An aggregation method for large-scale dynamic games

Research output: Working paper

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It is a well known fact that many dynamic games are subject to the curse of dimensionality, limiting the ability to use them in the study of real-world problems. I propose a new method to solve complex large-scale dynamic games using aggregation as an approximate solution. I obtain two fundamental characterization results. First, approximations with small within-state variation in the primitives have a smaller maximum error bound. I provide numerical results which compare the exact errors and the bound. Second, I find that for monotone games, order preserving aggregation is a necessary condition of any optimal aggregation. I suggest using quantiles as a straightforward implementation of an order preserving aggregation architecture for industry distributions. I conclude with an illustration, by solving and estimating a stylized dynamic reputation game for the hotel industry. Simulation results show maximal errors between the exact and approximated solutions below 6%, with average errors below 1%.
Original languageEnglish
Publication statusPublished - 2020

Publication series

NameSSRN Electronic Journal


  • Aggregation
  • Curse of Dimensionality
  • Dynamic Games
  • Reputation
  • Markov Perfect Equilibrium


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