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off_equilibrium

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Off-equilibrium deviation establishes that recognition cost J(r) exceeds zero for any positive real deviation parameter r not equal to unity. Researchers deriving Nash equilibria from recognition cost minimization would reference this result to confirm that unilateral deviations incur positive cost. The proof consists of a direct invocation of the J-cost positivity lemma under the given positivity and inequality hypotheses.

claimFor any real number $r > 0$ with $r ≠ 1$, the recognition cost satisfies $J(r) > 0$.

background

The module identifies Nash equilibrium with zero J-cost (minimum recognition cost, no beneficial unilateral deviation) and off-equilibrium states with positive J-cost. J-cost is defined via the square formula J(r) = (r-1)^2 divided by a positive denominator when r > 0, as established in the upstream Cost module. The local theoretical setting equates the five canonical game types to configDim D = 5 and treats Nash equilibrium as the J = 0 minimum.

proof idea

One-line wrapper that applies the Jcost_pos_of_ne_one lemma from the Cost module (and its duplicates in JcostCore and LocalCache).

why it matters in Recognition Science

This result supplies the off-equilibrium half of the game theory certification, feeding directly into gameTheoryCert which bundles five_types, nash_eq at J = 0, and off_eq at J > 0. It supports the RS derivation of Nash's theorem from recognition cost minimization, consistent with the framework's identification of equilibria as cost minima. No open questions are touched.

scope and limits

Lean usage

def gameTheoryCert : GameTheoryCert where five_types := gameTypeCount nash_eq := nash_equilibrium off_eq := off_equilibrium

formal statement (Lean)

  34theorem off_equilibrium {r : ℝ} (hr : 0 < r) (hne : r ≠ 1) :
  35    0 < Jcost r := Jcost_pos_of_ne_one r hr hne

proof body

  36

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depends on (3)

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