{"paper":{"title":"Principal-agent problems with adverse selection: A stochastic target problem formulation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"The agent's optimization in unique-contract adverse selection problems reformulates as a stochastic target problem, turning the principal's design into a stochastic control problem with partial information and state constraints.","cross_cats":["math.OC"],"primary_cat":"econ.TH","authors_text":"Guillermo Alonso Alvarez, Ibrahim Ekren, Liwei Huang","submitted_at":"2026-05-01T20:24:19Z","abstract_excerpt":"We study a principal-agent problem with adverse selection, where the principal does not know the agent's true cost but must design a contract to optimize a specific criterion. Unlike standard screening frameworks that allow for self-selection, we assume the principal can only offer a unique contract. We show that the agent's optimization problem can be reformulated as a stochastic target problem. After characterizing the credible domain of this target problem, we show that the principal's objective can be solved as a stochastic optimal control problem with partial information and state constra"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"We show that the agent's optimization problem can be reformulated as a stochastic target problem. After characterizing the credible domain of this target problem, we show that the principal's objective can be solved as a stochastic optimal control problem with partial information and state constraints.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The principal is restricted to offering a unique contract (rather than a menu), and the agent's problem admits an exact reformulation as a stochastic target problem whose credible domain can be characterized.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"Principal-agent adverse selection with unique contracts is reformulated as a stochastic target problem for the agent and a stochastic optimal control problem for the principal.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"The agent's optimization in unique-contract adverse selection problems reformulates as a stochastic target problem, turning the principal's design into a stochastic control problem with partial information and state constraints.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"7f6f7d5fa73d95fb9ebbaf3f200191c9271c7f12a2cf115b6f0b8c98ef6a0fd4"},"source":{"id":"2605.01080","kind":"arxiv","version":2},"verdict":{"id":"5d1231ae-fd7d-4633-b6d7-5cf835a90bc7","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-09T14:20:05.361274Z","strongest_claim":"We show that the agent's optimization problem can be reformulated as a stochastic target problem. After characterizing the credible domain of this target problem, we show that the principal's objective can be solved as a stochastic optimal control problem with partial information and state constraints.","one_line_summary":"Principal-agent adverse selection with unique contracts is reformulated as a stochastic target problem for the agent and a stochastic optimal control problem for the principal.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The principal is restricted to offering a unique contract (rather than a menu), and the agent's problem admits an exact reformulation as a stochastic target problem whose credible domain can be characterized.","pith_extraction_headline":"The agent's optimization in unique-contract adverse selection problems reformulates as a stochastic target problem, turning the principal's design into a stochastic control problem with partial information and state constraints."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2605.01080/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"doi_compliance","ran_at":"2026-05-19T17:38:25.811262Z","status":"completed","version":"1.0.0","findings_count":0}],"snapshot_sha256":"0ba5404a09770f18a9498b9b8e195564d0141ad8c0c21ec5456c04716442ca7c"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}