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Intuitive Mathematical Economics Series. General Equilibrium Models and the Gradient Field Method

Author

Listed:
  • Tomás Marinozzi
  • Leandro Nallar
  • Sergio Pernice

Abstract

General equilibrium models are typically presented with mathematical methods, such as the Edgeworth Box, that do not easily generalize to more than two goods and more than two agents. This is fine as a conceptual introduction, but it may be insufficient in the “Big-Data Machine-Learning Era”, with gigantic databases filled with data of extremely high dimensionality that are already changing the practice, and perhaps even the conceptual basis, of economics and other social sciences. In this paper present what we call the “Gradient Field Method” to solve these problems. It has the advantage of being, 1) as intuitive as the Edgeworth Box, 2) easily generalizes to far more complex situations, and 3) nicely mesh with the data friendly techniques of the new Era. In addition, it provides a unified framework to present both, partial equilibrium, and general equilibrium problems.

Suggested Citation

  • Tomás Marinozzi & Leandro Nallar & Sergio Pernice, 2021. "Intuitive Mathematical Economics Series. General Equilibrium Models and the Gradient Field Method," CEMA Working Papers: Serie Documentos de Trabajo. 820, Universidad del CEMA.
    • Handle: RePEc:cem:doctra:820
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    References listed on IDEAS

    as
    1. Sergio A. Pernice, 2019. "Intuitive Mathematical Economics Series. Linear Structures I. Linear Manifolds, Vector Spaces and Scalar Products," CEMA Working Papers: Serie Documentos de Trabajo. 689, Universidad del CEMA.
    2. Sergio A. Pernice, 2018. "Intuitive Mathematical Economics Series. Constrained Maximization and the Method of Lagrange Multipliers," CEMA Working Papers: Serie Documentos de Trabajo. 680, Universidad del CEMA.
    3. Sergio A. Pernice, 2020. "Serie de Machine Learning. Revisión de Algebra Lineal 1," CEMA Working Papers: Serie Documentos de Trabajo. 736, Universidad del CEMA.
    4. Susan Athey & Guido W. Imbens, 2019. "Machine Learning Methods That Economists Should Know About," Annual Review of Economics, Annual Reviews, vol. 11(1), pages 685-725, August.
    5. Athey, Susan & Imbens, Guido W., 2019. "Machine Learning Methods Economists Should Know About," Research Papers 3776, Stanford University, Graduate School of Business.
    6. Sergio A. Pernice, 2018. "Intuitive Mathematical Economics Series. Chain Rule and Derivatives of Functions Defined Implicitly," CEMA Working Papers: Serie Documentos de Trabajo. 679, Universidad del CEMA.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    microeconomics; general equilibrium; radient; gradient field; machine learning.;
    All these keywords.

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