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Preprint . 2026
License: CC BY
Data sources: Datacite
ZENODO
Preprint . 2026
License: CC BY
Data sources: Datacite
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Domain Shrinkage: A Constant-Memory (O(1)) Algorithm for Mixed Nash Equilibrium Computation via Coupled Interval Contraction

Authors: Daniel Luan;

Domain Shrinkage: A Constant-Memory (O(1)) Algorithm for Mixed Nash Equilibrium Computation via Coupled Interval Contraction

Abstract

Domain Shrinkage is a constant-memory (O(1)) algorithm for computing mixed-strategy Nash equilibria in finite 2×2 bimatrix games. Unlike fictitious play and replicator dynamics, which accumulate a belief history that grows with the number of iterations, Domain Shrinkage keeps only a bounded, non-accumulating record and converges by iteratively contracting a shared feasible-strategy corridor based solely on the players' current best responses. The paper analyzes convergence (linear in the number of contraction cycles), characterizes the running time as O(1/δ) for step floor δ, and presents an interactive 3D visualization of the expected-payoff surfaces that animates the shrinkage process in real time. This record contains the paper (PDF and LaTeX source), the figures, and a screen-recorded demo of the interactive visualization. Source code: https://github.com/daluan217/3D-Nash-EquilibriumLive interactive demo: https://nash-equilibrium-simulator.com Please email me with any questions, comments, or feedback: daluan217@g.ucla.edu

Keywords

Nash Equilibrium, Bimatrix Games, Constant-Memory Algorithms, Fictitious Play, Mixed Strategies, 3D Visualization, Learning Dynamics, Computational Game Theory, Interval Contraction, Domain Shrinkage, Game theory, Best-Response Dynamics

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
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