no complexity theory in economics
Roger E. Farmer has a blog post on why economists should not use complexity theory. At first, I though he was going to argue that complexity models have been dis-proven or they use unreasonable assumptions. Instead, he simply says we don’t have enough data:
The obvious question that Buzz asked was: are economic systems like this? The answer is: we have no way of knowing given current data limitations. Physicists can generate potentially infinite amounts of data by experiment. Macroeconomists have a few hundred data points at most. In finance we have daily data and potentially very large data sets, but the evidence there is disappointing. It’s been a while since I looked at that literature, but as I recall, there is no evidence of low dimensional chaos in financial data.
Where does that leave non-linear theory and chaos theory in economics? Is the economic world chaotic? Perhaps. But there is currently not enough data to tell a low dimensional chaotic system apart from a linear model hit by random shocks. Until we have better data, Occam’s razor argues for the linear stochastic model.
If someone can write down a three equation model that describes economic data as well as the Lorentz equations describe physical systems: I’m all on board. But in the absence of experimental data, lots and lots of experimental data, how would we know if the theory was correct?
On one level, this is a fair point. Macro-economics is notorious for having sparse data. We can’t re-run the US economy under different conditions a million times. We have quarterly unemployment rates and that’s it. On another level, this is an extremely lame criticism. One thing that we’ve learned is that we have access to all kinds of data. For example, could we have m-turker participate in an online market a million times? Or, could we mine eBay sales data? In other words, Farmer’s post doesn’t undermine the case for complexity. Rather, it suggests that we might search harder and build bigger tools. And, in the end, isn’t that how science progresses?