Farmers conclusion "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."

Occam's  razor ?


Now an as if approach might continue along the Lineraize and add  R  shocks framework

But what's so wrong with determined systems modeled by determined systems

Figure it out and agents can game it...as if they don't already game it
Albeit with a percentage of failures often big surprising ones even !

Yes once agents figure out the " workings "  given really good initial readings
And periodic re readings for steerage ...
But they won't figure it out
   it's too complex

Disregard this post
It's jumblicious

Can economic systems be chaotic ?

Perhaps not if agents can react to discovered patterns in say prices of stocks





Brock
"constant, the so-called Feigenbaum constant. It is so beautiful to play with these equations and that is what attracts me to it. It is tough though to find economic relevance of it. This is because a lot of economists, especially macro- economists, work with aggregate data. A lot of this stuff that might happen at a more micro-le- vel disappears when averaged out. Also, there are a lot of smoothing mechanisms in econo- mics."

 "In the stock-exchange for example; if you think a stock is going up or down on a weekly

basis, you construct a portfolio to exploit it. B basis, you construct a portfolio to exploit it. But then everyone else can do the same thing and the entire effect will vanish.

The only way to really get chaos going and be able to defend it, is that the economy has to have a large number of sectors. This means that the difference equation has to be replaced with vectors. However, the sufficient conditions to get chaos are just too tough, because of in- tertemporal and cross sectional smoothing."  



Farmer again and better

"Is it useful to approximate a complex system by a stochastic linear model. I personally think so. But it ultimately comes down to the ratio, in the data, of signal to noise. If an economy really does converge to a limit cycle, but the shocks that hit the system are large, the limits cycle will look like a point. If the shocks are small, relative to the underlying dynamics, we should be able to see that in data. Scatter plots of investment to GDP ratios at adjacent dates should, for example, should cluster around a doughnut. They don't.