arXiv subjects

Chupeng Xie

Publications and source records attributed to Chupeng Xie.

2 recordsLinked to original sources

arXiv2026

When Pricing Agents Meet Buying Agents: Personalized Pricing and Verifiable Trust

Same fairness rule, different posterior, no trade. We study personalized pricing when seller and buyer principals delegate to agents that receive different value signals and execute machine-enforced mandates. Equal nominal surplus rules can be incompatible because each is applied to its agent's posterior. In one common environment, we solve nested pricing, inspection, mandate-selection, and repeated-relationship subgames. Signal attestation and execution attestation have different effects: evidence of a high seller signal can legitimate a high price, whereas evidence of a restrained rule can prevent unauthorized extraction. Verification can recover trade and soften both principals' policies only when it covers the disputed proposition. In repeated transactions, attributable offers above a buyer's reference cap depreciate relationship capital, producing a state-dependent Markov policy and a reference-respecting region. Verification is privately underprovided when the seller does not internalize avoided buyer inspection and relationship spillovers, but can be overprovided when it facilitates extraction. The model links AI measurement, algorithmic extraction, and verifiable restraint without treating trust as software emotion or verified execution as proof of true value.

econ.TH↗

arXiv2026

When Trade Produces Knowledge: Dynamic Pricing, Bilateral Learning, and Trust

A transaction can allocate a product and create information unavailable before consumption. We study dynamic pricing when a pricing agent and a buying agent hold different estimates of match value, traded outcomes update a public belief, and attributed extraction depreciates relationship capital. Public learning polarizes exchange: greater precision drives compatible delegated policies toward trade and incompatible policies toward rejection. The seller's exact expected profit must account for the fact that acceptance selects its posterior margin. Its optimal policy therefore tracks the public margin, trust distance, and posterior precision. Higher extraction reduces both current acceptance and the arrival of future outcome signals, producing a reference-respecting region and an extraction-learning trap. With unequal signal precision, the trading rule additionally selects common quality; a selection-naive learner can become more confident and less accurate. Transactions create an information externality for later participants, while a shared misspecified model can make the agents agree without becoming correct. The analysis distinguishes data production, Bayesian knowledge, relationship capital, and verified computation, and identifies the state and protocol records required to test the mechanisms.

econ.TH↗