Antifragility, Modernity, and Dancing with Disorder

“Wind extinguishes a candle and energizes fire.”

I’m often asked what my favourite book is. I guess it’s all about being the right book at the right time. To date though, Antifragile by Nassim Taleb probably had the most influence on the way I think and see the world. 

But Antifragile is written in an overly intellectual style. Too many unnecessarily fancy words. This is very much intentional – Taleb doesn’t want skim readers to spread misrepresented ideas.

This is a shame. So much wisdom remains buried under pages, without getting a good chance to influence even more people. 

So, this post is my attempt at articulating some of its key concepts in the simplest way possible. 5 sections:

  1. Defining and Recognizing Antifragility: distinguish fragile vs robust vs antifragile; recognize that antifragile systems are everywhere (horizontally across different disciplines, and vertically across different scales)
  2. Modernity’s Denial of Antifragility: how modernity’s obsession with reducing disorder hurts antifragile systems; explore economic super-efficiency to illustrate this
  3. Dancing With Disorder: a deeper dive into more technical supporting concepts for practical applications of antifragility; i.e. minimize harm and maximize gain
  4. Misuse and Abuse of Antifragility: understand those with no skin in the game abuse this concept
  5. Wrap Up: summary and more personal reflection on why this is my favourite book

 


1. Defining and Recognizing Antifragility

1.1. Antifragile: things that gain from disorder

What are some things that are fragile? What are the properties of fragility?

Breaks easily?

Okay, then what’s the logical opposite of fragile?

Doesn’t break easily?

So something along the lines of: robust, resilient, strong, tough?

Not quite.

All of the above present a false dichotomy. Saying that robust is the opposite of fragile is like saying the opposite of positive is neutral (when it is in fact negative). 

“Some things benefit from shocks; they thrive and grow when exposed to volatility, randomness, disorder, and stressors and love adventure, risk, and uncertainty. Yet, in spite of the ubiquity of the phenomenon, there is no word for the exact opposite of fragile. Let us call it antifragile.”

Taleb defines fragility as things that dislike disorder, volatility, randomness, uncertainty, stress, time etc.

Fragile things are harmed by anything from the “disorder family“: disorder, volatility, uncertainty, stress, time, error, variability, dispersion of outcomes, randomness, the unknown ,turmoil, entropy, imperfect,  incomplete knowledge

Antifragile, being the exact opposite, would then be: things that gain from disorder, volatility etc.

Fragile dislikes volatility. Robust doesn’t care. Antifragile likes volatility.

Fragile breaks when dropped. Robust remains intact. Antifragile becomes even stronger than before.

Fragile wants status quo. Robust is indifferent. Antifragile craves changes.

Fragile is the Damocles dines with a sword hanging above his head. Robust is the Phoenix that gets reborn from its ashes. Antifragile is the Hydra that grows two heads for every head it loses.

From Greek mythology: Sword of Damocles, Phoenix, Hydra

Antifragile goods would love to be shipped in a box labeled “please mishandle, please be careless”.

Source

 

1.2. A Transdisciplinary Lens to Antifragility

Antifragility is everywhere, as long as you learn to recognize it. As a rule of thumb, inanimate, top-down mechanical systems tend to be fragile, while natural, self-organized, living systems tend to be antifragile.

Multi-disciplinary examples of antifragile systems:

(i) Natural selection. Recall high school biology: 1. There is a variation of characteristics in a population. 2. When there is a change in environment, those with favourable characteristics to this new environment survive, and those without perish. 3. Favourable characteristics are passed onto the next generation. 4. After many generations these favourable characteristics become common in the population.

But if there are no major environmental changes for a long time, quirky characteristics that are unfavourable in today’s environment becomes less and less common. Variation decreases and the population trends towards a monoculture. Then, if there is a big environmental change, the monoculture can easily be wiped out.

So natural selection is an antifragile mechanism because it feeds off continuous uncertainty and change to work.

Immune system. Our bodies give a more rapid and more intense immune response after each encounter with a pathogen. This is how natural immunity and vaccinations work. Secondary immune response is both faster and more intense (antibody count). As Nietzsche would put it, “What doesn’t kill you makes you stronger.”

Bone. Bone-building cells (osteoblasts), and bone-destroying cells (osteoclasts) are given instructions on where to build new bone or destroy old bone based on where there is most stress exposure. Add bone to high stress areas, and remove bone from low stress areas. This is why astronauts lose 1-2% of bone mineral density for every month spent in space: no gravity, no stress, less bone.

Stress profile on femur

Start-up ecosystems. Start-up concentrations like Silicon Valley thrive most when there are disruptive changes to society. Some directly commercialise new technologies (personal computing, the internet, smartphones, AI etc). Others do even better by bringing new business models that ride on a tailwind of social megatrends (shared economy, platform business models, social networks etc). Either way, if the market, or business landscape is not changing, the incumbents will just continue to dominate.

Capitalism. The market determines the demand and price of goods and services. This way, the ‘invisible hand’ directs more of society’s collective resources (in the form of capital) towards value-generating activities, and gives less from value-destroying ones. Disorder, volatility, change – continually forces the system to re-arrange itself to maximize value. We explore this concept of decentralized self-organization soon.

Military. Some say it won’t be long until China’s military power surpasses the US. Such predictions are typically based on: (i) China’s GDP having already surpassed the US on a purchasing power parity basis; (ii) US withdrawing its global presence and turning to protectionist policies, while China’s expands along its ambitious Belt and Road.

But one overlooked after is the fact that the US has been continually involved itself in wars even after WWII. China’s last major involvement, on the other hand, was in the Korean War in the 1950s. With continual military involvement comes: more highly-trained generals, a repertoire of field-validated strategies, and updates to equipment/internal processes/structures etc based on first-hand battle experience. For now, the Chinese military is like a heavyweight mixed martial artist that’s trained hard for years but never actually been in the ring. 

 

1.3. A Fractal Lens to Antifragility

Here’s when things get more interesting. Antifragility works in layers and hierarchies.

In order for a system to be antifragile, some of its components need to be fragile.

This may seem counter-intuitive. Perhaps we’re too familiar with robust things being made up of robust components. 

Start-up ecosystem. The fragility of start-ups and companies (high failure rate) is necessary for the broader the economy to be antifragile. Resources will always be in scarcity. The faster the low-value start-ups fail, the less resources they burn off, and this is better for the system as a whole.

Biology. For a multicellular organism, old and damaged cells absorb resources without adding much value. These cells need to be fragile so that they can easily be replaced with fresh ones. When you’re deprived of food (i.e. low in resources) – your body kills the old cells first. This fragile-sacrificing mechanism keeps the system (organism) stays healthy.

The above examples considered going from higher levels to lower levels in a system hierarchy. Systems contain subsystems, and those subsystems are compromised of even more subsystems, and so on.

But we can go the other way too. Lower level to higher level. Any system is a subsystem of a bigger system. And that bigger system is a component of an even higher level system above it. And so on.

So if a subsystem is antifragile, then at least some of the other subsystems on its same level need to be fragile for the parent system above it to be antifragile

Back to the biology example to illustrate this recursive antifragility:

  • A multicellular organism, say a human, is antifragile if some of its unicelluar components are fragile
  • A tribe of humans is antifragile, if some humans are fragile. For our late ancestors, fragility here meant the vulnerability of getting killed. Killed by being eaten by a lion, or being abandoned by the tribe for physical weakness/injury/disease etc. But over time, this also included more mild forms of fragility like being ignored or punished for violating social norms.
  • For a larger human society (where multiple tribes interact) to be antifragile, some tribes need to be fragile. Different tribes have certain norms and lifestyles.
  • Basically, recursive Darwinism at different scales.

Also note that an antifragile whole requiring fragile components does not mean that all fragile components give an antifragile whole. All dogs are mammals, but not all mammals are dogs.

Perhaps there are also ontological implications in this notion. From subatomic particles scaling up to galactic clusters with alternating degrees of antifragility and fragility at every network layer. Save this thought for another time…

 


2. Modernity’s Denial of Antifragility

2.1. Modernity’s Obsession with Reducing Disorder

Much of society’s resources goes into taming and reducing disorder. 

  • Agriculture stabilizes the food supply
  • Fridges keep food fresh by narrowing the temperature range
  • Dampening systems keep skyscrapers from wobbling from wind and earthquakes
  • Fuses protect electrical appliances from excess current damage
  • Safety rails in high places protect people from falling to their death
  • Insurance protects bank accounts from going bust from unexpected accidents/misfortunes

Shielding the fragile from disorder is a good thing. We wouldn’t have got this far without it.

Thing is modernity has become impressively effective at doing this. Excessively effective in fact.

The problem is we’re shielding disorder not just for fragile systems, but for antifragile ones too. This denial stems from a lack of understanding of antifragility. Our institutions have been designed and evolved to manage fragile systems, rather than nurture antifragile ones. 

“Modernity corresponds to the systematic extraction of humans from their randomness-laden ecology—physical and social, even epistemological. Modernity is not just the postmedieval, postagrarian, and postfeudal historical period as defined in sociology textbooks. It is rather the spirit of an age marked by rationalization (naive rationalism), the idea that society is understandable, hence must be designed, by humans. With it was born statistical theory, hence the beastly bell curve. So was linear science. So was the notion of “efficiency”—or optimization.”

This artificial suppression is bad for two reasons.

First, antifragile systems don’t just want disorder, they need it to stay alive. Without disorder, the antifragile actually suffers, stagnates, and eventually dies. Absence of stressors leads to atrophy.

“…if antifragility is the property of all those natural (and complex) systems that have survived, depriving these systems of volatility, randomness, and stressors will harm them. They will weaken, die, or blow up. We have been fragilizing the economy, our health, political life, education, almost everything … by suppressing randomness and volatility.”

Second, the longer an antifragile system is denied of disorder, the more damage it’ll cause when it inevitably blows up. Just because a system exhibits no visible risks doesn’t mean it’s risk-free. Silent risks creep and accumulate beneath the surface. When the fire eventually burns, it’ll really burn.

 

2.2. Wildfire example

Fires have gotten larger over the past century. Climate change is certainly a key contributing factor. But another has to do with forestry management.

In the US, for example, forests used to be more patchy. See 1936 vs 2012 below. 

TED talk: Why wildfires have gotten worse

Back then, wildfires were more frequent but smaller. 

“These different forest types, the environments that they grew in and fire severity – they all worked together to shape this historical patchwork… It provided a natural mechanism to resist the spread of future fires across the landscape. Once a patch of forest burned it helped to prevent the flow of fire across the landscape. A way to think about it is that the burned patches helped the rest of the forest to be forest.”

As roads and rail pierced through the forests, they acted as effective fire breaks. Forests that would otherwise burn became protected. Plus, logging removed the larger trees – the survivors of centuries of wildfire. The forest became more dense and filled with smaller, more flammable vegetation.

In 1910, there was a huge wildfire near Washington – the “Big Burn”. A large area burned and many died. From then on the newly formed national fire service was mandated to extinguish all wildfires across the country. But by continually suppressing every small fire, the forests accumulated dry dead matter, making it increasingly vulnerable to inevitable bigger ones. By suppressing disorder, the forest has become more fragile. 

So now, when a fire does go out of control, it really goes out of control.

This is the point of back burning, (aka prescribed burning): intentionally burning off parts of the forest in a controlled manner. We just don’t do enough of it.

 

2.3. Economic Superefficiency Example

Let’s turn to an economics example. For 200 years and running, globalized economies have embraced Ricardian comparative advantage. The idea that each country should focus on its relative advantages over other countries, rather than its absolute advantages.

For instance, suppose England can produce 1 unit of cloth for $100 while Portugal can do it for $90. England can produce 1 unit of wine for $110 while Portugal can do it for just $80. 

Even though Portugal can make its own cloth for $90, it should still pay $100 and import English ones.

This is because it’s better for Portugal to put all of its labour resources into wine production and sell it to England at $30. The $10 ‘loss’ of importing cloth is more than offset by the $30 ‘profit’ of selling wine.

This means that even though Portugal has an absolute advantage over England in per unit cloth production cost, England has the comparative advantage once we factor in wine too.

Extrapolate this example to millions of products and millions of trading entities, we see the global economy benefiting from a globally opportunity-cost-optimized marketplace. We’ve all benefited from this hyper-specialization.

Source

But there is a hidden price to superefficiency: fragility.

Industries are becoming increasingly concentrated. Winner-take-all effects are becoming more prevalent. Too many eggs in one basket. Pareto distributions are becoming super-Pareto. 

In 1978, the top 100 most profitable firms earned 48% of all publicly traded companies’ profits.

In 2015, the top 100 earned 84%.

Harvard Business Review’s 2019 article: Rethinking Efficiency illustrates this point. In the following charts, the left horizontal axis shows aggregate market share of the top 4 companies in a given industry in 1997. The right horizontal axis shows the same for 2012. So an upward slope indicates increasing concentration

Of the 850 industries studied…

…two thirds increased concentration (shown above on left in orange). Also note the emptiness in the top right, showing that most of the highly concentrated industries in 1997 amplified their concentration over time.

This trend is even more evident when we hone in on the industries where cumulative top-4 market share moved by more than 10 percent points.

Of the 285 industries that moved by 10 percentage points in concentration (either up or down), ~75% became more concentrated (shown by orange lines above on left).

Of the 92 industries that moved by more than 20 percentage points, a whopping ~90% increased concentration (shown by orange lines above on right).

Simply put, big changes are more likely to be increases rather than decreases in industry concentration.

So what’s driving this?

Generally speaking, it’s the irresistible efficiency gains from economies of scale and winner-take-all efforts at play. For instance, a large trucking company has several advantages over its smaller rivals: shared cost centers (fleet maintenance and repair, back office functions), greater buyer bargaining power to acquire fleet, greater supplier bargaining power to get better margins out of their customers etc.

 

Usually, this is great for most people – we get better, cheaper, faster stuff. Efficiency creates value for society.

But too much efficiency can actually destroy value in the long-run.

  • Big firms generally respond slower to change, and are less adaptable
  • Highly concentrated oligopolies can turn into quasi-cartels. Each benefiting from lucrative margins at the end user’s ultimate cost.
  • This is positively reinforcing. More profits, more power, more lobbying, more control, more profits, and so on. Big firms get bigger. The rich get richer.
  • Increased market power means the big company can more easily get away without responding to bottom-up trends/changes in the market. This accumulates hidden fragility.
  • When they fall, they fall hard: many lose their jobs, many customers are suddenly left without a supplier. And this creates much bigger second-order effects than if a couple of small ones had failed

A superefficient dominant model elevates the risk of catastrophic failure. Bigger fire.

In short, modernity’s suppression of disorder comes with good intentions. Modernity is a neurotic parent. This has been a good outcome for fragile systems (e.g. the helmet the child was forced to wear by mum prevented permanent brain injury). But a bad one for antifragile systems (e.g. preventing the child from ever going on a bicycle out of fear they’ll get hurt). 

We need to develop an eye to recognise such fragility, and minimise its effects…. Right?

Yes, but that’s just saying let’s move from a fragile design to robust one.

We can do better. Let’s move from a fragile design to an antifragile one. Gain from disorder, not just be immune to it.

 


3. Dancing with disorder

So what?

How to gain from disorder? How can I benefit from this concept? What are the practical applications?

We first need to dive deeper into some more technical concepts. Specifically, the mathematical properties of fragility, as well as a few closely-related ancillary concepts. Here we’ll cover:

  • Non-linearity
  • Convexity effects
  • Black Swans
  • Optionality
  • Event vs implication of event
  • Limitations of antifragility

 

3.1. Non-linearity

  • Jump from a 0.1 m height 1,000 times. Your knees would hurt a bit but otherwise you’re fine.
  • Jump from a 100 m height once and you’re dead. 1 x 100 m jump has a much bigger effect than 1000 x 0.1 m jumps.

This is non-linearity. A single large shock has a disproportionately greater effect than the equivalent cumulative effect of many small shocks.

More examples:

  • Drive into a wall at 5km/hr 20 times versus drive into a wall at 100km/hr once.
  • Drink 1 bottle of wine a day for 7 days versus drink 7 bottles of wine in 1 day. 

Fragility is generally non-linear. This is due to the structure of survival probabilities. While the fragile is still alive, it’s harmed a lot more by single large shocks than lots of small ones. Once it’s already dead, more shocks no longer affect it. The fragile is subject to accelerating harm.

Commute time is also subject to non-linearity.

  • Adding more cars into the road network when there’s very few cars doesn’t change your commute time very much. eg. going from 10k cars to 20k cars.
  • But if there’s already quite a lot of cars (e.g. 100k) on the road, adding another 10k won’t increase commute time by 10%, but something much more, say 50%.
  • So travel time is fragile to the volatility of the number of cars on the road. Travel time is subject to accelerating harm.
  • Having 90k cars for one hour and 110k cars for another would be much slower than two hours of 100k cars.
  • Since travel time is a negative, we can represent it as a cost. So graph going down is higher cost (longer commute time).

Liquidation cost is another non-linear function. The cost of selling off $70b worth of assets is significantly more than 7x the cost of selling off $10b worth.

Antifragile is also non-linear. Although in contrast to the fragile, big shocks bring more benefits than lots of small shocks. Up to a certain point, of course. The antifragile is subject to accelerating benefit.

 

3.2. Convexity effects

There are two kinds of non-linearity: convex (smile) and concave (frown).

Antifragile is convex. Fragile is concave. How so?

Consider the graph below. Horizontal axis is some variable (e.g. height you jump from). Vertical axis is gain/loss from that variable (e.g. your physical condition after you jump). Your health is a function (f(x)) of the jump height (x). Given this shape, you prefer less volatility in x. Because on average, you’re going to lose more than gain. 

Concave has more pains from volatility

The convex is the the same shape but flipped over.

Convex has more gains from volatility

For the convex, more volatility brings more gains than losses on average. So the convex likes volatility and disorder.

If you earn more than you lose from fluctuations, you want a lot of fluctuations.

A more technical way of interepreting convexity is: the average of the function is greater than the function of the average. Especially more so as x is more volatile and dispersed. This is illustrated for two x-values below, but can be generalised for more.

Jensen’s inequality: for convex functions, average of function is higher than function of average

Another implication: the convex does not need to be right that often. Suppose you were wrong 5 times, and right just once. If the gains you make from that one right time is greater than the total losses you make from all 5 wrongs combined, then you’re still up.

Someone with linear payoff needs to be right more than 50 percent of the time. Someone with convex can still be ahead even if they’re frequently wrong.

 

3.3. Black Swans

The metaphor originates from Europeans being shocked from seeing black swans for the first time when they first came to Australia. All swans they ever saw back home were white. And so, they presumed all swans were white.

Image source

Black Swan: an event that is: (i) an outlier, with (ii) extreme impact, that (iii) people make ex post facto explanations for (“I told you so”). 

Examples:

  • Natural disasters, especially those that affect humans: tsunamis, earthquakes
  • Non-natural disasters: terror attacks, underestimated extremist political candidates winning elections, Titanic sinking, Aztecs encountering the Spanish

And of course, the current COVID-19 pandemic is a Black Swan event…

  • …Economically…
    (On financial markets, I’d actually argue COVID-19 is more like the final straw that broke the camel’s back, but let’s keep this post on topic.)
  • …Politically…
    How effectively governments respond will drastically affect the next election results. And for non-democracies, it’ll affect how people view the current ruling party. All of these factors may reshape global order and leadership dynamics in ways we can’t foresee right now.
  • …Socially…
    Need I say more?

No one saw it coming. 

What makes Black Swans unpredictable is we don’t know what we don’t know.

The turkey problem. Imagine you’re a turkey. Every day that gets closer to Thanksgiving, a turkey is continued to be fed very well. As each day passes, a turkey can conclude with more and more statistical confidence that Thanksgiving is good for the turkey. Then, right before Thanksgiving we all know what happens.

This is a classic example of conflating absence of evidence for evidence of absence. Here, evidence is the turkey not being harmed by the farmer.

Many forecasts extrapolate historical data into the future. 

“The fool believes that the tallest mountain in the world will be equal to the tallest one he has observed.”

But the danger of this is that the risks remain hidden. And these hidden risks is what makes catastrophes catastrophes.

Take the Fukushima nuclear reactor, as another example. It was built to withstand the worst earthquake …that has been recorded in the past. It had no precedent for the magnitude of the 2011 earthquake. Catastrophe.

For the fragile, wins and losses over time may look something like this.

Effect of Black Swans on the fragile

Small frequent visible variations with large occasional hidden losses. Well, hidden, until it finally happens.

For the robust, the variations could small and insignificant. Black Swans don’t really affect them. Fluctuations are predictable.

Effect of Black Swans on the robust

For the antifragile, variations could also be small most of the time. But every now and then an unexpected event brings a monster win.

 
Effect of Black Swans on the antifragile

So the fragile is exposed to negative Black Swans, while the antifragile is exposed to positive ones.

 

3.4. Optionality

In a financial markets context, an option is a right, but not an obligation, to buy/sell something at a fixed price within an agreed time frame. They’re a type of derivative – a contract, a financial instrument, that has its value determined by some other underlying asset. There’s two types of options.

  • Call option: right to buy something at a fixed price within agreed time frame
  • Put option: right to sell something at a fixed price within agreed time frame

For example, a certain stock is at $100 per share. You think it’ll go up. You have $500.

  • You could buy 5 shares with your $500 ($100 x 5).
  • If stock goes up to $125, you make $125 profit ($25 x 5). Not bad.
  • But you’re greedy. So you borrow money from a friend to leverage up, say another $2k.
  • You buy 25 shares with $2.5k ($500 your money, $2k friend’s money)
  • If stock goes up to $125, you now make $625 profit ($25 x 25). Better.
  • But leverage can hurt you much in the same ratio. If the stock goes to $75, instead of losing only $125, you now lose $625.
  • And if the stock goes much lower, you lose so much money you can’t even pay your friend back.

Alternatively, you could buy call options.

  • Think of each option contract as a coupon that says “for a limited time only, with this coupon, 1 share of this stock can be bought for $100.” 
  • For this fixed price (aka. strike price) of $100, let’s say each option contract is $5 (aka. the premium).
  • So each coupon costs $5. You buy 100 of these coupons for $500.
  • If the stock goes up to $125 at any point within the agreed time frame, you have the right to use (aka. execute) your option, to buy stock worth $125 for just $100.
  • This advantageous price difference, makes each one of your ‘coupons’ have an intrinsic value of $25. Since you paid $5 for each one of these coupons, the profit you make for each coupon you own is $20.
  • 100 coupons each profiting $20 is a $2k profit (see below). $2k profit is better than the $625 profit in the previous example. 
  • Now imagine the same nightmare scenario of the stock plummeting to $30. At this price, you can just choose to not execute your options. There’s no reason to pay $100 for something that’s worth $30. You simply let your coupons’s time frame expire.
  • Your 100 coupons are now worth $0. So you lose the $500 you paid for these. $500 loss is much better than the $625 loss in the previous example.
  • If the stock went even lower, say to $10 a share, you still only lose $500.
Image: Long call option explained

There are 3 points to note here about options.

First, note the favourable asymmetry. You have a capped downside and uncapped upside.

Second, this means that if you’re holding such options, you want to see more price volatility. You don’t care about the average outcome, you only care about the most favourable one. Options like a dispersion of outcomes.  Options like volatility.

The luxury goods industry, for example, cares less about the average income/wealth in its target market. It only cares about having enough people that are super-rich.

  • Market A: 10m people with $50k income, and 100k people with $1m income
  • Market B: 5m people with $10k income, 1m people with $1m income

Market B is more attractive to luxury goods. Inequality doesn’t matter. Dispersion does.

Third, you don’t have to be right that often. So long as the cost of each time you’re wrong is low. You can lose small often, but win very big every now and then.

“If you “have optionality,” you don’t have much need for what is commonly called intelligence, knowledge, insight, skills, and these complicated things that take place in our brain cells. For you don’t have to be right that often. All you need is the wisdom to not do unintelligent things to hurt yourself (some acts of omission) and recognize favorable outcomes when they occur. (The key is that your assessment doesn’t need to be made beforehand, only after the outcome.)”

With this, we also define optionality as: optionality = asymmetry + rationality.

By rationality, we mean keeping the good and ditching the bad. That is, it does take some intelligence to recognise favourable outcomes and knowing what to discard. This also implies that action needs to be taken to realise the value of the options.

We actually pay for optionality all the time. One example is purchasing insurance. Pay some fixed amount per year. If something goes wrong (Black Swan), we have the option to neutralize the effect.

But explicit, well-defined options, are expensive to purchase. Especially when the value of the optionality is identified and mapped in a contract such as an insurance policy. An even more expensive example of the additional premium paid for an option is flight tickets with flexible cancellation/date change policies.

However, there are other places where options remain underpriced, or not priced at all. We just strrugle to recognise due to the domain dependence of our minds. It certainly helps to be transdisciplinary.

The antifragile has options, and the fragile does not. An option is the weapon of antifragility.

 

3.5. Event vs implication of event

Now let’s bring all these concepts together.

To recap:

  • Non-linear means accelerating harm or loss. Increasing x has a disproportionately larger effect on f(x). Many systems are non-linear. And many people mistake non-linear to be linear. 
  • Convex is the good type of linear where fluctuations lead to more gains than losses overall. The convex wants volatility. The antifragile is convex.
  • No one can predict Black Swans. We don’t know when they will come, where they will come from, and what exact impact it would have. All we know is that they’re always a possibility.
  • Asymmetry plus rationality means optionality. Options don’t care about average outcomes, it only cares about the most favourable ones. Options like a dispersion of outcomes. 

All of these more technical concepts, illustrated why the antifragile wants more volatility, dispersion, and randomness. 

Now when it comes to the applying of our understanding of antifragility, we actually care more about the consequence of an event rather than the event itself.

  • We care more about the payoff than the thing that causes the payoff
  • We care more about the value of f(x), rather than the value of x.
  • What happens to something as a consequence of some event, is not the same as the event itself.

“There is something (here, perception, ideas, theories) and a function of something (here, a price or reality, or something real). The conflation problem is to mistake one for the other, forgetting that there is a “function” and that such function has different properties.

Now, the more asymmetries there are between the something and the function of something, then the more difference there is between the two. They may end up having nothing to do with each other.”

This is the conflation problem. It’s very important not to conflate x with f(x).

Especially if f(x) is non-linear and asymmetrical. This would just amplify the difference between x and f(x) – especially for large x values.

We all do this all the time. Our brains are optimised to save energy. This is why it’s so important to put additional effort into re-training our neural networks, to sharpen our critical thinking continuously.

Oddly though, appreciating this distinction between x and f(x) is comforting.

Because x is hard to predict.

  • Causes are complex
  • Systems are interdependent
  • Second order effects are extremely difficult to foresee
  • In fact, for complex systems, there’s not enough atoms in the universe to compute all of the possible outcomes
  • There’s hidden chaos in order, and hidden order in chaos
  • Hence, the perpetual possibility of Black Swans

Even without fully understanding x, so long as we understand f(x), we can position ourselves to win more than lose.

We want favourable non-linearity: convexity. This exposes us to positive Black Swans rather than negative ones.

We want favourable asymmetry: where a few big, uncapped wins are much greater than the frequent small, capped losses.

We want optionality to ditch the bad and keep the good. 

This way, it’s less about how often you’re right, and more about how much you gain every time you are right.

This way, it’s possible to benefit from a world without fully understanding it.

Embrace uncertainty. Welcome volatility.

Focus on the payoffs of an event rather than the event itself.

 

3.6. Limitations of antifragility

There’s limitations and nuances to antfiragility though.

What doesn’t kill you makes you stronger.”

Yeah, but you can get permanently disabled.

Exposing your immune system to trace amounts (often deactivated/dead) of a pathogen makes you stronger. But injecting excessive amounts will just kill you. Your immune system is only antifragile up to a certain point.

Bone exposed to too little stress is weak. Bone exposed to the right amount is strong. But bone exposed to excessive stress, like jumping off a building, will break. Bone is only antifragile up to a certain point.

Children should not be over-protected, but neglecting them is even worse.

We can visualise this limitation as function with some convex sections, and some concave sections.

Fragility transfer: convex only likes volatility (up to a certain point) over some range

As the dose increases beyond a certain point, benefits reverse.

Convex exposure likes volatility (up to some point) over some range.

Balance matters. Context matters. It’s naive to take a mental model and apply it wholesale to everything. Many misinterpret a summary of Antifragile (and the summary itself likely a misinterpretation to begin with), and go around calling everything antifragile. “If you’re holding a hammer everything looks like a nail.”

I consider the learning-process-application cycle of any mental model in stages:

  • (i) Never heard of it
  • (ii) Heard of it
  • (iii) Read a summary / someone told me about it
  • (iv) Read the book. Recognise antifragility when it is present. “Wow I see antifragility everywhere”
  • (v) Thought more about it. Oh wait, I’m confused
  • (vi) Thought even more about it. Distinguish false antifragility from true antifragility, and recognise the nuances. 
  • (vii) Consciously apply it in life and work
  • (viii) Unconsciously apply it in life and work. You don’t even realise you’re doing it.
  • (ix) Go back to step (v) and repeat…

Stage (iii) and (iv) – “I think I know” – is most dangerous.

 


4. Potential misuse and abuse of antifragility

Furthering this warning on misintepreting antifragility is misusing and abusing it. 

 

4.1. Stealing an option from society

Unfortunately in modern society, fragile disadvantages are often transferred from some onto others. Without also transferring the antifragile advantages. Reaping the benefits while someone else take all the risk. It’s like eating the top part of a cupcake and giving someone the rest.

“Antifragility for one is fragility for someone else.”

Let’s take the 2008 GFC for example. A select few at a select few financial institutions were benefiting from frequent, large wins without consideration of the hidden risks. Or rather, knowing that they won’t be affected by the risks. When things finally did blow up, it was the taxpayers that bailed them out. Too big to fail. Taxpayers ended up paying for the fragile consequences while a select few took home the bacon.

Essentially, these select few were stealing an option from society. Benefiting from the value of optionality, while the rest of society ended up paying for the option ‘premiums.’

This is part of a broader problem of misaligned incentives. Or more specifically, incentives without disincentives. All carrots, no sticks. Action without consequences. Not just at an individual, or group level. But at an institutional / system-design level. The commercial world works by addition rather than subtraction. Big pharma doesn’t gain by people avoiding sugar. There are hidden dangers of laissez faire capitalism. 

So addressing such dangers goes beyond changing the behaviours of a few. The underlying design of the system needs to be updated. Perhaps, into a model that better distributes the benefits of antifragility more proportionally to the risks that are taken on. Not advocating socialism. Far from it. Rather, those that take risks should be paid their due rewards, instead of having it stolen by someone else.

 

4.2. Inconsistent utilitarianism

To be clear, this section is not drawn from the book. It’s just where my own thinking has taken me. I find exploring inconsistent utilitarianism to be relevant to the ethics of antifragility.

For those unfamiliar with ethical theories in philosophy:

  • Deontology: rule-based; intention matters more than results; the means justifies the ends.
  • Consequentialism: outcome-based; results matter more than intention; the ends justifies the means.

Utilitarianism is a subset of consequentialism where what’s morally right is determined by whatever gives the greatest number of people the greatest benefit.

In the interest of simplicity, I’ll walk through this point with the classic, and overly-used trolley problem.

