This software system provides a testbed for experimenting with economic theories in a simulated, AI-driven environment. It integrates historical data (to ensure realism), event-driven modules (for external shocks or policy changes), and agent-based modeling (to emulate complex market behaviors).
Key Features:
Purpose: Collect, clean, and standardize datasets that capture historical economic indicators and relevant event metadata.
Purpose: Learn patterns from historical data to generate predictive or adaptive agent behaviors in the simulation.
Purpose: Provide a controllable, sandboxed “world” in which AI agents interact according to economic rules, policies, and event triggers.
Purpose: Define how individual agents make decisions and react to the evolving economic environment.
Purpose: Simulate how real-world or hypothetical events might alter agent behavior and market outcomes.
Purpose: Provide real-time and post-simulation insights for researchers, policymakers, and other stakeholders.
Below is a detailed pseudocode example outlining a potential end-to-end workflow for building, running, and analyzing an AI-driven economic simulator. While this example remains language-agnostic and high-level, it uses Python-like syntax to illustrate concepts in a clear and approachable manner.
```python import os import json import logging from typing import Dict, Any, List, Optional
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#—————————————————————————–
LOG_FORMAT = “%(asctime)s - %(name)s - %(levelname)s - %(message)s” logging.basicConfig(level=logging.INFO, format=LOG_FORMAT) logger = logging.getLogger(“EconomicSimulationPipeline”)
SIMULATION_TIMESTEPS = 365 # e.g., 1-year daily steps EVENT_CALENDAR_FILE = “event_calendar.json” # JSON specifying events by day REFERENCE_DATA_FILE = “historical_reference.csv” # For validating results
def load_economic_time_series(db_name: str) -> Dict[str, Any]: “”” Simulates loading economic time-series data (e.g., GDP, inflation rates, interest rates, commodity prices) from a database or data warehouse.
:param db_name: The name or path of the database or dataset.
:return: A dictionary containing time-indexed economic indicators.
"""
logger.info(f"Loading economic data from {db_name}...")
# Pseudocode for data fetching:
# 1. Connect to DB or read from CSV/Parquet.
# 2. Parse data into a time-series structure (e.g., pandas DataFrame).
# 3. Return as a dictionary for simplicity.
econ_data = {
"dates": ["2020-01-01", "2020-01-02", ...],
"indicators": {
"gdp": [1.21, 1.22, ...],
"inflation": [0.02, 0.021, ...],
"interest_rate": [0.01, 0.01, ...]
}
}
return econ_data
def load_event_metadata(db_events: str) -> Dict[str, Any]: “”” Loads metadata for significant economic, cultural, or policy-related events that may affect the simulation (e.g., pandemics, technological breakthroughs, political elections).
:param db_events: The path or database reference for event data.
:return: A dictionary with event details keyed by date or time index.
"""
logger.info(f"Loading event data from {db_events}...")
# Pseudocode for event data:
event_data = {
"2020-01-05": {"type": "policy_change", "details": {"tax_rate_increase": 0.02}},
"2020-02-10": {"type": "natural_disaster", "details": {"region": "coastal"}},
...
}
return event_data
def preprocess_economic_data(econ_data: Dict[str, Any]) -> Dict[str, Any]: “”” Cleans, normalizes, and validates the raw economic data. Handles tasks such as: - Missing-value imputation - Outlier detection - Time alignment
:param econ_data: Raw economic data dictionary from load_economic_time_series().
:return: A cleaned and validated version of econ_data.
"""
logger.info("Preprocessing economic data...")
# Pseudocode for cleaning steps:
# 1. Check for NaNs, fill or remove them.
# 2. Identify and correct outliers using domain knowledge or statistical methods.
# 3. Reindex or resample data if needed for uniform daily intervals.
return econ_data # Return processed data structure
def preprocess_event_data(event_data: Dict[str, Any]) -> Dict[str, Any]: “”” Cleans and validates event metadata. Potential tasks: - Validate event date/time format - Ensure event details have necessary keys (type, magnitude, region, etc.)
:param event_data: Raw event data dictionary from load_event_metadata().
:return: A cleaned and validated version of event_data.
"""
logger.info("Preprocessing event data...")
# Basic checks and transformations:
# 1. Filter out incomplete or suspicious events.
# 2. Sort events chronologically if required.
return event_data
def train_forecasting_model(econ_cleaned: Dict[str, Any]) -> Any: “”” Trains a forecasting model (e.g., ARIMA, LSTM, Transformer-based model) to predict future economic indicators.
:param econ_cleaned: Preprocessed economic time-series data.
:return: A trained model object capable of producing forecasts.
"""
logger.info("Training macro-level forecasting model...")
# Pseudocode workflow:
# 1. Split data into train/validation sets.
# 2. Fit model (e.g., ARIMA, RNN, or ensemble methods).
# 3. Evaluate on validation set.
# 4. Return the trained model object.
macro_model = "TrainedMacroModelObject"
return macro_model
def train_agent_behavior_model(econ_cleaned: Dict[str, Any], events_cleaned: Dict[str, Any]) -> Any: “”” Trains an agent behavior model using techniques like: - Reinforcement Learning (RL) - Behavior Cloning from historical data - Hybrid approaches (RL with supervised pre-training)
:param econ_cleaned: Preprocessed economic data.
:param events_cleaned: Preprocessed event data.
:return: A trained agent model object.
"""
logger.info("Training agent behavior model...")
# Pseudocode workflow:
# 1. Extract relevant features: price movements, policy changes, shocks.
# 2. Define action space (buy, sell, invest, etc.) and reward functions.
# 3. Train using historical data as offline RL or direct imitation learning.
# 4. Return the trained agent model.
agent_model = "TrainedAgentModelObject"
return agent_model
class EconomicSimulationEngine: “”” The central engine that orchestrates agent interactions, market clearing, policy changes, and external events. “””
def __init__(self,
macro_model: Any,
agent_model: Any,
initial_params: Dict[str, Any]):
"""
:param macro_model: A forecasting model used for projecting macro trends.
:param agent_model: A model governing how agents make decisions.
:param initial_params: A dictionary containing initial simulation state,
such as interest rates, number of agents, etc.
"""
self.macro_model = macro_model
self.agent_model = agent_model
self.state = initial_params # e.g., global interest rates, money supply
self.agents = self._initialize_agents(initial_params)
self.current_day = 0
logger.info("EconomicSimulationEngine initialized.")
def _initialize_agents(self, init_params: Dict[str, Any]) -> List[Dict[str, Any]]:
"""
Creates a list or dictionary of agents with initial wealth, roles,
and other attributes.
:param init_params: Simulation-wide parameters (e.g., number of households).
:return: A list of agent data structures.
"""
logger.info("Initializing agents...")
# Pseudocode:
# 1. Create N agents with random or seeded initial capital.
# 2. Assign them different roles: consumer, producer, banker, regulator, etc.
# 3. Store them in a list or dictionary.
agents = []
for i in range(init_params.get("num_agents", 1000)):
agent_profile = {
"id": i,
"role": "consumer" if i < 800 else "producer",
"wealth": 1000.0, # example
"preferences": {}
}
agents.append(agent_profile)
return agents
def apply_event(self, event: Dict[str, Any]) -> None:
"""
Applies an external event to the simulation environment.
For instance, a policy change, disaster, or major financial shift.
:param event: Dictionary describing the event type and details.
"""
logger.info(f"Applying event: {event}")
# Modify self.state or agent properties based on event details
# e.g., if event is "tax_rate_increase", raise the global tax parameter
if event["type"] == "policy_change":
new_tax_rate = event["details"].get("tax_rate_increase", 0.0)
self.state["tax_rate"] = self.state.get("tax_rate", 0.0) + new_tax_rate
# Additional event logic can be added here
def step(self) -> None:
"""
Advances the simulation by one time step (e.g., one day).
During a step, each agent makes decisions, markets clear,
and macro parameters update based on model forecasts.
"""
logger.info(f"Stepping simulation: Day {self.current_day}")
# 1. Forecast Macro Trends
# (Use macro_model to estimate e.g. inflation, interest rates)
predicted_trends = self._forecast_macro_trends()
# 2. Update Global State
self._update_global_params(predicted_trends)
# 3. Agents Make Decisions
for agent in self.agents:
self._agent_action(agent)
# 4. Resolve Markets and Apply Transactions
# (e.g., sum supply/demand, recalc prices, wealth distribution)
self._market_clearing()
# 5. Increment Time
self.current_day += 1
def _forecast_macro_trends(self) -> Dict[str, float]:
"""
Uses the macro_model to forecast near-future trends
(e.g. inflation, interest rates, GDP growth, etc.).
"""
# Pseudocode:
forecast = {
"inflation": 0.02, # Example forecast
"interest_rate": 0.015
}
return forecast
def _update_global_params(self, forecast: Dict[str, float]) -> None:
"""
Incorporates forecasted trends into the global simulation state.
"""
self.state["inflation_rate"] = forecast["inflation"]
self.state["interest_rate"] = forecast["interest_rate"]
def _agent_action(self, agent: Dict[str, Any]) -> None:
"""
Uses the agent_model (behavior logic) to decide an agent's action
in this step, then updates the agent's state accordingly.
"""
# Pseudocode:
# 1. Observe environment (prices, inflation, interest_rate).
# 2. agent_model returns action (buy, sell, invest, produce, etc.).
# 3. Update agent state (wealth, inventory).
pass
def _market_clearing(self) -> None:
"""
Aggregates all buy/sell actions, determines new prices,
and updates agent wealth.
"""
# Pseudocode:
# 1. Sum supply and demand for each commodity or asset.
# 2. Adjust prices according to supply-demand gaps.
# 3. Transfer wealth between agents as needed.
logger.info("Market clearing for current timestep...")
pass
def get_simulation_output(self) -> Dict[str, Any]:
"""
Returns a snapshot of the current state and possibly
the entire simulation history if stored.
:return: Dictionary containing simulation metrics, agent states,
and historical logs of each timestep (optional).
"""
output = {
"global_state": self.state,
"agent_states": self.agents,
# "history": self.history # if you store a history of states
}
return output
def load_event_calendar(file_path: str) -> Dict[int, Any]: “”” Loads a calendar of events from a JSON or CSV file, indexed by day number. Example structure: { 10: {“type”: “policy_change”, “details”: {“tax_rate_increase”: 0.02}}, 30: {“type”: “natural_disaster”, “details”: {“region”: “coastal”}} } “”” logger.info(f”Loading event calendar from {file_path}”) if not os.path.exists(file_path): logger.warning(“Event calendar file not found. Returning empty.”) return {} with open(file_path, “r”) as f: calendar_data = json.load(f) # Convert day strings to integers as needed return {int(day): details for day, details in calendar_data.items()}
def compare_with_historical(sim_results: Dict[str, Any], reference_data: str) -> Dict[str, float]: “”” Compares simulation outcomes with real-world historical data to evaluate accuracy.
:param sim_results: Output from the simulation engine.
:param reference_data: Path to historical data for validation.
:return: A dictionary of metrics indicating how closely the simulation
matches real-world outcomes (e.g., MAPE, RMSE).
"""
logger.info("Validating simulation results against historical data...")
# Pseudocode:
# 1. Load reference data from CSV.
# 2. Compute error metrics (e.g., MAPE for inflation or GDP).
# 3. Return a dictionary of metrics, e.g. {"gdp_error": 0.05, "inflation_error": 0.02}.
validation_metrics = {
"inflation_rmse": 0.02,
"gdp_rmse": 0.05
}
return validation_metrics
def generate_dashboard(sim_results: Dict[str, Any], validation_metrics: Dict[str, float]) -> None: “”” Generates or updates a dashboard (e.g., Plotly, Dash, or a static HTML report) with key graphs: - Time-series of inflation vs. historical benchmarks - GDP evolution vs. forecasts - Wealth distribution among agents over time - Validation metrics summary “”” logger.info(“Generating simulation dashboard…”) # Pseudocode: # 1. Prepare figures for each key metric. # 2. Render or save to file/HTML. pass
def main_workflow() -> Dict[str, float]: “”” Orchestrates the entire economic simulation workflow. Returns validation metrics that quantify the simulation’s realism. “”” logger.info(“Starting main economic simulation workflow…”)
# 1. Data Ingestion
economic_data = load_economic_time_series("db_econ")
event_data = load_event_metadata("db_events")
# 2. Data Preprocessing
econ_cleaned = preprocess_economic_data(economic_data)
events_cleaned = preprocess_event_data(event_data)
# 3. Model Training
macro_model = train_forecasting_model(econ_cleaned)
agent_behavior_model = train_agent_behavior_model(econ_cleaned, events_cleaned)
# 4. Initialize Simulation
initial_world_state = {
"interest_rate": 0.01,
"tax_rate": 0.05,
"num_agents": 1000,
"money_supply": 1000000.0
}
sim_env = EconomicSimulationEngine(
macro_model=macro_model,
agent_model=agent_behavior_model,
initial_params=initial_world_state
)
# 5. Run Simulation with Event Triggers
event_calendar = load_event_calendar(EVENT_CALENDAR_FILE)
for t in range(SIMULATION_TIMESTEPS):
if t in event_calendar:
sim_env.apply_event(event_calendar[t])
sim_env.step()
# 6. Collect Results and Validate
sim_results = sim_env.get_simulation_output()
validation_metrics = compare_with_historical(sim_results, REFERENCE_DATA_FILE)
# 7. Visualization
generate_dashboard(sim_results, validation_metrics)
logger.info("Main workflow complete.")
return validation_metrics
if name == “main”: metrics = main_workflow() print(“Simulation complete. Validation metrics:”, metrics)