SciPy 2024

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.

Recent computing advances have made complex models, such as heterogeneous agent models, feasible to solve. But there is not yet a portable format for representing these models that is not coupled with a particular method of analysis. This talk discusses recent work to define a flexible format for economic models that integrates with tools in the SciPy stack. This system expresses dynamic stochastic models modularly, in ‘blocks’. These models are agnostic to solution methods, and can express models that can only be solved approximately. This talk is intended for those using economic models in research, policy-making, and industry practice.