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Home / Nature & Environment / Climate Change / Exploring Possible Futures: Modeling in Environmental and Energy Economics / What are the last essential steps for developing a consistent model?

What are the last essential steps for developing a consistent model?

The final step of modeling is putting all pieces together into a consistent story. Watch Hannes Weigt explain more.
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6.3
Hello and welcome to the last week of our course on Modeling in Environmental and Energy Economics. Up to now, we learned all the conceptual and mathematical details, how to design a model, and all those gritty elements, how to solve it and interpret the results. Now, we want to put all those elements together to get a consistent story. Now, how do you transfer your model into a story? Pretty simple, just follow this structure.
36.4
You should be more than familiar with this by now. It has been following us around since the first week, and there’s a good reason for this. This structure gives you a handy guide of all those elements you will need for your story. The first and most important part is not your model, it’s your problem. Tell your audience why you do the things you do. What is your initial motivation behind building your model? If you find it interesting enough to design a model for that problem, tell your audience why it’s so interesting. Model building and solving is the next step. But as mentioned in the first week, it’s essential to link this to the initial problem.
82.7
Why is your model a credible representation of your problem? You need to make sure to explain why the chosen design of your model fits your purpose. I’ve got to be honest with you, many people won’t read that part of your paper. Modeling is often a black box for a lot of people out there, but not for modellers. They will take your black box and figure out how it works. So better make sure you tell them how it works.
114.9
Finally, you already learned how to interpret and present your results. From a story point of view, the most important point is to link this back to your problem.
129
This provides the overarching frame of your model and thereby, your story. Don’t just present all your results and then call it a day. There’s a nice statement credited to George Box that summarises what modeling is all about. All models are wrong, but some are useful. The first part simply refers to the fact that all models are simplifications of reality based on assumptions and limitations. They cannot capture the full complexity of reality because they’re not designed to capture the full complexity. So in that sense, they are all wrong. But that’s not what matters.
176.4
The big question is: what are you doing with your model? In the end, the model is a tool to help you address your initial problem. A hammer is a perfect tool if you have a nail, but not quite as useful if you have a bolt. The context in which you apply your model defines if it’s useful, and since you decide about the context, it’s also you that decides if your model is useful. And telling that context is your story.
210.6
Now, the tricky question is: how to make sure your model is useful. First, you should make sure your model is only wrong, because it’s a model not because it’s a bad model. So know your model basics, know all those limitations we learned, all those mathematical aspects we have addressed. Then, make sure you know what drives your results. Figure out what are the important variables? What are the elements that have impact on your results? What elements are not so important. Make a sensitivity analysis. And finally, always relate your model and your problem results to your problem. What was your initial question? What was your motivation to design your model?
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If you keep all that in mind, you can ensure that your model is useful. And if you have a useful model, you should also have a useful story.

You got all the elements together: you have a clearly defined problem, a fitting model, you solved it and produced results, made robustness checks and sensitivities to understand what drives your results. Furthermore, you gathered figures, tables and examples for your result presentation. Now what?

The final step of modeling is putting all those pieces together into a consistent story. As a general guideline you can use the structure presented in this course and combine this with the hourglass structure of paper writing:

You should always start and end broadly to ensure a consistent frame for your story. Motivate your research so that anyone understands why this is interesting. Provide a clear research question (your ‘Problem’) and relate it to existing research and approaches thereby becoming more specific. Your ‘Model’ and ‘Solution’ methods are the most specific parts and should provide sufficient detail for others to reproduce your work. With your results you again broaden the scope. The ‘Interpretation’ will need to provide the link from your specific methods and results to the general problem you introduced (see pink elements in the visualization). The conclusion will then close your story and should again be written in a way that anyone can understand what you have done. Make sure that you provide your readers with all the elements they need to follow your story and try to avoid opening up side-stories that distract from your main story-arch.

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University of Basel online course,

Exploring Possible Futures: Modeling in Environmental and Energy Economics

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If you want to have some more guidelines on how to write a consistent research paper you may want to have a look at one of the following references:

Bem, D. J. (2003). Writing the empirical journal. In: Darley, J. M., Zanna, M. P., & Roediger III, H. L. (Eds) (2003). The Compleat Academic: A Practical Guide for the Beginning Social Scientist, 2nd Edition. Washington, DC: American Psychological Association.

Nikolov, P. (2013). Writing tips for economics research papers. Harvard University.

Varian, H. R. (1997). How to build an economic model in your spare time. The American Economist, 3-10

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This content is taken from University of Basel online course
Exploring Possible Futures: Modeling in Environmental and Energy Economics
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