I think you're just spouting a lot of buzzwords, and you're actually conflating (a) the relevance of chaos theory and (b) criticisms of normal distributions in

I think you're just spouting a lot of buzzwords, and you're actually conflating (a) the relevance of chaos theory and (b) criticisms of normal distributions in economics. A major recent criticism is of some economic models which have assumptions to the effect of "returns on this investment will be normally distributed." The criticism is that evidence seems to suggest that returns are not actually normally distributed; rather they have "fat tails," meaning that extreme events are more likely than with a normal distribution. And this error causes economic models to discount the role of these unlikely extreme events. The Black-Scholes model effectively models stock prices as Brownian motion. This boils down to a single assumption: stock prices are the buildup of lots of very small INDEPENDENT random events (with FINITE variance) that occur over short times periods; this leads straight to Brownian motion. Brownian motion is indeed continuous; however, it is nowhere differentiable with probability 1. Because of the assumptions of finite variance and independence, the CLT tells us that returns will be normally distributed. So here, people pretty much agree that it's always these assumptions of independence that cause these models to predict poorly. This is totally different from chaos theory. First of all, chaotic systems are not necessarily complex. Here's a chaotic map with a "nice little formula": f : [0,1) -> [0,1) f(x) = 2*x (mod 1) While this is chaotic, we can understand much about this map. For example, each application produces an entropy of 1 bit (see entropy of dynamical systems...). So this is a map that "produces randomness" in a way. This is philosophically nice, because probability theory itself doesn't at all explain how randomness comes about.