Pricing Information Cascades


March 11, 2010


Nicole Immorlica


Northwestern University


We consider the problem of optimal pricing of a common-value
product in the presence of social learning effects. A new product
reaches the market and agents obtain private signals that partially
inform them about the value of this product. Agents decide
sequentially whether to purchase this product. Before making their own
decisions, they also observe the purchasing decisions of agents who
acted previously and learn from those actions in a Bayesian rational
fashion. We address the problem of how a firm should price the product
when taking social learning into account.

Our first result shows that firms do best asymptotically if the firms
select prices that lead the customers to learn the true value of the
product. We show how the firm can induce learning at a low cost by
inducing a vanishing fraction of the agents to act according to their
private signals. We also show a lower bound on the agents’ regret of
T2/3 for a society of size T. We finally show a pricing policy
that achieves this lower bound.


Nicole Immorlica

Nicole Immorlica is an assistant professor in the theoretical computer science group at Northwestern University. She joined Northwestern in September 2008 after postdoctoral positions at Microsoft Research in Redmond, WA, and Centruum voor Wiskunde en Informatica (CWI) in Amsterdam, Netherlands. She received her Ph.D. from MIT 2005. Her work focuses on applying economic and computer science techniques to problems at the forefront of computer science research, including models of diffusion on social networks, the design and analysis of ad auction markets, and the development of general auction mechanisms.