An unresolved problem in Bayesian decision theory is how to value and price information. This paper resolves both problems by assuming inexpensive information. Building on Large Deviation Theory, we produce a generically complete asymptotic order on samples of i.i.d. signals in finite-state,...

We assume that an impatient decision maker (DM) runs variable-size experiments at an increasing, strictly convex cost before choosing an irreversible action. We introduce and solve a tractable continuous time version of this problem --- a control of variance of a diffusion with uncertain mean....

We assume that an impatient decision maker (DM) runs variable-size experiments at an increasing, strictly convex cost before choosing an irreversible action. We introduce and solve a tractable continuous time version of this problem - a control of variance of a diffusion with uncertain mean....

We study optimal manipulation of a Bayesian learner through adaptive provisioning of information. The problem is motivated by settings in which a firm can disseminate possibly biased information at a cost, to influence the public's belief about a hidden parameter related to the firm's payoffs....

We extend the comparison of experiments in Blackwell (1953) to a strategic setting that both simplifies and expands upon ideas in Gossner (2000). We introduce a partial order on correlating signals, called more strategically informative, and prove that it is equivalent to the partial order more...