Description
This talk discusses recent developments in open source computational economics, with a focus on the Econ-ARK project and dynamic stochastic optimization problems. Economics is often concerned with agents making choices across periods of time and interacting through a market. Historically, these problems have been solved using dynamic programming methods that are plagued by the curse of dimensionality. In practice, economics models were either dramatically simplified for tractability or solved to only rough approximation. Recent work has shown how deep learning can be used to solve these problems in a much more efficient way. Today, more models are computationally feasible, and we should expect general computing methods to continue to expand this horizon. Thus, what's needed is a portable way of representing economic models which is agnostic to solution methods. I'll present early-stage efforts to produce such a language as a flavor of language that is compatible with Sympy.