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In another discussion group I'm on, someone asked the question, "What's the
future value of 'pay it forward'?".  In case you're not familiar, "pay it
forward" is the idea, made popular by the movie of the same name, of not
"returning" a favor, but rather "pay it forward" by doing three big favors
for three other people. When they ask how they can pay you back, you say,
"You can't. Pay it forward."

Even if the trend is dampened with some kind of attrition rate, so long as
the attrition rate is less than 66% (i.e., at least 1 out of 3 people
continue the meme), it will continue to grow, exponentially.  Given a finite
number of people in the world, plus the nature of networks, then it's
obvious that sooner or later, you're going to get more favors coming back to
you then you did in the first place.

That got me to thinking. you could actually model this:

.         Assume a network of n people.

.         Assume some kind of attrition rate (not everyone pays it forward,
or at least not three times).

.         You don't pay it forward to the person who paid it forward to you,
but once you get to 2, 3, 4 degrees of separation and higher, there's a
certain possibility of paying it forward to someone who's already been
"tagged" at least once, i.e., that person sees a "return" on what they paid
forward.

.         As more and more people get tagged, there's an increasing
probability of tagging someone who's already been tagged.

Frankly, I don't have the knowledge of how to model this correctly, but I'd
love to work on it with someone who does - I've got some ideas about the
factors that would need to be considered.  For someone who knows what
they're doing, this isn't a very difficult model.

I've been Googling this and can't find anything useful.  If anyone's
interested in working with me on this, or if you know of any existing or

Scott Allen
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