Throwing Money Five
This time, I don’t have any simulations to share. Instead, I’d like to reflect on the sims that have already appeared in this series and follow through on a promise I made last time: to reveal what I thought was the deeper truth that these economic models provide.
But to make that point, it’ll help if I first cover a few other bases about what I’ve been trying to do.
Sims are simple
The first thing I want to do is encourage you to look at the page source for these posts and check out the code. Maybe you’re not a coder and you don’t care, but even so, it’s worth taking a look at just how much complexity there isn’t in these simulations. They’re tiny. Furthermore, they don’t require much effort to understand. I want you to look because I want you to see that the frontier of science is right in front of you.
Sure, there are plenty of scientific disciplines in which you have to get a PhD and study for years to make a difference, but simulation science isn’t one of them. And there are so many possible sims you can build that the chance of you writing one that the world has never seen is remarkably high.
Does that mean that everything you write will be wanted in science journals across the world? Of course not. But unless you’re trying to be a career scientist, who cares? You don’t have to be part of some grand department to be a seeker after truth. We live in an age when the tools for building thought experiments and testing our rational assumptions have never been better. And yet we’re seeing those very tools be used in a way that erodes reason instead of supporting it. The right solution isn’t to trust science to someone in a lab who we suspect is cleverer than we are. It’s to engage, because we can.
I don’t have a PhD. I got a C in my Physics A Level at school in England. I did no physics at university. And yet I’ve done quantum gravity research. How come? Just two things: curiosity and writing code for scientists without getting paid.
The next point I want to cover is about the reason for it all. You might say why bother with all this?. After all, these models are simple and people are complicated. How can we possibly hope to capture what makes the rich, vivid, challenging mess that is human society tick with just a couple of pages of code?
The answer is simple. We don’t try to model all of it. Just certain important parts. And furthermore, the idea that you can’t model things like human society presumes that if you put a lot of complicated people together that the combined result must necessarily be more complicated.
But the world doesn’t work that way. Consider traffic patterns. Every driver of a car is a complex, nuanced individual with a completely different set of desires and goals to the others around her. However, all traffic jams look pretty much the same.
This is because every pattern in the world that’s emergent (that seems to grow out of the behavior of many elements) operates as a kind of filter. Only certain actions that one person can take will result in visible changes when we look at a crowd. The rest of their differences get washed out.
Human social systems often work like this. Whenever you vote or buy soap, the options presented to you are finite. The reasons for your choices can be totally different from everyone else’s, but certain effects will aggregate and others won’t. Just having finite choices, or having to compensate for the choices of others, means that the behavior of the social whole is often different from that of its parts.
Testing ideas makes us wiser
There’s another key reason to bother with models, which hooks up with something I mentioned in the last post. And it’s this: even if the models we make don’t capture the phenomena we actually see in the world, they still force us to test our ideas and figure out what they actually mean.
There has recently been some lovely psychology research that shows how people imagine they know more than they do. This gap in our understanding of the world is never more acute than when we’re making decisions about the society we’re a part of, or systems in the world that are far larger than we are, like the environment. We usually don’t doubt our beliefs about such things because we imagine an ad-hoc theory or something we read on the web is the best we can do. But if an idea, when expressed in a model, doesn’t behave the way that guessed, then there must be something we’re missing.
What this means is that costly experiments and laborious data collection aren’t always necessary to improve our understanding of the world. They certainly help, but the easiest, cheapest place to refine our thinking is to use the tools right in front of us. Armchair pontification is cheap. Model-driven pontification is slight less cheap, which is a great start.
Which brings me to my core point about the simulations I’ve been posting this week: clues about how economies work are right in front of us in these models. If we think skeptically and test our understanding of the world, we can start to notice things that even many experts miss.
What we’ve learned is that the easiest way to generate a realistic wealth distribution is to play out the random motion of cash in a population. In other words: the action of entropy. Furthermore, that entropic process never runs backwards, because, after all, entropy seldom does. These wealth distributions have undergone something that physicists call called symmetry breaking.
When Thomas Piketty came out with his famous book, there were economists lining up to pour scorn on his ideas. But the process of escalating inequality he observes in the data he collected is just like the symetry breaking we’ve modeled here.
For me, that raises two questions:
One: If he’s wrong, how come wealth ineqality doesn’t behave like the action of entropy, why is the curve that it produces always exactly like it?
Two: Given what we see here, how can we possibly believe in the power of the free market to pick the right prices for things?
It seems pretty obvious here that in a free market, power is going to eventually slide into the hands of a few. At the creation of a market, when every trader has relatively equal power, the prices for things will reflect the needs of the many. But over time, the prices for things will inevitably start to reflect the desires of the few because only a few will have the spare resources required to set prices. Furthermore, adding talent or business acumen to that picture only accelerates this process, as we’ve seen.
Thus, believing in the power of unrestrained free markets to reflect fair prices indefinitely is equivalent to believing that entropy can go backwards.
“Hang on a minute!” says our Conservative voice, Ayn. “If that’s true, how come we don’t see that in markets? How come stock exchanges work at all? I don’t buy it!”
The point of Piketty’s book is that we do see it. How come markets work? Because large things like national economies take a long time to fall over. You can imagine a fair economy like a pencil balanced on its end. It has loads of potential energy. Once it chooses a direction to fall, potential energy gets converted into kinetic energy. Or in the case of the market, goods can be exchanged at reasonable prices. But then the pencil hits the table, bounces, and stops. Or, in market terms, you get something like the Credit Crunch and its aftermath. Wealth accretes around a handful of billionaires.
“Then how come we have viable markets in the first place?” says Ayn.
Because when truly disruptive events come along, like the Industrial Revolution or World War Two, they create relatively equal envionments in which a large number of parties can engage in the process of trying to better their lot. The problem we have now is that that’s not where we are.
“So we need a revolution,” says Karl. “A planned economy!”
I don’t think so. From what I’ve seen, top-down controlled economies tend to end up with inequality curves just like the ones we just looked at. If anything, they seem even steeper. The inexorable logic of inequality doesn’t really care what’s being traded, whether it’s money or political favors in a single-party system. I suspect that we need something else.
I want to come back to this topic with models that more closely resemble actual markets because I’d like to know what would work. But in the mean time, the meta-point stands: we understand the world when we look past just the specifics in front of us and start investigating the higher-level patterns. When we exercise our curiosity, we are stronger and wiser. And if you’ve read this far, you’re already doing that. So I salute you, dear reader. Thank you for going on this journey with me. Next time, I’ll give the wealth models a rest, and post about something a little different.