On decision-making (part 6): “One down, one to go”

This is part of a series of posts. If you’re coming in fresh, you probably want to start with this one. To reiterate my previous disclaimer, this series talks about my company’s product, but hopefully you’ll agree that it’s genuinely relevant to anyone who’s interested in judgment and decision-making.

So in the last post, I talked about why conventional decision-assisting tools (such as product selection tools on shopping sites) actually have little to no idea of what your decision-making criteria actually are, which is a key reason why they often return zero results, or 58 "matches", most of which aren’t very good matches.

The first challenge we had to overcome when developing ChoiceBot was to find a way for decision makers (such as consumers), to enter their decision-making criteria for features like brand, price, weight, etc accurately..

After many, many iterations, we came up with this:

ChoiceBot sliders (price feature)

The cool thing about this interface isn’t the sliders. Yes I know, we didn’t invent sliders. What we did come up with, however, was a way for you as a consumer to communicate any feeling you could have about any feature accurately. In the screenshot above, the sample criteria from the previous post are being communicated with 100% accuracy. In case you didn’t memorize it, the sample criteria from the last post were:

“I’d expect to pay about $300 for a digital camera that meets my needs. I might go to $400 if the other features are really awesome, but $500 is out of the question. $200 would be a good deal (i.e., I might be willing to put up with some minor weaknesses at that price), and $100 would be great.”

This interface seems to work pretty well, because we have yet to come across a set of criteria that a decision-maker could express in natural language to another human being, and that couldn’t be communicated accurately using it. (While it may look simple/obvious, it’s apparently the first and only criteria-entry interface that passes the natural language test -patent pending, baby).

Once we had solved the problem of enabling decision-makers to actually explain their decision-making criteria to a software application, we had to figure out what the software application should do with those criteria. Developing this software involved emulating how people combine information in their head when deciding which thing they like the best from an array of things, and is the subject of the next post in this series, entitled…

"How I reverse-engineered my brain"

By Nick Desbarats