We generalize the results on Bayesian learning based on the martingale convergence theorem to the sequential framework. We show that the variability in the sequential framework is sufficient under mild conditions to circumvent the incomplete learning results that characterize the optimal learning literature. We then give an alternative approach whereby the economist is Bayesian with a prior on the space of agent priors. We illustrate the usefulness of our approach by applying it to two popular economic examples: a monopolist who does not know the demand curve he faces, and the stochastic single-sector growth model with an unknown production function
Recent advances in Bayesian reinforcement learn-ing (BRL) have shown that Bayes-optimality is theore...
This paper continues the study of Bayesian learning processes for general finite-player, finite-str...
This paper examines the heterogeneous market in which economic agents of different information-proce...
We generalize the results on Bayesian learning based on the martingale convergence theorem to the se...
We study the framework of optimal decision making under uncertainty where the agents do not know the...
In "Bayesian Economists ... Bayesian Agents I" (BBI), we generalized the results on Bayesian learnin...
Bayesian analysts use a formal model, Bayes’ theorem to learn from their data in contrast to non-Bay...
This dissertation considers a particular aspect of sequential decision making under uncertainty in w...
www.princeton.edu/∼noahw/ We study the problem of a policymaker who seeks to set policy optimally in...
In this paper we investigate optimal Bayesian learning and control with lagged dependent vari-ables ...
We study abstract macroeconomic systems in which expectations play an important role. Consistent wit...
Motivated by applications in financial services, we consider a seller who offers prices sequen-tiall...
We study the problem of a policymaker who seeks to set policy optimally in an economy where the true...
In this work we consider probabilistic approaches to sequential decision making. The ultimate goal i...
We study the problem of a policymaker who seeks to set policy optimally in an economy where the true...
Recent advances in Bayesian reinforcement learn-ing (BRL) have shown that Bayes-optimality is theore...
This paper continues the study of Bayesian learning processes for general finite-player, finite-str...
This paper examines the heterogeneous market in which economic agents of different information-proce...
We generalize the results on Bayesian learning based on the martingale convergence theorem to the se...
We study the framework of optimal decision making under uncertainty where the agents do not know the...
In "Bayesian Economists ... Bayesian Agents I" (BBI), we generalized the results on Bayesian learnin...
Bayesian analysts use a formal model, Bayes’ theorem to learn from their data in contrast to non-Bay...
This dissertation considers a particular aspect of sequential decision making under uncertainty in w...
www.princeton.edu/∼noahw/ We study the problem of a policymaker who seeks to set policy optimally in...
In this paper we investigate optimal Bayesian learning and control with lagged dependent vari-ables ...
We study abstract macroeconomic systems in which expectations play an important role. Consistent wit...
Motivated by applications in financial services, we consider a seller who offers prices sequen-tiall...
We study the problem of a policymaker who seeks to set policy optimally in an economy where the true...
In this work we consider probabilistic approaches to sequential decision making. The ultimate goal i...
We study the problem of a policymaker who seeks to set policy optimally in an economy where the true...
Recent advances in Bayesian reinforcement learn-ing (BRL) have shown that Bayes-optimality is theore...
This paper continues the study of Bayesian learning processes for general finite-player, finite-str...
This paper examines the heterogeneous market in which economic agents of different information-proce...