We study how changes to the informativeness of signals in Bayesian games and single‐agent decision problems affect the distribution of equilibrium actions. Focusing on supermodular environments, we provide conditions under which a more precise private signal for one agent leads to an...

We study how information affects equilibria and welfare in games. For an agent, more precise information about an unknown state of the world leads to a mean-preserving spread of beliefs. We provide necessary and sufficient conditions to obtain either a non-increasing mean or a...

In empirical economics and finance, inference is often based on previously-used data. We show that valid Bayesian inference is noninformative for previously-used data. Given the prevalence of previously-used data and the success of ``Bayesian" methods in empirical economics and finance, this is...

This paper presents a modification of the inspection game: The Bayesian Monitoring model rests on the assumption that judges are interested in enforcing compliant behavior and making correct decisions. They may base their judgments on an informative but imperfect signal which can be generated...

We study how learning affects an uninformed monopolist's supply and investment decisions under multiplicative uncertainty in demand. The monopolist is uninformed because it does not know one of the parameters defining the distribution of the random demand. Observing prices reveals this...

The problem of modeling the revision of the information of a decision maker based on the information of the expert sources is considered. The basic model assumes that the information of the decision maker and expert sources is in the form of the probability mass functions. The modeling approach...

This paper analyzes the root cause of Ellsberg-type choices. This class of problems shares the feature that at the time of the decision, t=m, the decision maker (DM) possesses partial information about the events/propositions of interest: DM knows the objective probabilities of some sub-class...