially force us to undertake severe lifestyle changes. This is the deep uncertainty component in the Covid-19 event that will require us to engage in the last kind of ignorance, invincible ignorance. Invincible ignorance demands that we turn to our own epistemological structures and ask if they are hindering our comprehension of the situation. It is a kind of ignorance that requires that we examine the foundations of our worldviews to consider whether they are getting in the way of our ability to grasp the scope of the crisis and its consequences. Covid-19 seems, again, to be a perfect example of what we mean. If globalization dynamics have boosted the spread of the virus, it only makes sense to wonder how we could or should change these dynamics. If present supranational structures have failed, we need to develop new ones; if national governments cannot cope, we must improve (or get rid of) them. Yet, the key in this type of ignorance is not what we can learn from this experience but what we have to unlearn. If current capitalist logic impels us to the choice of saving people or saving the economy, then perhaps it is time to take a hard look at the extent to which the old ways were not sustainable or humane. As the imperfections of the system are laid bare, it may require our species to take a good hard look at our invincible ignorance deficits and imagine a better way.

The Normal is the Problem
From Delight Labs, Facebook 2020

Clearly, it makes sense to apply the right kind of ignorance depending on the situation. The challenge is being able to identify which level of uncertainty is being addressed and then applying the right ignorance. This is a demanding challenge. We have dedicated a significant amount of time to explore what constitutes the ‘normal’. Sardar devoted considerable space to the idea and it is clear to us that when trying to sharpen our anticipatory capabilities, normal becomes a big liability. Normalcy resists the consideration, and the wider use, of alternative futures approaches; it even restricts what is acceptable in the present. A point made well by an image, during the October 2019 riots,  from Santiago, Chile:  “We will not return to normal, because normal was the problem.”

Manufactured Normalcy Field


Our minds excel at normalizing whatever happens. It is an advantage when we have to adapt to something fast, but it becomes a hurdle when we need to open up to a range of possibilities. We use Venkatesh Rao’s notion of manufactured normalcy field (MNF) to address the impact of normalcy in our thinking. The manufactured normalcy field is the epistemological construction that reaffirms normalcy despite ontological shifts. In itself it is nothing negative. But the MNF tends to assume that resorting to conventional linear thinking and induction is the best way to deal with the ‘normal’. However, Covid-19 entails deeper layers of uncertainty that cannot be overcome with plain ignorance.

Postnormal Lag
Postnormal Lag

Maintaining a business as usual approach, in the face of emergent change, is what we refer to as the postnormal lag. The lag then is the concepts that represents the persistence in applying old recipes to new situations, while pretending that they work in spite of mounting evidence to the contrary (think Climate Change deniers!). Covid-19 has shown several examples of this: every time a government has declared that there was nothing to worry about; or when they said that their health system was more than ready to face SARS-CoV-2; when they declared that the measure that worked in one place would not work in our country; when they kept stating that the country had already reached the peak (for days and days); when they promise that their measures will keep the economy ready to go as soon as the confinement is over.

The Tilt

Of course, this is a question of failed leadership, and it is not as though this is entirely new, but it is fair to accept that some leaders really do believe that decisions they make – directions they give – are best.  But it also signals a great deal of arrogance; and we have already established that to move on from plain ignorance we need to acknowledge the voids and shortcomings in our knowledge. But then again, some countries seem to be doing a better job, even the case of China indicates that the lag can be beaten. It is quite obvious that in some places the attitude has been different- such as Hong Kong, Taiwan, South Korea, Denmark, Germany, or Andorra. Either they had the capacity to see the real potential impact of the pandemic; or, somehow, realised that business as usual would not do. Or, their data is inaccurate – and our level of ignorance is not what we assume it to be. We call this capacity to update the MNF the tilt. It means that the MNF may be altered; we could become aware of the emerging postnormal nature of a rapidly developing event or issue. No matter how compelling or pervasive the MNF, we do have the capacity to go beyond epistemological constructs and see phenomena for what they really are.

