Monday, May 16, 2016

Recognizing complexity by inspection

Eric Liu (speechwriter) and Nick Hanauer (business person) have a new article at Evonomics that is an excerpt from their book The Gardens of Democracy. Obviously, they have the requisite skills to identify a complex nonlinear system by inspection:
Traditional economic theory is rooted in a 19th- and 20th-century understanding of science and mathematics. At the simplest level, traditional theory assumes economies are linear systems filled with rational actors who seek to optimize their situation. Outputs reflect a sum of inputs, the system is closed, and if big change comes it comes as an external shock. The system’s default state is equilibrium. The prevailing metaphor is a machine. 
But this is not how economies are. It never has been. As anyone can see and feel today, economies behave in ways that are non-linear and irrational, and often violently so. These often-violent changes are not external shocks but emergent properties—the inevitable result—of the way economies behave.
Yes, it's so violent:

For reference, let's look at an actual complex biological system (Lynx population with predator-prey dynamics):

If the US economy was as violent changes as the population dynamics of a Lynx, the economy would have collapsed to approximately zero GDP and sprung back again [1]. During the past 70 years, the US economy has not received a quarterly shock of more than -10% (and that's annualized — equivalent to an actual quarterly shock of less than -2.5%). Shocks on the order of a few percent mean the economy is well within the realm of perturbation theory.

Whatever your theory of how economics works ...

\frac{\Delta NGDP}{NGDP} \approx 0

is an excellent starting point. Do I have to show this graph again:


Footnote added 5 June 2018

[1] Part of what defines a dynamical system's chaos is that it visits nearly all of its phase space and nearby elements separate exponentially in time. 


  1. Jason, this isn't directly related, but I found this from Nate Silver to be interesting, regarding his failures to predict the outcome of the GOP nomination. He talks about "chaos" a bit in there and the difficulties in producing a good model, especially for the early stages of the race. I don't know if there's any overlap in your opinion with making economic forecasts. I do like how he emphasizes the discipline of sticking to a mathematical model and that making predictions creates learning opportunities for the modeler (the "fail forward" concept).

    Off-hand, I don't see why making the forecasts that Nate does should be any less daunting than constructing models to make economic forecasts.

    1. In a sense Nate Silver's polls-only models are a kind of model-free estimation. They still involve models to understand e.g. registered voter polls versus polls that use cell phones, etc ... basically weighting = model.

      The polls plus version is more of a real model -- not just polls, but endorsements based on "the party decides" theory.

      In economics, some econometric forecasts are somewhat model free in the same sense as the polls only forecasts from Silver (e.g. I think GDPnow from the Atlanta Fed really just looks at the correlation of indicators that are available before the official GDP release -- a kind of "Big Data" approach). But for the most part in economics you need a model to even understand what the data means. It makes it harder. For example, you need to understand the theoretical concept of inflation to understand the difference between real and nominal gdp.

      I think GDPnow kind of does for macro what Silver does for elections. I have a post I am working on where I look at GDPnow ...


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