diff --git a/MyIA.AI.Notebooks/GameTheory/GameTheory-02-NormalForm-Python.ipynb b/MyIA.AI.Notebooks/GameTheory/GameTheory-02-NormalForm-Python.ipynb
index dc844dfaee..c3b2ac62b1 100644
--- a/MyIA.AI.Notebooks/GameTheory/GameTheory-02-NormalForm-Python.ipynb
+++ b/MyIA.AI.Notebooks/GameTheory/GameTheory-02-NormalForm-Python.ipynb
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@@ -231,10 +231,10 @@
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@@ -279,16 +279,16 @@
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@@ -392,10 +392,10 @@
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@@ -444,7 +444,17 @@
     "$$u_i(s_i, s_{-i}) \\geq u_i(s_i', s_{-i}) \\quad \\forall s_{-i}$$\n",
     "avec au moins une inegalite stricte.\n",
     "\n",
-    "Une stratégie est **dominante** si elle domine toutes les autres stratégies."
+    "Une stratégie est **dominante** si elle domine toutes les autres stratégies.\n",
+    "\n",
+    "> **Note — alias rétrograde.** Le nom court `find_dominant_strategy` reste\n",
+    "> disponible comme alias de `find_weakly_dominant_strategy`. Son comportement\n",
+    "> a changé sur un cas dégénéré : face à des stratégies *interchangeables*\n",
+    "> (mêmes gains contre toute réponse adverse), l'ancienne implémentation\n",
+    "> reclassait la première rencontrée comme dominante ; l'alias renvoie\n",
+    "> désormais `None`. Une stratégie interchangeable n'est pas dominante, elle\n",
+    "> est équivalente à une autre. Le premier exercice cite encore le nom court\n",
+    "> comme indice : si votre jeu comporte des stratégies interchangeables, lisez\n",
+    "> la sémantique ci-dessus comme faible et stricte."
    ]
   },
   {
@@ -453,16 +463,16 @@
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@@ -482,8 +492,8 @@
       "\n",
       "Analyse de dominance:\n",
       "----------------------------------------\n",
-      "Joueur Ligne: 'Defaire' est dominante\n",
-      "Joueur Colonne: 'Defaire' est dominante\n"
+      "Joueur Ligne: 'Defaire' est faiblement dominante\n",
+      "Joueur Colonne: 'Defaire' est faiblement dominante\n"
      ]
     }
    ],
@@ -522,12 +532,54 @@
     "    \n",
     "    return False, None\n",
     "\n",
-    "def find_dominant_strategy(game: NormalFormGame, player: int) -> Optional[int]:\n",
+    "def find_weakly_dominant_strategy(game: NormalFormGame, player: int) -> Optional[int]:\n",
     "    \"\"\"\n",
-    "    Trouve une strategie dominante pour un joueur.\n",
+    "    Trouve une strategie FAIBLEMENT dominante pour un joueur.\n",
+    "    \n",
+    "    Strategie s faiblement dominante si elle est AU MOINS aussi bonne que toute\n",
+    "    autre strategie 'other' sur tous les profils d'adversaire, avec AU MOINS une\n",
+    "    inegalite stricte. La clause stricte evite qu'une strategie identique a une\n",
+    "    autre soit declaree \"faiblement dominante\" -- deux strategies strictement\n",
+    "    identiques ne se dominent faiblement l'une l'autre (elles sont interchangeables).\n",
+    "    \n",
+    "    Returns:\n",
+    "        Index de la strategie faiblement dominante, ou None si aucune.\n",
+    "    \"\"\"\n",
+    "    n_strategies = game.m if player == 0 else game.n\n",
+    "    \n",
+    "    for s in range(n_strategies):\n",
+    "        is_dominant = True\n",
+    "        for other in range(n_strategies):\n",
+    "            if other == s:\n",
+    "                continue\n",
+    "            \n",
+    "            if player == 0:\n",
+    "                pays = [game.A[s, j] - game.A[other, j] for j in range(game.n)]\n",
+    "            else:\n",
