A sideways look at economics
Throughout history there have always been observations humans can’t explain. In the ancient world, unexplainable events were often attributed to the all-encompassing brush of ‘fate’. The Roman goddess Fortuna, goddess of luck and fate, was often depicted standing on a ball, indicating her uncertainty, and blindfolded to show her lack of bias. Romans could conveniently stop worrying about understanding things that, as far they knew, were out of their hands. Today, we rarely credit fate; but does it still influence our lives?
Over time, science has gradually explained away many uncertainties that baffled our ancestors. Poor health, for example, is explained not as fate but instead as the outcome of a combination of factors including our diets, exercise and environment. But there remain countless things modern humans cannot yet explain or predict with certainty. What will the weather be like next week in Corfu? What will be the outcome of a dice roll? These days, instead of attributing these things to fate, we credit randomness. But is there even such a thing as randomness? Perhaps we just lack information, like our ancestors.
Humans love identifying patterns. This has no doubt aided our evolution, from allowing us to learn which plants were safe to eat as hunter-gatherers to helping modern scientists find cures to complex diseases. However, this trait makes us pretty bad at defining or even accepting randomness. Let’s say we are flipping a coin ten times and noting down “H” for heads and “T” for tails for each flip. The results could look like either of the following:
H H T H H T H T T T
H T H T H T H T H T
But which do you think was more likely to be a random sequence? You’re probably aware, at least in theory, that each is just as likely as the other. However, there is a part of us that does find the first sequence more random than the other. The alternating pattern in the second is hard not to notice, and it creates doubt in our minds about its randomness. It also raises the question that if the first sequence were longer, would we eventually convince ourselves of a pattern there as well, and hence its predictability? Mankind is almost hardwired to reject randomness — yet at times we rely on it.
Economists rely on randomness to remove bias from our forecasting models, for example. But how can we model randomness using computers, which are designed to be predictable? There are many methods that attempt to do this. Most use an external variable (one that the computer itself cannot generate) and feed it into a fixed algorithm. This external variable could be the millimetres of rainfall in London yesterday or the currently highly unstable growth in energy prices. In the vast majority of cases this degree of unpredictability is sufficient, and is termed ‘pseudo randomness’. However, if we were to run the programme in the same conditions it would produce exactly the same ‘random’ number. Can we really say a generated number is random if, given the same conditions, it is repeatable?
Computers are valuable to us exactly because they are predictable. They are made to give the same output given the same inputs. Apart from when they don’t… Back in 2003, Belgium held its first ever electronic elections. When counting was taking place, one candidate was found to have more votes than there were eligible voters. Instantly, there were suspicions of foul play, but the real reason was much more innocent. A cosmic ray, travelling at the speed of light and smaller than an atom, was found to have hit a voting machine in just the right way to assign an extra 4,096 votes to the candidate! How’s that for predictable?!
The real world works in a similar way to computers, bound not by lines of code but by the laws of physics. Given all the variables affecting a leaf falling from a tree, such as air flow and gravity, the law of physics will determine exactly where it will land. Given a sufficiently powerful computer and knowledge of all the variables, then we could predict the leaf’s destination, right? If we can do this, then why not the movement of a fish? Or, even harder, how long the next UK prime minister will last? But maybe that’s too ambitious… This can crystallise into the depressing conclusion that all our actions can be predicted, given a powerful-enough computer. And there is no randomness.
One flaw in this argument is the assumption of a sufficiently powerful computer. In a virtual simulation, with a small number of parameters, it would be possible to calculate with certainty simple outcomes. The outcome of a dice-roll or coin-flip. But how big would your computer need to be to predict a cosmic ray hitting a specific part of a voting machine in central Belgium exactly on election day? In the real world, there are countless other variables that need to be factored in. You could go on indefinitely adding more and more variables and as such would need an increasingly powerful computer to process this information. To be able to predict events with absolute certainty you would need an infinitely powerful computer. One that not only contains all information of the universe but also the ability to process it.
There will always be events and realities that we will never be able to predict. For us, randomness is a constant, explaining all variables we cannot reliably factor in. We can and do get close to predicting the future, as long as we acknowledge the unknowns. Economists do this frequently, displaying results in confidence intervals that never reach 100%. Some do this better than others of course, but there is never certainty. We are as likely to be 100% certain of the future as blindfolded Fortuna balancing on a ball. Randomness, or fate, explains the incalculable residual of any forecast, no matter if it’s predicting GDP or what you’re having for lunch. Trying endlessly to understand why can lead to the dark sides of nihilism. The ability to accept our fate without understanding it is as important as our faith in the ability to sway it. We cannot escape fate, but, as paradoxical as it seems, we are not slaves to it either.
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