Synapse
⌘+K
Synapse
PulseExploreClubsResearchersJournals
Instagram
HomeClubsExplore
September 10, 2025Management Science

Utility Fairness in Contextual Dynamic Pricing with Demand Learning

View Full Paper
Ask AI
Bookmark
Share

Authors

XCXi ChenNew York UniversityDSDavid Simchi-LeviMassachusetts Institute of TechnologyYWYining WangThe University of Texas at Dallas

Discussion

Loading...

Member takes

Implication

Novel algorithm optimizes pricing under utility fairness constraints, indicating a trade-off with revenue maximization.

Key Points

  • Optimal pricing policy balances utility and fairness, reducing regret in uncertain demand scenarios.
  • Achieved a nonstandard regret lower bound, demonstrating the complexity of incorporating fairness in pricing.
  • Utilized a constrained optimization problem to develop an approximation algorithm for efficient pricing policies.
  • Research integrates ethical considerations into algorithmic pricing strategies, paving the way for future advancements.

Cite This Study

Chen et al. (2025) studied this question.

synapsesocial.com/papers/68c1afd354b1d3bfb60e8002https://doi.org/10.1287/mnsc.2023.03956
View Full Paper
Ask AI
Bookmark
Share

Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Simultaneously Learning and Optimizing Using Controlled Variance Pricing2013 · 240 citations
  2. 2Close the Gaps: A Learning-While-Doing Algorithm for Single-Product Revenue Management Problems2014 · 207 citations
  3. 3Personalized Dynamic Pricing with Machine Learning: High-Dimensional Features and Heterogeneous Elasticity2021 · 210 citations
  4. 4Linearly Parameterized Bandits2010 · 452 citations
  5. 5Multidimensional Binary Search for Contextual Decision-Making2018 · 33 citations