寡头数据市场中的均衡定价
Equilibrium Pricing in Oligopolistic Data Markets
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中文总结 AI 辅助
该研究针对寡头数据市场，发现数据非竞争性会导致精确及近似纳什均衡不存在，提出分段线性凸定价函数可保证近似稳定性，模拟显示其性能优于最坏情况边界。
中文摘要 AI 辅助
我们研究了存在预算受限买方（例如，为提升模型精度而购买数据的机器学习公司）与策略性数据卖方的寡头数据市场中的均衡定价问题。卖方通过为其数据集设定价格展开竞争，由此产生的定价博弈的纯纳什均衡对应于均衡价格。对于竞争性商品，均衡价格通过竞争均衡得到保证，但我们表明，数据的非竞争性从根本上改变了这一状况：精确纳什均衡（NE）可能不存在，且在统一定价下，1.363近似纳什均衡也可能不存在。因此，我们研究了放松的均衡概念。允许卖方使用超出统一定价的定价方式——具体而言，即分段线性凸定价函数——可保证在常数因子内的近似稳定性：存在一种定价方案，使得任何卖方都无法通过偏离至任何统一定价将收入提升两倍（即针对统一定价偏离的2近似纳什均衡）。最后，我们的模拟结果显示，其收敛速度快，且经验近似保证优于最坏情况下的2倍边界。
英文摘要
We study equilibrium pricing in oligopolistic data markets with budget-constrained buyers (e.g., machine learning companies purchasing data to improve model accuracy) and strategic data sellers. Sellers compete by setting prices for their datasets, giving rise to a pricing game whose pure Nash equilibria correspond to equilibrium prices. While equilibrium prices are guaranteed for rivalrous goods via competitive equilibrium, we show that the non-rivalry of data fundamentally alters this picture: an exact Nash equilibrium (NE) need not exist, and in fact, 1.363-approximate NE may also not exist under uniform pricing. We therefore investigate relaxed equilibrium notions. Allowing sellers to use beyond-uniform pricing---specifically, piecewise-linear convex pricing functions---guarantees approximate stability within a constant factor: there exists a pricing profile in which no seller can improve revenue by a factor of two by deviating to any uniform price (a 2-approximate NE against uniform deviations). Finally, our simulations demonstrate fast convergence and empirical approximation guarantees that outperform the worst-case bound of 2.
