We consider an assortment optimization problem where the customer may purchase multiple products and possibly more than one unit of each product. We adopt the customer consumption model based on the multiple-discrete-choice (MDC) model. We present an algorithmic framework that delivers near-optimal algorithms for different variations of the assortment problem.

Essentially every choice involves an information collection or search phase prior to a decision-making phase. To study how these phases interact, we embed the Exponomial Choice model (Alptekinoglu and Semple 2016) in a classical model of sequential search with perfect recall (Weitzman 1979). We derive the search path and final choice probabilities in closed form and develop all the analytical tools to enable joint optimization of assortment and prices efficiently.

Online discussion platforms facilitate remote discussions between users. This paper explores the impact of consensus on engagement and proposes algorithms to optimize rankings. Consensus is identified as a crucial engagement driver, and our proposed algorithm outperformed current approaches in an experiment. Promoting debate over echo chambers, consensus is essential for user engagement and platform design.