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agents.py Requirements

  • Define Agent Classes: Create classes for consumers, corporations, and government, each with relevant attributes.
  • Attributes for Agents: Include attributes such as wealth, consumption preferences, investment capabilities, policy-making powers, etc.
  • Behavioral Methods: Implement methods that dictate how agents behave or interact, such as spending, saving, investing, and taxing.
  • Initialization and Setup: Allow for initializing agents with specific parameters or default settings.
  • Decision-making Capabilities: Incorporate simple AI or rule-based logic so that agents can make decisions based on current economic conditions.
  • Data Collection Hooks: Each agent should be able to log or export their state for analysis.

market.py Requirements

  • Simulation of Economic Market: Develop a market system where agents can interact through buying, selling, and trading.
  • Implementation of Market Mechanisms: Include functionalities like pricing mechanisms, supply and demand influences, and transaction processing.
  • Economic Indicators: Calculate key market indicators such as market equilibrium, inflation rates, etc.
  • Agent Interaction Management: Handle the relationships and interactions between different types of agents.
  • Regulatory Frameworks: Simulate the impact of laws and regulations imposed by governmental agents.
  • Event Handling: Allow the system to handle and respond to random or scheduled economic events (e.g., economic downturn, policy changes).

analysis.py Requirements

  • Data Accumulation: Collect and store data generated from the simulation in a structured format.
  • Statistical Analysis Tools: Provide tools for basic statistical analysis (e.g., mean, median, variance).
  • Economic Health Indicators: Compute indicators such as GDP, unemployment rate, and others based on the simulation data.
  • Historical Comparison: Enable comparisons between the simulated data and real-world historical data.
  • Report Generation: Create reports summarizing the findings and trends established throughout the simulation.
  • Prediction Models: Employ machine learning models to predict future economic outcomes based on past trends (optional).

visualization.py Requirements

  • Dynamic Graphical Representations: Develop real-time graphical representations of economic data such as line charts, bar graphs, and pie charts.

  • Interactive Dashboard: Create an interactive dashboard that users can use to visualize different economic scenarios.

  • Data Filtering and Manipulation: Allow users to filter and manipulate the data visually to examine different aspects of the economy.

  • Simulation Control via UI: Enable users to control simulation parameters through the dashboard (e.g., adjusting tax rates, introducing new policies).

  • Visualization of Agent Interactions: Graphically represent the interactions between different agents and their effects on the economy.

  • Responsiveness and Scalability: Ensure the visualizations are responsive and scale to different screen sizes and amounts of data.

  • Programming Requirements for Economic Simulation Project

agents.py

This module is tasked with the creation and management of economic agents within the simulation.

Classes:

  • Consumer
  • Corporation
  • Government

Key Functionalities:

  • Attributes: Define attributes such as wealth, consumption preferences, investment capabilities, policy-making powers.
  • Methods:
    • spend(): Dictate how agents distribute resources for consumption.
    • save(): Outline the saving behavior.
    • invest(): How and what agents invest in.
    • pay_taxes(): Interaction with the government through tax payments.
  • Decision-making:
    • Implement simple AI or rule-based logic to allow agents to make economic decisions based on their state and market conditions.
  • Initialization:
    • Initialize agents with specific parameters or default settings to simulate diverse economic scenarios.
  • Data Export:
    • Methods to log and export each agent's state for analysis and visualization.

market.py

This module simulates the economic market where agents interact, trade, and are influenced by various factors.

Key Functionalities:

  • Trade Mechanisms:
    • trade_goods(): Enable buying and selling interactions among agents.
    • set_prices(): Adjust prices based on market dynamics of demand and supply.
  • Market Indicators:
    • calculate_equilibrium(): Evaluate and adjust market equilibriums.
    • track_inflation(): Monitor and calculate inflation rates.
  • Regulations and Events:
    • apply_regulations(): Simulate how government policies impact market operations.
    • simulate_event(): Handle market responses to economic events such as downturns or booms.

analysis.py

Responsible for collecting, processing, and presenting data generated from the simulation.

Key Functionalities:

  • Data Accumulation:
    • Implement structures to efficiently collect, store, and process simulation data.
  • Statistical Tools:
    • Provide functions for basic statistical analysis to evaluate the health of the simulation economy.
  • Economic Indicators:
    • Compute and report key metrics such as GDP and unemployment rates.
  • Report Generation:
    • Automated generation of reports detailing simulation outcomes and trends.
  • Predictive Analysis (Optional):
    • Use machine learning techniques to predict future economic conditions based on historical and simulated data.

These are the programming requirements detailed for each specific module involved in the Economic Simulation Project. Developers are expected to rigorously implement these functionalities to ensure the simulation's effectiveness and accuracy.