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Theorem 4.1 — engagement beats utility only through higher moreishness, or higher span with lower value

Proved
EngagementOpt.Misalign.theorem_4_1

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

behavioral-economicsengagementp2o-batch-pfp2ap2o-gran-per-chapterp2o-plan-paperp2o-v1recommender-systems

Let W>0W>0W>0 be the user's per-step outside option and let M⊆Ω=[0,1)×[0,1)×R+\mathcal M\subseteq\Omega=[0,1)\times[0,1)\times\mathbb R_+M⊆Ω=[0,1)×[0,1)×R+​ be a content manifold of content distributions ω=(p,q,vˉ)\omega=(p,q,\bar v)ω=(p,q,vˉ) (moreishness, span, value). Assume, as the paper does, that M\mathcal MM contains at least one point with gS(ω)>0g_S(\omega)>0gS​(ω)>0, where

gS(ω)=vˉ−W1−q−pW1−p,gT(ω)=11−q+p1−p.g_S(\omega)=\frac{\bar v-W}{1-q}-\frac{pW}{1-p},\qquad g_T(\omega)=\frac1{1-q}+\frac{p}{1-p}.gS​(ω)=1−qvˉ−W​−1−ppW​,gT​(ω)=1−q1​+1−pp​.

Let ωS=(pS,qS,vˉS)\omega_S=(p_S,q_S,\bar v_S)ωS​=(pS​,qS​,vˉS​) maximize gSg_SgS​ over M\mathcal MM (3), and let ωT=(pT,qT,vˉT)\omega_T=(p_T,q_T,\bar v_T)ωT​=(pT​,qT​,vˉT​) maximize gTg_TgT​ over {ω∈M:gS(ω)≥0}\{\omega\in\mathcal M: g_S(\omega)\ge0\}{ω∈M:gS​(ω)≥0}, with gS(ωT)≥0g_S(\omega_T)\ge0gS​(ωT​)≥0 (4).

Suppose ωS≠ωT\omega_S\ne\omega_TωS​=ωT​ and that the disagreement is strict:

gS(ωS)>gS(ωT)andgT(ωT)>gT(ωS).g_S(\omega_S)>g_S(\omega_T)\qquad\text{and}\qquad g_T(\omega_T)>g_T(\omega_S).gS​(ωS​)>gS​(ωT​)andgT​(ωT​)>gT​(ωS​).

Then at least one of the following holds:

  1. pT>pSp_T>p_SpT​>pS​ (ωT\omega_TωT​ has higher moreishness);
  2. qT>qSq_T>q_SqT​>qS​ and vˉT<vˉS\bar v_T<\bar v_SvˉT​<vˉS​ (ωT\omega_TωT​ has higher span but lower value).

The theorem names the only two ways in which a platform that maximizes engagement (expected session length) can end up serving content that is worse for the user's forward-looking self: the content is more habit-forming, or it holds the user longer while delivering less value per item.

Formalization Note W>0W>0W>0 is added (the paper fixes W=1W=1W=1 without loss of generality, p. 5). The maximizers are taken as hypotheses (any points attaining the maxima of (3) and (4)), so the paper's tie-breaking rule and its closedness assumption on M\mathcal MM, which only serve to make the arg⁡max⁡\arg\maxargmax well defined, are not needed. The standing assumption that M\mathcal MM has a point with gS>0g_S>0gS​>0 (p. 16) is kept as printed. "One of the following" is an inclusive or.

Preamble
import Mathlib
import Definitions.Def_EngagementOpt_Misalign_Model
Formal statement
namespace EngagementOpt.Misalign

/-- Theorem 4.1, p. 16. Let `M ⊆ Ω` be a content manifold containing a point with `g_S > 0`
(p. 16), let `W > 0`, and let `ω_S`, `ω_T` be maximizers (3), (4). If `ω_S ≠ ω_T` and the
disagreement is strict, `g_S(ω_S) > g_S(ω_T)` and `g_T(ω_T) > g_T(ω_S)`, then
`p_T > p_S`, or `q_T > q_S` and `v̄_T < v̄_S`. -/
theorem theorem_4_1 (W : ℝ) (hW : 0 < W) (M : Set Params) (hM : ∀ ω ∈ M, InOmega ω)
    (hpos : ∃ ω ∈ M, 0 < gS W ω) (ωS ωT : Params)
    (hS : IsUtilityMaximizer W M ωS) (hT : IsEngagementMaximizer W M ωT)
    (hne : ωS ≠ ωT) (hdisS : gS W ωT < gS W ωS) (hdisT : gT ωS < gT ωT) :
    ωS.p < ωT.p ∨ (ωS.q < ωT.q ∧ ωT.v < ωS.v) := by sorry

end EngagementOpt.Misalign
Source
Kleinberg, Mullainathan, Raghavan, The Challenge of Understanding What Users Want, arXiv:2202.11776v3, p. 16, Theorem 4.1 (with (3), (4) and the standing assumption on the same page; Ω on p. 15)
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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