Soft numbers

There is an interesting tendency nowadays, especially among economists, to present things in exact figures. Like they where true.

In particular this is, surprisingly enough, almost always the case when it comes to predictions. Future estimations. How can a future estimation ever be exact?

You often hear economists state things like “We expect a growth of 11% during next 6 months”.

How can he state something like that? What he has in fact done is taking some highly unsure, often estimated, figures. Applied them to one or several economic and/or mathematical models of his choice(!). Finally having selected(!) the most suitable output from them.

Now, how can he claim the outcome from that to be true?! It might be a fair estimate at best, but regarding the exact numbers the only things we can be sure about is that it's definitely not true. In fact we can be infinitely sure we will not hit that exact number.

Same thing comes down to how people make their filter selections on most flight-search sites. The figures they input are preferred figures not exact ones – in the same way the economist is presenting an estimate not an exact prediction. Still, most systems interpret the user input as if it was the exact wish of the user.

If a user for example set a filter to “leave after 08:00”, that's in most cases because he would prefer to leave after that time, rarely an absolute need. If I where to offer him a 200€ cheaper flight at 07:48 – he's very likely to take it.

In OpenJet we are trying to grasp this into the design, and our current approach on it is not to hide the results that most sites would have filtered out, but rather highlight the ones currently within the preferences of the user.

This makes it possible for the user to at all time have an idea of what has been “filtered out” from his listings, helping him to decide whenever it's worth to step up that one hour earlier or not.