Learning and Dynamic Efficiency
Learning and Dynamic Efficiency
In this chapter the authors derive conditions on the primitives of the learning environment - which encompasses both Bayesian and non-Bayesian processes - ensuring that an efficient allocation of resources is achievable in a dynamic allocation environment where impatient, privately informed agents arrive over time, and where the designer gradually learns about the distribution of agents' values. There are two main kind of conditions: 1) Higher observations should lead to more optimistic beliefs about the distribution of future values; 2) The allowed optimism associated with higher observations needs to be carefully bounded. The authors’ analysis reveals and exploits close, formal relations between the problem of ensuring monotone - and hence implementable - allocation rules in our dynamic allocation problems with incomplete information and learning, and between the classical problem of finding optimal search policies that are characterized by a reservation price property. Finally, the authors characterize the constrained efficient policy ("second-best") if the efficient policy is not implementable.
Keywords: Bayesian and non-Bayesian processes, Dynamic allocation environment
MIT Press Scholarship Online requires a subscription or purchase to access the full text of books within the service. Public users can however freely search the site and view the abstracts and keywords for each book and chapter.
Please, subscribe or login to access full text content.
If you think you should have access to this title, please contact your librarian.
To troubleshoot, please check our FAQs, and if you can't find the answer there, please contact us.
