In applied models, the choice of a particular incomplete information structure appears to have been motivated primarily by technical convenience. The information structures used can be classified as either probabilistic or partitional. Information is probabilistic if no agent can rule out any type profile of the remaining agents and, for at least one type of one agent, the conditional and marginal probability distributions over the remaining agents' types are not equal. Information is partitional if the only information the agents have is that one or more agents (individually) can rule out type profiles of the remaining agents and, for at least one type of one agent, that agent has information about the remaining agents. Partitional information includes complete information as a special case. Existing results on complete information environments suggest that partitional information might simplify implementation problems. Within the context of an applied agency model in which capacity is constrained, we provide results that seem to challenge this intuition.
Journal of Economic Theoryvol.
70, (January 01, 1996):