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math.LO — Logic

Finite Nash Equilibria and Dependent Mixed Strategies in Lean

Contributed by Arthur, David Barros Hulak, Ruy J. G. B. de Queiroz

We present two complementary Lean 4 developments of finite Nash equilibrium. The pure-game layer uses player-specific legal action sets, distinguishes one-way generalized ordinal potentials from bidirectional ordinal potentials, and proves potential-maximizer certificates, a local-maximum characterization, absence of strict-improvement cycles, and weak acyclicity. The mixed-game layers use either a common finite action type or a dependent finite action type for each player. They prove payoff equalization on equilibrium supports, Dirac payoff identities, continuity of the normalized payoff-excess map, and mixed equilibrium existence. We explain the finite-sum and sum-of-squares arguments and the reindexing bridge to an attributed, proved product-of-simplices Brouwer theorem. An additional legal-subtype interface is separated from the declarations selected by the registry comparator. The account is tied to two immutable source snapshots and dated verification records. It documents established mathematics and its formal interfaces, without claiming a new equilibrium theorem or formalization priority.

Primarily AI-generated textHuman understanding: some partsBrouwer fixed pointLean 4MathlibNash equilibriumdependent action typesmixed strategiespotential games

math.OC — Optimization and Control

A Lean Formalization of Finite Zero Sum Minimax via Sion's Theorem

Contributed by Arthur, David Barros Hulak, Ruy J. G. B. de Queiroz

We describe a Lean 4 formalization of the minimax theorem for finite nonempty two-player zero-sum games with arbitrary real payoffs. Mixed strategies are probability vectors, and the two game values are defined using real indexed suprema and infima. An explicit payoff bound supplies the boundedness obligations required by these conditional extrema. The reverse minimax inequality follows by applying Mathlib's proved Sion saddle-point theorem to compact convex coordinate simplices, with the minimizing player placed in its first argument. We explain the representation bridge, the proof obligations, and the passage from a saddle point to equality. The accompanying version-pinned artifact separates a reusable library from a statement-only challenge and its proved solution. This is an exposition of a finite-game specialization of existing formal analysis, with no claim of a new mathematical proof or formalization priority.

Primarily AI-generated textHuman understanding: some partsLeanSion's theoremformalizationminimax theoremzero-sum games

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