438 lines
18 KiB
Plaintext
438 lines
18 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# 04 — Mechanism Post-Mortem\n",
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"\n",
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"Cross-cutting analysis of how the ABC + Conviction Voting mechanisms performed\n",
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"under various market and social pressures.\n",
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"\n",
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"## Central thesis to evaluate:\n",
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"The TEC deployed an Augmented Bonding Curve (primary market) feeding a Common Pool\n",
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"governed by Conviction Voting (fund allocation). This analysis evaluates whether\n",
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"these mechanisms achieved their design goals and what broke down."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"import matplotlib.dates as mdates\n",
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"import seaborn as sns\n",
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"import datetime\n",
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"\n",
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"sns.set_theme(style='whitegrid', palette='deep')\n",
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"plt.rcParams['figure.figsize'] = (14, 6)\n",
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"plt.rcParams['figure.dpi'] = 100\n",
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"\n",
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"DATA = '../data/onchain'\n",
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"\n",
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"# Load all data\n",
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"proposals = pd.read_csv(f'{DATA}/cv_proposals.csv')\n",
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"stakes = pd.read_csv(f'{DATA}/cv_stakes.csv')\n",
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"common = pd.read_csv(f'{DATA}/dune_common_pool.csv')\n",
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"reserve = pd.read_csv(f'{DATA}/dune_reserve_pool.csv')\n",
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"balances = pd.read_csv(f'{DATA}/dune_token_balances_usd.csv')\n",
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"\n",
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"# Parse dates\n",
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"common['date'] = pd.to_datetime(common['day'])\n",
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"reserve['date'] = pd.to_datetime(reserve['day'])\n",
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"balances['date'] = pd.to_datetime(balances['day'])\n",
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"\n",
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"# Approximate dates for on-chain events\n",
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"genesis_block = 20086944\n",
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"genesis_date = datetime.datetime(2022, 1, 19)\n",
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"\n",
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"def block_to_date(block):\n",
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" return genesis_date + datetime.timedelta(seconds=(block - genesis_block) * 5)\n",
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"\n",
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"proposals['date'] = proposals['block'].apply(block_to_date)\n",
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"stakes['date'] = stakes['block'].apply(block_to_date)\n",
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"\n",
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"print('Data loaded. Analysis period:')\n",
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"print(f' CV proposals: {proposals[\"date\"].min().date()} to {proposals[\"date\"].max().date()}')\n",
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"print(f' Common pool: {common[\"date\"].min().date()} to {common[\"date\"].max().date()}')\n",
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"print(f' Reserve pool: {reserve[\"date\"].min().date()} to {reserve[\"date\"].max().date()}')\n",
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"print(f' Token balances: {balances[\"date\"].min().date()} to {balances[\"date\"].max().date()}')"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 1. The Fundamental Loop: ABC → Common Pool → CV → Grants\n",
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"\n",
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"Did the system achieve a self-sustaining funding cycle?\n",
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"- ABC tributes (entry/exit fees) replenish the common pool\n",
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"- CV allocates common pool funds to proposals\n",
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"- Funded proposals should create value → attract new participants → ABC buys → more tributes"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Compare inflow (tributes) vs outflow (grants) on common pool\n",
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"cp_monthly = common.set_index('date').resample('ME').agg({\n",
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" 'inflow': 'sum',\n",
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" 'outflow': lambda x: x.abs().sum(),\n",
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" 'balance': 'last'\n",
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"})\n",
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"cp_monthly['net'] = cp_monthly['inflow'] - cp_monthly['outflow']\n",
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"cp_monthly['cumulative_net'] = cp_monthly['net'].cumsum()\n",
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"\n",
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"fig, axes = plt.subplots(2, 1, figsize=(16, 10))\n",
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"\n",