+    "                pays = [game.B[i, s] - game.B[i, other] for i in range(game.m)]\n",
+    "            \n",
+    "            # Faible dominance = partout >= ET quelque part > (clause stricte)\n",
+    "            if not all(p >= 0 for p in pays) or not any(p > 0 for p in pays):\n",
+    "                is_dominant = False\n",
+    "                break\n",
+    "        \n",
+    "        if is_dominant:\n",
+    "            return s\n",
+    "    \n",
+    "    return None\n",
+    "\n",
+    "\n",
+    "def find_strictly_dominant_strategy(game: NormalFormGame, player: int) -> Optional[int]:\n",
+    "    \"\"\"\n",
+    "    Trouve une strategie STRICTEMENT dominante pour un joueur.\n",
+    "    \n",
+    "    Strategie s strictement dominante si elle est strictement meilleure que toute\n",
+    "    autre strategie 'other' sur TOUS les profils d'adversaire. Cette fonction est\n",
+    "    le pendant direct de `is_strictly_dominated` (qui teste une strategie donnee) :\n",
+    "    elle cherche la strategie dominante plutot que de la verifier.\n",
     "    \n",
     "    Returns:\n",
-    "        Index de la strategie dominante, ou None si aucune.\n",
+    "        Index de la strategie strictement dominante, ou None si aucune.\n",
     "    \"\"\"\n",
     "    n_strategies = game.m if player == 0 else game.n\n",
     "    \n",
@@ -538,9 +590,9 @@
     "                continue\n",
     "            \n",
     "            if player == 0:\n",
-    "                dominates = all(game.A[s, j] >= game.A[other, j] for j in range(game.n))\n",
+    "                dominates = all(game.A[s, j] > game.A[other, j] for j in range(game.n))\n",
     "            else:\n",
-    "                dominates = all(game.B[i, s] >= game.B[i, other] for i in range(game.m))\n",
+    "                dominates = all(game.B[i, s] > game.B[i, other] for i in range(game.m))\n",
     "            \n",
     "            if not dominates:\n",
     "                is_dominant = False\n",
@@ -551,6 +603,17 @@
     "    \n",
     "    return None\n",
     "\n",
+    "\n",
+    "# Alias retrograde -- le premier exercice et le materiel pedagogique anterieur\n",
+    "# utilisent encore le nom court `find_dominant_strategy`. L'alias preserve la\n",
+    "# compatibilite ; il est documente pour inviter les nouveaux appels a utiliser\n",
+    "# le nom explicite `find_weakly_dominant_strategy`.\n",
+    "# ATTENTION : sur des strategies interchangeables (memes gains contre toute\n",
+    "# reponse), l'ancienne version renvoyait l'index 0 ; l'alias, qui applique la\n",
+    "# semantique faible corrigee, renvoie None. C'est la correction portee par\n",
+    "# #21192 -- une strategie interchangeable n'est pas dominante.\n",
+    "find_dominant_strategy = find_weakly_dominant_strategy\n",
+    "\n",
     "# Analyser le Dilemme du Prisonnier\n",
     "pd = create_prisoners_dilemma()\n",
     "pd.display()\n",
@@ -559,16 +622,16 @@
     "print(\"-\" * 40)\n",
     "\n",
     "# Joueur Ligne\n",
-    "dom_row = find_dominant_strategy(pd, 0)\n",
+    "dom_row = find_weakly_dominant_strategy(pd, 0)\n",
     "if dom_row is not None:\n",
-    "    print(f\"Joueur Ligne: '{pd.row_actions[dom_row]}' est dominante\")\n",
+    "    print(f\"Joueur Ligne: '{pd.row_actions[dom_row]}' est faiblement dominante\")\n",
     "else:\n",
-    "    print(\"Joueur Ligne: pas de strategie dominante\")\n",
+    "    print(\"Joueur Ligne: pas de strategie faiblement dominante\")\n",
     "\n",
     "# Joueur Colonne\n",
-    "dom_col = find_dominant_strategy(pd, 1)\n",
+    "dom_col = find_weakly_dominant_strategy(pd, 1)\n",
