一种用于数据贫乏市场中定价复杂数据产品的先验预测蒙特卡洛框架
A Prior-Predictive Monte Carlo Framework for Pricing Complex Data Products in Data-Poor Markets
AI总结：
本文提出一种先验预测蒙特卡洛框架，用于在缺乏实证数据的情况下，为复杂数据产品生成公平且可审计的价格区间，结合专业经验与数据驱动方法。
摘要：
对高级数据产品进行定价，尤其是在半导体制造等复杂领域，是一项根本性的挑战，因为公开交易数据稀少，且经常具有异质性和保密性。尽管数据价值取决于多个相互作用的因素，如技术复杂性、质量、实用性以及许可权，但传统定价方法往往依赖于随意的启发式方法或需要大量的历史交易数据。在日益数据驱动的经济中，我们引入了一种先验预测蒙特卡洛框架，该框架能够在没有实证数据的情况下生成公平、一致且合理的数据产品价格范围。通过模拟许多可能的定价'世界'和交易配置，该框架生成稳定的概率价格区间（例如P5/P50/P95），而不是单点估计，从而创建了一个可审计且可重复的概率定价系统。通过约束截断先验，强制实施商业现实。所提出的模型将传统数据定价基于专业经验与一种基于数据的方法相结合，后者还允许在积累更多交易数据时进行经典贝叶斯更新。
英文摘要：
Pricing advanced data products - particularly in complex fields such as semiconductor manufacturing - is a fundamentally challenging task due to the sparsity of publicly available transaction data, and its frequent heterogeneity and confidentiality. While data value depends on multiple interacting factors, such as technical sophistication, quality, utility, and licensing rights, traditional pricing methods tend to rely on ad-hoc heuristics or require massive amounts of historical transaction data. In an increasingly data-based economy, we introduce a prior-predictive Monte Carlo framework that enables the generation of fair, consistent, and justified price ranges for data products in the absence of empirical data. By simulating many plausible pricing 'worlds' and deal configurations, the framework produces stable probabilistic price bands (e.g., P5/P50/P95) rather than single point estimates, creating an auditable and repeatable probabilistic pricing system with business realism enforced via constraint-truncated priors. The proposed model bridges traditional data pricing rooted in professional experience with a data-based approach that also allows for classical Bayesian updating as more transaction data is accumulated.
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