Black Swans, biases and organisational resilience

Black Swans, biases and organisational resilience

This article highlights the Black Swan concept, identifies a small selection of potential low probability/high impacts events that could cause large (and different) disruptions to policing in future, and suggests that ‘wargaming’ these unlikely scenarios could improve organisational resilience.

Idea in Brief 

2020 has highlighted the threat that so-called ‘Black Swan’ events can pose to business continuity planning, and the need to increase efforts to account for more low probability/high impact events that might challenge organisational resilience. This Critical Perspective highlights the Black Swan concept, identifies a small selection of potential low probability/high impacts events that could cause large (and different) disruptions to policing in future, and suggests that ‘wargaming’ these unlikely scenarios could improve organisational resilience. The value of this article is that it identifies a selection of biases that can blind us to future disruptions and impede our decision making. 

Strategic Considerations

• Are police in Australia and New Zealand ‘learning the lessons’ from COVID-19 to prepare for the next pandemic or improve practices into the future?
• To what extent are current business continuity plans in policing able to account for a wide variety of possible future disruptions?
• Could cross-jurisdictional ‘wargaming’ of various scenarios assist in building organisational resilience?
• Do police embed strategies to mitigate the impact of and management of potential cognitive biases? What, if any, new training might be required? 

Introduction

COVID-19 has created disruptions through global and domestic socio-economic systems, and in many ways, we’re running out of terms to describe the shock – the use of the term ‘unprecedented’ has itself been unprecedented!

Nevertheless, COVID-19 has shown that large scale disruptions can and do occur, and that such shocks force rapid re-orientations by police, and significant re-assessment of business continuity planning.

In some circles, there have been debates as to whether COVID-19 constituted a ‘Black Swan’ or not – a term coined by the author Nassim Nicholas Taleb to describe outlier events that lie outside the bounds of normal expectations.

Taleb argues that our collective track record at predicting Black Swans is dismal, yet we think we understand them due to hindsight bias – the tendency that most (if not all) people have to overestimate their own ability to have foreseen a low probability event occurring, having learnt that the event actually occurred.¹ Taleb also argues that humans’ instinctive propensity to flee from tigers and other predators suggests that our risk-avoidance mechanism is governed much more by the emotional part of our brains than the cognitive parts.² So how then should we engage with low probability/high impact events like Black Swans if we seem to be predisposed to mis-assess them?

This question has led other thinkers to expand on Taleb’s Black Swan concept, given than many emerging threats seems to be influenced by our cognitive biases. This Critical Perspective summarises the variants of the Black Swan concept and identifies a selection of occurrences that police could have to contend with in future.

Given that preparing for one specific crisis may assist in preparing for unforeseen crises, this article is designed to support risk assessment and business continuity planning processes within Australian and New Zealand police.

Blacks swans and variants

Different low probability/high impact events are typically described along the following lines, with some authors, such as the Singapore Government Centre for Strategic Foresight³ and others⁴, assigning the following examples.

• Black Swans - Highly disruptive events that are largely unpredictable. E.g. World War 1, Sept 11 attacks.

• Grey Swans - Highly disruptive events that are predictable to some degree. E.g. World War II, Super solar flares (see below).

• White Swans - Highly disruptive events than are mostly or entirely predictable. E.g. Extreme weather events, Global pandemics.

• Red Swans – Foreseen events that turn out to have low or no impacta (i.e. Red swans are ‘red herrings’). E.g. Y2K bug.

• Grey Rhinos (or Dirty-Grey Swans) – Obvious, disruptive events that are neglected due to cognitive biases or willful disregard. E.g. Climate change.

• Dragon Kings – Extreme disruptions produced by escalating events within dynamic or unstable systems. Dragon Kings produce different results to similar events occurring at smaller scales⁵. E.g. the Fukushima nuclear disaster.

Defining events as one of the above variants often depends on the observer, not the event itself.⁶ For example, COVID-19 could be thought of as a Black Swan, Grey Swan, White Swan, or Grey Rhino depending on how familiar an observer was with epidemiologists’ warnings over the years, how seriously warning were taken, and the extent to which they were included in organisational planning processes.

It stands to reason that a significant factor in preparing for the future is creatively identifying potential future disruptive events (effectively shifting Black Swans to Grey or White Swans) and ‘wargaming’ potential responses. While imagined future disruptions may not actually come to pass, the key point is that (even mentally) preparing for such events can contribute to improved organisational resilience.

For example, as explored in the below, preparing for the highly disruptive solar storm may also assist in preparing for other Black Swans events that might shut down communication technologies, e.g. large-scale cyberattacks (and vice versa).

Also important is deliberately evaluating and learning from previous or recent disruptions. Opportunities to improve organisational resilience to future crises can stem from assessing whether the lessons learned from those crises are transferrable, or whether the responses undertaken suggest new approaches should be normalised.

Example low probability/high impact events

Super Solar Flares

Constitutional reforms

• The passing of Queen Elizabeth II could substantially reinvigorate Australian and New Zealand republican movements, potentially leading to successful referenda on independence and significant constitutional reforms.
• Key challenge – Are police well positioned to influence the public and government leaders on how police might need to be conserved, or reimagined?
• Events with similar effects – Escalation of the ‘Defund the police’ movement in Australia and New Zealand.

