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Utility Fairness in Contextual Dynamic Pricing with Demand Learning (2311.16528v1)

Published 28 Nov 2023 in stat.ML and cs.LG

Abstract: This paper introduces a novel contextual bandit algorithm for personalized pricing under utility fairness constraints in scenarios with uncertain demand, achieving an optimal regret upper bound. Our approach, which incorporates dynamic pricing and demand learning, addresses the critical challenge of fairness in pricing strategies. We first delve into the static full-information setting to formulate an optimal pricing policy as a constrained optimization problem. Here, we propose an approximation algorithm for efficiently and approximately computing the ideal policy. We also use mathematical analysis and computational studies to characterize the structures of optimal contextual pricing policies subject to fairness constraints, deriving simplified policies which lays the foundations of more in-depth research and extensions. Further, we extend our study to dynamic pricing problems with demand learning, establishing a non-standard regret lower bound that highlights the complexity added by fairness constraints. Our research offers a comprehensive analysis of the cost of fairness and its impact on the balance between utility and revenue maximization. This work represents a step towards integrating ethical considerations into algorithmic efficiency in data-driven dynamic pricing.

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Citations (1)
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Summary

  • The paper introduces a novel contextual bandit algorithm that integrates fairness constraints into personalized pricing.
  • It formulates optimal pricing as a constrained optimization problem with an efficient approximation in static full-information settings.
  • It extends the analysis to dynamic pricing with demand learning, quantifying the trade-off between fairness and revenue.

The paper "Utility Fairness in Contextual Dynamic Pricing with Demand Learning" investigates the integration of fairness considerations into personalized pricing strategies, under scenarios of uncertain demand. This research is pivotal in addressing the ethical dimensions of pricing algorithms while maintaining algorithmic efficiency.

Key Contributions:

  1. Novel Contextual Bandit Algorithm: The authors propose a new algorithm that adapts to personalization needs, while adhering to fairness constraints. The goal is to formulate an optimal pricing policy that incorporates utility fairness into the dynamic pricing framework.
  2. Framework for Static Full-Information Setting: The research initially focuses on a static setting where full information is available. Here, they approach the optimal pricing policy as a constrained optimization problem. They introduce an approximation algorithm to efficiently compute ideal pricing strategies, which account for fairness constraints without excessive computational overhead.
  3. Characterization of Optimal Policies: Through mathematical analysis and computational studies, the paper explores the structural characterization of optimal contextual pricing policies. Key insights derived include simplified policy structures that could serve as foundational guides for further research and practical implementation.
  4. Extension to Dynamic Pricing with Demand Learning: Beyond the static setting, the research extends to dynamic pricing scenarios where demand learning is crucial. The authors explore the interplay between fairness constraints and learning efficiency, presenting a non-standard regret lower bound. This bound underscores the increased complexity introduced by incorporating fairness.
  5. Cost of Fairness: An in-depth examination of the cost associated with fairness is provided. The analysis considers how fairness constraints affect the equilibrium between utility (representing consumer satisfaction and equity) and revenue maximization. This balance is crucial for developing fair yet profitable pricing strategies.

Implications:

The paper contributes to advancing the integration of ethical considerations into algorithmic decision-making. By embedding utility fairness within the contextual dynamic pricing framework, it proposes a balanced approach that could potentially mitigate biases and inequalities arising from purely profit-driven algorithms. This work serves as an essential step toward making data-driven dynamic pricing strategies more equitable and socially responsible, offering significant implications for both academia and industry applications.

Overall, the paper presents a thorough analysis and introduces practical frameworks that blend fairness with optimization in personalized pricing—a critical evolution in algorithmic pricing methodologies.

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