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Theorem 3.1, p. 7 — customization gains at most a factor m, and the order is tight

Proved
CustAssort.Value.theorem_3_1

by mikedeng1 · Oct 9, 2026 · Mathlib 0df444a (Lean v4.33.1)

assortment-optimizationcustomizationmultinomial-logitp2o-batch-pfp2ap2o-gran-per-chapterp2o-plan-paperp2o-v1

Let nnn products have positive revenues, nonnegative type-dependent MNL preference weights, and a no-purchase weight of one. Let the mmm customer types arrive with nonnegative probabilities summing to one. For a first-stage assortment of size at most KKK, zCAPz_{\rm CAP}zCAP​ permits a separate subset for each type, while zMMNLz_{\rm MMNL}zMMNL​ offers the same assortment to every type. Then

zMMNL≤zCAP≤m zMMNL.z_{\rm MMNL}\le z_{\rm CAP}\le m\,z_{\rm MMNL}.zMMNL​≤zCAP​≤mzMMNL​.

The order is tight: for every m≥2m\ge2m≥2, the paper's family with n=K=mn=K=mn=K=m, b=m−1b=m-1b=m−1, a=2m(m−1)ma=2m(m-1)^ma=2m(m−1)m, θj=α/aj\theta_j=\alpha/a^jθj​=α/aj, ri=air_i=a^iri​=ai, and vij=bm−i+1v_{ij}=b^{m-i+1}vij​=bm−i+1 when i≤ji\le ji≤j (zero otherwise), satisfies the model assumptions and has zMMNL>0z_{\rm MMNL}>0zMMNL​>0 and

zCAP≥m6 zMMNL.z_{\rm CAP}\ge\frac{m}{6}\,z_{\rm MMNL}.zCAP​≥6m​zMMNL​.

This compares the best expected revenue with and without customer-type customization. The explicit family realizes the paper's Ω(m)\Omega(m)Ω(m) assertion with a positive MMNL benchmark.

Formalization Note Products and types are 0-based Fin indices. The explicit constant 1/61/61/6 is pinned from the proof's bounds zCAP≥αm/2z_{\rm CAP}\ge\alpha m/2zCAP​≥αm/2 and zMMNL≤3αz_{\rm MMNL}\le3\alphazMMNL​≤3α. No revenue-order hypothesis is imposed: the instance has increasing revenues.

Preamble
import Mathlib
import Definitions.Def_CustAssort_Value_Setting
Formal statement
namespace CustAssort.Value

/-- Theorem 3.1, page 7, including the explicit tightness family of pages 8–9. -/
theorem theorem_3_1 :
    (∀ (n m K : ℕ) (r : Fin n → ℝ) (v : Fin n → Fin m → ℝ)
      (θ : Fin m → ℝ),
      (∀ i, 0 < r i) → (∀ i j, 0 ≤ v i j) → (∀ j, 0 ≤ θ j) →
      (∑ j : Fin m, θ j) = 1 →
      zMMNL θ v r K ≤ zCAP θ v r K ∧
      zCAP θ v r K ≤ (m : ℝ) * zMMNL θ v r K) ∧
    (∀ m : ℕ, 2 ≤ m →
      (∀ i : Fin m, 0 < revI (aStar m) m i) ∧
      (∀ i j : Fin m, 0 ≤ vI (bStar m) m i j) ∧
      (∀ j : Fin m, 0 ≤ thetaI (aStar m) m j) ∧
      (∑ j : Fin m, thetaI (aStar m) m j) = 1 ∧
      0 < zMMNL (thetaI (aStar m) m) (vI (bStar m) m)
        (revI (aStar m) m) m ∧
      (m : ℝ) / 6 * zMMNL (thetaI (aStar m) m) (vI (bStar m) m)
        (revI (aStar m) m) m ≤
      zCAP (thetaI (aStar m) m) (vI (bStar m) m)
        (revI (aStar m) m) m) := by sorry

end CustAssort.Value
Source
El Housni & Topaloglu, Joint Assortment Optimization and Customization under a Mixture of Multinomial Logit Models: Value of Personalized Assortments, SSRN 3830082 (version of December 7, 2021), p. 7, Theorem 3.1; proof pp. 7–9
Human review
  • Endorsed by Shuze Chen · Oct 9, 2026

    Confirmed by the moderator at approval.

  • Endorsed by mikedeng1 · Oct 9, 2026

    Confirmed by the mission captain (proposal self-audit).

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