#3: Let your students test their abstractions.
The Google team has one hell of an abstraction on their hands. They’ve distilled the complicated process of driving a car and its infinite judgment calls, muscle twitches, and cursing into a finite set of variables. That set of variables is so finite, in fact, they say that a computer can compute it in real-time and drive a car by itself.
That just isn’t plausible. At various points, I’ll wager the Google team didn’t think their abstraction was plausible either.
I’ll put any sum of money on this: the team wanted to know if their abstraction was any good. They’d thrown away so much data for the sake of a manageable abstraction. Did they throw away too much? Could they have thrown away more? Is the abstraction just right? They could have turned to existing theory and models in artificial intelligence and said, “Well, the literature says the model should work.” But no one would have walked away from that conversation satisfied.
People like to test their abstractions. They want to see the driverless car drive.
- If we make the claim that the abstraction of a basketball shot is a parabola, that the ball goes in, students are going to want to test that claim.
- If we make the claim that the abstraction of Facebook’s user growth is a logistic curve, that it will reach one billion users in April 2013, we are going to be very interested in Facebook’s user count on April 1, 2013.
- If we make the claim that the App Store’s downloads grow linearly, that it will cross the twenty-five billion download mark in ten days, we’re going to want to know if we’re right.
The thing is, you and I are in on the joke. A lot of our abstractions are flimsy. If the basketball falls off the plane defined by the player and the hoop, the model falls apart. We abstract runners into particles moving at constant speed — no acceleration or deceleration. Try that abstraction with Playing Catch-Up. It falls apart. But it falls apart interestingly, and we win twice over. First, our students work on the abstractions we need them to work on. But we get a discussion about the limitations of those abstractions as a bonus. How much error should we tolerate? Are there ways we could improve the abstraction?
So for all these reasons and because there’s very little downside, give students the opportunity to test out and refine their abstractions.