Business models often elicit skepticism. “What is behind the model?” someone asks. Behind their polite question you can hear some less polite variants, questions they are thinking but do not say: “Why should I trust the results?” “How do I know you haven’t cooked this model for your own benefit?” Even: “I will not be fooled by your scam.”

Model credibility is often a problem. We are successful with our business models only to the extent that other people trust those models. And frankly their concerns are often warranted. As modelers, we know that all models are wrong. Some models are wrong in ways that undermine their conclusions. And some models are indeed cooked—not ours of course, but those created by less scrupulous modelers.

Model credibility is a fundamental problem with all kinds of modeling, not just business models. When we create a model we understand the details. The consumers of models do not understand the details. There is a fundamental tension between our deep knowledge and their lack of it, a tension that plays out in their skepticism.

This is similar to what happens when I drive a car for three years and then sell it to someone else. I know the defects. I know whether I crashed it on an icy road last January. I know whether I forgot to change the oil, and how aggressively I drive when listening to Big D and the Kids Table. My would-be buyer knows none of this. He reacts with skepticism.

To economists the used car sale is an example of information asymmetry. The seller has more information about the product than the buyer, and better information as well. The information is asymmetric.

Business models suffer from information asymmetry just as used cars do. Those of us who sell business models know more about the models we build. Our buyers know less. The credibility problem is inherent in this asymmetry.

What can we do to gain credibility? Some people suggest model validation (or verification). (We describe the validation and verification of business models in our book.) I think these people are mistaken: validation does not help. Validation and verification are techniques to make a model more accurate, a better reflection of reality. But the credibility problem is not about the model, it is about how the model is perceived. A skeptic can be just as skeptical about a more accurate model as about a less accurate one.

So what can we do? In practice, we use two approaches. First we engage the model consumers in the modeling process. We work with them in model-based workshops to create the model. In essence the model consumers become modelers, albeit modelers without the technical modeling skills. People who help us create models are never skeptical of the results.

Of course there are limits to engaging the model consumers. The workshops for creating the model rarely include more than nine or ten people. Everyone else is outside the room. And often when the model is created, we do not even know the ultimate consumers. Their experience with our model is later, only after the model is finished.

The second approach to creating credibility is to make the model transparent. Everything is shown. Everything is made clear and easy to understand. A model consumer who is sufficiently motivated should be able to open up the model and understand everything.

Making a transparent model is hard, harder than making an opaque one. Models often include messy details. These details must be cleaned up so they can be shown. Models often have complexities. These complexities must either be simplified, or at least explained in a simple way.

Making a transparent model is indeed hard, but it is worth the trouble. When someone is skeptical we show him the model and invite him to investigate.