The Interesting Times: The Model that Explains Political Revolutions and Financial Crises

Hi there,

This week's financial media channel Real Vision hosted a Festival of Learning. My Mutiny Fund Partner Jason and I both interviewed some great speakers including Hugh Hendry, Ed Misrahi, and Jerry Haworth. I'll share links here when they’re available.

I have also been invited by Chris Sparks from Lunch Hour to discuss how we can become more accurate predictors of the future and best position ourselves to be antifragile to an uncertain world. Sign up here to join in on the 25th of September.

The Mental Model that Explains Political Revolutions and Financial Crises

When asked how he went bankrupt, Ernest Hemingway replied “gradually, then suddenly”. It’s a great phrase because it seems to apply to so many things. Often times, things seem “stuck” until they change all of a sudden.

An industry that has seen no innovation or change is suddenly upended by a new entrant (E.g. Payment processing before Stripe, Cabs before Uber/Lyft).
A peaceful movement fights against violence and oppression for years, and nothing much changes. Then, everything changes. (e.g. Arab Spring, American Revolution)

There’s a helpful framework from complexity science for understanding this phenomenon: attractor landscapes.

Attractor landscapes were originally used in physics but are broadly applicable to genetics, business, politics, and options trading among many other fields, and getting a better understanding of them has helped me make better decisions in an understanding world.

Let’s take a simple example: you’re fishing in a small pond.

Fish reproduce, so you catching fish won’t lead to them disappearing from the pond as long as you don’t catch too many, too fast.

However, beyond some level, fish will die from overpopulation as there isn’t enough food for them to all feed on. And, below some threshold, fish are too sparse and can’t find a mate to reproduce so they die out.

We can represent this with a spectrum like the one below where the arrows represent the natural tendency of the fish population.

The arrows underneath the bar show the tendency of the population to grow or shrink. Attribution to https://ncase.me/attractors/

In this example, the fish population has two attractor points: seventy (70) fish and zero (0) fish.

Without fishing, the natural population is around 70. If you start catching fish then there will be fewer fish and more food for each fish so they will have plenty to eat so that they are healthy and reproduce.

So if you catch ten fish one week and then take a week off, the population will trend back towards 70.

Similarly, if you don't fish at all and the fish reproduce too much then overpopulation will cause there to be not enough food and so some fish will die out and the population will trend back towards a population of 70.

This makes the population of 70 an attractor point.

However, if you catch too many fish, too quickly then you pass a tipping point. In this model, once there are less than 30 fish, the fish won’t be able to reproduce and survive and the fish population will die out.

At 31 fish, if you stop fishing then the population will slowly recover to it's attractor point of 70 as the fish have plenty of food and are able to produce new little fishies.

However, At 29 fish, the tipping point has been crossed and there’s no going back. A process that starts out very gradual as you approach 30 fish, suddenly snaps in a very sharp and not so great way.

We can visualize this with valleys as attractor points and mountain tops as tipping points.

Metaphorically, the population of fish behaves like a ball rolling down a series of hills. As long as you are within the valley, the population tends to “roll” back towards 70.

This is a pretty unintuitive thing and you can often see people misunderstanding that. Can you imagine the public debates where there is an "unlimited fishing camp" and a "restricted fishing camp?"

The "restricted fishing camp" would be trying to explain that if there is too much overfishing then the pond will go barren and there will be no more fishing.

The "unlimited fishing camp" will point to years of historical data: "These restricted fishing people are just fear-mongering, the fish population has always rebounded in the past!"

This example is much simpler than real complex systems because no one knows where the tipping points actually are in real life. We can build models to estimate them, but those models are always going to be approximations with their own errors.

Attractor landscapes are a useful mental model for thinking about lots of complex systems from Political uprisings like the Arab Spring to financial market crashes like what we saw in March of 2020. Everything seems quiet for a long time until some precipitating event causes a tipping point to occur as COVID-19 did in March.

The main lessons I've taken away from attractor landscapes are.

1. In a complex system, just because the past has been characterized by stability or low volatility, does not mean the system is impervious to change. It may just mean that we have not yet reached the tipping point.

2. For systems where the cost of passing a tipping point can be catastrophic (e.g. a nuclear power plant reactor or your life savings), you want to paint a pretty wide margin of safety and make sure you aren't anywhere near the tipping point.

Can you think of other good examples of attractor landscapes in your own life? Or other lessons we can draw from them? Hit reply and let me know.

All images are from Nicky Case's wonderful interactive post on attractor landscapes. Highly recommended.

How the Eggheads Cracked
The New York Times Magazine

I love going back and reading old newspaper or magazine articles to see how narratives of events change over time.

It is doubly a pleasure when the writing is excellent. This piece was written by Michael Lewis (Moneyball, The Big Short) after the blow up of hedge fund Long-Term Capital Management (LTCM) in 1998. LTCM lost more than $4 billion in a bizarre six-week financial panic that makes a lot more sense in light of an understanding of attractor landscapes. Everything was fine, until suddenly, it was very, very bad.

The link above is paywalled for NYT subscribers but you can read the article on Archive.org.

The Case for Global Investing

Meb Faber

Investors (particularly Americans and Australians) tend to exhibit a well-known home country - they invest too much of their savings in their own country instead of diversifying internationally. This post goes through much of the research showing the value of diversifying globally.

The most interesting factoid for me: Investors with an equal weight investment into France, Great Britain, USA, Russia, and Germany in 1900 would have done almost as well just investing in the best performer (USA), even though Russia and Germany blew up.

This is very counterintuitive. "Picking the winner" added almost no value to just equal-weighted diversification across the five largest economies, but this is the magic of diversification!

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