{
 "cells": [
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   "cell_type": "markdown",
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   "source": [
    "# 13 — Test adverse : l'IDE se sature sans coût\n",
    "\n",
    "L'[audit critique](../docs/limites.md) relève une objection que le reste du dépôt n'avait pas\n",
    "traitée. Une plateforme contrainte de maintenir un IDE élevé peut servir des contenus\n",
    "**formellement divergents mais substantiellement vides** — un article étiqueté « point de vue\n",
    "opposé » dont le propos reste adjacent à celui du lecteur. Si l'index se sature ainsi, il est\n",
    "inutilisable comme norme.\n",
    "\n",
    "C'est un problème d'optimisation sous contrainte : **aucune donnée réelle n'est nécessaire\n",
    "pour le trancher**, et il valait mieux le trancher avant de proposer un seuil réglementaire.\n",
    "\n",
    "**Ce que ce notebook établit :**\n",
    "\n",
    "* l'objection est **fondée**, et sévèrement. Une plateforme qui peut dissocier l'étiquette du\n",
    "  contenu obtient un **IDE de 1,000 — la note parfaite — pour une diversité de contenu\n",
    "  strictement nulle**, et sans perdre un point d'engagement ;\n",
    "* il n'est même pas besoin d'aller jusque-là : à mi-découplage, la contrainte n'a plus que\n",
    "  **36 % de sa force** initiale ;\n",
    "* l'**entropie quadratique de Rao** résiste, et mieux qu'attendu : au-delà d'un découplage de\n",
    "  moitié, le plancher devient purement **inatteignable** — la plateforme ne peut plus s'y\n",
    "  conformer sans diversifier réellement ;\n",
    "* et l'écart entre les deux indices, mesuré contre sa contrefactuelle honnête, fournit une\n",
    "  **signature de manipulation** directement prescriptible."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66c67515",
   "metadata": {},
   "source": [
    "## 1. Le modèle, et ce qu'il met en jeu\n",
    "\n",
    "Un catalogue de $k$ points de vue, de positions canoniques $c_\\ell$ réparties sur un axe\n",
    "d'opinion. Un lecteur en $u$. L'engagement décroît avec la distance au lecteur :\n",
    "\n",
    "$$g(x) = \\exp\\left(-\\frac{(x-u)^2}{2w^2}\\right)$$\n",
    "\n",
    "C'est l'hypothèse de bulle, et elle est **défavorable à la plateforme** : elle suppose que\n",
    "conforter paie. Sans elle, il n'y aurait pas de conflit entre diversité et profit, donc pas de\n",
    "question.\n",
    "\n",
    "Le **découplage** $\\varphi \\in [0,1]$ mesure la latitude de la plateforme à dissocier\n",
    "l'étiquette du contenu. Le meilleur article portant l'étiquette $\\ell$ se trouve en\n",
    "\n",
    "$$x^*_\\ell = c_\\ell + \\varphi\\,(u - c_\\ell)$$\n",
    "\n",
    "À $\\varphi = 0$ l'étiquette prédit le contenu. À $\\varphi = 1$, toute étiquette est disponible\n",
    "en version vide, arbitrairement proche du lecteur. C'est là, et nulle part ailleurs, que se\n",
    "joue la manipulation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "fc72b372",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-23T13:39:57.853144Z",
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     "shell.execute_reply": "2026-08-23T13:39:58.398133Z"
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "catalogue de 8 points de vue :\n",
      "  positions canoniques  -1.00  -0.71  -0.43  -0.14  +0.14  +0.43  +0.71  +1.00\n",
      "  lecteur en +0.60\n",
      "\n",
      "  φ = 0.0  contenus servis  -1.00  -0.71  -0.43  -0.14  +0.14  +0.43  +0.71  +1.00\n",
      "  φ = 0.5  contenus servis  -0.20  -0.06  +0.09  +0.23  +0.37  +0.51  +0.66  +0.80\n",
      "  φ = 1.0  contenus servis  +0.60  +0.60  +0.60  +0.60  +0.60  +0.60  +0.60  +0.60\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from ide.gaming import (\n",
    "    canonical_positions,\n",
    "    engagement,\n",
    "    excess_signature,\n",
    "    optimal_feed_under_ide,\n",
    "    optimal_feed_under_rao,\n",
    "    served_positions,\n",
    ")\n",
    "from ide.plotting import PALETTE, save_figure, use_project_style\n",
    "\n",
    "use_project_style()\n",
    "\n",
    "VIEWPOINTS = 8      # points de vue du catalogue réglementaire\n",
    "USER = 0.6          # position du lecteur, décentrée : la bulle a un côté\n",
    "WIDTH = 0.5         # largeur d'engagement\n",
    "FLOOR = 0.80        # plancher d'IDE de référence\n",
    "\n",
    "canonical = canonical_positions(VIEWPOINTS)\n",
    "print(f\"catalogue de {VIEWPOINTS} points de vue :\")\n",
    "print(\"  positions canoniques  \" + \"  \".join(f\"{c:+.2f}\" for c in canonical))\n",
    "print(f\"  lecteur en {USER:+.2f}\\n\")\n",
    "\n",
    "for decoupling in (0.0, 0.5, 1.0):\n",
    "    served = served_positions(canonical, USER, decoupling)\n",
    "    print(f\"  φ = {decoupling:.1f}  contenus servis  \" + \"  \".join(f\"{x:+.2f}\" for x in served))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "176615ce",
   "metadata": {},
   "source": [
    "## 2. Un plancher d'entropie est une température\n",
    "\n",
    "La plateforme maximise $\\sum_\\ell q_\\ell\\,g(x^*_\\ell)$ sous $\\mathrm{IDE}(q) \\geq \\tau$. Le\n",
    "maximum d'une forme linéaire à entropie fixée est une distribution de Boltzmann :\n",
    "\n",
    "$$q_\\ell \\propto \\exp\\big(g(x^*_\\ell)/T\\big)$$\n",
    "\n",
    "où $T$ est le multiplicateur qui sature le plancher. **La contrainte réglementaire agit\n",
    "exactement comme la température sociale du reste du dépôt.** Ce n'est pas une analogie : c'est\n",
    "la même algèbre, et elle rend la solution exacte plutôt qu'approchée — ce qui importe pour un\n",
    "résultat négatif, où une heuristique de solveur pourrait porter la conclusion.\n",
    "\n",
    "Première vérification : la plateforme **sature** le plancher, elle ne le dépasse jamais."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7b1ab28e",
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    "execution": {
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " plancher τ   IDE atteint   engagement     étiquettes servies\n",
      "--------------------------------------------------------------\n",
      "       0.00         0.000        0.974                   1 / 8\n",
      "       0.30         0.300        0.964                   2 / 8\n",
      "       0.50         0.500        0.931                   4 / 8\n",
      "       0.80         0.800        0.798                   8 / 8\n",
      "       0.95         0.950        0.643                   8 / 8\n",
