a roundup of operations research models for finding love

Happy Valentine’s Day from Punk Rock OR! I decided to do a little sleuthing about operations research and love. I was pleasantly surprised to find several mathematical models for finding love (optimally or not).

I blogged about optimally allocating marriages yesterday, but it’s still worth reading another blog post on it. Anna Nagurney blogged about a paper in EJOR about a linear assignment model that optimally matches couples in Switzerland. The authors identified weights for the pairings using output from logistic regression models that predict marital success using socioeconomic factors.

Tallys Yunes developed an transportation model for Excel Solver that determines how someone can optimally send flowers to a nine love interests. It’s cute, but I would not recommend actually using operations research to juggle so many love interests!

Mike Trick beautifully blogged about using–and not using–the Secretary Problem to optimally find love.

Thaddeus Sim blogged about consolidating flower deliveries to optimally deliver flowers on Valentine’s Day.

The following TED video explains OKCupid’s matching algorithm for predicting who should go on a date.

The prologue for This American Life episode #486 “Valentine’s Day 2013” features several physicists discussing how they computed the mathematical probability that they would find girlfriends using the Drake equation. If you’re not familiar with This American Life, it is non-fiction.

Speaking of the Drake equation, Brain Pickings has a link to a PBS video (below) about computing the probability of ideal love matches for you using the Drake equation. It’s apparently 871 (give or take).

5 responses to “a roundup of operations research models for finding love

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