     "if dom_col is not None:\n",
-    "    print(f\"Joueur Colonne: '{pd.col_actions[dom_col]}' est dominante\")"
+    "    print(f\"Joueur Colonne: '{pd.col_actions[dom_col]}' est faiblement dominante\")"
    ]
   },
   {
@@ -576,10 +639,10 @@
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@@ -596,16 +659,16 @@
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      "exception": false,
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@@ -619,24 +682,24 @@
       "==================================================\n",
       "\n",
       "Dilemme du Prisonnier:\n",
-      "  Ligne: 'Defaire' dominante\n",
-      "  Colonne: 'Defaire' dominante\n",
+      "  Ligne: 'Defaire' est faiblement dominante\n",
+      "  Colonne: 'Defaire' est faiblement dominante\n",
       "\n",
       "Chasse au Cerf (Stag Hunt):\n",
-      "  Ligne: aucune strategie dominante\n",
-      "  Colonne: aucune strategie dominante\n",
+      "  Ligne: pas de strategie faiblement dominante\n",
+      "  Colonne: pas de strategie faiblement dominante\n",
       "\n",
       "Bataille des Sexes:\n",
-      "  Ligne: aucune strategie dominante\n",
-      "  Colonne: aucune strategie dominante\n",
+      "  Ligne: pas de strategie faiblement dominante\n",
+      "  Colonne: pas de strategie faiblement dominante\n",
       "\n",
       "Jeu du Poulet (Chicken):\n",
-      "  Ligne: aucune strategie dominante\n",
-      "  Colonne: aucune strategie dominante\n",
+      "  Ligne: pas de strategie faiblement dominante\n",
+      "  Colonne: pas de strategie faiblement dominante\n",
       "\n",
       "Matching Pennies:\n",
-      "  Ligne: aucune strategie dominante\n",
-      "  Colonne: aucune strategie dominante\n"
+      "  Ligne: pas de strategie faiblement dominante\n",
+      "  Colonne: pas de strategie faiblement dominante\n"
      ]
     }
    ],
@@ -648,18 +711,18 @@
     "for game in games:\n",
     "    print(f\"\\n{game.name}:\")\n",
     "    \n",
-    "    dom_row = find_dominant_strategy(game, 0)\n",
-    "    dom_col = find_dominant_strategy(game, 1)\n",
+    "    dom_row = find_weakly_dominant_strategy(game, 0)\n",
+    "    dom_col = find_weakly_dominant_strategy(game, 1)\n",
     "    \n",
     "    if dom_row is not None:\n",
-    "        print(f\"  Ligne: '{game.row_actions[dom_row]}' dominante\")\n",
+    "        print(f\"  Ligne: '{game.row_actions[dom_row]}' est faiblement dominante\")\n",
     "    else:\n",
-    "        print(f\"  Ligne: aucune strategie dominante\")\n",
+    "        print(f\"  Ligne: pas de strategie faiblement dominante\")\n",
     "    \n",
     "    if dom_col is not None:\n",
-    "        print(f\"  Colonne: '{game.col_actions[dom_col]}' dominante\")\n",
+    "        print(f\"  Colonne: '{game.col_actions[dom_col]}' est faiblement dominante\")\n",
     "    else:\n",
-    "        print(f\"  Colonne: aucune strategie dominante\")"
+    "        print(f\"  Colonne: pas de strategie faiblement dominante\")"
    ]
   },
   {
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@@ -761,16 +824,16 @@
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    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-06-03T09:03:14.693720Z",
-     "iopub.status.busy": "2026-06-03T09:03:14.692600Z",
-     "iopub.status.idle": "2026-06-03T09:03:14.722126Z",
-     "shell.execute_reply": "2026-06-03T09:03:14.720799Z"
+     "iopub.execute_input": "2026-10-20T00:50:26.881001Z",
+     "iopub.status.busy": "2026-10-20T00:50:26.880205Z",
+     "iopub.status.idle": "2026-10-20T00:50:26.893390Z",
+     "shell.execute_reply": "2026-10-20T00:50:26.891747Z"
     },
