The Curvature Shadow: An Apparent Failure of Maximum-Entropy Equilibrium Selection is a Removable Artifact
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Abstract (arXiv)
In two-player zero-sum games whose Nash equilibria form a convex set, regularized solvers such as Regularized Nash Dynamics (R-NaD) empirically select the maximum-entropy member: the information projection (I-projection) of a uniform reference onto the Nash set. On a panel of small games this match is exact, with one apparent exception: in Kuhn poker R-NaD lands at bluff coordinate 0.180 while the maximum-entropy member sits at 0.201, a coordinate gap of about 0.021, even though R-NaD attains 99.7 percent of the maximum entropy. We ask whether this gap is a genuine selection bias or an artifact, and answer it quantitatively. We show that for selection on a one-dimensional Nash manifold the coordinate gap factorizes as gap ≈ √2δ/kappa, where δ is the entropy shortfall of the solver and kappa is the curvature of the entropy landscape at its peak. Across five games this relation holds to within 2 × 10-4 (under 1 percent relative error). The four matrix games have δ ≈ 0 (R-NaD reaches the maximum-entropy member exactly) and therefore no gap regardless of curvature; only the sequential game (Kuhn) has δ > 0. A causal sweep of the magnet strength drives δ → 0 and the gap toward zero along the predicted curve (fitted scaling exponent 0.50, R2 > 0.999999, against the exact prediction of 1/2), until the dynamics destabilize at a stability floor: behavior consistent with a removable shortfall and inconsistent with a fixed bias. We quantify the curvature half of the law from measured curvatures and flag a moving-target pitfall in the natural Tsallis-entropy experiment. The Kuhn gap is thus the curvature shadow of a small, removable entropy shortfall on an unusually flat peak; the I-projection account is upheld up to a flatness-limited residual.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 4 of its 8 references that are in the map. First posted on arXiv 20 Jul 2026.
Builds on
4 of 8 references.
References · 4
- 2022Mastering the Game of Stratego with Model-Free Multiagent Reinforcement Learningcited by 131
- 2022A Unified Approach to Reinforcement Learning, Quantal Response Equilibria, and Two-Player Zero-Sum Gamescited by 66
- 2020From Poincar'e Recurrence to Convergence in Imperfect Information Games: Finding Equilibrium via Regularizationcited by 61
- 2026Which Nash Equilibrium? Solver-Dependent Selection on Zero-Sum Nash Polytopescited by 2
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Citing papers per year: 2026: 1.
1 paper.