In the field of artificial intelligence, there’s a fairly simple (compared to other AI techniques) method called A* (pronounced A-star) that’s used to solve a lot of problems. It can be used in any situation where you’d want to find the shortest path from where you are now to some goal. This is a very common problem, and shows up everywhere from calculating the best airline routes to video game NPC AI. It’s useful in any kind of route planning, whether it’s a route through space or through some other set of sequential decisions.
A* works by comparing the state of the world now with the state of the world if a certain decision were made. Since it has to choose which decision to make before it knows what the result of a decision is, it uses a heuristic to approximate the result of different decisions and then chooses between those decisions. The heuristic approximates the distance to the goal after each given decision. There’s a bit more to it than that, but you can read about it on Wikipedia or something if you’re interested. What I find really intriguing about A* is the importance of the heuristic.
It turns out that A* doesn’t always work well. If the heuristic isn’t chosen well, then A* will do very poorly at finding a way to the goal. It may find a route that is much worse than what’s possible. Whatever heuristic is chosen needs to never over-estimate the true distance to the goal. AI researchers will spend a lot of time thinking up good heuristics for their application, often much more time than they spend implementing the path-finding A* algorithm.
What AI researchers realize that a lot of other people don’t is that the way you estimate the distance to your goal has a huge impact on how fast you reach it. I’ve been noticing recently that the standard heuristic is very poor in a lot of areas in life where people are trying to achieve something. I first noticed that in the field of education when I was having trouble learning about AI in the first place.
When I took my first few algorithms and AI classes, I got very high grades. I’m good at school and test-taking, so I got 4.0s in most of the classes that I took. Unfortunately, I was often unable to apply the information or implement the algorithms after the class ended. The heuristic I’d been using to see if I learned, my grade, was often over-estimating my progress. I was eventually able to truly learn and implement the different algorithms that were important, but I had to completely ignore grades while doing so. Instead, I just focused on small projects that included the algorithms. These small projects (what homework aspires but often fails to be) were a truer measure of progress than grades ever were.
Since noticing that grades were such a poor metric for education goals, I’ve been on the lookout for other places that this appears in people’s daily lives. Here’s what I’ve noticed so far. Can you think of any that I’ve missed?
- Education: The default heuristic is grades, but number of completed projects is probably a better metric.
- Life-success: It seems like a lot of people use income to measure this, but that’s a pretty limited metric and often seems to lead people to unhappiness. I would argue that autonomy is probably a better metric here, but I’m not sure how to measure autonomy.
- Weight loss: I know a lot of people who have been working on losing weight and being healthier in general, but I think that weight is probably not a good heuristic for this. Body Mass Index does a lot better, but it’s a bit harder for the average person to measure accurately.
- Relationship health: People tend to measure the health of a relationship by how long it’s lasted, but this is a very inaccurate guide at best. Lots of long relationships are pretty bad, but for one reason or another, the people don’t break up. Other relationships are short, but end up being very positive experiences for everyone involved. It seems like the happiness of the partners is a much better heuristic, but it’s also harder to measure.
In each of these cases, it’s pretty hard to measure something that’s actually a good heuristic so people choose a less useful heuristic. Using the worse heuristic, people can easily make measurements, but those measurements aren’t actually very useful. Even though good heuristics are often harder to measure, they offer much better results.