     "papermill": {
-     "duration": 0.063609,
-     "end_time": "2026-06-03T09:03:14.724991",
+     "duration": 0.027652,
+     "end_time": "2026-10-20T00:50:26.895831+00:00",
      "exception": false,
-     "start_time": "2026-06-03T09:03:14.661382",
+     "start_time": "2026-10-20T00:50:26.868179+00:00",
      "status": "completed"
     },
     "tags": []
@@ -843,10 +906,10 @@
    "id": "30a2fe15",
    "metadata": {
     "papermill": {
-     "duration": 0.01284,
-     "end_time": "2026-06-03T09:03:14.751179",
+     "duration": 0.010012,
+     "end_time": "2026-10-20T00:50:26.916185+00:00",
      "exception": false,
-     "start_time": "2026-06-03T09:03:14.738339",
+     "start_time": "2026-10-20T00:50:26.906173+00:00",
      "status": "completed"
     },
     "tags": []
@@ -863,16 +926,16 @@
    "id": "e061e72d",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-06-03T09:03:14.839725Z",
-     "iopub.status.busy": "2026-06-03T09:03:14.837826Z",
-     "iopub.status.idle": "2026-06-03T09:03:14.864985Z",
-     "shell.execute_reply": "2026-06-03T09:03:14.858968Z"
+     "iopub.execute_input": "2026-10-20T00:50:26.940318Z",
+     "iopub.status.busy": "2026-10-20T00:50:26.939484Z",
+     "iopub.status.idle": "2026-10-20T00:50:26.948292Z",
+     "shell.execute_reply": "2026-10-20T00:50:26.946671Z"
     },
     "papermill": {
-     "duration": 0.062185,
-     "end_time": "2026-06-03T09:03:14.871756",
+     "duration": 0.023283,
+     "end_time": "2026-10-20T00:50:26.950558+00:00",
      "exception": false,
-     "start_time": "2026-06-03T09:03:14.809571",
+     "start_time": "2026-10-20T00:50:26.927275+00:00",
      "status": "completed"
     },
     "tags": []
@@ -915,10 +978,10 @@
    "id": "d9a8f9d7",
    "metadata": {
     "papermill": {
-     "duration": 0.012851,
-     "end_time": "2026-06-03T09:03:14.898337",
+     "duration": 0.009987,
+     "end_time": "2026-10-20T00:50:26.970203+00:00",
      "exception": false,
-     "start_time": "2026-06-03T09:03:14.885486",
+     "start_time": "2026-10-20T00:50:26.960216+00:00",
      "status": "completed"
     },
     "tags": []
@@ -937,10 +1000,10 @@
    "id": "271fb8a5",
    "metadata": {
     "papermill": {
-     "duration": 0.015601,
-     "end_time": "2026-06-03T09:03:14.930064",
+     "duration": 0.010215,
+     "end_time": "2026-10-20T00:50:26.991018+00:00",
      "exception": false,
-     "start_time": "2026-06-03T09:03:14.914463",
+     "start_time": "2026-10-20T00:50:26.980803+00:00",
      "status": "completed"
     },
     "tags": []
@@ -969,16 +1032,16 @@
    "id": "260aae30",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-06-03T09:03:14.971022Z",
-     "iopub.status.busy": "2026-06-03T09:03:14.970538Z",
-     "iopub.status.idle": "2026-06-03T09:03:14.984384Z",
-     "shell.execute_reply": "2026-06-03T09:03:14.981953Z"
+     "iopub.execute_input": "2026-10-20T00:50:27.014800Z",
+     "iopub.status.busy": "2026-10-20T00:50:27.013899Z",
+     "iopub.status.idle": "2026-10-20T00:50:27.025811Z",
+     "shell.execute_reply": "2026-10-20T00:50:27.024189Z"
     },
     "papermill": {
-     "duration": 0.040976,
-     "end_time": "2026-06-03T09:03:14.986768",
+     "duration": 0.026226,
+     "end_time": "2026-10-20T00:50:27.027800+00:00",
      "exception": false,
-     "start_time": "2026-06-03T09:03:14.945792",
+     "start_time": "2026-10-20T00:50:27.001574+00:00",
      "status": "completed"
     },
     "tags": []
@@ -992,19 +1055,19 @@
       "==================================================\n",
       "\n",
       "Dilemme du Prisonnier:\n",
-      "  (Defaire, Defaire) -> gains (np.int64(1), np.int64(1))\n",