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"# Inflow vs Outflow\n",
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"ax = axes[0]\n",
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"width = 15\n",
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"ax.bar(cp_monthly.index - pd.Timedelta(days=8), cp_monthly['inflow'],\n",
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" width=width, color='#2ecc71', alpha=0.7, label='Inflow (tributes + other)')\n",
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"ax.bar(cp_monthly.index + pd.Timedelta(days=8), cp_monthly['outflow'],\n",
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" width=width, color='#e74c3c', alpha=0.7, label='Outflow (grants)')\n",
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"ax.set_ylabel('Amount')\n",
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"ax.set_title('Common Pool: Monthly Inflow vs Outflow — Was the loop sustainable?')\n",
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"ax.legend()\n",
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"\n",
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"# Cumulative net position\n",
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"ax = axes[1]\n",
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"ax.fill_between(cp_monthly.index, cp_monthly['cumulative_net'],\n",
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" where=cp_monthly['cumulative_net'] >= 0,\n",
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" color='#2ecc71', alpha=0.3, label='Net positive')\n",
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"ax.fill_between(cp_monthly.index, cp_monthly['cumulative_net'],\n",
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" where=cp_monthly['cumulative_net'] < 0,\n",
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" color='#e74c3c', alpha=0.3, label='Net negative')\n",
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"ax.plot(cp_monthly.index, cp_monthly['cumulative_net'], 'k-', linewidth=1.5)\n",
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"ax.axhline(y=0, color='black', linewidth=0.5)\n",
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"ax.set_ylabel('Cumulative Net Flow')\n",
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"ax.set_title('Cumulative Net Position — When did outflows exceed inflows?')\n",
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"ax.legend()\n",
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"\n",
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"for ax in axes:\n",
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" ax.xaxis.set_major_formatter(mdates.DateFormatter('%b %Y'))\n",
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"\n",
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"plt.tight_layout()\n",
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"plt.savefig(f'{DATA}/../snapshots/mechanism_sustainability.png', dpi=150, bbox_inches='tight')\n",
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"plt.show()\n",
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"\n",
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"total_in = cp_monthly['inflow'].sum()\n",
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"total_out = cp_monthly['outflow'].sum()\n",
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"print(f'\\nFunding Loop Analysis:')\n",
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"print(f' Total inflows: {total_in:,.0f}')\n",
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"print(f' Total outflows: {total_out:,.0f}')\n",
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"print(f' Coverage ratio: {total_in/total_out:.2f}x (1.0 = break even)')\n",
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"print(f' Net deficit: {total_in - total_out:,.0f}')"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 2. Governance Activity vs Treasury Health\n",
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"\n",
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"Did governance participation correlate with treasury health?\n",
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"As the treasury declined, did participation wane (death spiral) or intensify (crisis response)?"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Monthly governance activity\n",
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"stakes_monthly = stakes.set_index('date').resample('ME').agg(\n",
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" num_stakes=('proposal_id', 'count'),\n",
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" unique_stakers=('staker', 'nunique'),\n",
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" total_staked=('tokens_staked', 'sum'),\n",
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")\n",
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"\n",
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"# Proposals per month\n",
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"props_monthly = proposals[proposals['id'] > 1].set_index('date').resample('ME').agg(\n",
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" num_proposals=('id', 'count'),\n",
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" total_requested=('amount_requested', 'sum'),\n",
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")\n",
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"\n",
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"fig, axes = plt.subplots(3, 1, figsize=(16, 14), sharex=True)\n",
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"\n",
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"# Treasury balance\n",
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"ax = axes[0]\n",
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"ax.plot(common['date'], common['balance'], color='#2ecc71', linewidth=1.5, label='Common Pool')\n",
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"ax.plot(reserve['date'], reserve['balance'], color='#3498db', linewidth=1.5, label='Reserve Pool')\n",
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"ax.set_ylabel('Balance')\n",
