Indecisive? Try Hunch.

The advent of the internet is called the “information age.” We use the web to help us make decisions by easily finding information about things that would have been out of our reach a generation ago. This is obvious with research material – we are no longer limited to our local libraries’ catalogues, we can find information from books and journals around the globe.

Web 2.0 adds to published information, helping us make decisions based on others’ ratings and comments. If ten people dislike a new restaurant, I probably will too, so I can decide not to go there. If a bridesmaid’s dress gets a 4 ½ star rating from 244 amazon users, but the top 5 ratings state the dress is larger than the size chart suggests, I will buy the dress a size small. Their experience leads to my decision.

But – what about tough decisions, like whether to buy a Mac or a PC if you are looking for a new laptop? There are too many opinions on this one to sort through the user reviews, and there is too much information published by Apple, IBM, CNET, and other experts to determine which is really better for me.

There is now an online tool that helps people make these decisions - Hunch. Hunch quizzes users on their likes and dislikes, then helps them figure how to handle an unknown. Their algorithm is based on other users’ similar responses.

For instance, Hunch has determined that the choice between a Mac and a PC can be based on whether a person likes to dance or not. Dancers prefer the Mac.
http://www.techcrunch.com/2009/04/17/people-who-switch-to-macs-like-to-dance-and-other-strange-hunches/

Only problem so far, I haven’t been able to try it - I’m still waiting for the invitation to come through in my email account.

More good information about Hunch from the Hunch fact sheet http://www.hunch.com/fact-sheet/:

“What problem does Hunch solve?
Our long-term goal is for a user to be able to come to Hunch with any decision she is pondering, and after answering a handful of questions, get as good a decision as if she had interviewed a group of knowledgeable people or done hours of careful research online.
“Eventually, when Hunch gets good enough, we hope users will trust it to make an informed decision without having to turn to lots of external time-consuming sources of information.”

“Hunch uses machine learning to get smarter in two ways:
“User contributions train Hunch to be smarter overall. Contributions can take many forms, from correcting a fact that Hunch got wrong, to suggesting new decision topics to feature, follow-up questions to ask or decision results to propose.
“The more Hunch learns about each individual user's personality and preferences, the better Hunch can customize decision results for that user. It's like a friend getting to know someone's taste and preferences over time, so they can provide sound and trusted advice.”