#Inverseincentivesplan

import numpy as np import pandas as pd # Step 1: Define States and Initial Budgets states = ['StateA', 'StateB', 'StateC'] current_reimbursements = np.array([1000, 1500, 2000]) # in millions base_budgets = np.array([800, 1200, 1600]) # in millions profit_percentage = 0.50 population_growth = np.array([0.02, 0.03, 0.01]) # 2%, 3%, 1% penalty_rate = 0.10 # 10% penalty bonus_percentage = 0.05 # 5% bonus # Step 2: Define Current Expenses current_expenses = np.array([900, 1400, 1800]) # in millions # Step 3: Run Simulation def simulate_savings(expenses, base_budget, profit_percentage, pop_growth, penalty_rate, bonus_percentage): potential_savings = expenses - base_budget profit = potential_savings * profit_percentage new_budget = base_budget + profit # Penalty if budget increases more than population growth budget_increase = new_budget - base_budget penalty = 0 if budget_increase > base_budget * pop_growth: penalty = (budget_increase - base_budget * pop_growth) * penalty_rate # Apply savings to cover penalty if potential_savings > 0: penalty = max(0, penalty - potential_savings) # Bonus for reducing budget needs bonus = (base_budget - new_budget) * bonus_percentage if new_budget < base_budget else 0 # Adjust final budget with penalty and bonus adjusted_budget = new_budget + penalty - bonus taxpayer_savings = np.sum(current_reimbursements) - np.sum(adjusted_budget) return adjusted_budget, profit, taxpayer_savings, penalty, bonus # Step 4: Run Scenarios scenarios = [] for i in range(len(states)): expenses = current_expenses[i] base_budget = base_budgets[i] pop_growth = population_growth[i] adjusted_budget, profit, taxpayer_savings, penalty, bonus = simulate_savings( expenses, base_budget, profit_percentage, pop_growth, penalty_rate, bonus_percentage ) scenarios.append({ 'State': states[i], 'Adjusted Budget': adjusted_budget, 'Profit': profit, 'Taxpayer Savings': taxpayer_savings, 'Penalty': penalty, 'Bonus': bonus }) results_df = pd.DataFrame(scenarios) # Step 5: Print Results print("Simulation Results:") print(results_df) # Step 6: Economic Impact Analysis # This is a simplified placeholder for economic impact analysis. employment_reduction = np.random.uniform(-0.05, 0.05, len(states)) # 5% reduction to 5% increase economic_impact = results_df['Taxpayer Savings'] * (1 + employment_reduction) results_df['Economic Impact'] = economic_impact print("\nEconomic Impact Analysis:") print(results_df[['State', 'Taxpayer Savings', 'Economic Impact']])