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Life sciences · Preprint
arXiv · August 17, 2026
Raises a question worth testing. It does not answer one.
This is a preprint proposing OnGameLearn, an online learning algorithm for contextual matrix games that combines multi-player strategic decision-making with contextual information. The work provides theoretical statistical guarantees (tail bounds, convergence, asymptotic normality, sublinear regret) and demonstrates feasibility in simulation and a single hotel pricing case, but has not undergone peer review and carries no clinical or validated operational evidence.
Algorithm development with theoretical analysis, simulation validation, and single real-world case study. Intervention: OnGameLearn: an online learning algorithm integrating contextual information into multi-player online games to balance exploration and exploitation across player actions and contexts.
Algorithm achieves sublinear regret bound under theoretical analysis Doubly robust, √T-consistent estimator developed for policy value in matrix games Convergence of estimated Nash equilibrium established analytically
Safety was not reported in the material analysed. Check the source before drawing any conclusion about harm.
The source did not state who this applies to in practice.
This is a theoretical computer science preprint proposing a novel algorithmic framework with mathematical guarantees but no clinical, patient, or validated real-world outcome evidence; it demonstrates proof-of-concept in simulation and one unvalidated pricing application.
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Online decision making often requires navigating a landscape shaped by both dynamic contexts and strategic interactions. In competitive pricing, for example, hotels must account for both dynamic contextual factors and rivals' strategic responses. Existing approaches address only part of this challenge: contextual bandits optimize single-agent decisions using observable features but ignore multi-player interactions, while online matrix games capture strategic behavior through Nash equilibrium but assume fixed payoffs, ignoring contextual information. How should agents act then when strategic payoffs evolve with contextual signals? We introduce \emph{online contextual matrix games} to integrate contextual information into multi-player online games. We further propose \emph{OnGameLearn}, an online learning algorithm that efficiently balances exploration and exploitation across both player actions and contexts. This approach comes with statistical guarantees: tail bounds for the estimated payoff matrix, the convergence of the estimated Nash equilibrium, the asymptotic normality of the parameter estimators, and the sublinear regret bound. We also develop the notion of \emph{policy value} in matrix games and develop a doubly robust, $\sqrt{T}$-consistent estimator for it. Across simulated studies and a real-world hotel pricing application, we find that OnGameLearn effectively navigates the intertwined challenges of strategic and contextual decision-making.
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