A principal delegates multiple decisions to an agent, who has private information relevant to each decision. The principal is uncertain about the agent's preferences. I solve for max-min optimal mechanisms—those which maximize the principal's payoff against the worst case agent preference types. These mechanisms are characterized by a property I call “aligned delegation”: all agent types play identically, as if they shared the principal's preferences. Max-min optimal mechanisms may take the simple forms of ranking mechanisms, budgets, or sequential quotas. (JEL D44, D83, J16)

We examine ways to measure the amount of information generated by a piece of news and the amount of uncertainty implicit in a given belief. Say a measure of information is valid if it corresponds to the value of news in some decision problem. Say a measure of uncertainty is valid if it corresponds to expected utility loss from not knowing the state in some decision problem. We axiomatically characterize all valid measures of information and uncertainty. We show that if measures of information and uncertainty arise from the same decision problem, then they are coupled in that the expected reduction in uncertainty always equals the expected amount of information generated. We provide explicit formulas for the measure of information that is coupled with any given measure of uncertainty and vice versa. Finally, we show that valid measures of information are the only payment schemes that never provide incentives to delay information revelation. (JEL D81, D83)

We model demand for noninstrumental information, drawing on the idea that people derive entertainment utility from suspense and surprise. A period has more suspense if the variance of the next period’s beliefs is greater. A period has more surprise if the current belief is further from the last period’s belief. Under these definitions, we analyze the optimal way to reveal information over time so as to maximize expected suspense or surprise experienced by a Bayesian audience. We apply our results to the design of mystery novels, political primaries, casinos, game shows, auctions, and sports.

If experiment A is Blackwell more informative than experiment B , it is always possible that A and B are induced by signals A′ and B′ such that A′ is a refinement of B′, that is, A′ entails observing B′ plus some additional information. We first show that this result does not extend beyond pairs of experiments: There exist collections of experiments that cannot be induced by a collection of signals so that whenever two experiments are Blackwell ordered, the associated signals are refinement ordered. In other words, sometimes it is impossible for more informed agents to know everything that less informed agents know. More broadly, define an information hierarchy to be a partially ordered set that ranks experiments in terms of informativeness. Is it the case that for any choice of experiments indexed on the hierarchy such that higher experiments are Blackwell more informative, there are signals that induce these experiments with higher signals being refinements of lower signals? We show that the answer is affirmative if and only if the undirected graph of the information hierarchy is a forest.

A signal is a description of an information source that specifies both its correlation with the state and its correlation with other signals. Extending Blackwell (1953), we characterize when one signal is more valuable than another regardless of preferences and regardless of access to other signals. This comparison is equivalent to reveal-or-refine: every realization of the more valuable signal reveals the state or refines the realization of the less valuable signal. We also study other comparisons of signals, including sufficiency, martingale dominance, and Lehmann. Reveal-or-refine is also equivalent to making any of these comparisons robust to access to other signals. (JEL C90, D82, D83)