reinforcement learning approach to solving incomplete market models with aggregate uncertainty

  1. Andrei Jirnyi 1
  2. Vadym Lepetyuk 2
  1. 1 Kellogg School of Management, Northwestern University
  2. 2 Universitat d'Alacant
    info

    Universitat d'Alacant

    Alicante, España

    ROR https://ror.org/05t8bcz72

Zeitschrift:
Working papers = Documentos de trabajo: Serie AD

Datum der Publikation: 2011

Nummer: 21

Seiten: 1-22

Art: Arbeitsdokument

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Zusammenfassung

We develop a method of solving heterogeneous agent models in which individual decisions depend on the entire cross-sectional distribution of individual state variables, such as incomplete market models with liquidity constraints. Our method is based on the principle of reinforcement learning, and does not require parametric assumptions on either the agents' information set, or on the functional form of the aggregate dynamics.

Fuente de los datos: Dialnet