      "       1.00         1.000        0.474                   8 / 8\n",
      "\n",
      "À τ = 0 la plateforme sert son unique meilleur contenu ; à τ = 1, l'uniforme.\n",
      "Entre les deux, elle atteint exactement la contrainte — jamais davantage.\n"
     ]
    }
   ],
   "source": [
    "print(f\"{'plancher τ':>11s} {'IDE atteint':>13s} {'engagement':>12s} {'étiquettes servies':>22s}\")\n",
    "print(\"-\" * 62)\n",
    "for floor in (0.0, 0.3, 0.5, 0.8, 0.95, 1.0):\n",
    "    feed = optimal_feed_under_ide(VIEWPOINTS, USER, floor=floor, width=WIDTH)\n",
    "    spread = int(np.sum(feed.weights > 0.01))\n",
    "    print(f\"{floor:11.2f} {feed.ide:13.3f} {feed.engagement:12.3f} {spread:19d} / {VIEWPOINTS}\")\n",
    "\n",
    "print(\"\\nÀ τ = 0 la plateforme sert son unique meilleur contenu ; à τ = 1, l'uniforme.\")\n",
    "print(\"Entre les deux, elle atteint exactement la contrainte — jamais davantage.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5cee84b6",
   "metadata": {},
   "source": [
    "## 3. Sur un catalogue honnête, la contrainte coûte\n",
    "\n",
    "C'est la vérification qui rend le test adverse non trivial. Si le plancher ne coûtait rien même\n",
    "sans manipulation, il n'y aurait rien à saturer et le résultat serait vide."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "985b30cd",
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2026-08-23T13:39:58.406457Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " plancher   engagement     coût\n",
      "-------------------------------\n",
      "     0.50        0.931     4.4 %\n",
      "     0.70        0.858    12.0 %\n",
      "     0.80        0.798    18.1 %\n",
      "     0.90        0.710    27.1 %\n",
      "     0.95        0.643    34.0 %\n",
      "     1.00        0.474    51.4 %\n",
      "\n",
      "Un plancher d'IDE à 0,80 coûte 18 % d'engagement à une plateforme honnête.\n",
      "La contrainte mord. Reste à savoir si elle mord une plateforme qui triche.\n"
     ]
    }
   ],
   "source": [
    "free_honest = optimal_feed_under_ide(VIEWPOINTS, USER, floor=0.0, width=WIDTH).engagement\n",
    "\n",
    "print(f\"{'plancher':>9s} {'engagement':>12s} {'coût':>8s}\")\n",
    "print(\"-\" * 31)\n",
    "for floor in (0.5, 0.7, 0.8, 0.9, 0.95, 1.0):\n",
    "    feed = optimal_feed_under_ide(VIEWPOINTS, USER, floor=floor, width=WIDTH)\n",
    "    cost = 1.0 - feed.engagement / free_honest\n",
    "    print(f\"{floor:9.2f} {feed.engagement:12.3f} {100 * cost:7.1f} %\")\n",
    "\n",
    "print(\"\\nUn plancher d'IDE à 0,80 coûte 18 % d'engagement à une plateforme honnête.\")\n",
    "print(\"La contrainte mord. Reste à savoir si elle mord une plateforme qui triche.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de1cad76",
   "metadata": {},
   "source": [
    "## 4. Le test adverse\n",
    "\n",
    "Même plancher, même catalogue réglementaire — mais la plateforme peut désormais faire porter\n",
    "une étiquette éloignée par un contenu proche."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1040257f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-23T13:39:58.407526Z",
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     "shell.execute_reply": "2026-08-23T13:39:58.412807Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "coût du plancher d'IDE, en % d'engagement perdu\n",
      " plancher   φ = 0.00   φ = 0.25   φ = 0.50   φ = 0.75   φ = 1.00\n",
      "----------------------------------------------------------------\n",
      "     0.50       4.4%       2.6%       1.2%       0.3%       0.0%\n",
      "     0.70      12.0%       7.8%       3.8%       1.0%       0.0%\n",
      "     0.80      18.1%      12.5%       6.6%       1.8%       0.0%\n",
      "     0.90      27.1%      20.0%      11.4%       3.4%       0.0%\n",
      "     0.95      34.0%      25.9%      15.4%       4.8%       0.0%\n",
      "     1.00      51.4%      41.4%      26.4%       8.7%       0.0%\n"
     ]
    }
   ],
   "source": [
    "DECOUPLINGS = (0.0, 0.25, 0.5, 0.75, 1.0)\n",
    "\n",
    "baselines = {\n",
    "    d: optimal_feed_under_ide(VIEWPOINTS, USER, floor=0.0, decoupling=d, width=WIDTH).engagement\n",
    "    for d in DECOUPLINGS\n",
    "}\n",
    "\n",
    "print(\"coût du plancher d'IDE, en % d'engagement perdu\")\n",
    "print(f\"{'plancher':>9s}\" + \"\".join(f\"{f'φ = {d:.2f}':>11s}\" for d in DECOUPLINGS))\n",
    "print(\"-\" * 64)\n",
    "for floor in (0.5, 0.7, 0.8, 0.9, 0.95, 1.0):\n",
    "    row = \"\"\n",
    "    for d in DECOUPLINGS:\n",
    "        feed = optimal_feed_under_ide(VIEWPOINTS, USER, floor=floor, decoupling=d, width=WIDTH)\n",
    "        row += f\"{100 * (1 - feed.engagement / baselines[d]):10.1f}%\"\n",
    "    print(f\"{floor:9.2f}\" + row)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "474cdd8a",
   "metadata": {},
   "source": [
    "### Le résultat, en une ligne\n",
    "\n",
    "La dernière colonne est **zéro partout**, y compris pour un plancher d'IDE de 1,00 — la note\n",
    "maximale. Une plateforme qui peut découpler entièrement l'étiquette du contenu obtient la\n",
    "diversité parfaite sans céder un point d'engagement.\n",
    "\n",
    "Regardons ce que ce fil contient réellement."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "02609585",
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2026-08-23T13:39:58.416620Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     φ      IDE      Rao    écart   engagement     coût\n",
      "------------------------------------------------------\n",
      "  0.00    0.800    0.443    0.357        0.798    18.1 %\n",
      "  0.25    0.800    0.324    0.476        0.862    12.5 %\n",
      "  0.50    0.800    0.215    0.585        0.928     6.6 %\n",
      "  0.75    0.800    0.108    0.692        0.980     1.8 %\n",
      "  1.00    1.000    0.000    1.000        1.000     0.0 %\n",
      "\n",
      "À φ = 1 : IDE = 1,000 — la note parfaite — pour une diversité de contenu de 0,000.\n",
      "L'index décerne son meilleur score à un fil qui ne contient qu'un seul point de vue.\n"
     ]
    }
   ],
   "source": [
    "print(f\"{'φ':>6s} {'IDE':>8s} {'Rao':>8s} {'écart':>8s} {'engagement':>12s} {'coût':>8s}\")\n",
    "print(\"-\" * 54)\n",
    "for d in DECOUPLINGS:\n",
    "    feed = optimal_feed_under_ide(VIEWPOINTS, USER, floor=FLOOR, decoupling=d, width=WIDTH)\n",
    "    cost = 100 * (1 - feed.engagement / baselines[d])\n",
    "    print(f\"{d:6.2f} {feed.ide:8.3f} {feed.rao:8.3f} {feed.signature:8.3f}\"\n",
