Why would it be helpful, from a modeling perspective, to view economics as a science even if it is ...

When it comes to economics, a model is a simplified framework designed to illustrate complex processes. These models are produced to yield precise and verifiable implications about a certain phenomena it is trying to explain. Formal evaluation involves testing the model’s key implications and assessing its ability to reproduce stylized facts. Economists use many tools to test their models, including case studies, lab-based experimental studies, and statistics. Many organizations, such as large businesses and the government use economic models to help predict future conditions. When using these economic models, big corporations want to reduce risk by making sure they're making the best decisions regarding their financials, much the same way that doctors trust medicines that are backed up by scientific evidence.

Still, the randomness of economic data often gets in the way, so economists must be precise when saying that a model "successfully explains" something. From a forecasting perspective that means errors are unpredictable and irrelevant (zero) on average. When two or more models satisfy this condition, economists generally use the volatility of the forecast errors to break the tie - smaller volatility is generally preferred.

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