The objective of this research is to introduce an innovative macroeconomic digital twin framework intended for policymakers. By constructing a twin digital of real-world economic phenomena, we can analyse the potential impact of macroeconomic policies within a digital environment before their actual implementation in reality. Our contingency is based on the recognition that forecasting in social sciences, particularly in economics, often needs a reasonable degree of accuracy in predicting events. The lack of accuracy in forecasting in economics is primarily due to the underutilization of appropriate statistical and computational techniques. Instead, policymakers frequently rely on methods that often yield impractical outcomes. Leveraging twin technologies can be a valuable tool, empowering policymakers to explore and assess various scenarios effectively before implementation.
The aim of the research is to:
“Create a twin digital of an economic system (society, country, or industry) that allow us to simulate and reproduce different economic policies implementation and outcomes”.
The objectives of the research are:
- To review twin technologies methodologies, policy making theories, forecasting methods in economics, and behavioural economics.
- To create a twin model of an economic system.
- To create an agent based model in Python that will be the foundation of the twin model.
- To validate our model using historical data and traditional statistical tools such r square, standard error and mean average performance error (MAPE).
- Agent Based Model (ABM) will be the technology chosen to create the digital twin simulation. ABMs are able to create intricate social systems, incorporating different agents and allowing these agents to dynamically change. This in turn, allow us to simulate different social contexts
After reviewing existing literature, we define the components of our ABM,
- The agent/s. This can be one ore more agents. For instance, if the aim is to replicate a entire society, the agents could be the public sector, the private sector, households, and international relations.
- The agent/s rules. These rules must be based on behavioural economics and general economic principles.
- The environment. The environment will be the economic ecosystem. For instance, if the model aims to replicate a specific country then variables like the base level of pricing, debt, interest rates and other macroeconomic variables must be set prior to run the simulation.
- The environment rules. Regulatory policies, regulations and economic principles that dictate the how these variables act. Decision rules. Once the agent and the environment is defined, then the decision rules that trigger behaviour and the magnitude of the effect must be defined. Here we also define loops and reactions.
Once the ABM is created and in working conditions, we validate our model by using historical data. For instance, we can validate our simulation by forecasting GDP growth and running the model under different scenarios (actual vs forecasted) and compare the outcome and the performance against historical models.
