Quality information on online platforms is often conveyed through simple, percentile-based badges and tiers that remain stable across different market environments. Motivated by this empirical evidence, we study robust quality disclosure in a market where a platform commits to a public disclosure policy mapping the seller's product quality into a signal, and the seller subsequently sets a downstream monopoly price. Buyers have heterogeneous private types and valuations that are linear in quality. We evaluate a disclosure policy via a minimax competitive ratio: its worst-case revenue relative to the Bayesian-optimal disclosure-and-pricing benchmark, uniformly over all prior quality distributions, type distributions, and admissible valuations. Our main results provide a sharp theoretical justification for quantile-partition disclosure. For K-quantile partition policies, we fully characterize the robust optimum: the optimal worst-case ratio is pinned down by a one-dimensional fixed-point equation and the optimal thresholds follow a backward recursion. We also give an explicit formula for the robust ratio of any quantile partition as a simple "max-over-bins" expression, which explains why the robust-optimal partition allocates finer resolution to upper quantiles and yields tight guarantees such as 1 + 1/K for uniform percentile buckets. In contrast, we show a robustness limit for finite-signal monotone (quality-threshold) partitions, which cannot beat a factor-2 approximation. Technically, our analysis reduces the robust quality disclosure to a robust disclosure design program by establishing a tight functional characterization of all feasible indirect revenue functions.

翻译：在线平台上的质量信息通常通过简单、基于百分位数的徽章和等级来传达，这些标识在不同市场环境中保持稳定。受这一实证证据启发，我们研究了一个市场中的稳健质量披露问题：平台承诺采用一个公开的披露策略，将卖家的产品质量映射为信号，随后卖家设定下游垄断价格。买家具有异质的私有类型，其估值与质量呈线性关系。我们通过最小化最大竞争比来评估披露策略：该策略在最坏情况下的收益相对于贝叶斯最优的披露与定价基准，且该评估在所有先验质量分布、类型分布及可容许估值上保持一致。我们的主要结果为分位数划分披露提供了明确的理论依据。对于K-分位数划分策略，我们完整刻画了稳健最优解：最优最坏情况比由一维不动点方程确定，最优阈值遵循后向递归。我们还给出了任意分位数划分稳健比的显式公式，即简单的“分箱最大值”表达式，这解释了为何稳健最优划分将更精细的分辨率分配给上分位数，并产生紧致的保证（例如均匀百分位桶的1 + 1/K）。相比之下，我们证明了有限信号单调（质量阈值）划分存在稳健性极限，其无法超越因子2的近似比。在技术上，我们的分析通过建立所有可行间接收益函数的紧致函数刻画，将稳健质量披露问题简化为一个稳健披露设计规划。