    "          f\" {feed.engagement:12.3f} {cost:7.1f} %\")\n",
    "\n",
    "print(\"\\nÀ φ = 1 : IDE = 1,000 — la note parfaite — pour une diversité de contenu de 0,000.\")\n",
    "print(\"L'index décerne son meilleur score à un fil qui ne contient qu'un seul point de vue.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61964821",
   "metadata": {},
   "source": [
    "### Et il n'est pas besoin d'aller au bout\n",
    "\n",
    "Le découplage complet est une caricature : aucune plateforme ne peut vider toutes ses\n",
    "étiquettes. La question qui compte pour un régulateur est **à quelle vitesse la contrainte perd\n",
    "sa force**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8617aad3",
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2026-08-23T13:39:58.421452Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     φ     coût   force restante\n",
      "--------------------------------\n",
      "   0.0    18.1 %          100.0 %\n",
      "   0.1    16.0 %           88.2 %\n",
      "   0.2    13.7 %           75.7 %\n",
      "   0.3    11.3 %           62.6 %\n",
      "   0.4     8.9 %           49.4 %\n",
      "   0.5     6.6 %           36.5 %\n",
      "   0.6     4.5 %           24.6 %\n",
      "   0.7     2.6 %           14.4 %\n",
      "   0.8     1.2 %            6.6 %\n",
      "   0.9     0.3 %            1.7 %\n",
      "   1.0     0.0 %            0.0 %\n",
      "\n",
      "À mi-découplage la contrainte n'a plus que 36 % de sa force ;\n",
      "à φ = 0,8, il en reste 7 %. La dégradation est bien plus rapide que le découplage.\n"
     ]
    }
   ],
   "source": [
    "reference_cost = 1 - optimal_feed_under_ide(\n",
    "    VIEWPOINTS, USER, floor=FLOOR, decoupling=0.0, width=WIDTH\n",
    ").engagement / baselines[0.0]\n",
    "\n",
    "print(f\"{'φ':>6s} {'coût':>8s} {'force restante':>16s}\")\n",
    "print(\"-\" * 32)\n",
    "for d in np.linspace(0.0, 1.0, 11):\n",
    "    free = optimal_feed_under_ide(\n",
    "        VIEWPOINTS, USER, floor=0.0, decoupling=float(d), width=WIDTH\n",
    "    ).engagement\n",
    "    feed = optimal_feed_under_ide(\n",
    "        VIEWPOINTS, USER, floor=FLOOR, decoupling=float(d), width=WIDTH\n",
    "    )\n",
    "    cost = 1 - feed.engagement / free\n",
    "    print(f\"{d:6.1f} {100 * cost:7.1f} % {100 * cost / reference_cost:14.1f} %\")\n",
    "\n",
    "print(\"\\nÀ mi-découplage la contrainte n'a plus que 36 % de sa force ;\")\n",
    "print(\"à φ = 0,8, il en reste 7 %. La dégradation est bien plus rapide que le découplage.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7e50857",
   "metadata": {},
   "source": [
    "## 5. L'entropie quadratique de Rao résiste\n",
    "\n",
    "$$Q = \\frac{2}{D}\\sum_{\\ell m} q_\\ell q_m \\, |x^*_\\ell - x^*_m|$$\n",
    "\n",
    "Elle ne compte pas les étiquettes, elle compte les **écarts entre contenus servis**, rapportés\n",
    "à l'étendue $D$ du catalogue de référence.\n",
    "\n",
    "!!! danger \"Un piège de normalisation, et ce qu'il aurait coûté\"\n",
    "    Une première version de ce module normalisait $Q$ par l'étalement **effectivement servi**.\n",
    "    La mesure devenait alors invariante d'échelle, et un fil réduit à un point y marquait\n",
    "    $Q \\approx 1$ sur du bruit d'arrondi. Le notebook aurait conclu que l'entropie de Rao est\n",
    "    manipulable elle aussi — c'est-à-dire l'inverse de la vérité. L'unité est donc l'étendue\n",
    "    du catalogue **de référence**, fixée par le régulateur."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f918b46e",
   "metadata": {
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     "shell.execute_reply": "2026-08-23T13:39:59.251244Z"
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "plancher de Rao à 0.50\n",
      "\n",
      "     φ   Q atteignable   Q servi   conforme      coût\n",
      "------------------------------------------------------\n",
      "  0.00           1.000     0.500        oui    15.6 %\n",
      "  0.25           0.750     0.500        oui    21.1 %\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  0.50           0.500     0.500        oui    39.5 %\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  0.75           0.250     0.250        NON         —\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  1.00           0.000     0.000        NON         —\n"
     ]
    }
   ],
   "source": [
    "free_rao = {\n",
    "    d: optimal_feed_under_rao(VIEWPOINTS, USER, floor=0.0, decoupling=d, width=WIDTH).engagement\n",
    "    for d in DECOUPLINGS\n",
    "}\n",
    "\n",
    "RAO_FLOOR = 0.50\n",
    "print(f\"plancher de Rao à {RAO_FLOOR:.2f}\\n\")\n",
    "print(f\"{'φ':>6s} {'Q atteignable':>15s} {'Q servi':>9s} {'conforme':>10s} {'coût':>9s}\")\n",
    "print(\"-\" * 54)\n",
    "for d in DECOUPLINGS:\n",
    "    feed = optimal_feed_under_rao(VIEWPOINTS, USER, floor=RAO_FLOOR, decoupling=d, width=WIDTH)\n",
    "    complies = feed.rao >= RAO_FLOOR - 1e-6\n",
    "    cost = f\"{100 * (1 - feed.engagement / free_rao[d]):7.1f} %\" if complies else \"     —\"\n",
    "    print(f\"{d:6.2f} {feed.reachable_rao:15.3f} {feed.rao:9.3f} \"\n",
    "          f\"{'oui' if complies else 'NON':>10s} {cost:>9s}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e580b03",
   "metadata": {},
   "source": [
    "### Deux propriétés, et la seconde est inattendue\n",
    "\n",
    "**Le plancher devient inatteignable.** Au-delà d'un découplage de moitié, aucune distribution\n",
    "d'étiquettes ne permet de satisfaire $Q \\geq 0{,}5$ : la plateforme qui a vidé ses étiquettes\n",
    "**ne peut plus se conformer**, quoi qu'elle fasse. Là où l'IDE offrait une échappatoire\n",
    "gratuite, l'entropie de Rao ferme la porte.\n",
    "\n",
    "**Et le coût augmente avec le découplage**, au lieu de diminuer : 16 % à $\\varphi = 0$, 40 % à\n",
    "$\\varphi = 0{,}5$. C'est exactement l'inverse du comportement de l'IDE, et la raison en est\n",
    "mécanique — vider ses étiquettes réduit la diversité atteignable, donc rend la conformité plus\n",
    "chère. **Manipuler l'étiquetage se retourne contre la plateforme.**\n",
    "\n",
    "C'est cette propriété, plus que la simple résistance, qui fait de $Q$ une norme tenable."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a128093",
   "metadata": {},
   "source": [
    "## 6. Une signature de manipulation, sans seuil inventé\n",
    "\n",
    "L'écart brut $\\mathrm{IDE} - Q$ n'est **pas** interprétable seul : les deux indices ne sont pas\n",