Rapid acceleration of climate change

• The environmental impacts of climate change could be grossly underestimated, leading to far more severe and frequent extreme weather events, and threatened food supplies.
• Key challenge – Are police able to pivot towards a heightened focus on emergency management and disruptions to population movements?
• Events with similar effects – Another global pandemic.

The sudden birth of Quantum Computing

• Private sector or government researchers could develop true quantum computing well ahead of expectations, rendering most encryption useless and threatening global information security.
• Key challenge – Are police able to preserve sensitive information when digital security cannot be trusted?
• Events with similar effects – Significant cyberattacks on police.

Cognitive biases and decision-making

As described above, disruptive ‘Swan events’ can often surprise organisations as they are deemed to (or assumed to) have a low probability of occurring, but actually occur. While chance may play a role in this, of concern is the natural human bias to blind oneself to the Grey Rhino events heading straight for us.

Below is a small selection of the cognitive biases that have been identified to affect cognitive thought¹⁴. While many/most of these biases can be overcome through processes or methodological means, most are resistant to change. Simply giving the biases a name can assist, as naming enables individuals to reflect on their own experiences based on those biases - wilfully or not.

• Confirmation bias – we actively seek out information that supports our existing beliefs. Given emerging change often occurs first on the fringes of societies and economies, confirmation bias blinds us to such changes, or encourages us to rationalise them. This bias is arguably the most important or influential bias we experience, it paves the way for us to become more susceptible to other social issues like echo chambers and vulnerability to misinformation.

• Group think – we allow social dynamics to override sound decision-making. Individuals in group settings may not want to appear contrarian by challenging the accepted wisdom (or superiors). In a black swan or futures contexts, group think can preclude a group from considering change emerging in unexpected areas. A related bias is ‘in-group bias’, the tendency to unfairly favour those who belong to a common social group.

• Declinism - we tend to see the past as better than it was, and the future as worse than it is likely to be. Commonly referred to as ‘rose-coloured glasses’, declinism risks creating a self- fulfilling prophecy in the future (we fear for the future, and so the future becomes shaped by that fear). Related biases are optimism bias, pessimism bias and negativity bias – the tendency to disproportionately weight bad news over good news.

• The sunk-cost fallacy – the tendency to irrationally cling to investments already made that cannot be recovered. In a futures context, sunk-cost fallacy may prevent an organisation from taking new strategic directions in response to how the world has changed around it.

• Clustering illusion and illusory correlation – we tend to find patterns in unrelated datapoints or information. This tendency can see us jumping at shadows, and in some ways, this bias explains the apparent emergence of many conspiracy theorists.

There are many other biases that can cloud our thinking when assessing emerging disruptions and determining actions to address them. While simply recognising that we might suffer from biases is a great step towards managing its effects, other approaches to managing biases can involve:

• Avoiding critical decisions when hungry, angry, tired or alone.¹⁵
• Reflecting on one’s own history of decision-making¹⁶
• Considering who is impacted by the decisions being made or adopting an ‘outsiders’ perspective’.
• Actively engaging diverse perspectives into decision making processes.
• Assigning a designated ‘black hat’ or’ red team’ in group decision-making processes to respectfully challenge accepted courses of action.

At an individual level, and with respect to other people, implicit bias tests such as those run by Harvard University¹⁷ can assist in identifying what kind of people we may be unconsciously biased against. A recent study suggests that 75% of Australians are implicitly biased against Aboriginal and/or Torres Strait Islander people.¹⁸

Harvard University Implicit Association Test

Footnotes

¹  https://www.britannica.com/topic/hindsight-bias
²  https://www.edge.org/3rd_culture/taleb04/taleb_indexx.html
³  https://www.csf.gov.sg/files/media-centre/publications/csf-csc_foresight--a-glossary.pdf
⁴  https://www.axios.com/coronavirus-climate-change-risks-bc81ec96-ca03-4af7-867f-2aac2648b2d5.html
⁵  https://www.csf.gov.sg/files/media-centre/publications/csf-csc_foresight--a-glossary.pdf
⁶  https://strategiccfo.com/black-swan-events/
⁷  https://www.nationalgeographic.com/news/2011/3/110302-solar-flares-sun-storms-earth-danger-carrington-event-science/
⁸  https://astrobites.org/2020/05/08/a-different-kind-of-world-changing-disaster-another-carrington-event/
⁹  https://science.nasa.gov/science-news/science-at-nasa/2014/23jul_superstorm
¹⁰ https://www.who.int/bulletin/volumes/96/2/17-199588/en/#:~:text=In%202017%2C%20this%20range%20would,between%200.5%25%20and%201.0%25
¹¹ https://www.sciencealert.com/here-s-what-would-happen-if-solar-storm-wiped-out-technology-geomagnetic-carrington-event-coronal-mass-ejection
¹² https://www.swpc.noaa.gov/content/space-weather-faq-frequently-asked-questions
¹³ https://www.lloyds.com/media/lloyds/reports/emerging-risk-reports/solar-storm-risk-to-the-north-american-electric-grid.pdf
¹⁴ https://www.yourbias.is/anchoring
¹⁵ https://www.theguardian.com/law/2011/apr/11/judges-lenient-break
¹⁶  https://medium.com/swlh/how-to-overcome-cognitive-biases-and-make-better-decisions-daeecd38f910
¹⁷ https://implicit.harvard.edu/implicit/takeatest.html
¹⁸ https://www.abc.net.au/news/2020-06-14/implicit-association-test-indigenous-australia-negative-bias/12344930

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