Will Hunch Help You Make Decisions? Signs Point to Yes
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the room for doubt left by my “Yes” answers to the seventh and eighth questions, which would militate against buying one of the e-book reading devices. (Or maybe Hunch knows somehow that I’ve been trying desperately to come up with reasons not to spend $350 on a Kindle.)
The Kindle question was constructed with a yes/no answer, but most decision trees on Hunch can lead to a variety of results. And if the site is missing an important result, you’re free to add it, and to specify which questions in the tree should lead to that result. (I couldn’t help adding my own name to the list of results for the question “Which technology writer would I like?”)
For certain questions, Hunch can hit surprisingly close to the target. When I played through the “Where should I go on vacation?” topic, Hunch guided me straight to the answer that was already at the top of my personal list: Florence. I wasn’t even trying to steer the answers toward Italy, at least not consciously. When I played through the topic “What’s the best dog breed for me?” I ended up, reassuringly, with Australian Shepherd—which is, of course, the breed I already own. I was less happy with Hunch’s answer to “Which superhero am I?”: Watchmen‘s Dr. Manhattan, who, while certainly a hunk, is too aloof for my taste.
But at this early stage, with so few people using the site, it would be hard to portray Hunch as a place to turn for consistently trustworthy recommendations. There just hasn’t been enough time for users to fill out the branches of the trees. The topic “What’s a good spa in Boston?” for example, has only four possible outcomes—which is embarrassingly incomplete when you consider that local review site Yelp lists more than 110 day spas around Boston. And some of the questions users have programmed into Hunch are so predictable and simplistic that they’re essentially rephrasings of common knowledge. For the topic “Where should I live in the Bay Area?” the first question in the tree is “Would you rather have: Great weather, with a subdued, suburban lifestyle, or iffy weather, but with an exciting, urban lifestyle?” To me, that’s precisely the same as asking “Would you rather live in Palo Alto or San Francisco?”
Still, the more people who use Hunch, the smarter it will get. The process may be slow—I suspect that building a truly useful decision tree is harder than it looks, and that it will take a while for Hunch to build up a community of volunteers with the requisite thoughtfulness and expertise. But it’s happened before. Just look at Wikipedia.
And Hunch’s general model feels new and exciting. My own prediction is that millions of users will be drawn to the site, which turns the potentially stressful process of reaching a decision into a fun, interactive quiz. The decision-tree format may not respect the subtlety and grayness of the real world; Hunch’s style guide insists that the answers to each question in a decision tree be mutually exclusive, which, in real life, they rarely are. But the trees do offer a convenient way to navigate through a mess of possibilities, and perhaps to reach unexpected and thought-provoking answers. And hey, it’s got to work better than a Magic 8-Ball.
Hunch was testing its system privately until March 27, when it took the lid off and started letting in a few outsiders. I got an invitation to open a beta account less than a week after requesting one, and from what I’m hearing, Hunch is responding to account requests even faster now. But if you want to try Hunch and they don’t send you an invitation right away, I’ve got a handful to give out. Be one of the first five people to write in to send your e-mail to firstname.lastname@example.org, and I’ll send you one.
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