    "sur la même échelle, et un fil parfaitement honnête en affiche déjà 0,36. Publier un seuil\n",
    "là-dessus reviendrait à fabriquer un chiffre — ce que ce dépôt a déjà eu à retirer une fois.\n",
    "\n",
    "La grandeur interprétable est la **différence à la contrefactuelle honnête** : ce qu'un\n",
    "catalogue dont les étiquettes prédisent le contenu afficherait au même IDE. Elle vaut zéro par\n",
    "construction pour une plateforme honnête, et elle est calculable par le régulateur puisqu'elle\n",
    "ne dépend que du catalogue de référence, qu'il fixe lui-même."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "75ce213d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-23T13:39:59.252649Z",
     "iopub.status.busy": "2026-08-23T13:39:59.252571Z",
     "iopub.status.idle": "2026-08-23T13:39:59.257528Z",
     "shell.execute_reply": "2026-08-23T13:39:59.257075Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     φ   écart brut     excès\n",
      "-----------------------------\n",
      "  0.00        0.357     0.000\n",
      "  0.25        0.476     0.119\n",
      "  0.50        0.585     0.228\n",
      "  0.75        0.692     0.335\n",
      "  1.00        1.000     0.643\n",
      "\n",
      "L'excès est nul pour une plateforme honnête et croît de façon monotone.\n",
      "C'est la seule des trois grandeurs qui soit directement prescriptible.\n"
     ]
    }
   ],
   "source": [
    "print(f\"{'φ':>6s} {'écart brut':>12s} {'excès':>9s}\")\n",
    "print(\"-\" * 29)\n",
    "for d in DECOUPLINGS:\n",
    "    feed = optimal_feed_under_ide(VIEWPOINTS, USER, floor=FLOOR, decoupling=d, width=WIDTH)\n",
    "    print(f\"{d:6.2f} {feed.signature:12.3f} \"\n",
    "          f\"{excess_signature(VIEWPOINTS, USER, FLOOR, d, WIDTH):9.3f}\")\n",
    "\n",
    "print(\"\\nL'excès est nul pour une plateforme honnête et croît de façon monotone.\")\n",
    "print(\"C'est la seule des trois grandeurs qui soit directement prescriptible.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1db1c214",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-23T13:39:59.258336Z",
     "iopub.status.busy": "2026-08-23T13:39:59.258270Z",
     "iopub.status.idle": "2026-08-23T13:39:59.649526Z",
     "shell.execute_reply": "2026-08-23T13:39:59.649070Z"
    }
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1380x936 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure, axes = plt.subplots(2, 2, figsize=(11.5, 7.8))\n",
    "served, cost_curve, scissors, resistance = axes.ravel()\n",
    "\n",
    "honest = optimal_feed_under_ide(VIEWPOINTS, USER, floor=FLOOR, decoupling=0.0, width=WIDTH)\n",
    "gamed = optimal_feed_under_ide(VIEWPOINTS, USER, floor=FLOOR, decoupling=1.0, width=WIDTH)\n",
    "\n",
    "# (a) Ce que les deux plateformes servent réellement, sur deux rangs.\n",
    "for row, (feed, colour, name) in enumerate(\n",
    "    [(gamed, PALETTE[\"field\"], \"manipulée\"), (honest, PALETTE[\"remedy\"], \"honnête\")]\n",
    "):\n",
    "    served.scatter(feed.positions, np.full(VIEWPOINTS, row),\n",
    "                   s=40 + 5200 * feed.weights, color=colour, alpha=0.55,\n",
    "                   edgecolors=colour, linewidths=1.2, zorder=3)\n",
    "    served.text(-1.08, row + 0.30, f\"{name} · IDE {feed.ide:.2f} · Rao {feed.rao:.2f}\",\n",
    "                fontsize=8.5, color=colour, fontweight=\"bold\")\n",
    "served.axvline(USER, color=PALETTE[\"neutral\"], linestyle=\"--\", linewidth=1.2, zorder=1)\n",
    "served.text(USER + 0.05, -0.42, \"lecteur\", fontsize=8, color=PALETTE[\"neutral\"])\n",
    "served.annotate(\"huit étiquettes,\\nun seul contenu\", xy=(USER, 0.0), xytext=(-0.55, 0.10),\n",
    "                fontsize=8, color=PALETTE[\"field\"],\n",
    "                arrowprops={\"arrowstyle\": \"->\", \"color\": PALETTE[\"field\"], \"lw\": 1.1})\n",
    "served.set_xlim(-1.15, 1.15)\n",
    "served.set_ylim(-0.55, 1.55)\n",
    "served.set_yticks([])\n",
    "served.set_xlabel(\"position du contenu servi\")\n",
    "served.set_title(\"Même contrainte, deux fils sans rapport\", fontsize=10)\n",
    "served.spines[\"left\"].set_visible(False)\n",
    "\n",
    "# (b) Coût du plancher selon le découplage.\n",
    "floors = np.linspace(0.4, 1.0, 25)\n",
    "shades = [PALETTE[\"remedy\"], \"#3f8f6a\", PALETTE[\"neutral\"], \"#a35bb0\", PALETTE[\"field\"]]\n",
    "for decoupling, colour in zip(DECOUPLINGS, shades, strict=True):\n",
    "    base = baselines[decoupling]\n",
    "    costs = [\n",
    "        100 * (1 - optimal_feed_under_ide(\n",
    "            VIEWPOINTS, USER, floor=float(f), decoupling=decoupling, width=WIDTH\n",
    "        ).engagement / base)\n",
    "        for f in floors\n",
    "    ]\n",
    "    cost_curve.plot(floors, costs, color=colour, linewidth=1.8, label=f\"φ = {decoupling:.2f}\")\n",
    "cost_curve.set_xlabel(\"plancher d'IDE imposé\")\n",
    "cost_curve.set_ylabel(\"engagement perdu  [%]\")\n",
    "cost_curve.set_title(\"La contrainte s'efface avec le découplage\", fontsize=10)\n",
    "cost_curve.legend(fontsize=8)\n",
    "\n",
    "# (c) Les ciseaux : diversité affichée contre diversité servie.\n",
    "grid = np.linspace(0.0, 1.0, 41)\n",
    "displayed, actual = [], []\n",
    "for decoupling in grid:\n",
    "    feed = optimal_feed_under_ide(\n",
    "        VIEWPOINTS, USER, floor=FLOOR, decoupling=float(decoupling), width=WIDTH\n",
    "    )\n",
    "    displayed.append(feed.ide)\n",
    "    actual.append(feed.rao)\n",
    "scissors.plot(grid, displayed, color=PALETTE[\"field\"], linewidth=2.0,\n",
    "              label=\"IDE — diversité affichée\")\n",
    "scissors.plot(grid, actual, color=PALETTE[\"remedy\"], linewidth=2.0,\n",
    "              label=\"Rao — diversité servie\")\n",
    "scissors.fill_between(grid, actual, displayed, color=PALETTE[\"field\"], alpha=0.12)\n",
    "scissors.set_xlabel(\"découplage φ de l'étiquette et du contenu\")\n",
    "scissors.set_ylabel(\"indice\")\n",
    "scissors.set_ylim(-0.03, 1.05)\n",
    "scissors.set_title(f\"À plancher d'IDE {FLOOR:.2f} imposé\", fontsize=10)\n",
    "scissors.legend(fontsize=8, loc=\"center left\")\n",
    "\n",
    "# (d) Ce qu'un plancher de Rao rend atteignable.\n",
    "reachable = [\n",
    "    optimal_feed_under_rao(\n",
    "        VIEWPOINTS, USER, floor=0.0, decoupling=float(d), width=WIDTH\n",
    "    ).reachable_rao\n",
    "    for d in grid\n",
    "]\n",