+      "  (Defaire, Defaire) -> gains (1, 1)\n",
       "\n",
       "Chasse au Cerf (Stag Hunt):\n",
-      "  (Cerf, Cerf) -> gains (np.int64(4), np.int64(4))\n",
-      "  (Lievre, Lievre) -> gains (np.int64(3), np.int64(3))\n",
+      "  (Cerf, Cerf) -> gains (4, 4)\n",
+      "  (Lievre, Lievre) -> gains (3, 3)\n",
       "\n",
       "Bataille des Sexes:\n",
-      "  (Opera, Opera) -> gains (np.int64(3), np.int64(2))\n",
-      "  (Football, Football) -> gains (np.int64(2), np.int64(3))\n",
+      "  (Opera, Opera) -> gains (3, 2)\n",
+      "  (Football, Football) -> gains (2, 3)\n",
       "\n",
       "Jeu du Poulet (Chicken):\n",
-      "  (Devier, Continuer) -> gains (np.int64(-1), np.int64(1))\n",
-      "  (Continuer, Devier) -> gains (np.int64(1), np.int64(-1))\n",
+      "  (Devier, Continuer) -> gains (-1, 1)\n",
+      "  (Continuer, Devier) -> gains (1, -1)\n",
       "\n",
       "Matching Pennies:\n",
       "  Aucun equilibre en strategies pures\n"
@@ -1053,7 +1116,16 @@
   {
    "cell_type": "markdown",
    "id": "gt2-nash-count-interp",
-   "metadata": {},
+   "metadata": {
+    "papermill": {
+     "duration": 0.01102,
+     "end_time": "2026-10-20T00:50:27.049401+00:00",
+     "exception": false,
+     "start_time": "2026-10-20T00:50:27.038381+00:00",
+     "status": "completed"
+    },
+    "tags": []
+   },
    "source": [
     "### Interprétation — ce que révèle le NOMBRE d'équilibres (1, 2, 2, 2, 0)\n",
     "\n",
@@ -1081,10 +1153,10 @@
    "id": "7aa2b5c3",
    "metadata": {
     "papermill": {
-     "duration": 0.01799,
-     "end_time": "2026-06-03T09:03:15.025524",
+     "duration": 0.009886,
+     "end_time": "2026-10-20T00:50:27.068713+00:00",
      "exception": false,
-     "start_time": "2026-06-03T09:03:15.007534",
+     "start_time": "2026-10-20T00:50:27.058827+00:00",
      "status": "completed"
     },
     "tags": []
@@ -1101,16 +1173,16 @@
    "id": "c9ac80b3",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-06-03T09:03:15.060360Z",
-     "iopub.status.busy": "2026-06-03T09:03:15.059761Z",
-     "iopub.status.idle": "2026-06-03T09:03:16.227810Z",
-     "shell.execute_reply": "2026-06-03T09:03:16.225594Z"
+     "iopub.execute_input": "2026-10-20T00:50:27.092857Z",
+     "iopub.status.busy": "2026-10-20T00:50:27.092415Z",
+     "iopub.status.idle": "2026-10-20T00:50:27.551686Z",
+     "shell.execute_reply": "2026-10-20T00:50:27.550325Z"
     },
     "papermill": {
-     "duration": 1.186871,
-     "end_time": "2026-06-03T09:03:16.230790",
+     "duration": 0.473236,
+     "end_time": "2026-10-20T00:50:27.553335+00:00",
      "exception": false,
-     "start_time": "2026-06-03T09:03:15.043919",
+     "start_time": "2026-10-20T00:50:27.080099+00:00",
      "status": "completed"
     },
     "tags": []
@@ -1118,7 +1190,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 800x600 with 1 Axes>"
       ]
@@ -1128,7 +1200,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 800x600 with 1 Axes>"
       ]
@@ -1138,7 +1210,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 800x600 with 1 Axes>"
       ]
@@ -1222,10 +1294,10 @@
    "id": "60a55803",
    "metadata": {
     "papermill": {
-     "duration": 0.025806,
-     "end_time": "2026-06-03T09:03:14.556304",
+     "duration": 0.011472,
+     "end_time": "2026-10-20T00:50:27.575109+00:00",
      "exception": false,
-     "start_time": "2026-06-03T09:03:14.530498",
+     "start_time": "2026-10-20T00:50:27.563637+00:00",
      "status": "completed"
     },
     "tags": []
@@ -1251,10 +1323,10 @@
    "id": "5f2cddcb",
    "metadata": {
     "papermill": {
-     "duration": 0.015302,
