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Lemma B.2, p. 28 — for max_{A∈[0,Ā]} y·u(A/y, d) − C(A): A*(y) increasing, A*(y)/y decreasing, V*(y) concave, dV*/dy decreasing in d

Proved
AddictivePlatforms.Compete.lemma_B_2

by mikedeng1 · Oct 16, 2026 · Mathlib 0df444a (Lean v4.33.1)

attentioncomparative-staticsconcavityp2o-batch-pfp2ap2o-gran-per-chapterp2o-plan-paperp2o-v1value-function

Assume Assumptions 1–2 (with the mission's standing pins). For y>0y > 0y>0 and d≥0d \ge 0d≥0 consider

max⁡A∈[0,A‾]  y⋅u ⁣(Ay,d)−C(A),(A.1)\max_{A\in[0,\overline{A}]} \; y\cdot u\!\left(\frac{A}{y}, d\right) - C(A), \qquad (A.1)A∈[0,A]max​y⋅u(yA​,d)−C(A),(A.1)

the consumer's problem when she spreads total attention AAA evenly over "yyy platforms" of addictiveness ddd. Let A∗(y,d)A^*(y,d)A∗(y,d) be a maximizer and V∗(y,d)V^*(y,d)V∗(y,d) the maximized value. Then for every d≥0d \ge 0d≥0:

  1. A∗(y,d)A^*(y,d)A∗(y,d) is (weakly) increasing in yyy on (0,∞)(0,\infty)(0,∞);
  2. A∗(y,d)/yA^*(y,d)/yA∗(y,d)/y is (weakly) decreasing in yyy on (0,∞)(0,\infty)(0,∞);
  3. V∗(⋅,d)V^*(\cdot,d)V∗(⋅,d) is concave on (0,∞)(0,\infty)(0,∞);

and 4. ∂V∗/∂y\partial V^*/\partial y∂V∗/∂y is decreasing in ddd, in integrated form: for all 0<y1<y20 < y_1 < y_20<y1​<y2​, the map

d⟼V∗(y2,d)−V∗(y1,d)d \longmapsto V^*(y_2,d) - V^*(y_1,d)d⟼V∗(y2​,d)−V∗(y1​,d)

is (weakly) decreasing on [0,∞)[0,\infty)[0,∞).

At y=Ky = Ky=K the problem defines AK(d)A_K(d)AK​(d), and V∗(K,d)−V∗(K−1,d)V^*(K,d) - V^*(K-1,d)V∗(K,d)−V∗(K−1,d) is the consumer's gain from joining the KKK-th platform; the lemma is the engine of Step 1 of Proposition 2.

Formalization Note. The page says "V∗(y)V^*(y)V∗(y) is strictly concave in yyy". That is false: on the paper's Figure 3 instance u(a,d)=1−e−(a−d)u(a,d) = 1 - e^{-(a-d)}u(a,d)=1−e−(a−d), C(a)=a/2C(a) = a/2C(a)=a/2 with a large cap, V∗(y,d)=y (1/2−(d+ln⁡2)/2)V^*(y,d) = y\,(1/2 - (d + \ln 2)/2)V∗(y,d)=y(1/2−(d+ln2)/2) is linear in yyy. The proof shows concavity, which is all that is used downstream, so concavity is stated. The derivative claim is stated as a decreasing difference because the paper does not establish differentiability of V∗V^*V∗. The maximizer is a parameter Astar assumed to maximize (A.1) for every y>0y > 0y>0, d≥0d \ge 0d≥0 (it is unique by strict concavity, and exists when A‾<∞\overline{A} < \inftyA<∞ by compactness).

Preamble
import Mathlib
import Definitions.Def_AddictivePlatforms_Compete_Model

open Set Filter Topology
open scoped ENNReal
Formal statement
namespace AddictivePlatforms.Compete

/-- Lemma B.2 (p. 28). With `A*(y, d)` a maximizer of `y · u(A/y, d) − C(A)` over `A ∈ [0, Ā]` and
`V*(y, d)` the maximized value: `A*` is increasing in `y`, `A*(y)/y` is decreasing in `y`, `V*` is
concave in `y` on `(0, ∞)` (the page says "strictly concave", which is false; see the mission
notes), and `dV*/dy` is decreasing in `d`, stated in integrated form: for `0 < y₁ < y₂`,
`V*(y₂, d) − V*(y₁, d)` is decreasing in `d ≥ 0`. -/
theorem lemma_B_2 (u : ℝ → ℝ → ℝ) (C : ℝ → ℝ) (Abar : ℝ≥0∞) (hS : Standing u C)
    (Astar : ℝ → ℝ → ℝ)
    (hA : ∀ y : ℝ, 0 < y → ∀ d : ℝ, 0 ≤ d → IsPerspMax u C Abar y d (Astar y d)) :
    (∀ d : ℝ, 0 ≤ d →
      MonotoneOn (fun y => Astar y d) (Ioi 0) ∧
      AntitoneOn (fun y => Astar y d / y) (Ioi 0) ∧
      ConcaveOn ℝ (Ioi 0) (fun y => perspObj u C y d (Astar y d))) ∧
    ∀ y₁ y₂ : ℝ, 0 < y₁ → y₁ < y₂ →
      AntitoneOn (fun d => perspObj u C y₂ d (Astar y₂ d) - perspObj u C y₁ d (Astar y₁ d))
        (Ici 0) := by sorry

end AddictivePlatforms.Compete
Source
Ichihashi & Kim, Addictive Platforms, Bank of Canada SWP 2022-16 (Apr. 1, 2022), p. 28, Lemma B.2 and (A.1); proof pp. 28–29
Human review
  • Endorsed by Shuze Chen · Oct 16, 2026

    Confirmed by the moderator at approval.

  • Endorsed by mikedeng1 · Oct 16, 2026

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

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