    "resistance.plot(grid, reachable, color=PALETTE[\"remedy\"], linewidth=2.0,\n",
    "                label=\"diversité de Rao atteignable\")\n",
    "resistance.axhline(RAO_FLOOR, color=PALETTE[\"disorder\"], linestyle=\"--\", linewidth=1.4)\n",
    "resistance.fill_between(grid, 0, reachable, where=np.array(reachable) < RAO_FLOOR,\n",
    "                        color=PALETTE[\"disorder\"], alpha=0.15)\n",
    "crossing = float(np.interp(RAO_FLOOR, np.array(reachable)[::-1], grid[::-1]))\n",
    "resistance.axvline(crossing, color=PALETTE[\"neutral\"], linestyle=\":\", linewidth=1.2)\n",
    "resistance.text(crossing + 0.02, 0.9, f\"conformité\\nimpossible\\nau-delà de φ = {crossing:.2f}\",\n",
    "                fontsize=8, color=PALETTE[\"neutral\"], va=\"top\")\n",
    "resistance.set_xlabel(\"découplage φ de l'étiquette et du contenu\")\n",
    "resistance.set_ylabel(\"entropie de Rao atteignable\")\n",
    "resistance.set_title(f\"Un plancher de Rao à {RAO_FLOOR:.2f} devient inatteignable\", fontsize=10)\n",
    "resistance.legend(fontsize=8, loc=\"lower left\")\n",
    "\n",
    "figure.suptitle(\"Test adverse : l'IDE se sature sans coût, l'entropie de Rao non\", fontsize=12)\n",
    "figure.tight_layout(rect=(0, 0, 1, 0.96))\n",
    "save_figure(figure, \"fig13_test_adverse\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba1f77ba",
   "metadata": {},
   "source": [
    "## 7. Ce que le notebook établit\n",
    "\n",
    "**L'objection est fondée, et l'IDE seul est inutilisable comme norme.** Une plateforme capable\n",
    "de dissocier l'étiquette du contenu obtient un IDE de 1,000 — la note maximale — pour une\n",
    "diversité de contenu strictement nulle, sans céder un point d'engagement. Et la dégradation est\n",
    "bien plus rapide que le découplage : à mi-chemin, la contrainte n'a plus que 36 % de sa force.\n",
    "\n",
    "**L'entropie quadratique de Rao résiste, et se retourne contre le manipulateur.** Au-delà d'un\n",
    "découplage de moitié, le plancher devient purement inatteignable ; en deçà, il coûte *plus*\n",
    "cher à mesure que la plateforme vide ses étiquettes. C'est une propriété plus forte que la\n",
    "simple robustesse, et c'est elle qui rend $Q$ prescriptible.\n",
    "\n",
    "**Une signature de manipulation existe**, à condition de la définir comme un excès sur la\n",
    "contrefactuelle honnête plutôt que comme un écart brut. Elle est nulle pour une plateforme\n",
    "honnête et se calcule à partir du seul catalogue de référence, que le régulateur fixe.\n",
    "\n",
    "> **Le résultat ne détruit pas l'index : il en déplace la définition.** Ce qu'il faut mesurer\n",
    "> n'est pas la diversité des étiquettes servies, c'est la distance sémantique entre les\n",
    "> contenus qu'elles portent.\n",
    "\n",
    "### Ce que le modèle suppose, et qui pourrait le retourner\n",
    "\n",
    "Le résultat est un théorème sur un modèle, pas une mesure. Trois hypothèses le portent :\n",
    "\n",
    "* **la bulle paie** — l'engagement décroît avec la distance au lecteur. Si les lecteurs\n",
    "  valorisaient la contradiction, il n'y aurait pas de conflit à arbitrer ;\n",
    "* **le découplage est gratuit** — produire un contenu vide sous une étiquette éloignée ne coûte\n",
    "  rien à la plateforme. Un coût de production réduirait $\\varphi$ atteignable, sans changer la\n",
    "  forme du résultat ;\n",
    "* **l'axe d'opinion est unidimensionnel.** En dimension supérieure, une plateforme dispose de\n",
    "  plus de directions où se cacher, ce qui va dans le sens du résultat plutôt que contre lui.\n",
    "\n",
    "Aucune de ces hypothèses n'est vérifiée empiriquement ici, et la première est la plus\n",
    "contestable."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "756a794d",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## 8. Le correctif proposé était lui-même défectueux\n",
    "\n",
    "La section précédente concluait que l'entropie quadratique de Rao devait remplacer l'IDE. Une\n",
    "lecture de la littérature d'évaluation des recommandeurs a montré que cette conclusion était\n",
    "fausse — et le défaut est du plus mauvais genre pour ce projet.\n",
    "\n",
    "**L'entropie de Rao est la *distance intra-liste*** (ILD), l'objectif de diversité le plus\n",
    "employé du domaine. Ohsaka et Togashi ([SIGIR 2023](https://arxiv.org/abs/2305.13801)) en ont\n",
    "donné une réexamination critique : l'ILD admet des **optima dégénérés**, parce qu'elle\n",
    "récompense l'écart sans jamais récompenser l'occupation.\n",
    "\n",
    "Sur un axe d'opinion, le dégénéré porte un nom."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "04c6deb3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-23T13:39:59.650446Z",
     "iopub.status.busy": "2026-08-23T13:39:59.650352Z",
     "iopub.status.idle": "2026-08-23T13:39:59.653465Z",
     "shell.execute_reply": "2026-08-23T13:39:59.653082Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Deux fils, et ce que l'entropie de Rao en pense :\n",
      "\n",
      "fil                                               Rao\n",
      "------------------------------------------------------\n",
      "uniforme sur les huit points de vue             0.750\n",
      "50 % à chaque bord, RIEN entre les deux         1.000\n",
      "\n",
      "Un plancher de Rao récompense la polarisation maximale.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from ide.gaming import (\n",
    "    canonical_positions,\n",
    "    centre_share,\n",
    "    gaussian_ild,\n",
    "    largest_gap,\n",
    "    optimal_feed_under,\n",
    "    position_entropy,\n",
    "    rao_entropy,\n",
    "    served_positions,\n",
    "    target_divergence,\n",
    ")\n",
    "from ide.plotting import PALETTE, save_figure, use_project_style\n",
    "\n",
    "use_project_style()\n",
    "\n",
    "CATALOGUE = canonical_positions(VIEWPOINTS)\n",
    "REFERENCE = float(np.ptp(CATALOGUE))\n",
    "BANDWIDTH = float(CATALOGUE[1] - CATALOGUE[0])\n",
    "\n",
    "bimodal = np.zeros(VIEWPOINTS)\n",
    "bimodal[0] = bimodal[-1] = 0.5\n",
    "uniform = np.full(VIEWPOINTS, 1.0 / VIEWPOINTS)\n",
    "\n",
    "print(\"Deux fils, et ce que l'entropie de Rao en pense :\\n\")\n",
    "print(f\"{'fil':44s} {'Rao':>8s}\")\n",
    "print(\"-\" * 54)\n",
    "print(f\"{'uniforme sur les huit points de vue':44s} \"\n",
    "      f\"{rao_entropy(uniform, CATALOGUE, REFERENCE):8.3f}\")\n",
    "print(f\"{'50 % à chaque bord, RIEN entre les deux':44s} \"\n",
