Probability and Nanaya’s Predictive Power

When you get your Nanaya Romance report, one of the first things you’ll see is a chart predicting your odds of finding love over the next seven years. For everyone, this is a line that starts at 0% as we calculate odds based on the moment you take the test. As you meet people in time, your odds increase.

But what do these odds even mean? For instance, if you see your odds of finding a match are 50% three years from now, what does that actually mean for finding love? Is it actually a coin flip? How can we even test that this is accurate when the future hasn’t happened?

To better answer that question, let’s consider the odds of roulette. For those who have seen the move Run Lola Run, it’s hard to forget the scene at the casino. Lola, the movie’s protagonist, needs to raise 100,000 Deutsche Marks to save her boyfriend. She goes into a casino with hardly enough cash to enter and places everything on Black 20…

She wins, but why stop? She doubles down on Black 20…and wins again.

What are the odds? On a standard European roulette wheel there are 37 numbers, from 0 to 36. So the odds of a bet are 1/37. For a successful double-or-nothing wager those odds are (1/37)2. I could have told her that as she was walking in that it’s all but impossible to save her boyfriend in a casino. After all, casinos make money because are odds are always on their side.

It’s easy to make that argument. I can look at a roulette table and know how many choices there are. For any spin of the wheel, there are 37 possible outcomes and only one number will be picked. If you play an infinite number of games, you start to see what the odds 1/37 actually mean in practical terms. To some extent, the Nanaya algorithm works like this. We look at our database to determine the odds of the person you run into at work, through your friends, around town, etc. being the sort of person you would be compatible with and with which there may be mutual attraction. So these odds can be interpreted like the odds in roulette – if you play the game many times (i.e. meet a lot of people), you’ll start to see the effect of those odds.

The Nanaya algorithm predicts the number of people you’ll actually meet and how often you’ll meet them to create a probability distribution based on all those interactions and single-event probabilities. This tells us in time what the net odds are of finding love. But interpreting that probability is a lot trickier than a game of roulette.

Imagine Lola placing her first bet. She knows the odds are 1/37 – but does that practically matter when she can either win or lose. The odds can be 50% or even 99.9% and she can still lose. If you’re only playing one game – how you interpret odds becomes rather subjective.

Like a single game of roulette, we only live one life. So when Nanaya says your odds of finding love are, for instance, 75% four years from now – what does that mean? How can it even be confirmed?

One of the most famous examples of this is how Nate Silver applied statistical simulations to the presidential election of 2008. Even though there can only be one presidential election, he used poll data and their margins of error to simulate 10,000 virtual elections to see what all possible outcomes would look like. He took the average of all those possible outcomes to assess some prediction of the winner. So a result may look like Obama winning 5,800 of those virtual elections, thus giving Obama a 58% chance of winning the election.

“The most important thing to remember in reviewing your Nanaya results is that it’s not about the number itself, it’s about what you do with the number.”

But there’s a lot of subjectivity in this calculation and how it’s interpreted. This is a fundamental problem in the philosophy of probability. What odds mean to practical, subjective human beings has always been contested. After all there can only be one election. The paper The Probabilities of Unique Events by Khemlani, et al. has a great discussion of interpretation of probabilities and how subjective reasoning and belief plays into prediction and decision-making. For an overview of the entire subject, Wikipedia provides a glimpse into the rabbit hole of the philosophy of probabilities.

The way I personally perceive our Nanaya predicts is as a propensity. The odds indicate a degree of likelihood that an event will happen. Otherwise, living only a single life, it’s impossible to perceive the odds meaningfully. It gives us a basis for suggestions as how to improve odds and perform other calculations, so long as probabilities are treated consistently. Nate Silver’s guide to how he predicts senate campaigns is rather similar to how we’ve gone about what we’re doing. While for any person the odds are immediately unverifiable – the model is well-behaved, probabilistically determined*, and incredibly responsive to inputs. Because of this, we expect that no two Nanaya reports will be identical. Our model for predicting the odds of finding love was validated by people’s past decisions to leave and enter relationships.

To validate our modeling, there’ll be an extra question in our “Final Questions” portion of the questionnaire that asks if we can follow up for research. For those that select yes, we may send an email in the future to see if you’ve entered a long-term relationship by clicking “yes” or “no.” It’s by checking in on the many thousands lives of users that be certain Nanaya is calculating probabilities the way it should.

Love may come easily, but predicting love and interpreting probabilities is a tricky subject. The most important thing to remember in reviewing your Nanaya results is that it’s not about the number itself, it’s about what you do with the number. Our goal is that Nanaya helps you reflect on what makes you truly happy and your life goals – and that’s a whole lot more than a single number.

Odds & Ends

*The way we currently model sociological interactions is wholly deterministic, based on the inputs from the questionnaire. We have ideas for how to modify this in the future, but given the complexity of human interactions it’s unclear if probabilistically-derived social modeling using our data set will yield better results than our deterministic models.

As always, direct all questions, media inquiries, mathematical and metaphysical objections, and invectives to Don’t worry, we’ll always love you..