-     "end_time": "2026-06-03T09:03:16.262754",
+     "duration": 0.009853,
+     "end_time": "2026-10-20T00:50:27.596784+00:00",
      "exception": false,
-     "start_time": "2026-06-03T09:03:16.247452",
+     "start_time": "2026-10-20T00:50:27.586931+00:00",
      "status": "completed"
     },
     "tags": []
@@ -1271,16 +1343,16 @@
    "id": "46286901",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-06-03T09:03:16.293925Z",
-     "iopub.status.busy": "2026-06-03T09:03:16.293317Z",
-     "iopub.status.idle": "2026-06-03T09:03:16.314125Z",
-     "shell.execute_reply": "2026-06-03T09:03:16.312566Z"
+     "iopub.execute_input": "2026-10-20T00:50:27.618451Z",
+     "iopub.status.busy": "2026-10-20T00:50:27.617837Z",
+     "iopub.status.idle": "2026-10-20T00:50:27.634736Z",
+     "shell.execute_reply": "2026-10-20T00:50:27.633566Z"
     },
     "papermill": {
-     "duration": 0.040982,
-     "end_time": "2026-06-03T09:03:16.316713",
+     "duration": 0.029703,
+     "end_time": "2026-10-20T00:50:27.636539+00:00",
      "exception": false,
-     "start_time": "2026-06-03T09:03:16.275731",
+     "start_time": "2026-10-20T00:50:27.606836+00:00",
      "status": "completed"
     },
     "tags": []
@@ -1310,7 +1382,7 @@
       "\n",
       "Jeu reduit: 1x1\n",
       "Strategies restantes: Ligne=['R2'], Colonne=['C2']\n",
-      "Issue predite: (R2, C2) avec gains (4, 3)\n"
+      "Issue predite (IESDS) : (R2, C2) avec gains (4, 3)\n"
      ]
     }
    ],
@@ -1407,7 +1479,18 @@
     "\n",
     "print(f\"\\nJeu reduit: {len(row_red)}x{len(col_red)}\")\n",
     "print(f\"Strategies restantes: Ligne={row_red}, Colonne={col_red}\")\n",
-    "print(f\"Issue predite: ({row_red[0]}, {col_red[0]}) avec gains ({A_red[0,0]}, {B_red[0,0]})\")"
+    "\n",
+    "# Garantie : l'IESDS STRICTE ne predit un equilibre de Nash que si le jeu\n",
+    "# reduit est de taille 1x1. Sinon, plusieurs issues coexistent et leur\n",
+    "# statut d'equilibre doit etre verifie par find_pure_nash_equilibria.\n",
+    "if len(row_red) == 1 and len(col_red) == 1:\n",
+    "    print(f\"Issue predite (IESDS) : ({row_red[0]}, {col_red[0]}) avec gains ({A_red[0,0]}, {B_red[0,0]})\")\n",
+    "else:\n",
+    "    red_game = NormalFormGame(A_red, B_red, row_red, col_red, \"Jeu reduit IESDS\")\n",
+    "    candidates = find_pure_nash_equilibria(red_game)\n",
+    "    print(f\"Jeu reduit non trivial ({len(row_red)}x{len(col_red)}), IESDS strict ne suffit pas : {len(candidates)} issue(s) candidate(s)\")\n",
+    "    for (i, j) in candidates:\n",
+    "        print(f\"  candidate IESDS : ({row_red[i]}, {col_red[j]}) avec gains ({A_red[i, j]}, {B_red[i, j]}) -- a verifier comme equilibre de Nash\")\n"
    ]
   },
   {
@@ -1415,10 +1498,10 @@
    "id": "b8e131a0",
    "metadata": {
     "papermill": {
-     "duration": 0.016673,
-     "end_time": "2026-06-03T09:03:14.634908",
+     "duration": 0.008223,
+     "end_time": "2026-10-20T00:50:27.656035+00:00",
      "exception": false,
-     "start_time": "2026-06-03T09:03:14.618235",
+     "start_time": "2026-10-20T00:50:27.647812+00:00",
      "status": "completed"
     },
     "tags": []
@@ -1446,10 +1529,10 @@
    "id": "d319f493",
    "metadata": {
     "papermill": {
-     "duration": 0.011832,
-     "end_time": "2026-06-03T09:03:16.342540",
+     "duration": 0.009772,
+     "end_time": "2026-10-20T00:50:27.675953+00:00",
      "exception": false,
-     "start_time": "2026-06-03T09:03:16.330708",
+     "start_time": "2026-10-20T00:50:27.666181+00:00",
      "status": "completed"
     },
     "tags": []
@@ -1466,16 +1549,16 @@
