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Ross School of Business, University of Michigan, United States of America
We consider the firm maximizes the total expected revenue over a finite time horizon by optimizing the assortment/pricing of each time period.
Customers make choices under MNL with popularity effect, which also considers the historical sales.
The optimal prices can be solved by concave programming.
The heuristic algorithms we propose for assortment optimization have a 1/T performance ratio in the general case, and the ratio improves to 1/ln(T) when the product's utility is constant over time.
Centralized versus decentralized pricing controls for dynamic matching platforms
Ömer Sarıtaç1, Ali Aouad1, Chiwei Yan2
1London Business School; 2University of Washington, Seattle
TBD
Social learning with polarized preferences on content platforms
Dongwook Shin1, Bharadwaj Kadiyala2
1HKUST Business School; 2David Eccles School of Business, University of Utah
