The primary objective of economic research is to uncover statistical relationships 📊 and leverage these insights to improve the world 🌍. Economists typically have a broad range of interests, allowing them to explore various facets of economic behavior and policy.
Computational economics involves constructing mathematical models of economic systems 🧮 to conduct experiments 🔬. This approach allows researchers to investigate how changes in input variables affect economic outcomes, offering a unique perspective on real-world scenarios.
Conducting real-world economic experiments can be prohibitively expensive 💸 and often limits the scale and generalizability of findings. By using computational models, we can simulate various economic conditions and policies without the high costs associated with physical experiments.
One critical insight from economic theory is the Lucas Critique, which states that changing equilibrium conditions can alter behavioral responses ⚡. When implementing new policies, previous statistical relationships may not hold unless there is a causal explanation for these changes. Simply put, people will adjust their behavior in response to new incentives.
To provide a more accurate assessment of potential outcomes under different economic policies, we must build slightly more complex models of the economy 📉. This approach, known as quantitative realism, aims to enhance our understanding of the dynamic interplay between various economic factors.
Dynamic programming is a powerful tool in computational economics, with the cake-eating problem serving as a core example.
Imagine you are given an entire cake today, which you need to ration for the rest of your life 🎉. The challenge is to determine how much cake to consume each day to maximize your overall utility 🍽️. This problem exemplifies the trade-offs that individuals must navigate to extend or maximize their utility over time.