    "id": "6201ed54",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-06-03T09:03:16.374780Z",
-     "iopub.status.busy": "2026-06-03T09:03:16.373969Z",
-     "iopub.status.idle": "2026-06-03T09:03:16.385173Z",
-     "shell.execute_reply": "2026-06-03T09:03:16.383738Z"
+     "iopub.execute_input": "2026-10-20T00:50:27.700336Z",
+     "iopub.status.busy": "2026-10-20T00:50:27.699587Z",
+     "iopub.status.idle": "2026-10-20T00:50:27.708652Z",
+     "shell.execute_reply": "2026-10-20T00:50:27.707123Z"
     },
     "papermill": {
-     "duration": 0.030476,
-     "end_time": "2026-06-03T09:03:16.388025",
+     "duration": 0.024662,
+     "end_time": "2026-10-20T00:50:27.710754+00:00",
      "exception": false,
-     "start_time": "2026-06-03T09:03:16.357549",
+     "start_time": "2026-10-20T00:50:27.686092+00:00",
      "status": "completed"
     },
     "tags": []
@@ -1542,10 +1625,10 @@
    "id": "aa48b333",
    "metadata": {
     "papermill": {
-     "duration": 0.014588,
-     "end_time": "2026-06-03T09:03:16.421082",
+     "duration": 0.012186,
+     "end_time": "2026-10-20T00:50:27.734450+00:00",
      "exception": false,
-     "start_time": "2026-06-03T09:03:16.406494",
+     "start_time": "2026-10-20T00:50:27.722264+00:00",
      "status": "completed"
     },
     "tags": []
@@ -1580,16 +1663,16 @@
    "id": "18ef8f3d",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-06-03T09:03:16.456735Z",
-     "iopub.status.busy": "2026-06-03T09:03:16.455861Z",
-     "iopub.status.idle": "2026-06-03T09:03:16.493328Z",
-     "shell.execute_reply": "2026-06-03T09:03:16.490872Z"
+     "iopub.execute_input": "2026-10-20T00:50:27.758526Z",
+     "iopub.status.busy": "2026-10-20T00:50:27.758110Z",
+     "iopub.status.idle": "2026-10-20T00:50:27.780715Z",
+     "shell.execute_reply": "2026-10-20T00:50:27.779170Z"
     },
     "papermill": {
-     "duration": 0.059476,
-     "end_time": "2026-06-03T09:03:16.496053",
+     "duration": 0.037027,
+     "end_time": "2026-10-20T00:50:27.783056+00:00",
      "exception": false,
-     "start_time": "2026-06-03T09:03:16.436577",
+     "start_time": "2026-10-20T00:50:27.746029+00:00",
      "status": "completed"
     },
     "tags": []
@@ -1633,10 +1716,10 @@
    "id": "73c26033",
    "metadata": {
     "papermill": {
-     "duration": 0.01787,
-     "end_time": "2026-06-03T09:03:16.530370",
+     "duration": 0.011679,
+     "end_time": "2026-10-20T00:50:27.804884+00:00",
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@@ -1681,10 +1764,10 @@
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@@ -1720,10 +1803,10 @@
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@@ -1765,10 +1848,10 @@
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@@ -1776,7 +1859,7 @@
    "source": [
     "### Exercice 1 : Variante du Dilemme du Prisonnier (3 actions)\n",
     "\n",
-    "Reprenez la variante a 3 actions (Cooperer, Defaire, Punir) decrite ci-dessus. Tous les outils necessaires ont ete construits dans les sections précédentes et restent disponibles : la classe `NormalFormGame` (section 1), `find_dominant_strategy` (section 3), `find_pure_nash_equilibria` (section 4) et `best_response_row` / `best_response_col`. Completez les `# TODO` ci-dessous."
+    "Reprenez la variante a 3 actions (Cooperer, Defaire, Punir) decrite ci-dessus. Tous les outils necessaires ont ete construits dans les sections précédentes et restent disponibles : la classe `NormalFormGame` (section 1), `find_strictly_dominant_strategy` / `find_weakly_dominant_strategy` (section 3), `find_pure_nash_equilibria` (section 4) et `best_response_row` / `best_response_col`. Completez les `# TODO` ci-dessous."
