The Process of Modeling: Why Model? [Systems thinking & modelling series]

This is part 45 of a series of articles featuring the book Beyond Connecting the Dots, Modeling for Meaningful Results.

The first step to building a model is answering the simple question: Why am I building this model?

This question seems obvious, but in practice it is often hard to answer. Let’s try answering it for our hamster population model: Why are we building this model? The truth is that so far we do not have a real understanding of this.

Oftentimes, the lack of focus begins with the friend/client/colleague who commissioned the model. Laypeople frequently do not have a strong understanding of what modeling is, including what modeling can accomplish and what it cannot. Instead, your friend might have a simplistic view of a model, almost as if it were a magic wand. He feels he just needs a model and then, abracadabra, it will solve his problem. His thought process on what to do with a model might be as bareboned as:

1. Build Model.
2. …
3. Hamsters Saved.

Of course this is not the case. You build a model with a specific purpose in mind, otherwise it will most likely accomplish nothing. Worse yet, when it comes to the hamsters, it will be too little too late. Your first action should be to work with your friend to make sure you have filled in the “…” step. The best way to do this is generally working backwards from the final step rather than working forwards from the first one. For us that would be to first figure out how the hamster population is to be protected.

Paradoxically, in order to answer the question of why we are building a model, we are going to need to ask many questions of our own. Why should we protect the hamsters? What risks do the hamsters face? What do the hamsters need to be protected from? What avenues to obtaining these protections are there? What techniques to protecting the hamsters are most effective? Cheapest? Most expedient? And so on. We need to obtain a good understanding of the root cause of the problem your friend wants to tackle with this model and force out the concrete steps to getting there.

After discussing this with your friend let’s say the two of you come to the conclusion that you will need two things in order to reliably protect the hamster population. First, government regulatory agencies need to pass (stronger) rules protecting the hamster habitat. Second, non-governmental organizations (NGO’s) need to provide funds for hamster conservation and protection efforts.

Using this, we can expand our plan with more details:

1. Build Model.
2. …
3. Agencies enact rules to reliably protect hamsters. NGO’s provide money for conservation efforts.
4. Hamsters Saved.

This focuses things for us. Rather than “Building a model to save the hamsters” (which is too vague and completely unactionable, leading to our quandary about what to model), we are building a model designed to persuade governmental regulators and NGO’s that they should devote resources to protecting the hamsters.

So how do we do that? Let’s simplify the complex issue into two specific goals for our model:

• Show that given the status quo (business as usual) the hamster population will go extinct.
• Show that alternatives to the status quo exist (which require regulatory action or investments) that enable the hamster population not only to survive, but also to thrive.

If our model demonstrates both these things it could be a highly persuasive tool to shape decisions and policies. By building a model that does these two things¹ we will have given our friend a powerful tool to push for regulatory action and financial support.

When building your own models you’ll want to go through a similar thought process to get at the core goal or question the model should address. Going into a modeling project with the attitude “First we’ll build a great model, then we’ll figure out how to apply it” is a prescription for failure. Of course, as you go through the process you might discover insights you never expected or you might determine that your original hypothesis was wrong. Such discovery is always a great outcome, but you can never count on it happening in the course of building your model. It’s best to start very focused in your modeling efforts and treat any discoveries or broadening of scope later on as a lucky bonus.

Next edition: The Process of Modeling: Model Project Management.

Article sources: Beyond Connecting the Dots, Insight Maker. Reproduced by permission.

Header image source: Beyond Connecting the Dots.

Notes: