Suppose an economist develops an economic model and finds that it works great in theory but fails in practice. What should the economist do next?

The importance of model re-evaluation cannot be overstated in the field of economics. When an economist's theoretical model does not work in practice, this is a clear signal that it is time for re-evaluation.

This step involves a careful examination of how the model is applied to real-world situations. Economists must question whether the scope of the model is too broad or too narrow and consider if any essential variables have been omitted. For instance, has the model accounted for all possible market conditions, or is it too focused on a specific scenario? Re-evaluation is essentially about finding the gaps between theory and practice and investigating the root causes behind a model's failure to accurately predict or explain economic phenomena.

Incorporating this feedback loop is critical. Without it, the model may remain rigid and unresponsive to the dynamic nature of economic landscapes, thus limiting its practical usefulness.

Diving into the theoretical assumptions of an economic model is the linchpin of understanding where the model might miss its mark. These are the foundational beliefs upon which all analyses and predictions are built. In the context of economic modeling, simplifications are often necessary, but they can also lead to significant inaccuracies if not closely examined.

Assumptions may include perfectly rational behavior of agents, complete information, or frictionless markets – all of which are rarely present in real life. An economist should critically assess these assumptions for their realism and pertinence to current economic conditions, identifying which assumptions are causing the model to diverge from actual economic behavior. It often is the misalignment of these foundational premises with reality that causes the model to fail when applied.

The robustness of any economic model heavily relies on the quality of data it uses. Appropriate data collection methods are paramount to ensure accurate and relevant input into the model. An economist must consider the sources of their data, the methodologies used in gathering it, and the potential biases that could emerge.

For instance, if data is collected through surveys, how are the questions framed? What is the sample size and is it representative of the wider population? Are there any technological means, such as big data analytics, that could provide more comprehensive insights? Answering these questions can lead to improvements in data collection that, in turn, enhance the model's performance by providing a more precise depiction of economic conditions.

Once assumptions have been examined and data collection methods refined, model testing and adjustment stand as the iterative steps toward refinement. A reworked model must be subject to rigorous testing to validate its predictions against empirical evidence. If discrepancies still exist, further adjustments may be necessary.

Whether it’s through statistical analysis, comparing outcomes with historical data, or peer review, each test uncovers strengths and weaknesses in the model's design. An adaptive approach is crucial—one that recognizes the iterative nature of model development. By making incremental improvements in response to each round of testing, an economist can gradually enhance the model's accuracy and reliability, thereby moving closer to a representation of the economic world that is both theoretical sound and practically viable.