On learning and growth

Leonard J. Mirman, Kevin Reffett, Marc Santugini

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


We study optimal growth under learning. We extend the Mirman–Zilcha stochastic growth results characterizing optimal programs for general utility and production functions to the case of learning. We then use recursive methods to study the effect of learning on the dynamic program by considering the case of iso-elastic utility and linear production, for general distributions of the random shocks and beliefs (i.e., without the use of conjugate priors), for any horizon. Finally, we address the issue of experimentation by providing a solution to an infinite-horizon optimal dynamic program.

Original languageEnglish (US)
Pages (from-to)1-44
Number of pages44
JournalEconomic Theory
StateAccepted/In press - Dec 28 2015


  • Brock–Mirman environment
  • Dynamic programming
  • Euler equation
  • Experimentation
  • Learning
  • Optimal growth

ASJC Scopus subject areas

  • Economics and Econometrics


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