pith:N3ZCCGVG
Principal-agent problems with adverse selection: A stochastic target problem formulation
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.
arxiv:2605.01080 v2 · 2026-05-01 · econ.TH · math.OC
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Claims
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.
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.
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.
Receipt and verification
| First computed | 2026-05-20T00:03:13.343928Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
6ef2211aa6f41d31f022cfa4222b642764cd8a8c0c5f10e0c1a77bee7862a064
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/N3ZCCGVG6QOTD4BCZ6SCEK3EE5 \
| jq -c '.canonical_record' \
| python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
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Canonical record JSON
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