Postnormal Burst
Postnormal Burst

And like so many other things in life, creeping processes come to an endpoint. In PNT theory this moment is represented by the postnormal burst. The postnormal burst not only signals the end of creep it also forces the MNF to reset as all the accumulated uncertainty is cleared, or resolved, one way or another. The things we do not presently know, like true SARS-CoV-2 infectiousness, including its incidence and virulence; its lethality; the Covid-19 pandemic’s total effect on the economy; and, the actual impact on our lifestyles and ordinary activity. All these questions, so uncertain now, will become facts that we will be able to gather, measure and process with a mere plain ignorance approach. Even the remaining unclear fringe points will fall under surface uncertainty. Right now, the burst may seem to be a bad omen. But it may also mean that we will become fully conscious of the situation, that no lag will result in the face of the new evidence, and we will gain experience and capacity to respond to future coronaviruses or other zoonotic outbreaks.

Nevertheless, the body count of the Covid-19 pandemic makes it painfully clear that adjusting the MNF is a very difficult thing to do.

This is where the Menagerie of Postnormal Potentialities can help. It is a set of tools that help us to better understand change in PNT and aid us to weed out our thinking from the biases that may cloud our judgment. There menagerie consist of three symbolic animals.

Black Elephant
Black Elephant

The first is the black elephant. Developed from the notion of the “elephant in the room,” a black elephant, as noted by Vinay Gupta, “is an event which is extremely likely and widely predicted by experts, but people attempt to pass it off as a Black Swan when it finally happens.” In line with Gupta’s concept, Oliver Markley argued that they are “high probability and high impact as seen by experts if present trends continue, but low credibility for non-expert stakeholders…”

Black Swan
Black Swan

Next, we find the black swan. Black swans are ‘outliers’, things totally outside and way beyond our observations. Nasim Nicholas Taleb’s coined the concept and in contrast to black elephants, they appear to come ‘out of the blue.’ As Taleb notes, they are “very fragile to miscalculation, with a general severe underestimation mixed with an occasional severe overestimation.”

Black Jellyfish
Black Jellyfish

Finally, the black jellyfish, the only menagerie element of which the CPPFS claims authorship. Black jellyfish represent events and phenomenon that have the potential of going postnormal by escalating rapidly – even instantaneously. Black jellyfish have a high impact potential, but they are ‘normal’ phenomena, and this is why most of times we rule them out. However, under certain circumstances they converge and are driven toward postnormalcy by positive feedback, or exponential growth leading toward systemic instability.

A frequent question we are asked these days:  Is the Covid-19 crisis a Black Elephant, or a Black Swan or a Black Jellyfish?

There is no straight forward answer to this question. Phenomena are not one or another element of the menagerie. It is how we process them that can make us see the phenomena as one or the other. What may be perceived as a  Black Elephant in one context may be observed as a Black Jellyfish in another. Context and perceptions really matter. The function of the menagerie is to help us spot novelty and incipient change and orient our gaze towards particular events or issues that may be creeping towards postnormalcy or approach a postnormal tilt.

Covid19 was a black elephant. Many people have been forecasting potential pandemics. For example, the Millennium Project’s State of the Future reports have been including this possibility since 1997. However, given its complex and behavioural components, and its sudden emergence and rapid expansion, it can also be characterised as a black jellyfish.

Beware the Black Jellyphant!
© 2020 Travels of Adam

Perhaps Covid19 is a combination of black elephant and black Jellyfish: a black Jellyphant, as represented in the mural painting in East London by Puerto Rican artist Alexis Diaz.

We are left with a begging question: how can this crisis evolve?

We will Conclude the Postnormal Perfect Storm Next week in Part 3...


The PNT icons are copyright © cppfs 2020.


The following are cited from the blog above:

Warsal, Charlie (2020). "When Will Life Be Normal Again? We Just Don't Know," The New York Times. April 13.

GUPTA, Vinay (2009). On Black Elephants.

MARKLEY, Oliver (2011). "A New Methodology for Anticipating STEEP Surprises” in Technological Forecasting & Social Change 78 1079-1097

TALEB, Nassim Nicholas (2007). The Black Swan: The Impact of the Highly Improbable. New York: Random House


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