    ]
   },
   {
@@ -1785,16 +1868,16 @@
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@@ -1828,9 +1911,9 @@
     "#                           name=\"Dilemme du Prisonnier (3 actions)\")\n",
     "# game_ex1.display()\n",
     "\n",
-    "# TODO: y a-t-il une strategie strictement dominante ? (find_dominant_strategy)\n",
+    "# TODO: y a-t-il une strategie strictement dominante ? (find_strictly_dominant_strategy)\n",
     "# Indice: comparez avec le cas 2x2 classique ou 'Defaire' domine 'Cooperer'.\n",
-    "# dom_row = find_dominant_strategy(game_ex1, 0)\n",
+    "# dom_row = find_strictly_dominant_strategy(game_ex1, 0)\n",
     "\n",
     "# TODO: enumerer les equilibres de Nash purs (find_pure_nash_equilibria)\n",
     "# Question d'analyse: l'ajout de l'action 'Punir' cree-t-il de nouveaux equilibres ?\n",
@@ -1842,10 +1925,10 @@
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    "metadata": {
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@@ -1871,16 +1954,16 @@
    "id": "ex2-mp-code",
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@@ -1923,10 +2006,10 @@
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@@ -1966,16 +2049,16 @@
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@@ -2026,10 +2109,10 @@
    "id": "ex-iewds-md",
    "metadata": {
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      "exception": false,
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@@ -2053,16 +2136,16 @@
    "id": "ex-iewds-code",
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-     "shell.execute_reply": "2026-06-03T09:03:16.970321Z"
+     "iopub.execute_input": "2026-10-20T00:50:28.073088Z",
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      "status": "completed"
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@@ -2132,10 +2215,10 @@
    "id": "33048a4b",
    "metadata": {
     "papermill": {
-     "duration": 0.035605,
-     "end_time": "2026-06-03T09:03:17.035543",
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      "status": "completed"
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     "tags": []
@@ -2162,7 +2245,16 @@
   {
    "cell_type": "markdown",
    "id": "aa3557a1",
-   "metadata": {},
+   "metadata": {
+    "papermill": {
+     "duration": 0.012315,
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+     "status": "completed"
+    },
+    "tags": []
+   },
    "source": [
     "> **Lien avec la formalisation Lean** : Les concepts de ce notebook — jeu en forme normale, stratégies pures et mixtes, équilibre de Nash — sont formalisés dans le notebook compagnon [GT-2b-Lean-Definitions](GameTheory-02b-Lean-Definitions-Lean.ipynb) (kernel Lean 4). On y trouve les structures `NormalFormGame`, `FiniteGame`, `Game2x2`, les types `PureStrategy`, `MixedStrategy`, le simplexe standard, et la définition `isNashEquilibrium` pour jeux 2×2, le tout construit interactivement depuis zéro (sans import Mathlib). Le Dilemme du Prisonnier, la Bataille des Sexes et le jeu du Poulet y sont prouvés comme équilibres de Nash en stratégies pures."
    ]
@@ -2172,10 +2264,10 @@
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@@ -2226,15 +2318,15 @@
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    "environment_variables": {},
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-   "input_path": "MyIA.AI.Notebooks/GameTheory/GameTheory-2-NormalForm.ipynb",
-   "output_path": "GameTheory-2-NormalForm.ipynb",
+   "input_path": "GameTheory-02-NormalForm-Python.ipynb",
+   "output_path": "GameTheory-02-NormalForm-Python_output_20261021_142021.ipynb",
    "parameters": {},
-   "start_time": "2026-06-03T09:03:07.402343",
-   "version": "2.7.0"
+   "start_time": "2026-10-21T12:37:51.411187",
+   "version": "2.6.0"
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diff --git a/scripts/notebook_tools/twin_pairs.d/gametheory-2-normalform/0024-2026-10-22-myia-po-2023-CoursIA-2.yaml b/scripts/notebook_tools/twin_pairs.d/gametheory-2-normalform/0024-2026-10-22-myia-po-2023-CoursIA-2.yaml
new file mode 100644
index 0000000000..f604e67db4
--- /dev/null
+++ b/scripts/notebook_tools/twin_pairs.d/gametheory-2-normalform/0024-2026-10-22-myia-po-2023-CoursIA-2.yaml
@@ -0,0 +1,6 @@
+date: '2026-10-22'
+by: myia-po-2023:CoursIA-2
+python_sha: 2f7c8ee9d62d8c9bb879e5db6eae7bab024a4bb2
+csharp_sha: a13bd5766cbb98747b843cf84574e0f9a5d69a60
+content_python_sha: 57a67be4c2a7278ffb9dc18a93aa3cefbcbb062b73bf12874afc408dd7d32a7e
+content_csharp_sha: 8839e0c29cf643eda5075c07e684512b418f8e9567878423a17a1b59727906c0