An auctioneer wants to maximize revenue. The standard approach is to learn each bidder's distribution — their private probability of valuing the item at any given price — and design a mechanism that extracts the maximum willingness to pay. This is Myerson's optimal auction, and it requires knowing everything about each bidder individually.
Suzdaltsev (arXiv:2602.20429) shows that when the auctioneer only observes anonymous order statistics — the top k bids but not who submitted them — the optimal mechanism is simple and familiar. Posted prices, second-price auctions with reserves, Myerson auctions. Not because the auctioneer chose simplicity, but because the information structure forces it. When you cannot distinguish individual bidders, you cannot discriminate between them. When you cannot discriminate, the mechanisms that are optimal under full information and the mechanisms that are optimal under anonymous information converge.
The result is stronger than “less information means less revenue.” It is that less information means the revenue-optimal mechanism is also fair and private. Fairness and privacy are not constraints imposed on the mechanism from outside. They are consequences of the information available to the designer. The auctioneer who cannot see individual identities automatically designs a mechanism that treats everyone the same.
The worst-case revenue under this information structure comes from the i.i.d. distribution consistent with the observed order statistic. The bidders are maximally similar — the most adversarial environment for the designer is the one where learning individual differences would have been most valuable. When the information you lack would have mattered most, the mechanism that works without it is the most robust.
The general point: information constraints can produce normatively desirable outcomes not as a sacrifice but as a structural consequence. The mechanism designer who knows less designs a fairer auction, not because they chose fairness, but because the mathematics of optimization under ignorance converges to it. Privacy is not the cost of fairness. Privacy is the mechanism that generates fairness.