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doesn’t have anything to do with the technique itself. Dalton just called the technique a regression because the first things that he happened to estimate displayed this tendency—what Galton called “regression toward mediocrity”—and what we now call “regression toward the mean.”
    The regression literally produces an equation that best fits the data. Even though the regression equation is estimated using historical data, the equation can be used to predict what will happen in the future. Dalton’s first equation predicted seed and child size as a function of their progenitors’ size. Orley Ashenfelter’s wine equation predicted how temperature and rain would impact wine quality.
    eHarmony produced a formula to predict preference. Unlike the Netflix or Amazon preference engines, the eHarmony regression is trying to match compatible people by using personality and character traits that people may not even know they have or be able to articulate. Indeed, eHarmony might match you with someone who you might never have imagined that you could like. This is the wisdom of crowds that goes beyond the conscious choices of individual members to see what works at unconscious, hidden levels.
    eHarmony is not alone in trying to use data-driven matching. Perfectmatch matches users based on a modified version of the Myers-Briggs personality test. In the 1940s, Isabel Briggs Myers and her mother Katharine Briggs developed a test based on psychiatrist Carl Jung’s theory of personality types. The Myers-Briggs test classifies people into sixteen different basic types. Perfectmatch uses this M-B classification to pair people who have personalities that historically have the highest probability of forming lasting relationships.
    Not to be outdone, True.com collects data from its clients on ninety-nine relationship factors and feeds the results into a regression formula to calculate the compatibility index score between any two members. In essence, True.com will tell you the likelihood you will get along with anyone else.
    While all three services crunch numbers to make their compatibility predictions, their results are markedly different. eHarmony believes in finding people who are a lot like you. “What our research kept saying,” Warren has observed, “is [to] find somebody whose intelligence is a lot like yours, whose ambition is a lot like yours, whose energy is a lot like yours, whose spirituality is a lot like yours, whose curiosity is a lot like yours. It was a similarity model.”
    Perfectmatch and True.com in contrast look for complementary personalities. “We all know, not just in our heart of hearts, but in our experience, that sometimes we’re attracted [to], indeed get along better with, somebody different from us,” says Pepper Schwartz, the empiricist behind Perfectmatch. “So the nice thing about the Myers-Briggs was it’s not just characteristics, but how they fit together.”
    This disagreement over results isn’t the way data-driven decision making is supposed to work. The data should be able to adjudicate whether similar or complementary people make better matches. It’s hard to tell who’s right, because the industry keeps its analysis and the data on which the analysis is based a tightly held secret. Unlike the data from a bunch of my studies (on taxicab tipping, affirmative action, and concealed handguns) that anyone can freely download from the Internet, the data behind the matching rules at the Internet dating services are proprietary.
    Mark Thompson, who developed Yahoo! Personals, says it’s impractical to apply social science standards to the market. “The peer-review system is not going to apply here,” Thompson says. “We had two months to develop the system for Yahoo! We literally worked around the clock. We did studies on 50,000 people.”
    The matching sites, meanwhile, are starting to compete on