LambdaSim's main goal is to act as a digital twin of the world from a macroeconomic perspective and to simulate future scenarios by taking advantage of agent based modeing and various methods from multivariat data analysis.

Here is a peek at what's driving this project along with a project timeline.

Complexity arises when there are nonlinear relationships, feedback loops, and unstable dynamics. Examples range from predicting traffic jams in a bustling city to understanding how the weather will unfold based solely on initial conditions.

Fortunately, we can make significant progress in predicting complex systems by breaking down the system into smaller components often called agents. These agents follow a simpler set of rules regarding how they act with their environment. This approach allows us to iteratively simulate the behavior of the entire system. By trying different values for the variables that we can change in real-world scenarios within the simulation, we gain valuable insights about how the system as a whole will be affected. As a consequence, this insight enables us to make better, more informed decisions in the real world.

In science, testing a hypothesis is crucial for proving or disproving a theory. However, in economics, especially on large scales, it is both analytically unpredictable and often very costly or impossible to test new government policies, such as those listed here, before analyzing their real-world implementation results and aftereffects. Additionally, in democratic governments, where new politicians are elected every four years, a policy might be reversed before any meaningful impact, positive or negative, can be measured.

This is the problem that Lambdasim aims to tackle. By creating detailed and dynamic models of economic systems, simulations can predict the potential outcomes of various policies without the need for real-world implementation. These simulations can account for numerous variables and their interactions, providing a more comprehensive analysis of possible results. This approach allows policymakers to evaluate the potential impacts of their decisions in a controlled environment, reducing the risks, costs, and ambiguities associated with trial-and-error in real-world policy testing.

Examples of societal issues that could be simulated in this may include, but is not limited to:

• Economic Stimulus Package: How a large economic stimulus, such as a package of tax cuts and public investments, affects economic growth, unemployment, and inflation over time.
• Climate Quotas: The impact of how an implementation of a national system for trading climate quotas will impact greenhouse gas emissions, industry, and the economy.
• Pandemic Preparedness: The effects of various strategies for managing a pandemic crisis, including testing, tracing, vaccination, and lockdown measures, on both public health and the economy.
• Tax Reform: The introduction of a new tax reform that changes tax rates for different income groups and businesses, and its impact on income distribution, innovation, and economic growth.
• Public Transportation Expansion: The development of a new, comprehensive public transportation system in urban areas and its impact on traffic patterns, air pollution, and societal development.
• Social Housing Policy: The effects of a large-scale public investment in affordable housing, and how this influences the housing market, social mobility, and public wellness.
• Education Reform: The implementation of a reform that introduces free higher education, and how this affects student enrollment, education quality, and the labor market.