Watching his teams put together the faulty puzzles, he noticed a number of interesting traits. One obvious one is that the larger the puzzle, the more time it takes to complete. "As the working space expands the computational effort increases," he said. Ambiguity also increases computational complexity. Puzzle pieces that have the same colors and shapes were harder to fit together than those with distinct details.

"Excessive ambiguity really drives up the computational cost," Jonas said.

Jonas was also impressed with how little information someone needed to get an idea of the image that the puzzle held. After assembling only four pieces, one of his teams was able to guess that its puzzle depicted a Las Vegas vista. "That is not a lot of fidelity to figure that out," he said. Having only about 50 percent of the puzzle pieces fitted together provided enough detail to show the outline of the entire puzzle image. This is good news for organizations unable to capture all the data they are studying -- even a statistical sampling might be enough to provide the big picture, so to speak.

"When you have less than half the observation space, you can make a fairly good claim about what you are seeing," Jonas said.

Also, studying how his teams finish the puzzles gave Jonas a new appreciation in batch processing, he said.