Less Is More

Error, Human

Much market analysis operates on the assumption that more data is better – more data leads to more accurate results. More data may require more complex processing, leading to greater and greater requirements for computing power but, in principle, the idea is that more is better.

Out in the real world, however, we don’t have the luxury of this kind of analysis. This leads to errors which sometimes we call biases. But surprisingly it also, often, leads to better results. It may just be that the reason we make so many mistakes is because we’re trying to process too much information, not because we’re naturally error prone.

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The Curse Of Seven

I’d like you to memorise a number for me. The number is 111011101001101010010. Easy, huh? Now, read on.

Shouting at the Plastic

We humans have many unusual traits. We’re the only mammal that can’t swim when it's born, the only mammal whose male can’t tell when its female is fertile, we have large brains which we habitually attack with fermented grains, we walk upright other than in ongoing fermented grain situations, and, perhaps most importantly, we use language to communicate (even when fermented grains are involved).

Now language, of course, is a curious thing. Given the huge amount of brain space and energy it consumes it was clearly very important to our survival as a species. So it’s odd that many of us then use this gift to shout inanely at mobile phones in public spaces. That’s what we call an “unintended consequence” and it helps lead to a big investing problem, the issue of information overload.

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Satisficing Stockpicking

Logical Lab Rats

When we make decisions we nearly always do so in the context of something or other. In fact about the only time we’re asked to make contextless choices is in academic exams and laboratory based psychology experiments. As these are the two most familiar situations faced by the academics generating the theories that underpin most of modern finance we shouldn’t be awfully surprised if their great ideas are somewhat lacking in any understanding of … well, anything, really.

Being trained to think logically and probabilistically is a necessary part of being a modern economist, but it’s hardly a requirement for most people in most professions most of the time. You don’t find many baristas trying to make Bayesian inferences about which particular coffee to pour next. We clearly don’t rationalise most decisions, we make them quickly and effortlessly. We don’t optimise, we satisfice.

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