    "      f\"{rao_entropy(bimodal, CATALOGUE, REFERENCE):8.3f}\")\n",
    "print(\"\\nUn plancher de Rao récompense la polarisation maximale.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1c210eb",
   "metadata": {},
   "source": [
    "### Ce n'est pas une manipulation, c'est la réponse optimale\n",
    "\n",
    "Le point est plus grave qu'une faille exploitable. La plateforme n'a **pas besoin de tricher**\n",
    "pour vider le centre : c'est ce que lui dicte la maximisation de l'engagement sous contrainte\n",
    "de Rao. Servir une masse près du lecteur et le complément au bord opposé est la façon la moins\n",
    "chère de satisfaire le plancher."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b16a5653",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-23T13:39:59.654174Z",
     "iopub.status.busy": "2026-08-23T13:39:59.654101Z",
     "iopub.status.idle": "2026-08-23T13:40:01.244437Z",
     "shell.execute_reply": "2026-08-23T13:40:01.243910Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "plancher = 0.6\n",
      "  mesure                           coût    servis   centre   plus grand vide\n",
      "  -------------------------------------------------------------------------\n",
      "  Rao (ILD)                      20.5 %      4/8     0.75             0.71\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  entropie de position            7.6 %      5/8     0.89             0.14\n",
      "  Gaussian ILD                   25.4 %      7/8     0.70             0.29\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  proximité à la cible            4.2 %      4/8     0.95             0.14\n",
      "\n",
      "plancher = 0.8\n",
      "  mesure                           coût    servis   centre   plus grand vide\n",
      "  -------------------------------------------------------------------------\n",
      "  Rao (ILD)                      32.8 %      4/8     0.53             0.71\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  entropie de position           18.1 %      8/8     0.83             0.14\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gaussian ILD            inatteignable      2/8     0.00             1.00\n",
      "  proximité à la cible           14.4 %      8/8     0.87             0.14\n",
      "\n"
     ]
    }
   ],
   "source": [
    "MEASURES = {\n",
    "    \"Rao (ILD)\": lambda q, x: rao_entropy(q, x, REFERENCE),\n",
    "    \"entropie de position\": lambda q, x: position_entropy(q, x, CATALOGUE),\n",
    "    \"Gaussian ILD\": lambda q, x: gaussian_ild(q, x, BANDWIDTH),\n",
    "    \"proximité à la cible\": lambda q, x: target_divergence(q, x, CATALOGUE),\n",
    "}\n",
    "\n",
    "free = optimal_feed_under(MEASURES[\"Rao (ILD)\"], VIEWPOINTS, USER, 0.0, width=WIDTH).engagement\n",
    "\n",
    "for floor in (0.6, 0.8):\n",
    "    print(f\"plancher = {floor:.1f}\")\n",
    "    print(f\"  {'mesure':22s} {'coût':>14s} {'servis':>9s} {'centre':>8s} {'plus grand vide':>17s}\")\n",
    "    print(\"  \" + \"-\" * 73)\n",
    "    for name, measure in MEASURES.items():\n",
    "        feed = optimal_feed_under(measure, VIEWPOINTS, USER, floor, width=WIDTH)\n",
    "        complies = measure(feed.weights, feed.positions) >= floor - 1e-6\n",
    "        cost = f\"{100 * (1 - feed.engagement / free):6.1f} %\" if complies else \"inatteignable\"\n",
    "        served = int(np.sum(feed.weights > 0.01))\n",
    "        print(f\"  {name:22s} {cost:>14s} {served:6d}/{VIEWPOINTS}\"\n",
    "              f\" {feed.centre_share:8.2f} {feed.largest_gap:16.2f}\")\n",
    "    print()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "953cef79",
   "metadata": {},
   "source": [
    "### Lecture\n",
    "\n",
    "Sous plancher de Rao à 0,80, la plateforme sert **quatre points de vue sur huit**, laisse un\n",
    "vide de 0,71 — les sept dixièmes de l'axe — et ne place plus que la moitié de son fil ailleurs\n",
    "qu'aux bords. **Le plancher réglementaire produit lui-même l'exposition bimodale que le projet\n",
    "cherchait à mesurer.**\n",
    "\n",
    "C'est la seule des quatre mesures qui le fasse. Et ce n'est pas un détail d'implémentation :\n",
    "c'est la conséquence directe de ce que mesure l'ILD."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16069ae7",
   "metadata": {},
   "source": [
    "## 9. Trois remplaçantes, et ce qu'elles coûtent\n",
    "\n",
    "**L'entropie de position** est l'IDE à une substitution près : la distribution mesurée n'est\n",
    "plus celle des étiquettes déclarées mais celle des **positions effectivement servies**,\n",
    "projetées sur les bacs du catalogue de référence. Elle garde l'interprétation de l'index\n",
    "d'origine — nulle pour un fil gelé, 1 pour l'uniforme — et refuse les deux échappatoires.\n",
    "\n",
    "**La Gaussian ILD** est la proposition d'Ohsaka et Togashi : un noyau gaussien qui sature, si\n",
    "bien qu'au-delà de quelques largeurs de bande, s'éloigner davantage ne rapporte plus rien et\n",
    "la seule façon de gagner est d'occuper des endroits différents.\n",
    "\n",
    "**La proximité à une cible déclarée** répond à un défaut de principe que les trois autres\n",
    "partagent : elles supposent qu'une forme d'exposition est bonne sans le dire. L'entropie\n",
    "suppose l'uniforme, l'entropie de Rao suppose l'écartement — et c'est ce non-dit qui la conduit\n",
    "à prescrire la bimodalité. Une divergence rend l'hypothèse explicite : le régulateur *déclare*\n",
    "la distribution visée."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "368b8f7d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-23T13:40:01.245618Z",
     "iopub.status.busy": "2026-08-23T13:40:01.245528Z",
     "iopub.status.idle": "2026-08-23T13:40:01.249568Z",
     "shell.execute_reply": "2026-08-23T13:40:01.249126Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A. Le fil bimodal, vu par chaque mesure\n",
      "\n",
      "mesure                     bimodal   uniforme   verdict\n",
      "----------------------------------------------------------------------------\n",
      "Rao (ILD)                    1.000      0.750   PRÉFÈRE la polarisation\n",
      "entropie de position         0.333      1.000   préfère l'étalement\n",
      "Gaussian ILD                 0.500      0.715   préfère l'étalement\n",
      "proximité à la cible         0.451      1.000   préfère l'étalement\n",
      "\n",
      "\n",
      "B. La faille d'origine reste-t-elle fermée ? (découplage total)\n",
