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Ramandeep S. Randhawa is an operations research scholar whose research interests include designing service systems, revenue management, stochastic modeling, and mechanism and incentive design. His work has been published in journals that include Management Science, Manufacturing and Service Operations Management, and Operations Research. He currently serves as an Area Editor for the Stochastic Models area in Operations Research.
Areas of Expertise
INSIGHT + ANALYSIS
Interview: Raman Randhawa at Retailers.MX
Raman Randhawa, Professor of Data Sciences and Operations, speaks to Retailers.MX about new frontiers in data analytics.
NEWS + EVENTS
USC Marshall Announces New Joint Undergraduate Degree: AI for Business
The degree continues Marshall’s mission to prepare the next generation of business leaders for a rapidly evolving tech environment.
Helping First-Gen Marshall Students
A Lasting Legacy
USC Marshall and Leventhal scholarship recipients thank their generous donors at the 2021 Marshall Scholarship Celebration.
RESEARCH + PUBLICATIONS
Information products provide agents with additional information that can be used to update actions. In many situations, access to such products can be quite limited. For instance, in epidemics, there tends to be a limited supply of medical testing kits, or tests. These tests are information products because their output of a positive or a negative answer informs individuals and authorities on the underlying state and the appropriate course of action. In this paper, using an analytical model, we show how the accuracy of a test in detecting the underlying state affects the demand for the information product differentially across heterogeneous agents. Correspondingly, the test accuracy can serve as a rationing device to ensure that the limited supply of information products is appropriately allocated to the heterogeneous agents. When test availability is low and the social planner is unable to allocate tests in a targeted manner to the agents, we find that moderately good tests can outperform perfect tests in terms of social outcome.
We study a pricing and information provisioning game between a better-informed seller (such as a retailer) and its customers. The seller is (ex post) better informed about product availability and can choose how to communicate this information to the customers. The customers are heterogeneous in their valuation for the product. The firm optimizes on publicly posted prices (which are the same for all customers) and its information provisioning (which can be personalized). Using a Bayesian persuasion framework, we find that public information provisioning, in which the firm sends the same information to all customers, has limited value. However, personalized information provisioning, in which the firm can share different information with different customers, has significant value and has attributes very similar to personalized pricing.