学习公平需求模型
Learning Fair Demand Models
AI总结：
研究数据驱动定价中的公平性问题，通过比较在需求估计或价格优化阶段施加公平约束的策略，分析其对消费者福利和社会效益的影响。
摘要：
数据驱动定价在航空、贷款、保险和零售等领域日益普遍。通过从客户特征中学习需求模型并据此定价，这些系统可能产生歧视性结果，引发公平性问题。这引出了基本问题——系统应如何在定价流程中纳入公平考量，以及最终如何影响社会结果？为回答这些问题，我们研究了一个简化模型，其中卖方有一个两阶段决策流程：线性需求模型估计，随后是价格优化。卖方在训练损失、价格和需求方面考虑公平概念，包括均等主义和罗尔斯主义视角。我们表明，跨消费者群体均等化训练损失会导致多个解，进而可能产生不良结果，尽管这是公平机器学习中的标准方法。相反，关注直接应用于价格或需求的公平性，我们比较了两种策略：在需求估计阶段或价格优化阶段强制执行公平性。对于均等主义公平，我们刻画了在较小公平水平下每种策略何时产生更高的社会福利。我们表明，当数据集中的市场规模和价格相似时，在估计阶段施加价格公平对消费者更有利，而在优化阶段施加需求公平则带来更好的消费者结果。对于罗尔斯主义公平，两种策略完全一致。最后，我们将模型扩展到其他需求函数，并使用真实世界疫苗定价数据进行案例研究。
英文摘要：
Data-driven pricing is increasingly prevalent in sectors such as airlines, lending, insurance, and retail. By learning demand models from customer features and setting prices accordingly, these systems may generate discriminatory outcomes that raise fairness concerns. This leads to fundamental questions - how and where should systems incorporate fairness considerations in the pricing pipeline, and how does it ultimately affect societal outcomes? To answer these, we study a stylized model where a seller has a two-stage decision pipeline comprising linear demand model estimation followed by price optimization. The seller considers fairness notions in training loss, price, and demand, under both parity-wise and Rawlsian perspectives. We show that equalizing training loss across consumer groups leads to multiple solutions, which in turn can result in undesirable outcomes despite being a standard approach in fair machine learning. Focusing instead on fairness applied directly to prices or demand, we compare two strategies that enforce fairness in either the demand estimation stage or the price optimization stage. For parity-wise fairness, we characterize when each strategy yields higher social welfare under small fairness levels. We show that when market sizes and prices in the dataset are similar, imposing price fairness in the estimation stage is more beneficial to consumers, whereas imposing demand fairness in the optimization stage yields better consumer outcomes. For Rawlsian fairness, the two strategies coincide exactly. Lastly, we extend our model to alternate demand functions and conduct a case study using real-world vaccine pricing data.
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