Aversion to Ambiguity and Model Misspecification in Dynamic Stochastic Environments

Aversion to Ambiguity and Model Misspecification in Dynamic Stochastic Environments. Hansen, L. P. & Miao, J. 115(37):9163–9168.

Paper doi abstract bibtex

[Significance] In many dynamic economic settings, a decision maker finds it challenging to quantify the uncertainty or assess the potential for mistakes in models. We explore alternative ways of acknowledging these challenges by drawing on insights from decision theory as conceptualized in statistics, engineering, and economics. We suggest tractable and revealing ways to incorporate behavioral responses to uncertainty, broadly conceived. Our analysis adopts recursive intertemporal preferences for decision makers that allow them to be ambiguity averse and concerned about the potential misspecification of subjective uncertainty. By design, these representations have revealing implications for continuous time environments with Brownian information structures. Problems where uncertainty's structure is obscure, such as macroeconomics, finance, and climate change, are promising areas for application of these tools. [Abstract] Preferences that accommodate aversion to subjective uncertainty and its potential misspecification in dynamic settings are a valuable tool of analysis in many disciplines. By generalizing previous analyses, we propose a tractable approach to incorporating broadly conceived responses to uncertainty. We illustrate our approach on some stylized stochastic environments. By design, these discrete time environments have revealing continuous time limits. Drawing on these illustrations, we construct recursive representations of intertemporal preferences that allow for penalized and smooth ambiguity aversion to subjective uncertainty. These recursive representations imply continuous time limiting Hamilton-Jacobi-Bellman equations for solving control problems in the presence of uncertainty.

@article{hansenAversionAmbiguityModel2018,
  title = {Aversion to Ambiguity and Model Misspecification in Dynamic Stochastic Environments},
  author = {Hansen, Lars P. and Miao, Jianjun},
  date = {2018-09},
  journaltitle = {Proceedings of the National Academy of Sciences},
  volume = {115},
  pages = {9163--9168},
  issn = {0027-8424},
  doi = {10.1073/pnas.1811243115},
  url = {https://doi.org/10.1073/pnas.1811243115},
  abstract = {[Significance] In many dynamic economic settings, a decision maker finds it challenging to quantify the uncertainty or assess the potential for mistakes in models. We explore alternative ways of acknowledging these challenges by drawing on insights from decision theory as conceptualized in statistics, engineering, and economics. We suggest tractable and revealing ways to incorporate behavioral responses to uncertainty, broadly conceived. Our analysis adopts recursive intertemporal preferences for decision makers that allow them to be ambiguity averse and concerned about the potential misspecification of subjective uncertainty. By design, these representations have revealing implications for continuous time environments with Brownian information structures. Problems where uncertainty's structure is obscure, such as macroeconomics, finance, and climate change, are promising areas for application of these tools.

[Abstract] Preferences that accommodate aversion to subjective uncertainty and its potential misspecification in dynamic settings are a valuable tool of analysis in many disciplines. By generalizing previous analyses, we propose a tractable approach to incorporating broadly conceived responses to uncertainty. We illustrate our approach on some stylized stochastic environments. By design, these discrete time environments have revealing continuous time limits. Drawing on these illustrations, we construct recursive representations of intertemporal preferences that allow for penalized and smooth ambiguity aversion to subjective uncertainty. These recursive representations imply continuous time limiting Hamilton-Jacobi-Bellman equations for solving control problems in the presence of uncertainty.},
  keywords = {*imported-from-citeulike-INRMM,~INRMM-MiD:c-14637346,ambiguity,communicating-uncertainty,control-problem,dynamic-system,entropy,mathematical-reasoning,mathematics,modelling-uncertainty,optimisation,scientific-communication,statistics,technology-mediated-communication,uncertainty},
  number = {37}
}

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