Research

2022

1. OR

   Learning Manipulation Through Information Dissemination

   J. Keppo, M. Kim, and X. Zhang

   Operations Research, 2022

   Abs Bib HTML

   We study the optimal manipulation of a Bayesian learner through adaptive information provisioning, motivated by firms employing false advertising on e-commerce platforms and social networks. Specifically, we consider a firm that disseminates biased information to manipulate public beliefs about a parameter related to its payoffs. In our sequential optimization model, the firm dynamically decides on both the quantity and content of information sent to the public to maximize its expected total profits. By solving the Bayesian dynamic programming equation, we explicitly characterize the optimal manipulation strategy and also the evolution of public belief under such manipulation over time. We also consider the public as partially Bayesian social learners who utilize public reviews to mitigate manipulation. We show that the public eventually learns the truth from misinformation using social learning.

   @article{keppoetal2023learnmanip,
     title = {Learning Manipulation Through Information Dissemination},
     author = {Keppo, J. and Kim, M. and Zhang, X.},
     journal = {Operations Research},
     volume = {70},
     number = {8},
     pages = {3490-3510},
     year = {2022}
   }

Runner-up, INFORMS DAS Best Student Paper Award, 2022
SSRN Version