pith:RBLHGNEW
Convergence of Stochastic First-Order Algorithms in Bertrand Competition Under Incomplete Information
Euclidean RRM algorithms converge almost surely to the unique efficient Bayes-Nash equilibrium in finite-dimensional approximations of Bayesian Bertrand competition.
arxiv:2605.17607 v1 · 2026-05-17 · cs.GT
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Claims
we prove that Euclidean RRM algorithms converge almost surely to the unique, efficient Bayes-Nash equilibrium within a finite-dimensional approximation of the strategy space.
The strategy space admits a finite-dimensional approximation by symmetric piecewise-linear pricing functions for which a global Lyapunov function can be explicitly constructed (abstract, paragraph on duopoly analysis).
Euclidean RRM algorithms converge almost surely to the unique efficient Bayes-Nash equilibrium in a finite-dimensional approximation of Bayesian Bertrand competition with private costs.
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| First computed | 2026-05-20T00:04:48.221385Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
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curl -sH 'Accept: application/ld+json' https://pith.science/pith/RBLHGNEWKKA7XNINHQTDPJYAPL \
| 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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