We study the revenue-maximizing allocation of several heterogeneous,
commonly ranked objects to impatient agents with privately
known characteristics who arrive sequentially. There is a deadline
after which no more objects can be allocated. We first characterize
implementable allocation schemes, and compute the expected revenue
for any implementable, deterministic and Markovian allocation
policy. The revenue-maximizing policy is obtained by a variational
argument which sheds more light on its properties than the usual
dynamic programming approach. Finally, we use our main result in
order to derive the optimal inventory choice, and explain empirical
regularities about pricing in clearance sales. (JEL C61, D21, D82)
Gershkov, Alex, and Benny Moldovanu.
"Dynamic Revenue Maximization with Heterogeneous Objects: A Mechanism Design Approach."
American Economic Journal: Microeconomics,
Optimization Techniques; Programming Models; Dynamic Analysis
Asymmetric and Private Information