The question in which we are interested is how a market inhabited by multiple agents, about whom we are differentially uncertain, and who trade goods the use of which imposes a negative effect on others, is to be ideally regulated. We show that a priori asymmetric uncertainty, when combined with a posteriori observed outcomes, is a rich source of information that can be used to reduce aggregate uncertainty. The observation implies that whereas asymmetric information usually entails a cost on welfare, it can help achieve greater efficiency in regulation.
CentER Discussion Paper,
We develop a dynamic regulation game for a stock externality under asymmetric information and future market uncertainty. Within this framework, regulation is characterized as the implementation of a welfare-maximization program conditional on informational constraints. We identify the most general executable such programs and find these yield simple and intuitive policy rules. We apply our theory to carbon dioxide emissions trading schemes and find substantial welfare gains are possible, compared to current practices.