  • A trolley is fast-approaching 5 people. They’re all about to die.
  • Frame 1. There is an option to pull a lever that diverts the trolley onto a different track. But this will kill 1 other bystander that would not have otherwise died. Would you pull the lever?
  • Utilitarian ethics says yes. Divert the trolley, save the 5 and kill 1. This is a better outcome for the collective whole.
  • Deontological ethics says no. Leave the trolley as is, let it kill 5 people. Because who gives you the right to determine who lives and who dies? Diverting the trolley would kill a person that would not have otherwise been killed.
  • 70% say yes (i.e. choose the utilitarian solution). “Killing 1 person is an unfortunate side effect of saving 5 people. This is morally right.”
  • But strangely enough this number changes when you frame the same options in a different way.
  • Frame 2. Instead of pulling the lever, you now have to the push that 1 bystander onto the tracks with your own hands to save the 5. Would you push the bystander to their death?
  • Only 30% say yes. The visceral reality of actually touching the person you’re about to assign a death note to is deeply discomforting. “5 people were saved because 1 person was killed. This is wrong.”
  • Frame 3. This time, in order to pull the lever to divert the trolley, you have to quickly push someone standing in front of the lever out of the way, and as that person falls, they die. No other bystanders this time. 5 live saved, 1 killed.  Would you do it?
  • 80% say yes. “Pushing someone out of way is one thing. Them dying from the fall is another. It’s just an unfortunate side effect. They weren’t killed as a means to save 5.”

“Killing someone intentionally as a means to save five feels intuitively wrong, but the intuition is strongest when the killing would occur right here, right now; doing it in more complicated sequences of intentionality doesn’t feel as bad. This is not because of a cognitive limit – it’s not that subjects don’t realize the necessity of killing the person in the loop scenario. It just doesn’t feel the same. In other words, intuitions discount heavily over space and time.” – Behave, Robert Sapolsky

Bringing this back to antifragility and ethics. Earlier we said that antifragile systems require some of its components to be fragile. This is the mechanism of how it gains from disorder.

Sacrifice a few to save the many.

The first ethical warning here is recognising when antifragility is used as a philosophical justification for extreme utilitarianism. There’s political precedents for this: the rise of Nazi Germany.

The second, more prevalent, but more subtle warning is recognising different frames under which antifragility is articulated around utilitarian positions. Some could argue that efforts to protect the weak denies antifragile systems of their much needed disorder (let the weak die), and this ends up hurting the whole even more.

Because much like the trolley problem, people register very different opinions on what’s morally right based on how things are framed.

Biology. “Weak and damaged cells need to die so that the organism can survive and grow.” Fair enough. That’s the reality of nature. I don’t have a moral position on the live-or-kill decisions over my damaged cells. Actually, I want them to die.

Start-ups. “Weak and damaged businesses need to die so that the broader economy can survive and grow.” Fair enough. That’s the mechanism of capitalism. Efficiently allocate society’s finite resources into more value-adding ventures. In fact, I actually think shit start-ups (much more about leadership and people, and much less about the problem they’re solving, market they’re in etc) should die fast. This is better for the system.

Nation states. “Weak and damaged states need to die so that the future world can be better off.” Well history tells us that states come and go. But the part about whether a weaker state falling is better for the world is questionable – both logically and morally. Let’s explore this case in more depth to illustrate the challenges with using antifragility as a moral justification.

 

4.3. Extreme Darwinism at a nation state level

Empires and nation states rise and fall.

Consider the consolidation of Chinese political units between 2000 BC and 221 BC:

  • 2,000 BC: 3,000 polities (Xia dynasty – Three Dynasties period)
  • 1,500 BC: 1,800 polities (Shang dynasty)
  • 1,200BC: 170 polities (Western Zhou dynasty)
  • 770 BC: 23 polities (Eastern Zhou dynasty – Spring and Autumn (770-476 BC) period)
  • 475 BC: 7 polities, (Eastern Zhou dynasty – Warring States (475-221 BC) period)
  • 221 BC: 1 polity (Qin dynasty)

(Source: Origins of Political Order, Francis Fukuyama)

Basically, the Qin started a meritocracy experiment. They assigned high-level military positions based on competence rather than kinship ties. They won more battles. Soon, other polities began to copy their meritocratic recruitment process. Those that didn’t were conquered. Later on, the Qin started assigning government positions based on merit too. Any peasant that can read and write could sit an exam and work their way up to very senior positions. Meanwhile, most other states around the world didn’t do this until much later. This is supposedly one reason why Asians value studying so much. Education is a path out of poverty.

With morals aside and only looking at the mechanisms, I’d argue this is natural selection at a nation state level. Some were more politically organised than others. Some adapted. Others died. Over time, the politically organised became the structural norm.

Now consider the Spanish conquest of the Aztecs. Long story short: deception, fear, steel weapons, guns, and most catastrophically, germs. Is this also natural selection? Well, many would argue that the circumstances are materially different in this example. There’s a lot more factors at play. It’s not that simple.

Another consideration. Many pacific island nations are at the mercy of rising sea levels. If they die off, is that just disorder feeding into antifragility? Is it an insult to antifragility to step in and help them? I’m sure most would of course not. Because rising sea levels was not their fault. It was the industrial nations with high energy usage per capita, and high capita. But the weak pays the biggest price. Okay, so if it’s not someone’s fault they don’t deserve to be sacrificed for the greater whole? What about the hundreds of thousands peasants and soldiers in during the Warring States period in China. Was it their fault? 

You can see where this is going. But I think this post is long enough.

These are not easy questions. The more you look into it the more tangled you realise it is. The more you learn, the more you realise how complex the world is.

