What should I be for Halloween? Do I need a Porsche? Should I dump that loser? Is Phoenix a good place to retire? Whom should I vote for? What toe ring should I buy?
In 10 questions or less, Hunch will offer you a great solution to your problem, concern or dilemma, on hundreds of topics. Hunch’s answers are based on the collective knowledge of the entire Hunch community, narrowed down to people like you, or just enough like you that you might be mistaken for each other in a dark room. Hunch is designed so that every time it’s used, it learns something new. That means Hunch’s hunches are always getting better. It’s absolutely free and easy too so why you are not trying it? You can create your account here.
The theory behind using collective knowledge for decision making
Take, for example, expertise about colleges or cars. In a random, large group of people, most probably know something about a few examples (say, the college someone attended or the car they currently drive) but are not experts on the topic as a whole (as a college guidance counselor or auto executive might be). If you were able to collect and organize all the various bits of individual knowledge that the large group possesses, youâ€™d have a pretty complete picture of the topic overall.
The theory in practice: The Hunch algorithm
The algorithm is always asking itself, “What can I ask you next which will lead to the best possible result for this decision?” The choice of which questions to ask and when to ask them will vary based on what you’ve already been asked (and how you’ve answered) so far, the same way that a human expert would adjust a line of questioning based on your responses. The idea is that if someone says they’re a vegetarian, you don’t want to then immediately ask them how they want their steak cooked.
User contributions are mostly what makes Hunch smart
As you answer questions, Hunch can narrow down your possible decision outcomes because each outcome can be “trained” to correspond with each question’s answers. Any logged in user can set initial training or correct existing training, in addition to proposing new topics, questions to ask, and decision outcomes. This is how Hunch is truly a collection of common knowledge. So whether you happen to know a great question that would lead someone to a Sancerre vs. a Pinot Grigio, or you’d like to clarify that “Whatever Happened to Baby Jane” is probably more of a “campy” than a “cult” movie, Hunch absorbs your input and uses it to provide smarter decision results for the next user.
Hunch also uses machine learning based on statistical inferences
Besides users explicitly training and contributing to Hunch, there’s a second way that Hunch learns, especially for what we call ‘Teach Hunch About You’ questions which have more to do with you as a person than with your preferences for a specific topic’s objective decision criteria. When a user clicks “Yes” or “No” to indicate whether or not they like a decision result, Hunch incrementally strengthens or weakens the mathematical correlation between that result and any ‘Teach Hunch About You’ questions that have been answered so far. So over time, Hunch might learn that people living in cities tend to prefer diet sodas, or that SCUBA divers tend to like bicycles with lots of gears.
Hunch is designed to soak up collective knowledge and then organize it in a useful way to help you make smart decisions. Hunch proposes custom decision results for you that it wouldn’t necessarily give to somebody else. But at its core, Hunch’s decision making algorithm is just a mathematical framework. It’s the users of Hunch who give the algorithm proper training and personality by contributing to it and making it clever, funny, and nuanced…. but most of all very useful in helping everyone to make smart, efficient decisions.