For those of you who regularly follow my writings, am sure you have observed by know my fascination with behavioral economics/ finance & the psychology of crowds. One of my major insights from studying the work of OG investors like Charlie Munger, Howard Marks and Bruce Flatt is that the key to superior (i.e., above market average) returns is to be non-consensus & right. Getting a read on how the crowd is behaving at any point in time is one of the important analytical tools necessary to achieve non-consensus behavior.

To simplify, a crowd is a set of largely independent & uncoordinated entities, though you can define it in many other ways as per your context. There are many mental models to visualize the properties & behavior of a crowd. These include the Madness of Crowds, Herd Behavior, Social Proof, Incentives etc. However, during a recent trip to London, the city’s “Tube” train system brought back the most fundamental of these models right in front of my eyes – the normal distribution, popularly called the bell curve.

So, here’s the story. Last week, I landed at Gatwick on a busy morning, and boarded the train to Heathrow. The first thing I observed is how significantly better the London transit system is compared to anything I have experienced in the US. Even the NYC subway is nowhere close in terms of quality, multi-modality & cleanliness.

This particular train (I think it was called the Southeastern) had a very cool feature wherein it displayed how crowded each carriage was in the train, so people could shuffle around. Check out the below pic I took of the display in my train – do you notice an interesting pattern within it?

The distribution of the crowd across carriages is very close to a bell curve. Out of 12 carriages, the middle 5 are “standing room only” (yellow), 3 on the right and 2 on the left are “few seats available” (dark green) and the 2 carriages on extreme left & right are “plenty of seats available” (light green).

Seeing this pattern in a random, real-life event involving hundreds of independent & uncoordinated strangers blew my mind. I couldn’t resist taking its picture even while hanging on to 2 large bags while getting jostled in a..wait for it..middle carriage (see the bottom part of the above pic, it says “you are in coach 7”). I was myself in the middle bulge of the bell curve!

Now, besides this being a nerdy but cool anecdote, is there anything to learn from it? The applicability or importance of a normal distribution is not the main point here. The real insight is that attempting to decode & model how the crowd is behaving in a certain environment, as well as its potential implications, can by itself give investors a massive head start.

As Howard Marks says in his latest memo “Taking the Temperature“:

So, to be successful at contrarianism, you have to understand (a) what the herd is doing, (b) why it’s doing it, (c) what’s wrong with it, and (d) what should be done instead & why.

Howard Marks (Taking the Temperature)

The importance of rigorously decoding crowd behavior (or what we often call “the Market”) can’t be emphasized enough due to the simple reason that the crowd is right most of the time. When the investor-crowd is signaling that a company is un-fundable, most of the time it has correctly identified a weak business. If the market is predicting an interest rate cut by the Fed in the next few quarters, its combined wisdom is likely to be more accurate than most experts. If investors at large are investing in the AI wave or piling into an EV stock, they are indeed spotting a market opportunity that is likely to be exponential. If investor interest is low in a particular real estate location or type, most of the times it’s due to the right reasons.

While going blindly against the market consensus is flawed, first-order thinking, asking the right questions around “what” the market is doing & “why” is the first step of rigorous, second-order thinking.

The difference between “the market has spotted/ rejected an opportunity correctly” vs “the market is overly optimistic/ pessimistic on the said opportunity” is a fine nuance that can create a big delta on long term returns.

In particular, second-level thinkers understand that the convictions of the masses shape the market, but if those convictions are based on emotion instead of sober analysis, they should often be bet against, not backed.

Howard Marks (Taking the Temperature)

Abstracting this idea of understanding patterns in crowd behavior a bit more, I believe there is tremendous value in seeing various aspects of life as a distribution of outcomes. Personally, I find probability distributions more helpful in understanding how the real world works in a continuum, as opposed to statistical distributions, which are like static snapshots of reality & more academic in their usefulness.

Probability reflects how life operates in the “grey”. I have found viewing the world probabilistically to be immensely helpful in managing risk & uncertainty in every aspect of life. Too bad they don’t teach these applications while covering the subject in school!

Btw, coming back to the earlier train story, I practically used the bell curve pattern in how Londoners board trains by myself lining up either in the extreme beginning or extreme end of the platform during subsequent trips. Oh, the joy of boarding an empty carriage from the busy London Bridge station. Just goes to show that being a bit nerdy can sometimes be useful in practice!