 


5. Closing thoughts

5.1. Summary

  • Antifragility is a powerful mental model
  • Antifragile is beyond the resilient., It gains from disorder, volatility, uncertainty, stress, time etc.
  • It’s applicable in so many domains
  • An antifragile system needs some fragile components
  • Modernity unfortunately artificially denies the antifragile from its much needed disorder
  • This is particularly notable in the current economic paradigm – the relentless pursuit of efficiency
  • Non-linear means accelerating harm or loss. Many mistake non-linear to be linear. 
  • Convex is the favourable type of linear. It wins more than it loses from volatility. The antifragile is convex.
  • Black Swans are always out there.
  • When you have optionality, you want dispersion. Because you have favourable asymmetry and you don’t care about average outcomes, only the most favourable ones.
  • But, as with all mental models, we need to be wary of its limitations and misinterpretations
  • The notion of antifragility can easily be misused and abused by justifying extreme utilitarian solutions

I’ll now wrap up by going back to why this book made such an impact on me so you can see where I’m coming from.

 

5.2. Personal development

Why was this the right book at the right time for me?

Exactly five years ago, I was the national strategy and operations director for a not-for-profit, a youth leadership development organisation in Vietnam. Unimaginably rewarding but excruciatingly demanding. Shit happened everyday. There were always multiple fires burning. It never ended.

During such times, I lived by the energy bank concept. Start the day at a neutral zero. Some things/ moments/ thoughts etc are energy gains/deposits: powerful conversations, small wins, good food etc. And some things are energy drains/withdrawals: arguing with idiots, when someone breaks a promise, shit weather etc. Hopefully the day ends with a net positive. Usually it’ll reset. But sometimes the balance can rollover to the next day and accumulate.

But after reading Antifragile, I rigged my energy bank. Pretty much like what central banks are doing right now… I forced what used to energy drains to flip into energy gains. Instead of letting shit drain my energy, I figured why not re-train my perception of reality to gain from it. Let shit that flies at you strengthen you rather than hurt you. Don’t just survive the storm, thrive in it. Instead of weathering challenges, welcome them. Better yet, proactively seek them. Disorder and volatility is bitter medicine. Unpleasant now, but I know this is good for me. 

“Transmit positivity throughout uncertainty.” 

(This was one of the ‘leadership development characteristics’ explicitly defined by that not-for-profit I worked for that I valued the most.)

“The problem is not the problem, the problem is how you see the problem.”

(Don’t know where I first got this from. But I went around saying it all the time, during my director tenure. And successfully annoyed lots of people with it.)

Going further, when I looked back at all my shitty moments in life, I realised I always come out in a better position. A better person. Sometimes, the most effective way forward was by taking a much needed a slap in the face. Wake up. Grow up. 

“If you’re going to eat a shit sandwich, don’t nibble.”  

(Ben Horowitz, Hard Thing About Hard Things)

Of course, this is not easy. If it were easy, everyone would do it and there’s no point in writing this. So I’m still learning. But the consciousness of the idea and intent was the start that made all the difference. 

 

5.3. Antifragility as a complexity science mental model

So I valued Antifragile for its merits for practical application to personal development. But so many other books, articles, podcasts etc influenced me too. Mark Manson’s article Subtle Art of Not Giving a Fuck , for example, also influenced me a lot around the same time (in 2014). This was years before it turned into an immensely popular book.

The Subtle Art of Not Giving a Fuck, Mark Manson

So what made Antifragile so different? Why did it stand out so much?

Only recently I started to understand why.

It was my first proper glimpse into complexity science. An introduction to how its concepts serve as mental models (and meta mental models) that help with understanding the world. It had subconsciously made me see things with complexity lens all this time without explicitly knowing it was just complexity science. Complexity includes topics like:

  • Modular silos vs systems view
  • Networks
  • Domain dependence vs transdisciplinarity
  • Non-linear vs linear
  • First order effects vs second order effects
  • Chaos theory
  • Scaling laws and fractals
  • Emergence, self-organising, cellular automata
  • Information and computation
  • Modelling
  • Dimensionality
  • Ergodicity
  • …and more

But I could only appreciate this complexity science value the second time I read the book (in 2018). When I actually put the mental effort into digesting the technical/mathematical supporting concepts. Or, maybe I tried back in 2014 and I was just too stupid to get it. (This is why I wrote an 8k word blog post that could have been much shorter had I took out and further simplified the more technical sections. But I didn’t want to deny you from potentially getting as much out of it as I did.)

The interesting thing about studying complexity is, it puts you on this path to want to learn more horizontally. History, economics, business, philosophy, sociology, mathematics, science, finance, psychology, etc. That’s what it is. The book was kind of like the catalyst that turned my interest in how the world worked into an obsession.  

 

5.4. If you wish to continue exploring

This post is by no means a summary of the book. It’s more like study notes for some of the key ideas in the book. Maybe I briefly covered on half or so.

It may not be the right book at the right time for you as it was for me. But hey, if you found the concepts interesting, I’d encourage you to consider exploring it further. Read the actual book.

If some his other books are on your reading list: Fooled by Randomness, Black Swan, Skin in the Game – you can actually cover most of them by just reading Antifragile.

“…This makes this book my central work. I’ve had only one master idea, each time taken to its next step, the last step—this book—being more like a big jump.” – Taleb

Thank you for reading. I hope this post serves you as well as it has served me in writing it.


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