Modeling the expectations of inflation in the OLG model with genetic programming   [GP]

by

Chen, S.-H. and Yeh, C.-H.

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Info: Soft Computing - A Fusion of Foundations, Methodologies and Applications (Journal), 1999, p. 53-62
Keywords:genetic algorithms, genetic programming, overlapping generations models, bounded rationality, agent-based computational economics, Pareto-superior equilibrium
Abstract:
genetic programming (GP) [GP] [GPG] is employed to model learning [ML] and adaptation in the overlapping generations model, one [OG] of the most popular dynamic economic models. Using a model of inflation with multiple equilibria [ME] as an illustrative example, we show that our GP-based agents are able to coordinate their actions to achieve the Pareto-superior equilibrium [PE] (the low-inflation steady state) [SS] rather than the Pareto inferior equilibrium (the high-inflation steady state). [SS] We also test the robustness of this result with different initial conditions, economic parameters, GP control parameters, [CP] and the selection mechanism. [SM] 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.
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BibTex:
@Article{chen:1999:SC,
  author =       "Shu-Heng Chen and Chia-Hsuan Yeh",
  title =        "Modeling the expectations of inflation in the {OLG}
                 model with genetic programming",
  journal =      "Soft Computing - A Fusion of Foundations,
                 Methodologies and Applications",
  year =         "1999",
  volume =       "3",
  number =       "3",
  pages =        "53--62",
  month =        sep,
  keywords =     "genetic algorithms, genetic programming, overlapping
                 generations models, bounded rationality, agent-based
                 computational economics, Pareto-superior equilibrium",
  ISSN =         "1432-7643",
  doi =          "doi:10.1007/s005000050053",
  abstract =     "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.",
}