The Flaws of Fermi Estimates

Why don’t we use more Fermi estimates?[1] Many of us want to become more rational. We have lots of numbers we can think of and important variables to consider. There are a few reasons.

Fermi calculations get really messy. After a few variables introduced, they could quickly become difficult to imagine and outline a problem. Many people, especially those who were not used to writing academic papers, do not practice the skills of formalizing inputs and outputs. It can be tedious for those who do.

Fermi models typically do not include estimates of certainty. Certainty propagates. It creates bottlenecks. As a Fermi model grows, specific uncertain assumptions could underscore the result. Certainty estimates are typically not measured, and when they are they require formalization and significant calculation.

Fermi calculations are not fun to share. Most of them are pretty simple; they just involve multiplication and addition and 3–5 variables. However, in order to write them one must formalize them as few lines of math him or few long paragraphs which really should be math.

Graphical Assumption Modeling

We propose the use of simple graphical models in order to represent estimates and Fermi models. We think these have the capacity to solve the issues mentioned above and make complex estimations more simple, more sharable, and more calculable. A formal and rigorous graphical model could not only improve on existing Fermi calculations, but it could also extend them to functions they have not yet been used for.

Multiplication

Let’s say we are trying to estimate the number of smiles per day in a park. A first attempt at this may be to guess the number of people in the park and to estimate the number of smiles on average per person in the park.

This is easy to calculate directly. 100 People x 10 smiles/(day * person) = 1000 smiles/day.

As a model, we can represent the variables as lines and the function as a box in between them. This fits nicely with similar diagramming standards. The function of multiplication acts as an object with inputs and outputs.

Independent variables, or user selected variables, are shown in black, and dependent variables are shown in blue.

We can condense this diagram by moving the number of smiles per day per person into the multiplication block.

Say we wanted to find the total smiles per year in the park. We can simply extend ...

Dan Gustafsson

Dan Gustafsson