The Economic Engineering of Personalized Experiences

ABSTRACT

Consumer applications employ algorithms to deliver personalized experiences to users, among others, in search, e-commerce, online streaming, and social media, impacting how users spend their time and money. The dissertation studies the design of such personalization algorithms and the social consequences of their deployment.

The first chapter analyzes how preference measurement error differentially affects user groups in optimal personalization. Under such measurement error, welfare maximization is incompatible with equalizing the utility of (statistical) majority and minority users, and requires delivering majority-preferred experiences at a rate beyond their proportion in the user population. Participants in a survey of TikTok users say that they engage in costly actions, such as explicit feedback and changes to their consumption, to signal more accurately to the algorithm.

The second chapter focuses on the impacts of reward signal precision on online learning algorithms frequently used for personalization. Reward signals are precise when individual measurement is accurate and heterogeneity is low. While some algorithms favor experiences that yield more precise reward signals, and hence favor measurability and homogeneity, others, in the limit, choose experiences independently of the precision of their associated reward signals.

The third chapter studies, through the introduction of a new desideratum for market design, how to achieve personalization without infringing user privacy. Contextual privacy demands that all (preference) information elicited by an algorithm is necessary for computing an outcome of interest in all possible configurations of users’ information. This property is demanding, as it requires that no two pieces of information can jointly but not unilaterally influence the outcome. Algorithms can protect the privacy of users that are queried late, achieving the relaxed notion of maximal contextual privacy.

COMMITTEE

Alessandro Bonatti (chair, advisor), Dylan Hadfield-Menell (advisor), Eric Maskin, David Parkes

EVENT INFORMATION

Hybrid event. To attend virtually, please contact the IDSS Academic Office (idss_academic_office@mit.edu) for connection information.