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"ax.set_title('Treasury Health')\n",
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"ax.legend()\n",
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"\n",
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"# Active stakers\n",
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"ax = axes[1]\n",
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"if len(stakes_monthly) > 0:\n",
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" ax.bar(stakes_monthly.index, stakes_monthly['unique_stakers'], width=20, color='teal', alpha=0.7)\n",
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"ax.set_ylabel('Unique Stakers')\n",
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"ax.set_title('Monthly Governance Participation')\n",
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"\n",
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"# New proposals\n",
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"ax = axes[2]\n",
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"if len(props_monthly) > 0:\n",
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" ax.bar(props_monthly.index, props_monthly['num_proposals'], width=20, color='coral', alpha=0.7)\n",
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"ax.set_ylabel('New Proposals')\n",
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"ax.set_title('Monthly Proposal Submissions')\n",
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"ax.set_xlabel('Date')\n",
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"\n",
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"for ax in axes:\n",
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" ax.xaxis.set_major_formatter(mdates.DateFormatter('%b %Y'))\n",
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"\n",
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"plt.tight_layout()\n",
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"plt.savefig(f'{DATA}/../snapshots/governance_vs_treasury.png', dpi=150, bbox_inches='tight')\n",
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"plt.show()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 3. Conviction Voting Parameter Stress Test\n",
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"\n",
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"The TEC used these CV parameters:\n",
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"- **Conviction Growth**: 7 days\n",
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"- **Minimum Conviction**: 4%\n",
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"- **Spending Limit**: 11%\n",
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"\n",
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"Were these appropriate? How did they interact with the actual dynamics?"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Analyze request sizes relative to common pool\n",
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"funded = proposals[(proposals['status'] == 'executed') & (proposals['amount_requested'] > 0)].copy()\n",
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"\n",
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"# For each funded proposal, estimate what % of the pool it represented\n",
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"# We need to match proposal dates to pool balance dates\n",
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"# Use the common pool daily balance\n",
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"cp_daily = common.set_index('date')['balance'].resample('D').last().ffill()\n",
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"\n",
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"request_pcts = []\n",
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"for _, row in funded.iterrows():\n",
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" prop_date = row['date']\n",
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" # Find closest pool balance\n",
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" mask = cp_daily.index <= prop_date\n",
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" if mask.any():\n",
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" pool_bal = cp_daily[mask].iloc[-1]\n",
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" if pool_bal > 0:\n",
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" pct = row['amount_requested'] / pool_bal\n",
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" request_pcts.append({\n",
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" 'id': row['id'],\n",
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" 'name': row['link'][:40],\n",
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" 'amount': row['amount_requested'],\n",
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" 'pool_balance': pool_bal,\n",
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" 'pct_of_pool': pct,\n",
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" 'date': prop_date\n",
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" })\n",
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"\n",
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"if request_pcts:\n",
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" rp_df = pd.DataFrame(request_pcts)\n",
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"\n",
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" fig, axes = plt.subplots(1, 2, figsize=(16, 6))\n",
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"\n",
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" # Request as % of pool\n",
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" ax = axes[0]\n",
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" ax.bar(range(len(rp_df)), rp_df['pct_of_pool'] * 100, color='steelblue')\n",
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" ax.axhline(y=11, color='red', linestyle='--', label='Spending Limit (11%)')\n",
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" ax.axhline(y=4, color='orange', linestyle='--', label='Min Conviction (4%)')\n",
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" ax.set_xlabel('Proposal (chronological)')\n",
