【公开学术报告】A Step Towards Fairer Assortments: Algorithms and Welfare Implications

A Step Towards Fairer Assortments: Algorithms and Welfare Implications

Speaker: LU Wentao (John Hopkins University)

Date & Time: Mon.30th, December 2024, from 10:00 AM to 11:30 AM (Beijing Time)

Place: Meeting Room 2101, Tongji Building A.

ABSTRACT

Large online retailers and department stores function as marketplaces for many other sellers in addition to themselves, and consumers rely on the platform’s assortment and display decisions to examine different sellers (or products) and make purchase decisions. Traditionally, the primary objective of these marketplaces for assortment planning is to maximize revenue (or profit), which may create unfairness among sellers. This is because only a single assortment with the highest expected revenue is chosen resulting in some sellers being excluded from recommendations or assortments, with minimal market exposure and revenue. To address this issue, we incorporate constraints that ensure ’fairer’ market exposures, market shares or revenues for all sellers. These constraints ensure each seller to have a minimum market exposure or market share or revenue, which may depend on the seller’s reputation, product quality, and price, among other features. When the minimum market exposure constraint is imposed, we show that the optimal solution with these constraints is to randomize over at most n nested assortments, where n is the number of sellers (or products), and the optimal solution can be found in polynomial time. We then consider the case where the quality of exposure is considered and propose approximation algorithms for this case. We consider alternative constraints (minimum market share/revenue constraints) and objective (welfare maximization) and show that the nested structure remains optimal for those cases under certain conditions. We further investigate the impact of ’fairness’ constraints on consumer welfare, and show that it always increases when such constraints are imposed. We also identify cases in which the total welfare improves and therefore propose a revenue redistributing mechanism to achieve a win-win-win solution.

Key Words: Fairness; Randomized Assortment; Discrete Choice Model; Revenue Management; Welfare Implications