What is resiliency?

Resiliency is the ability to bounce back and adapt after difficulties. This term is easy to understand but the ways to achieve it are counter intuitive. To a great degree, that is due to the fact that our cultural, economic and political structures have been rewarding behaviours and traits that are the opposite: fragile. In many ways, the current state of our minds, bodies, economy and political landscape is a consequence of the use of a single metric to measure success: economic output.

Single metric focus on economic performance aligned with free market capitalism and its offshoots fosters efficiency, centralization and specialization at  any cost.  Well, among the costs is a complete lack of resilience. By seeking efficiency, redundancy is removed, by seeking specialization, flexibility is gone, by seeking centralization  errors are generalized. The minds and systems our current culture has created are highly sensitive to stress and variability. Any disruption that is slightly outside of what is easily predictable becomes a major issue with consequences that ripple to the most remote corners of the mind and the planet. 

Resiliency is the opposite of achieving a single metric. It is based on an dynamic act of balancing different aspects of reality. In order to properly understand resiliency, it is most important to understand a key phenomenon: The Black Swan Event.

What is a Black Swan Event? 

A long time ago in Rome, there was a saying used to describe something impossible or never seen: “This is as likely to happen as seeing a black swan! ”…until somebody went to Australia to realize that there is a breed of swans that is black. Nassim Thaleb first coined this term in his book The Black Swan (2007).

A Black Swan Event is an event of low probability but high severity. A Black Swan Event is an event that is impossible to predict and is huge in magnitude. Examples include: the 2008 financial crisis, Covid-19, September 11th. On September 10th, no one, even the finest analyst, was expecting that the twin towers would no longer be the next day.  A Black Swan Event is what life always ends up manifesting. The pursuit of happiness and financial success are core principles of our north american culture. This state of affairs has brought about tremendous wealth. On the flip side, our society and the systems we rely on to live and enjoy our lives have become highly vulnerable to anything out of the ordinary. A key feature of resiliency is the understanding that Black Swan events are not the exception, but the rule.

 https://en.wikipedia.org/wiki/Black_swan_theory

Why are Black Swans the rule?

Clearly, we haven’t been very good at predicting events like the 2008 financial crisis or the Covid pandemic. This is partly due to human biases  (like the recency bias – assumption that tomorrow will be like yesterday). Further the use of mathematical models to calculate risk and how it is distributed cannot integrate the infinite amount of variables that the world is made of.  “All models are wrong but some are useful.” – George E.P.Box. Models always fall short of the complexities of reality. We won’t go into mathematical / statistical details here. What matters is that: the probability of Black Swan events is impossible to predict because they are so rare and usually the result of the interaction of a multitude of dynamic systems involving an incalculable amount of variables. What is needed are attitudes and systems that might not be as efficient but are better equipped to deal with volatility or stress. In some ways what is needed is a change in our collective narrative – from a mechanistic outlook of the world to a dynamic interaction of self-aware systems – from a world where everything is predictable to a world that is much more complex and dynamic, a world where things can change on a dime.

Why are the statistical models we use faulty? 

Modern science, including statistical models, relies on two basic paradigms: analysis and synthesis. On one hand, analysis breaks down the subject into its parts, analyses those and then infers the characteristics of the whole based on the sum of its parts. On the other hand, synthesis looks at the whole and looks at the relationships between the parts. Synthesis would be your tool of choice when the characteristics of the subject are an emergence of the parts that cannot be understood by looking at the parts only. Those two paradigms are complementary but their use depends on the nature of your topic of interest. For instance, physics relies much more on analysis because the subjects of its observation are static and easily isolated. On the other hand, natural and social systems are inherently interconnected and as such, their observation relied much more on synthesis.

Our current narrative is based on the modern western scientific worldview which assumes that growth, efficiency and profit lead to the greatest common good. This assumption is based mostly on an analysis of the world and is very good at defining, quantifying and making up ingenious ways to use the material world (Artificial Intelligence, cars, drones…). It has inherent limitations though. This assumption neglects synthesis, and as such offers a sorely incomplete representation of reality. When what we practice is out of line with truth, a usually painful readjustment occurs. Interestingly, Indigenous world-views use synthesis as their dominant basic paradigm.

Short YouTube about Systems Thinking

Short YouTube video about analysis and synthesis  

What are Complex Adaptive Systems?

A system is what emerges out of the interaction of the parts it is made of. A house in not a pile of bricks. A house is the result of bricks with a specific arrangement and relationship between them.

