Modeling the expectations of inflation in the OLG model with genetic programming

Abstract

 In this paper, genetic programming (GP) is employed to model learning and adaptation in the overlapping generations model, one of the most popular dynamic economic models. Using a model of inflation with multiple equilibria as an illustrative example, we show that our GP-based agents are able to coordinate their actions to achieve the Pareto-superior equilibrium (the low-inflation steady state) rather than the Pareto inferior equilibrium (the high-inflation steady state). We also test the robustness of this result with different initial conditions, economic parameters, GP control parameters, and the selection mechanism. We find that as long as the survival-of-the-fittest principle is maintained, the evolutionary operators are only secondarily important. However, once the survival-of-the-fittest principle is absent, the well-coordinated economy is also gone and the inflation rate can jump quite wildly. To some extent, these results shed light on the biological foundations of economics.