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# Adjusted parameters for more pronounced simulation adjusted_investment_multiplier = 8 # Increased investment multiplier minimum_investment = 2 # Increased minimum investment demand_growth_factor = 0.3 # Increased demand growth factor extended_simulation_time = np.arange(0, 20, dt) # Longer simulation time # Re-initialize lists for the adjusted simulation adjusted_capacity = [initial_capacity] adjusted_demand = [initial_demand] adjusted_performance = [] adjusted_investment = [] # Perform the adjusted simulation for time in extended_simulation_time: current_capacity = adjusted_capacity[-1] current_demand = adjusted_demand[-1] # Calculate performance current_performance = current_capacity - current_demand adjusted_performance.append(current_performance) # Adjusted investment strategy if current_performance < performance_standard: invest_amount = adjusted_investment_multiplier * (performance_standard - current_performance) else: invest_amount = minimum_investment adjusted_investment.append(invest_amount) # Update capacity and demand with adjusted factors new_capacity = current_capacity + dt * invest_amount growing_action = 0.01 * current_demand new_demand = current_demand + dt * demand_growth_factor * growing_action * current_performance adjusted_capacity.append(new_capacity) adjusted_demand.append(new_demand) # Plotting the adjusted results plt.figure(figsize=(12, 6)) plt.plot(extended_simulation_time, adjusted_capacity[:-1], 'bv', label='Adjusted Capacity') plt.plot(extended_simulation_time, adjusted_investment, 'ro', label='Adjusted Investment') plt.plot(extended_simulation_time, adjusted_performance, 'g.', label='Adjusted Performance') plt.xlabel('Time') plt.ylabel('Value') plt.title('Adjusted Simulation of Growth and Underinvestment') plt.legend() plt.show() # Output the final performance for the adjusted model final_adjusted_performance = adjusted_performance[-1] final_adjusted_performance
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