pith:CRWCBII7
Profit Maximization in Bilateral Trade against a Smooth Adversary
A broker can achieve near-optimal regret in bilateral trade when valuations follow a smooth adversary.
arxiv:2605.12664 v1 · 2026-05-12 · cs.GT · cs.LG
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Claims
We devise a learning algorithm that guarantees a Õ(√T) regret bound, which is tight in the time horizon T up to poly-logarithmic factors. This matches the minimax rate for the stochastic i.i.d. case, and is also well separated from the adversarial setting, where sublinear-regret is unattainable.
The valuations are generated by a smooth adversary, which allows leveraging a continuity property of smooth instances combined with hierarchical net-construction of the broker's action space analyzed via algorithmic chaining.
A learning algorithm achieves tight Õ(√T) regret for profit maximization in bilateral trade against smooth adversaries, matching stochastic rates via continuity and algorithmic chaining.
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Receipt and verification
| First computed | 2026-05-18T03:09:50.384462Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
146c20a11fff1e705730c21f50a1a73d16771609892699ce78039db2df0e985b
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Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/CRWCBII774PHAVZQYIPVBINHHU \
| 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())"
# expect: 146c20a11fff1e705730c21f50a1a73d16771609892699ce78039db2df0e985b
Canonical record JSON
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