      "\n",
      "mesure                    valeur maximale atteignable   plancher 0,6 tenable ?\n",
      "----------------------------------------------------------------------------------\n",
      "Rao (ILD)                                       0.000   non\n",
      "entropie de position                            0.000   non\n",
      "Gaussian ILD                                    0.000   non\n",
      "proximité à la cible                            0.283   non\n"
     ]
    }
   ],
   "source": [
    "print(\"A. Le fil bimodal, vu par chaque mesure\\n\")\n",
    "print(f\"{'mesure':24s} {'bimodal':>9s} {'uniforme':>10s}   verdict\")\n",
    "print(\"-\" * 76)\n",
    "for name, measure in MEASURES.items():\n",
    "    polarised, spread = measure(bimodal, CATALOGUE), measure(uniform, CATALOGUE)\n",
    "    verdict = \"PRÉFÈRE la polarisation\" if polarised > spread else \"préfère l'étalement\"\n",
    "    print(f\"{name:24s} {polarised:9.3f} {spread:10.3f}   {verdict}\")\n",
    "\n",
    "print(\"\\n\\nB. La faille d'origine reste-t-elle fermée ? (découplage total)\\n\")\n",
    "collapsed = served_positions(CATALOGUE, USER, decoupling=1.0)\n",
    "attempts = (uniform, bimodal, np.eye(VIEWPOINTS)[0])\n",
    "print(f\"{'mesure':24s} {'valeur maximale atteignable':>28s}   plancher 0,6 tenable ?\")\n",
    "print(\"-\" * 82)\n",
    "for name, measure in MEASURES.items():\n",
    "    best = max(measure(weights, collapsed) for weights in attempts)\n",
    "    print(f\"{name:24s} {best:28.3f}   {'OUI — saturable' if best >= 0.6 else 'non'}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26c227cd",
   "metadata": {},
   "source": [
    "### Les trois tiennent, mais elles ne se valent pas\n",
    "\n",
    "Aucune des trois ne rouvre la faille d'origine : sur des contenus tous identiques, elles\n",
    "tombent toutes sous le plancher. Et aucune ne préfère la polarisation. Restent leurs défauts\n",
    "propres, qu'il faut nommer avant de recommander."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5dcbd724",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-23T13:40:01.250849Z",
     "iopub.status.busy": "2026-08-23T13:40:01.250766Z",
     "iopub.status.idle": "2026-08-23T13:40:01.255048Z",
     "shell.execute_reply": "2026-08-23T13:40:01.253626Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trois fils occupant chacun QUATRE bacs, mais pas de la même façon\n",
      "\n",
      "fil                                 ent. position   Gaussian ILD   plus grand vide\n",
      "----------------------------------------------------------------------------------\n",
      "groupés à gauche                            0.667          0.487             0.14\n",
      "étalés                                      0.667          0.715             0.43\n",
      "deux blocs aux bords                        0.667          0.598             0.71\n",
      "\n",
      "L'entropie de position est **nominale** : elle compte les bacs occupés et ne voit\n",
      "pas leur écartement. La Gaussian ILD est métrique et les distingue.\n",
      "\n",
      "Mais la Gaussian ILD plafonne à 0.715 sur\n",
      "l'uniforme : sa borne dépend de k et de la largeur de bande, donc un seuil chiffré\n",
      "n'y serait pas lisible pour un régulateur.\n"
     ]
    }
   ],
   "source": [
    "clustered = np.zeros(VIEWPOINTS)\n",
    "clustered[[0, 1, 2, 3]] = 0.25\n",
    "spread_out = np.zeros(VIEWPOINTS)\n",
    "spread_out[[0, 2, 5, 7]] = 0.25\n",
    "two_blocks = np.zeros(VIEWPOINTS)\n",
    "two_blocks[[0, 1, 6, 7]] = 0.25\n",
    "\n",
    "print(\"Trois fils occupant chacun QUATRE bacs, mais pas de la même façon\\n\")\n",
    "print(f\"{'fil':34s} {'ent. position':>14s} {'Gaussian ILD':>14s} {'plus grand vide':>17s}\")\n",
    "print(\"-\" * 82)\n",
    "for name, weights in ((\"groupés à gauche\", clustered),\n",
    "                      (\"étalés\", spread_out),\n",
    "                      (\"deux blocs aux bords\", two_blocks)):\n",
    "    print(f\"{name:34s} {position_entropy(weights, CATALOGUE, CATALOGUE):14.3f}\"\n",
    "          f\" {gaussian_ild(weights, CATALOGUE, BANDWIDTH):14.3f}\"\n",
    "          f\" {largest_gap(weights, CATALOGUE, CATALOGUE):16.2f}\")\n",
    "\n",
    "print(\"\\nL'entropie de position est **nominale** : elle compte les bacs occupés et ne voit\")\n",
    "print(\"pas leur écartement. La Gaussian ILD est métrique et les distingue.\")\n",
    "print(f\"\\nMais la Gaussian ILD plafonne à {gaussian_ild(uniform, CATALOGUE, BANDWIDTH):.3f} sur\")\n",
    "print(\"l'uniforme : sa borne dépend de k et de la largeur de bande, donc un seuil chiffré\")\n",
    "print(\"n'y serait pas lisible pour un régulateur.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3d87bc00",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-23T13:40:01.256216Z",
     "iopub.status.busy": "2026-08-23T13:40:01.256129Z",
     "iopub.status.idle": "2026-08-23T13:40:15.991101Z",
     "shell.execute_reply": "2026-08-23T13:40:15.990591Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1512x480 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure, axes = plt.subplots(1, 3, figsize=(12.6, 4.0))\n",
    "verdicts, shapes, costs = axes\n",
    "\n",
    "# (a) Ce que chaque mesure pense du fil polarisé.\n",
    "names = list(MEASURES)\n",
    "polarised = [MEASURES[n](bimodal, CATALOGUE) for n in names]\n",
    "spread = [MEASURES[n](uniform, CATALOGUE) for n in names]\n",
    "positions = np.arange(len(names))\n",
    "verdicts.bar(positions - 0.19, polarised, 0.36, color=PALETTE[\"disorder\"],\n",
    "             label=\"fil polarisé (deux bords)\")\n",
    "verdicts.bar(positions + 0.19, spread, 0.36, color=PALETTE[\"remedy\"], label=\"fil étalé\")\n",
    "verdicts.set_xticks(positions)\n",
    "verdicts.set_xticklabels([\"Rao\\n(ILD)\", \"entropie\\nde position\", \"Gaussian\\nILD\",\n",
    "                          \"proximité\\nà la cible\"], fontsize=7.5)\n",
    "verdicts.set_ylabel(\"valeur de la mesure\")\n",
    "verdicts.set_ylim(0, 1.18)\n",
    "verdicts.annotate(\"seule mesure qui\\npréfère la polarisation\", xy=(-0.19, 1.0),\n",
    "                  xytext=(0.35, 1.12), fontsize=7.5, color=PALETTE[\"disorder\"],\n",
    "                  ha=\"center\", arrowprops={\"arrowstyle\": \"->\",\n",
    "                                           \"color\": PALETTE[\"disorder\"], \"lw\": 1.1})\n",
    "verdicts.set_title(\"Le défaut, en une image\", fontsize=10)\n",
