Super Mario Is Mathier Than You Think
Studying the epic journey of the iconic jumping plumber can lead to new insights in theoretical computer science — and may help researchers understand the tractability of problems in other areas.
By Veronika Moroz ’28
MIT Alumni News: Feature
June 23, 2026
For decades, video games have been dismissed as mere entertainment, but a growing body of research suggests that the beloved plumber Mario may be a surprisingly rich subject for mathematical inquiry. In 2026, a team of computer scientists at MIT demonstrated that analyzing the classic platformer can yield new tools for understanding computational complexity — specifically, how certain problems can be solved efficiently (in polynomial time) or may be fundamentally intractable (NP-hard).
At its core, Super Mario is a game about movement, obstacles, and resource management. The player must navigate a series of levels, jumping over gaps, avoiding enemies, and collecting coins, all while racing against a timer. This deceptively simple premise, the researchers argue, encodes a number of deep mathematical structures. By mapping the game’s levels onto formal logic and graph theory, they have shown that determining whether a given configuration is solvable under specific constraints can be reduced to classic problems in constraint satisfaction and path planning.
One of the key findings, published in the Journal of the ACM, is that the generalized version of Super Mario — where levels can be arbitrarily large and include all standard game mechanics — is PSPACE-complete. This means it is at least as hard as any problem that can be solved using a polynomial amount of memory. The result places Mario alongside other classic games like Tetris and Sokoban in the pantheon of computationally difficult pastimes.
“People often think of video games as simple rule systems,” says lead researcher Dr. Elena Martinez, a professor of computer science at MIT. “But when you start to ask questions like ‘Can the player finish this level in a given number of moves?’ or ‘Is it possible to collect all coins before time runs out?’ you quickly realize you’re dealing with problems that have profound implications for algorithm design.”
The research also has practical applications. By studying how players intuitively solve these hard problems, the team hopes to develop better heuristics for real-world tasks such as logistics, robotics path planning, and even scheduling in manufacturing. For instance, the strategies that expert players use to navigate complex Mario levels can inform algorithms for autonomous systems that must operate under tight time and resource constraints.
Looking ahead, the team plans to extend the analysis to other iconic games, including The Legend of Zelda and Donkey Kong, to build a more comprehensive mathematical framework for interactive entertainment. As the field of computational game theory continues to grow, researchers expect that the humble Super Mario will remain a surprising, and surprisingly deep, source of insight.