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Data output

Limit the number of scenarios and highlight the insights. Watch Hannes Weigt explain simple rules of result interpretation.
We now turn to the final part of numerical modeling, the results. In the next week, you will learn how to interpret the economic aspects of your results. So in this video, we will focus on all the aspects that are relevant for the number crunching you have to carry out with numerical model results.
If you have the results in front of you, and you’ve finished your first review, there are two important rules you should check before making the effort of writing up your interpretation. Rule number one relates to the fact that we try to keep our models simple. This means you normally should have a pretty good idea about the results. If this is not the case, and you are really surprised by your findings, chances are high that they’re caused by some model error. Maybe there’s a problem with the data upload, or your model code and the model you think you coded are not the same. So take really care to figure out what drives your results. Those errors can be hidden really well.
Rule number two, is basically the same story. Even if your results look reasonable, there’s always a chance that there’s a model error in the background. I’ve already written up whole results sections, multiple times, every time with really good looking results, but there was a data upload error once and a misplaced bracket the second time. Well, that’s the life of a modeller. So essentially, make sure you know your model is working as you think it should work. That’s why you should always make a small traceable example model first, and go big later.
If you’re convinced enough that your results are correct, you need to put them into a story. Numerical modeling is about quantification of effects. We want to simulate real world systems under specific conditions and figure out what would happen. Keep this in mind when analysing your results. How do the numbers fit the original problem? One issue you have to face in numerical modeling is the fact that you often derive multiple interesting findings, and try to make a coherent story out of them. But what is picked up is often a single number or finding not the whole story. You make a complicated market power analysis. What sticks is how much the prices are higher due to the market power.
If you’re aware of this effect, you can help to make sure that it is you choose the number that is going to stick. Nothing is more frustrating than carrying out a complicated analysis and somebody else picks this unimportant result of a side scenario as main finding.
When it comes to present your findings, there’s some quite general recommendations. Make sure you have a proper scenario layout the reader can follow. Don’t add too many layers or sub-scenarios. Keep things focused. Since we analyse virtual worlds, the benchmark helps to relate findings. Don’t claim that you’re able to forecast reality, but you can differentiate the effect a policy or market change would have compared to a reference case. It’s about the difference between the cases, tendencies and trends, not about exact numbers. Remember, all models are wrong, so don’t think your model is the exception. Since you will have to deal with thousands or millions of numbers, you will have to do the filtering for the readers.
Keep the results focused on your story. Aggregate if needed, but keep detailed findings if they are important. An example, like a representative daily pattern, can help to make a point. Tables can also help you to present large quantities in a condensed format. But please, don’t overuse them. If your results consist of only big chunky tables with little text in between, you won’t keep the read awake. And finally, make figures if possible. A well designed diagram, a mapping of spacial effects, a trend development, those are helpful tools to make a complicated story easy understandable. Only some people will understand the physics of power flows and matched electricity networks.
But everybody understands a simple figure showing it’s more expensive on this side of a line than on the other. You’re modeling job is to make sure the prices and physics are correct. You’ll storytelling job is to make a point.

When you finally got your model results there are two things to do: first, you need to make sure the numbers are correct; and second, you need to transfer the numbers into understandable results for your audience.

If you follow the steps provided during this course you hopefully have a working model that indeed produces correct results. But there is always the risk that you made some small errors while writing your model code or constructing your data upload. Always remember: your model software will do whatever you told her, not what you think you told her. So double-check that your coding is correct.

Presenting your results can sometimes be a challenge as you literally produce thousands of numbers. Boil it down to a reasonable reader-friendly summary: use descriptive statistics, aggregate results, show examples, limit the number of scenarios and highlight the insights.

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