    "verdicts.legend(fontsize=7.5, loc=\"upper right\", framealpha=0.95)\n",
    "\n",
    "# (b) Le fil que chaque plancher fait servir.\n",
    "FLOOR_SHAPE = 0.8\n",
    "for row, (key, label, colour) in enumerate(\n",
    "    ((\"Rao (ILD)\", \"plancher de Rao\", PALETTE[\"disorder\"]),\n",
    "     (\"entropie de position\", \"plancher d'entropie de position\", PALETTE[\"remedy\"]))\n",
    "):\n",
    "    feed = optimal_feed_under(MEASURES[key], VIEWPOINTS, USER, FLOOR_SHAPE, width=WIDTH)\n",
    "    # Seuls les points de vue réellement servis sont tracés : une masse sous le seuil du\n",
    "    # diagnostic n'est pas une exposition, et la dessiner masquerait le vide qu'elle laisse.\n",
    "    shown = feed.weights > 0.01\n",
    "    shapes.scatter(feed.positions[shown], np.full(int(shown.sum()), row),\n",
    "                   s=60 + 3000 * feed.weights[shown], color=colour, alpha=0.55,\n",
    "                   edgecolors=colour, linewidths=1.2, zorder=3)\n",
    "    shapes.text(-1.08, row + 0.30, f\"{label} · vide = {feed.largest_gap:.2f}\",\n",
    "                fontsize=8, color=colour, fontweight=\"bold\")\n",
    "shapes.axvline(USER, color=PALETTE[\"neutral\"], linestyle=\"--\", linewidth=1.1)\n",
    "shapes.text(USER + 0.05, -0.42, \"lecteur\", fontsize=8, color=PALETTE[\"neutral\"])\n",
    "shapes.annotate(\"le centre est vidé\", xy=(-0.15, 0.0), xytext=(-0.72, 0.52), fontsize=8,\n",
    "                color=PALETTE[\"disorder\"],\n",
    "                arrowprops={\"arrowstyle\": \"->\", \"color\": PALETTE[\"disorder\"], \"lw\": 1.1})\n",
    "shapes.set_xlim(-1.15, 1.15)\n",
    "shapes.set_ylim(-0.55, 1.55)\n",
    "shapes.set_yticks([])\n",
    "shapes.set_xlabel(\"position du contenu servi\")\n",
    "shapes.set_title(f\"Le fil optimal sous plancher {FLOOR_SHAPE:.1f}\", fontsize=10)\n",
    "shapes.spines[\"left\"].set_visible(False)\n",
    "\n",
    "# (c) Ce que chaque norme coûte, et jusqu'où elle se laisse imposer.\n",
    "grid = np.linspace(0.3, 0.95, 14)\n",
    "palette = [PALETTE[\"disorder\"], PALETTE[\"remedy\"], PALETTE[\"neutral\"], PALETTE[\"order\"]]\n",
    "for name, colour in zip(names, palette, strict=True):\n",
    "    curve = []\n",
    "    for floor in grid:\n",
    "        feed = optimal_feed_under(MEASURES[name], VIEWPOINTS, USER, float(floor), width=WIDTH)\n",
    "        complies = MEASURES[name](feed.weights, feed.positions) >= floor - 1e-6\n",
    "        curve.append(100 * (1 - feed.engagement / free) if complies else np.nan)\n",
    "    costs.plot(grid, curve, color=colour, linewidth=1.8, marker=\"o\", markersize=3, label=name)\n",
    "costs.set_xlabel(\"plancher imposé\")\n",
    "costs.set_ylabel(\"engagement perdu  [%]\")\n",
    "costs.set_title(\"Une courbe qui s'interrompt : plancher hors d'atteinte\", fontsize=10)\n",
    "costs.legend(fontsize=7.5)\n",
    "\n",
    "figure.suptitle(\"Correctif : l'entropie de Rao prescrivait la polarisation\", fontsize=12)\n",
    "figure.tight_layout(rect=(0, 0, 1, 0.92))\n",
    "save_figure(figure, \"fig13b_correctif_rao\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a59c605f",
   "metadata": {},
   "source": [
    "## 10. Ce que ce correctif change\n",
    "\n",
    "**L'entropie de Rao est retirée de la recommandation.** Elle résiste au bourrage d'étiquettes,\n",
    "mais son optimum sous contrainte est bimodal : elle prescrirait la polarisation. C'est le\n",
    "deuxième correctif apporté à la recommandation 1 du mémorandum, et il porte cette fois sur le\n",
    "remplacement proposé la veille.\n",
    "\n",
    "**Ce qui la remplace :**\n",
    "\n",
    "| Rôle | Mesure | Motif |\n",
    "|---|---|---|\n",
    "| **plancher** | entropie de position | garde l'interprétation de l'IDE, résiste aux trois adversaires testés, coûte moins cher que Rao |\n",
    "| **publié à côté** | plus grand vide | l'entropie est nominale : elle ne voit pas la géométrie que ce diagnostic expose |\n",
    "| **successeur** | proximité à une cible déclarée | seule à rendre explicite la forme d'exposition visée, au lieu de la supposer |\n",
    "\n",
    "**Et une leçon de méthode.** L'erreur n'a pas été trouvée en relisant le code : elle a été\n",
    "trouvée en lisant ce que le domaine avait déjà publié sur la mesure employée. Le test adverse\n",
    "de la veille était correct dans ce qu'il réfutait, et faux dans ce qu'il proposait — parce\n",
    "qu'il avait éprouvé le remplaçant contre **un seul** adversaire.\n",
    "\n",
    "> **Une norme ne se valide pas contre l'attaque qu'on a imaginée, mais contre celles qu'on n'a\n",
    "> pas imaginées.** C'est un argument pour aller chercher la littérature avant de prescrire, et\n",
    "> non après.\n",
    "\n",
    "### La limite qui subsiste\n",
    "\n",
    "Les quatre mesures reposent sur une **projection des contenus sur un axe d'opinion**, supposé\n",
    "connu et unidimensionnel. Sur données réelles, cette projection est précisément ce qui manque :\n",
    "c'est l'objet du chantier suivant, et la raison pour laquelle l'évaluation sur un jeu de\n",
    "données de recommandation ne peut pas se faire avec des catégories éditoriales pour points de\n",
    "vue."
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    "## Pistes ouvertes\n",
    "\n",
    "1. **Reprendre le test sur des *embeddings* réels** plutôt que sur un axe synthétique. Le jeu\n",
    "   de données MIND fournit des historiques de consultation et des catégories éditoriales : on\n",
    "   pourrait y mesurer la distance sémantique effective entre contenus d'une même étiquette, et\n",
    "   donc estimer le $\\varphi$ dont une plateforme dispose réellement.\n",
    "2. **Chiffrer le coût de production du découplage.** Le modèle le suppose nul ; s'il ne l'est\n",
    "   pas, il existe un $\\varphi$ d'équilibre, et c'est lui qui détermine si la manipulation est\n",
    "   rentable.\n",
    "3. **Étendre au jeu de Stackelberg** de la [feuille de route §4.2](../docs/feuille-de-route.md) :\n",
    "   ici la plateforme optimise sous une contrainte fixée, mais le régulateur devrait anticiper\n",
    "   la réponse et choisir le plancher en conséquence.\n",
    "4. **Traiter le choix du catalogue de référence.** Toute la résistance de $Q$ repose sur une\n",
    "   étendue fixée par le régulateur. Qui la fixe, et comment, redevient la question politique\n",
    "   que l'[audit §2.1](../docs/limites.md) avait déjà posée pour $k$."
   ]
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