During the pandemic, I started doing more jigsaw puzzles. Not real puzzles mind you—I found a jigsaw simulator on Steam that was fairly authentic to the real experience. And since I was doing jigsaw puzzles through the medium of video games, I couldn’t help but think about them in the context of puzzle video games. I realized, jigsaw puzzles are kind of weird! In your typical puzzle video game, the ideal is to have a set of levels, each of which require some crucial insight. In contrast, a jigsaw puzzle is more like a large task that you chip away at.

One way of thinking about this is through the lens of computational complexity. Take Sokoban, the classic block pushing puzzle upon which many puzzle video games are founded. In general, a Sokoban puzzle of size N requires exp(N) time to solve, in the worst case. However, the typical Sokoban puzzle does not present the worst case, it presents a curated selection of puzzles that can be solved more quickly. This gives the solver an opportunity to feel clever, rather than just performing a computation.

Jigsaw puzzles, on the other hand, are about performing a computation. And, if you wish to do a large jigsaw puzzle in a reasonable amount of time, you look for ways to perform that computation efficiently. This raises the question: what is the computational complexity of a jigsaw puzzle?

According to the open access paper, “No easy puzzles: Hardness results for jigsaw puzzles” by Michael Brand, realistic jigsaw puzzles require Θ(N^{2}) steps both in the worst case and on average. On the other hand, this is not born out by my own statistics, which seem to fit a straight line.