{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "67d644ad-6b26-4f07-b347-cf4cf43eee76",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Simulated data exported as 'simulated_experiment_data.csv' and return paths as 'return_paths.csv'\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# Parameters\n",
    "n_participants = 500\n",
    "np.random.seed(42)\n",
    "\n",
    "# Experiment parameters\n",
    "mu = 0.06\n",
    "sigma = 0.20\n",
    "\n",
    "# Participant demographics\n",
    "gender = np.random.choice(['male', 'female'], size=n_participants, p=[0.6, 0.4])\n",
    "age_raw = np.random.lognormal(mean=2.8, sigma=0.3, size=n_participants)\n",
    "age = np.clip(np.round(age_raw + 15), 18, 65).astype(int)\n",
    "education = np.random.choice(['high_school', 'bachelor', 'master', 'phd'], size=n_participants, p=[0.2, 0.5, 0.25, 0.05])\n",
    "occupation = np.random.choice(['student', 'employed'], size=n_participants, p=[0.7, 0.3])\n",
    "income = np.random.choice(['low', 'medium', 'high'], size=n_participants, p=[0.5, 0.3, 0.2])\n",
    "stock_exp = np.random.choice(['yes', 'no'], size=n_participants, p=[0.3, 0.7])\n",
    "\n",
    "# Risk aversion baseline (lottery allocation)\n",
    "lottery_allocation = (\n",
    "    70 + 10 * (gender == 'male')\n",
    "    + 10 * (age < 30)\n",
    "    + 15 * (stock_exp == 'yes')\n",
    "    - 5 * (income == 'low')\n",
    "    + np.random.normal(0, 10, n_participants)\n",
    ").clip(10, 100)\n",
    "\n",
    "# Calculate risk aversion parameter\n",
    "RA = mu / (lottery_allocation / 100 * sigma)\n",
    "\n",
    "# Robo conditions\n",
    "robo_condition = np.random.choice([1, 2, 3, 4], size=n_participants)\n",
    "\n",
    "# Generate return paths dataframe\n",
    "paths = pd.DataFrame({\n",
    "    'path_id': np.arange(1, 31),\n",
    "    'best_forecast': np.random.normal(0.0607, 0.20, 30),\n",
    "    'last_period_return': np.random.normal(0.0607, 0.20, 30)\n",
    "})\n",
    "\n",
    "# DataFrame initialization\n",
    "data = pd.DataFrame({\n",
    "    'gender': gender,\n",
    "    'age': age,\n",
    "    'education': education,\n",
    "    'occupation': occupation,\n",
    "    'income': income,\n",
    "    'stock_exp': stock_exp,\n",
    "    'lottery_allocation': lottery_allocation.round(),\n",
    "    'RA_parameter': RA,\n",
    "    'robo_condition': robo_condition\n",
    "})\n",
    "\n",
    "# Generate decisions for 30 rounds\n",
    "for round_num in range(1, 31):\n",
    "    chosen_path = np.random.choice(paths['path_id'], n_participants)\n",
    "    round_info = paths.set_index('path_id').loc[chosen_path]\n",
    "    best_forecast = round_info['best_forecast'].values\n",
    "    last_return = round_info['last_period_return'].values\n",
    "\n",
    "    use_last_return = np.random.rand(n_participants) < 0.1\n",
    "    forecast = np.where(use_last_return, last_return, best_forecast)\n",
    "\n",
    "    optimal_allocation = (forecast / (RA * sigma)).clip(0, 100)\n",
    "\n",
    "    if 6 <= round_num <= 25:\n",
    "        follow_robo_prob = np.random.rand(n_participants)\n",
    "        follow_robo = (follow_robo_prob < 0.7)\n",
    "        if robo_condition.any() in [2, 4]:\n",
    "            allocation = np.where(follow_robo, optimal_allocation, optimal_allocation + np.random.normal(0, 10, n_participants))\n",
    "        else:\n",
    "            allocation = optimal_allocation + np.random.normal(0, 10, n_participants)\n",
    "\n",
    "        if robo_condition.any() in [3, 4]:\n",
    "            forecast = np.where(follow_robo, best_forecast, forecast)\n",
    "    else:\n",
    "        allocation = optimal_allocation + np.random.normal(0, 10, n_participants)\n",
    "\n",
    "    allocation = allocation.clip(0, 100).round()\n",
    "\n",
    "    data[f'forecast_round_{round_num}'] = forecast.round(4)\n",
    "    data[f'allocation_round_{round_num}'] = allocation\n",
    "    data[f'chosen_path_round_{round_num}'] = chosen_path\n",
    "\n",
    "# Export to CSV\n",
    "data.to_csv('simulated_experiment_data.csv', index=False)\n",
    "paths.to_csv('return_paths.csv', index=False)\n",
    "print(\"Simulated data exported as 'simulated_experiment_data.csv' and return paths as 'return_paths.csv'\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1189ed11-2363-4dbe-a645-c6969edfc106",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'plt' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[4], line 5\u001b[0m\n\u001b[1;32m      3\u001b[0m allocation_cols \u001b[38;5;241m=\u001b[39m [\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mallocation_round_\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mround\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m \u001b[38;5;28mround\u001b[39m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m31\u001b[39m)]\n\u001b[1;32m      4\u001b[0m data[allocation_cols]\u001b[38;5;241m.\u001b[39mboxplot(figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m15\u001b[39m, \u001b[38;5;241m6\u001b[39m))\n\u001b[0;32m----> 5\u001b[0m \u001b[43mplt\u001b[49m\u001b[38;5;241m.\u001b[39mtitle(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mInvestment Allocation per Round\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m      6\u001b[0m plt\u001b[38;5;241m.\u001b[39mxlabel(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mRound\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m      7\u001b[0m plt\u001b[38;5;241m.\u001b[39mylabel(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mAllocation\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
      "\u001b[0;31mNameError\u001b[0m: name 'plt' is not defined"
     ]
    },
    {
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",
      "text/plain": [
       "<Figure size 1500x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# Analysis\n",
    "# Investment per round (box plots)\n",
    "allocation_cols = [f'allocation_round_{round}' for round in range(1, 31)]\n",
    "data[allocation_cols].boxplot(figsize=(15, 6))\n",
    "plt.title('Investment Allocation per Round')\n",
    "plt.xlabel('Round')\n",
    "plt.ylabel('Allocation')\n",
    "plt.xticks(rotation=90)\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# Investment by gender and education\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.boxplot(x='gender', y=data[allocation_cols].mean(axis=1), hue='education', data=data)\n",
    "plt.title('Average Investment Allocation by Gender and Education')\n",
    "plt.ylabel('Average Allocation')\n",
    "plt.xlabel('Gender')\n",
    "plt.legend(title='Education')\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# Investment by treatment group\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.boxplot(x='robo_condition', y=data[allocation_cols].mean(axis=1), data=data)\n",
    "plt.title('Average Investment Allocation by Treatment Group')\n",
    "plt.ylabel('Average Allocation')\n",
    "plt.xlabel('Robo Condition')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "094b68c9-b9ba-4394-81ac-9ae2ec29fdd3",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}