Bayesians Learn while Waiting

Bayesians Can Learn From Old Data

In a widely-cited paper, Glymour [1] claims to show that Bayesians cannot learn from old data. His argument contains an elementary error. I explain exactly where Glymour went wrong, and how the problem should be handled correctly. When the problem is fixed, it is seen that Bayesians, just like logicians, can indeed learn from old data. Outline of the Paper I first review some aspects of standar...

Do Bayesians Learn Their Way Out of Ambiguity?

In standard models of Bayesian learning agents reduce their uncertainty about an event’s true probability because their consistent estimator concentrates almost surely around this probability’s true value as the number of observations becomes large. This paper takes the empirically observed violations of Savage’s (1954) sure thing principle seriously and asks whether Bayesian learners with ambi...

Learn While You Sleep

The Value of Waiting to Learn

The goal of this paper is to study irreversible investment under incomplete information. We extend McDonald and Siegel’s (1986) model to the case where the expected rate of return of the project cannot be observed but is known to be either low or high. Waiting and observing the realizations of the value of the project provides information to the investor who can update her beliefs about the tru...

Abducted by Bayesians?

This paper discusses the role of theoretical notions in making predictions and evaluating statistical models. The core idea of the paper is that such theoretical notions can be spelled out in terms of priors over statistical models, and that such priors can themselves be assigned probabilities. The discussion substantiates the claim that the use of theoretical notions may offer specific empiric...