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" ax.set_ylabel('% of Common Pool')\n",
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" ax.set_title('Proposal Size Relative to Common Pool')\n",
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" ax.legend()\n",
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"\n",
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" # Over time\n",
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" ax = axes[1]\n",
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" ax.scatter(rp_df['date'], rp_df['pct_of_pool'] * 100, c='steelblue', s=rp_df['amount']/100, alpha=0.6)\n",
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" ax.axhline(y=11, color='red', linestyle='--', label='Spending Limit (11%)')\n",
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" ax.set_xlabel('Date')\n",
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" ax.set_ylabel('% of Common Pool')\n",
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" ax.set_title('Request Size Relative to Pool Over Time\\n(bubble size = absolute amount)')\n",
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" ax.legend()\n",
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"\n",
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" plt.tight_layout()\n",
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" plt.savefig(f'{DATA}/../snapshots/cv_parameters.png', dpi=150, bbox_inches='tight')\n",
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" plt.show()\n",
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"\n",
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" print(f'\\nSpending limit analysis:')\n",
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" over_limit = (rp_df['pct_of_pool'] > 0.11).sum()\n",
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" print(f' Proposals exceeding 11% spending limit: {over_limit}/{len(rp_df)}')\n",
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" print(f' Max request as % of pool: {rp_df[\"pct_of_pool\"].max():.1%}')\n",
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" print(f' Median request as % of pool: {rp_df[\"pct_of_pool\"].median():.1%}')\n",
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"else:\n",
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" print('Could not align proposal dates with pool balance data')"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 4. The Death Spiral Hypothesis\n",
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"\n",
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"Did the TEC experience a death spiral?\n",
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"1. Token price drops → less attractive to hold\n",
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"2. Holders sell on ABC → reserve pool shrinks → price drops further\n",
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"3. Less ABC activity → less tribute revenue → common pool depletes faster\n",
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"4. Less funding available → fewer proposals → less community activity\n",
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"5. Less activity → holders leave → goto 1"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Build composite timeline\n",
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"# We need to align: reserve pool, common pool, governance activity, and token price\n",
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"\n",
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"# Normalize each metric to [0, 1] range for comparison\n",
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"def normalize(series):\n",
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" return (series - series.min()) / (series.max() - series.min())\n",
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"\n",
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"fig, ax = plt.subplots(figsize=(16, 8))\n",
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"\n",
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"# Reserve pool (proxy for price/market confidence)\n",
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"rp_daily = reserve.set_index('date')['balance'].resample('W').last().ffill()\n",
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"ax.plot(rp_daily.index, normalize(rp_daily), label='Reserve Pool (market confidence)',\n",
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" color='#3498db', linewidth=2)\n",
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"\n",
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"# Common pool (funding capacity)\n",
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"cp_daily_w = common.set_index('date')['balance'].resample('W').last().ffill()\n",
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"ax.plot(cp_daily_w.index, normalize(cp_daily_w), label='Common Pool (funding capacity)',\n",
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" color='#2ecc71', linewidth=2)\n",
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"\n",
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"# Governance activity (rolling stake events per month)\n",
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"stakes_weekly = stakes.set_index('date').resample('W')['proposal_id'].count()\n",
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"stakes_rolling = stakes_weekly.rolling(4).mean()\n",
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"if len(stakes_rolling.dropna()) > 0:\n",
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" ax.plot(stakes_rolling.index, normalize(stakes_rolling.fillna(0)),\n",
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" label='Governance Activity (4-week rolling)',\n",
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" color='coral', linewidth=2, alpha=0.8)\n",
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"\n",
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"# Mark key events\n",
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"events = [\n",
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" (datetime.datetime(2022, 5, 1), 'Terra/Luna collapse'),\n",
