Fast and Slow Learning from Reviews

The amount of goods and services transacted on online platforms is set to grow several folds over the next decade. These platforms face several critical challenges in creating a seamless interaction between diverse sellers and service providers, often with unknown provenance on the one hand, and millions of users on the other. Chief among them is how to provide reliable information on the reputation of sellers and the quality of goods and services they are providing. Though peer reviews and recommendations have emerged as the dominant approach to address this problem, the properties of different rating systems and the incentives facing users are poorly understood. Both the potential bias in reviews and the fact that those selecting to leave reviews are not representative of the average user complicate the reliability of the information provided by these rating systems.
Ali Makhdoumi is an assistant professor of Decisions Sciences at Fuqua School of business, Duke University. He is broadly interested in learning theory, optimization, game theory, and network science with applications to social and technological systems.