Why does an algorithm working for buyers treat a seller's real costs as higher than they actually are?

This explores why an algorithm that recommends sellers on the buyer's behalf, rather than for overall efficiency or for platform profit, ends up treating seller costs as more than their face value. The core reason is that sellers know their own costs and the algorithm doesn't. Any seller who gets picked can use that private knowledge to collect extra margin, which economists call an information rent. An algorithm working for buyers wants to shrink that margin, and the strongest tool it has is the threat of not trading. The model in Does algorithmic recommendation lose its edge as sellers compete more? puts it this way: the optimal recommender beats ordinary price competition only because it can withhold trades to limit information rents. In effect, it treats a high-cost seller as more expensive than that seller really is. Sometimes it passes up a deal that would have been worth making, so that every other seller has a reason to price more sharply.

The surprising part is that this advantage is temporary and depends on market structure. The same paper shows the algorithm's edge over plain competition gets smaller with each seller added, and nearly disappears in crowded markets. When many sellers compete, rivalry already pushes margins down, so walking away from deals adds little. A buyer-aligned algorithm matters most in thin markets with few sellers, which is also where it most often leaves trades undone.

Change whose interests the algorithm serves and seller costs get weighed very differently. When a platform optimizes for its own profit, Does platform pricing of inspection tokens encourage excess search? finds it prices search in a way that makes buyers over-inspect sellers. Buyers end up no better off, while surplus moves from sellers to the platform and total efficiency falls. In that case, seller costs become a source of revenue for the platform, not something to discipline on the buyer's behalf. How do seller subsidies sort quality in search markets? shows a third pattern: sellers pay subsidies to get inspected, and those payments sort sellers by quality without any auction. Do advertising-funded shopping agents favor paid product placements? makes the practical version of the same point: if a shopping agent is funded by advertising, paid placement becomes part of how it weighs sellers, however well it is engineered.

The theory assumes an algorithm that actually carries out its objective, and the evidence on LLM agents is less reassuring. Do agent mistakes or misaligned incentives cause marketplace welfare loss? traces lost welfare mainly to agents making execution mistakes, not to agents serving the wrong side. Do LLM negotiation agents reliably follow budget and pricing rules? finds buyer agents revealing their budgets and overpaying, which hands sellers the very rents a buyer-aligned design is meant to remove. Can AI seller agents shift buyer preferences through description edits? shows sellers can shift a buyer agent's choices just by rewriting product descriptions. The upshot is that whether the algorithm is aligned with buyers may matter less than whether it can follow its own rules.

The corpus has one formal source on this exact mechanism. For the full derivation of how the optimal algorithm adjusts each seller's cost, the welfare-edge paper is the place to start.