Appraising Your Assumptions

We make models every day. And if we were robots, we'd be making simulations every day. This post is about making you (more) aware that you build models yourself every day (not only when you follow my dreary/delightful tutorials), and to help you a little bit to scrutinize them.

Climate change is real, and the world is roughly spherical. These are facts, because there is a huge amount of rigorously reviewed literature confirming it, as well as a huge amount of high-quality observational evidence. But most of the other things we assume to be true are not so well substantiated. And those I refer to as assumptions. They are things that we believe in, or take for granted, but where the evidence isn't always as rock-solid. For example:

Now let me make a small side-track for those interested, before I proceed with the main content on appraising your assumptions.

Side-Track: facts and assumptions in science & simulation

Some disciplines in science find themselves in a position of luxury. They can base their works on physical laws, which have been tested and proven right for countless decades. Laws such as Newton's Law of Gravity for stellar dynamics, or the Navier-Stokes equation in fluid dynamics, can be treated as facts in many situations, and provide a solid foundation for those who want to do science and build simulations in these fields.

In other disciplines, such as many studies of human behavior, we do not possess these luxuries, and we have little choice but to resort to assumptions. Especially in those cases, for instance when I was working on a new rule set for a migration simulation this week, we need to be able to distinguish the stronger assumptions from the weaker ones. And that last thing is exactly why I felt about making this blog post.

Back to the main track: why do we need assumptions?

So first of all, we need to have our mental models to make decisions, and almost of all of these models will need assumptions, because:

Let me give you an example of the third one:

Image courtesy of *Tumisu at*.

It's universally established that the planet is undergoing climate change, and that the temperature is increasing. Most (unfortunately not all) of us have internalized that, and have a mental image of what that means. However, the interpretation of the details of climate change and its effects vary per person. For instance, you may have assumptions about the expected:

Without these assumptions, we struggle to estimate the concrete effects of climate change. And in general, assumptions are the glue we use, and need, to make decisions.

What matters with assumptions?

And that's all fine, but we can't rest on our laurels, for two reasons:

One, because assumptions aren't as solid as facts, there is a big risk that we are wrong, or that we will be wrong in the future. The worse the justification of our assumption is, the more likely to happen.

Two, some assumptions are just minor figments of our mind (indeed, some may only appear in a dream), but we may use other assumptions to make important decisions, or to spread them to other people if we are firmly convinced and find them rather important.

To keep matters very simple at first, we therefore end up with this appraisal spectrum of assumptions:

The diagonal arrow represents the ideal arrangement for assumptions. We'd like the better-justified assumptions to be more important, and the ill-justified ones to be irrelevant. After all, the assumptions in the bottom right may end up as trivia, because they are unimportant yet well-justified. The ones in the top-left are the ones to watch for, as they are highly important but poorly justified. In particularly, these could create problems for yourself and your credibility, as well as to others and society at large.

But to make that call whether your assumptions are roughly on the diagonal arrow, and indeed map any assumption to this diagram, you'll first need to appraise the assumption :).

Now making a detailed appraisal scheme is beyond my expertise, and would make this blog post enormous in any case. But I am happy to provide a few (hopefully useful) general pointers.

0: What are your assumptions?

Are you really aware of what your own assumptions are? There are probably a near-infinite number, but how about you try to write down a couple (say, 5 to 20 or so) that you have on your mind now?

1: How important is your assumption?

In simulation-building we can quantify this relatively easily, namely by doing something called Sensitivity Analysis, where we estimate the effects of changing one or more assumptions on the outputs of our model.

Though it'll be hard to produce exact numbers in your own context, doing a “sensitivity analysis” on your own assumptions can be very helpful. In essence it's asking yourself what would change in your life, and around you, if you were to invert the assumption that you're appraising?

When thinking of what would/could change, you may want to keep in mind the following possibilities:

I think you can appreciate that an assumption will be more important if you tend to find more evidence that falls within the scope of the four points above :). And perhaps that allows you to rank your assumptions in importance?

2: How good is the justification of your assumption?

To put your assumption on the horizontal axis of the diagram above, it's important to appraise how well you can justify it. However, this can be a bit harder to assess.

There is a lot of literature about quantifying the level of justifications, and in this post I will scratch the surface of it at the very most. I thought for a whole how I could make something that is likely to be reasonably on target, and relatively easy to consult. So, I hereby present the Assumption Quality Checklist Infographic:

Using it is pretty simple. The more green items you find vs. red items (and the more important the green items are vs. the red), the further you can move your assumption to the right on the diagram above :). As for the Tie-breakers, simply treat a “Yes” answer as a Quality Booster and a “No” answer as a Quality Buster :).

3: Putting the assumption on the diagram

Once you've gone through those steps, you can put the assumption on the diagram. And you can do the same with all the others that you want to appraise.

What kind of patterns emerges for you? Do you get a diagonal line, or are many assumptions actually in the top left and bottom right corner?

If any of your assumptions end up far above the diagonal arrow, you may want to either reconsider their importance, or try to see if there's any way you can justify it better.

And if they end up well below the arrow... well, I guess you identified some trivia then ;).

Closing thoughts

There may well be material around online that covers this better than I did. But honestly, I had trouble finding it!

I am very curious to learn (you can reach me at @whydoitweet on Twitter) how easy you found it to map your assumptions to the appraisal spectrum. And did you get the patterns you expected? And did you find it useful?

For subscribers I provide a few interesting links related to all this below.


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