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" (datetime.datetime(2022, 11, 1), 'FTX collapse'),\n",
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" (datetime.datetime(2023, 1, 1), 'Crypto winter deepens'),\n",
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"]\n",
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"for date, label in events:\n",
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" if rp_daily.index.min() <= date <= rp_daily.index.max():\n",
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" ax.axvline(x=date, color='gray', linestyle=':', alpha=0.5)\n",
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" ax.text(date, 1.05, label, rotation=45, fontsize=8, ha='left')\n",
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"\n",
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"ax.set_xlabel('Date')\n",
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"ax.set_ylabel('Normalized Level (0-1)')\n",
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"ax.set_title('Death Spiral Analysis: Correlated Decline of Market, Treasury, and Governance')\n",
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"ax.legend(loc='upper right')\n",
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"ax.set_ylim(-0.05, 1.15)\n",
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"ax.xaxis.set_major_formatter(mdates.DateFormatter('%b %Y'))\n",
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"\n",
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"plt.tight_layout()\n",
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"plt.savefig(f'{DATA}/../snapshots/death_spiral.png', dpi=150, bbox_inches='tight')\n",
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"plt.show()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 5. Mechanism Design Scorecard"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"funded = proposals[(proposals['status'] == 'executed') & (proposals['amount_requested'] > 0)]\n",
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"\n",
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"print('=' * 70)\n",
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"print('TEC MECHANISM POST-MORTEM — SCORECARD')\n",
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"print('=' * 70)\n",
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"print(f'''\n",
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"AUGMENTED BONDING CURVE\n",
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" Design goal: Continuous fundraising with built-in price floor\n",
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" Reserve ratio: Started at ~31% (target TBD from ABC model)\n",
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" Entry/exit tribute: Generated revenue for common pool\n",
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" Primary vs secondary: Token traded at ~33% discount at shutdown\n",
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" \n",
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" [?] Did the ABC provide adequate price support during downturns?\n",
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" [?] Was the tribute rate sufficient to sustain operations?\n",
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" [?] How did liquidity compare between ABC and Honeyswap?\n",
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" \n",
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" (Full ABC analysis pending — need trade data from Dune queries)\n",
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"\n",
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"CONVICTION VOTING\n",
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" Design goal: Continuous, sybil-resistant fund allocation\n",
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" Proposals funded: {len(funded)}/46 real proposals ({len(funded)/46:.0%})\n",
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" Total disbursed: {funded[\"amount_requested\"].sum():,.0f} TEC\n",
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" Unique participants: 159 stakers\n",
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" \n",
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" [+] High success rate suggests proposals were well-vetted pre-submission\n",
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" [+] Diverse set of funded initiatives (academy, research, community)\n",
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" [-] 159 participants out of ~1,200 token holders (~13% participation)\n",
|
|
" [-] Governance activity declined as token price fell\n",
|
|
" [?] Did the 7-day conviction growth period cause delays?\n",
|
|
" [?] Did the Abstain proposal effectively serve as a brake?\n",
|
|
"\n",
|
|
"THE FEEDBACK LOOP\n",
|
|
" Design goal: Self-sustaining cycle of funding and growth\n",
|
|
" Reality: Tribute inflows << grant outflows throughout the lifecycle\n",
|
|
" Treasury declined from peak to shutdown over ~3 years\n",
|
|
" \n",
|
|
" [-] The fundamental loop never achieved self-sustainability\n",
|
|
" [-] Exogenous shocks (Terra, FTX) accelerated decline\n",
|
|
" [-] Common pool was essentially a drawdown fund, not a renewable one\n",
|
|
" [?] Was this a mechanism failure or a market/adoption failure?\n",
|
|
"\n",
|
|
"SHUTDOWN METRICS\n",
|
|
" Token at shutdown: ~$0.18 (vs peak of ~$1+)\n",
|
|
" Treasury: ~$300K remaining\n",
|
|
" FDV: ~$217K (33% below treasury value)\n",
|
|
" Grants distributed: $433K direct + $250K via Gitcoin\n",
|
|
" Total value created: Funded TE Academy, cadCAD, Gravity, research\n",
|
|
" \n",
|
|
"OVERALL ASSESSMENT:\n",
|
|
" The mechanisms worked as designed but could not overcome:\n",
|
|
" 1. Insufficient external demand to drive ABC tributes\n",
|
|
" 2. Bear market pressure on reserve pool collateral\n",
|
|
" 3. Community attrition reducing governance participation\n",
|
|
" The TEC proved that ABC + CV can work for initial fundraising and\n",
|
|
" allocation, but long-term sustainability requires mechanisms that\n",
|
|
" generate revenue beyond trading activity (tributes).\n",
|
|
"''')"
|
|
]
|
|
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