**Editor's Note:** This delightful analogy on the Drake Equation is excerpted from Dr. Grinspoon's new book __Lonely Planets__, ISBN 0-06-018540-6, © 2003, Harper Collins Publishers, New York.

Say you are a single person going to a large dance party, and you would like to come away with a date for the following weekend. Arriving in front of the house, you can hear the music pumping and feel the bass rattling your gut. You are excited, but nervous as hell, so you decide to calm yourself with some math. Before going inside, you try to calculate your chances of getting lucky. You start by guessing the total number of people at the party. You notice that people are arriving at a rate of three per minute. We'll call this rate of arrival R. People are leaving at roughly the same rate, but you realize that you can estimate the number of people inside if you know how long they are staying. Let's call this length of stay L. The number of people inside will be roughly R times L. So, if people on average are staying for, say, one hundred minutes, there will be about three hundred inside.

But they are not all potential dates. After all, you have standards and preferences, and some may not be available. So you multiply the total number at the party by several factors, each expressing the probability that the average partygoer will meet one of your requirements. Each of these probability factors will have a value between zero and one. Zero means that nobody measures up to a particular requirement. One means that anyone will do. If half of them are okay, the probability is one-half, or 0.5, and so on.

For instance, you might want to rule out potential dates because they don't fit your sexual *p*reference. We will call this factor f_{p} (pronounced "f-sub-p") and assume that this is roughly 0.5, meaning that it rules out half of the people there. Then you are going to multiply that by the fraction that you find yourself *at*tracted to. If you are being picky, we'll say that f_{at}=0.1. In other words, one in ten, meet this criterion. Again, it cannot be higher than 1, even if you are drunk or desperate. Now, some people are not going to be *av*ailable because they are already hooked up and not interested in multiple partners. Let's say optimistically that a quarter of the people (or 25 percent) you are interested in are free. So f_{av}=0.25.

You also have to factor in your own behavior. Some are just so hot, you can't get up the *n*erve to talk to or dance with them. But all this math is making you feel pretty confident, so we'll say you can deal with approaching three-quarters of them: f_{n}=0.75. Then we have to multiply again by the fraction who turn out to actually be *i*nterested in you. Because you are fascinating and fun to dance with, and because you can talk knowingly and winningly of probability (chicks and cats dig that), no one can refuse you, so f_{i}=1. Assuming you have not forgotten any important factors, you can now estimate your chances of scoring at the party. The total number of likely candidates, N, will follow the formula

N = R x f_{p}x f_{at}x f_{av}x f_{n}x f_{i}x L

This is the "date equation." Given the numbers we've estimated, N = 3 x .5 x .1 x .25 x .75 x 1 x 100. So, N = 2.8. 2.8 people at the party will go out with you next weekend. Jackpot! Although, this is just a rough estimate, since we had to estimate the various factors, your best guesses lead to an N greater than one, so you figure your chances for success are pretty good. Thus emboldened, you check your hair one last time and enter the party....

The Drake Equation is parallel to the date equation, but the party we wish to crash is much larger, more frightening, and more enticing.

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