Systems can be simple, complicated or complex. A simple system brings up tame problems – or clock problems – that are easily resolved in a well known deterministic way, e.g. What to do about a flat tire?  A complicated system brings up more difficult problems, that often need multiple parties, a high level of expertise and there is a high level of certainty about the outcome e.g. How to send a rocket to the moon?  A complex system brings up wicked problems – or cloud problems. These wicked problems are unstructured, open-ended, interconnected, interdependent, multidimensional, systemic, unbounded in space and time, interdisciplinary, cross borders and departments. This makes them difficult to contain and structure. These problems have deep and unknown interconnections. When you try to pull on one part, you end up getting the whole thing. Complex problems are dynamic as they evolve over time. Problems and solutions are entwined as they coevolve. In response to the nature of complex problems, people often feel overwhelmed and might respond with denial, resignation, determinism, inertia… Trying to tame the problem by simplifying it is another pitfall. Complex problems signal a need for a shift in paradigm and/or approach in order to recontextualize the issue. Addressing a complex problem means breaking out of established assumptions. Truly tackling complex problems requires systems thinking: one needs to integrate the various perspectives and dimensions of the problem by seeing the system as a whole, so that one can get a real idea of what makes up the wicked problems he wrestles with. e.g. How to reduce poverty? How to reduce green house gases? How to raise a child?

Complex systems (CS) are characterized by:

1) Numerosity: CSs are made of many parts and do not rely on centralized control but are self organizing. As such new patterns of organisation emerge.  This process repeats. Self organizing complex systems have many elements on many different scales with all of these levels affecting each other e.g. humans, social groups, society, humanity. The parts of a CS are interconnected and interdependent. You cannot isolate or reduce the whole thing to one level.

2) Interdependence and non-linearity:  CSs parts are interdependent which creates non linearity:  1+1 does not equal 2. For example, the division of labour creates synergies leading to higher output. Due to feedback loops complex systems can show exponential growth or exponential decay. A CS can flip into new regimes in a very short time. A small change in input value in the system can bring system wide effects because of feed back loops. This is what is known as sensitivity to initial conditions (Butterfly effect)

3) Connectivity: CSs involve dense or high levels of interconnectivity with their parts. With increasing connectivity, the CSs will become increasingly defined by the connections between the parts rather than by the properties of its parts. CSs becomes a network of connections.

4) Autonomy and adaptation: CSs are often heterogeneous and have a high level of diversity. They do not have centralized system of coordination and they obey to the rule of evolution. The parts of CSs have the capacity to adapt to their local environment according to their own set of rules and are autonomous. Through this behaviour, they create structures from the bottom up.

In other words, dynamic systems are self evolving, self reproducing, self healing. 

In conclusion:

• Most of human culture still thinks about the world in partial ways, and tries to fix complex problems with solutions that are partial at best, counter productive most of the time. 
• The technological infrastructure we rely on is becoming more complicated at an exponential rate while our ability to understand our impact only grows linearly.

Complex problems require complex thinking and a lot of precaution. The current economic and political systems are currently using a sledge hammer to maintain stability instead of working with natural change.  We are looking forward to major systemic changes down the road. These kinds of changes have always be long and painful changes throughout history. There is no reason to believe that it is different now.

Refering back to the question about faulty mathematics:

In the great majority of cases, the distribution of happenings in the world follows the Normal Distribution curve (see the figure below). For example, the size of people, the length of pine cones or the weight of seeds all follow the Normal distribution shape (or bell curve).

https://en.wikipedia.org/wiki/Normal_distribution

https://www.mathsisfun.com/data/standard-normal-distribution.html

When trying to predict what the range of events will be in the future of CSs, one might try to use the Standard Normal Distribution and extrapolate the range of what could happen. Medicine, finance, social sciences are a few examples where this kind of predictions are used. In many cases, it is assumed that the distribution of events follows the Standard Normal Distribution. This is where the models fail. The more variables and the more interconnected the system measured is and the less the distribution is likely to look like a standard distribution; the curve will be skewed, the standard deviation might be a lot bigger than expected and many other parameters might fluctuate making it impossible to accurately predict the future. This is where the Black Swans hide. 

The economy and social sciences are two examples where a multitude of CSs are interacting and the range of variation and scale of events varies wildly over time. The distribution of events, in this case relevant to economy and social sciences, is what is called a fat-tailed distribution. 

https://en.wikipedia.org/wiki/Fat-tailed_distribution

In finance for example, using the Normal Distribution does not apply as it does not accurately predict the behaviour of the markets considering their complex structure. As an observation, we can see that many financial advisors use the standard distribution to sell their products as it gives the illusion of certainty. Looking at the past, practically every truly significant change or shift was unpredictable. A good financial product is not only diversified and conservative, it takes advantage of the downside of the market; when the market goes down, you win. (To further explore: Skin in the Game: Hidden Asymmetries in Daily Life by Nassim Taleb)

Ultimately, since we cannot know whether tomorrow will be like yesterday, one needs to design every system one relies on with redundancies, optionality and margins. This is not financially efficient, but considering that failure to consider this aspect of the world leads to catastrophic loss, it is necessary – and that is resiliency. That being said, knowing that things change drastically and that most do not plan for the Black Swan events, one can invest in ways that take advantage of that and have a drastically improved ability to cope, adapt and benefit when change comes – and it always does – and that is antifragility.

With all this in mind, the question we ask is: how much comfort does one need before thinking about resiliency for oneself, their loved ones and their community? We think that the answer is: very little.