Economics is sometimes called the social science most similar to physics. Both try to understand how complex systems change over time, depending on many different factors that affect each other.

Economists usually use statistics and econometrics to study these changes. But ideas from physics can also help, especially those that look at how things move, interact, and evolve in dynamic systems. Examples include models from fluid dynamics, particle behaviour, thermodynamics, and chaos theory. These often use equations and computer simulations to predict what might happen next.  Why is this? Economies are complex systems that are always changing, i.e., dynamic. They include people, businesses, and governments (the main players or interacting agents), and things like money, labour, goods, and information (and the flows between them). Models inspired by physics can help us understand how money and resources move through an economy. For example, cashflow between different sectors can be thought of as liquid flowing through pipes. When something unexpected happens, like a new policy, a natural disaster, or a new technology, the economy reacts, just like when a pipe is blocked and the water pressure and flow change immediately. Thus, changes to the economic environment can be modelled in a similar way to how physical systems respond to shocks.

The modelling approaches borrowed from the world of physics are differential equation models (calculating the rate at which things change with time) that can model things like GDP growth, inflation and underemployment dynamics over time, or agent-based simulations (inspired by particle simulations) where each economic agent follows simple rules, and then their collective behaviour emerges dynamically. Other modelling approaches are network theory which helps model trade networks, financial contagion and supply chains, stochastic models to model economic uncertainty, market volatility or risk (like random fluctuations in particle behaviour) and system dynamics and feedback loops which can model competition between firms, sectors or nations. 

Using models from physics and life sciences in economics has several advantages. They can show complex, non-linear interactions and how new patterns emerge. They also allow us to test different scenarios, connect small-scale behaviour (like individual choices) with large-scale outcomes (like national trends), and even run real-time simulations to test policies before applying them.

However, there are also challenges. Human behaviour is much harder to predict than physical forces. Data can be limited or noisy, which makes it difficult to fine-tune models. And if models are too simple, they may miss important cultural, psychological, or institutional factors that also shape economic systems.

By borrowing models from the physical sciences, we can better understand how real economies behave and change over time. Human behaviour is harder to predict than physical forces, but these mathematical and simulation-based methods still offer powerful tools for forecasting, testing policies, and exploring possible futures. Combining the structured approach of physics with insights into human behaviour can help create more flexible and resilient economic models.