A problem-solving method worth borrowing: means-ends analysis

I’d like to share a powerful problem solving method that I stumbled upon while doing the literature review of my dissertation. It’s called Means-Ends analysis and it’s the backbone of many AI models. However, even when applied the old-fashioned way – it can yield some pretty spectacular results.

Typical problem solving methods take the problem and work forwards from there to try to uncover a solution. “Guess-and-check” falls into that category, but the issue with that approach is that it can feel like a game of whack-a-mole when each new implemented solution creates a new problem.

Means-Ends analysis, in contrast, is a bi-directional method that works forwards from the problem and backwards from the solution – simplifying the process and avoiding a lot of follow-up problems at the same time.

Here’s how it works:

• Starting with the problem, make a note of the resources you have available.
• Then, starting with the solution, make a note of the goals you hope to accomplish.
• Then, taking these two extremes, you work forwards from the problem and resources available while at the same time working backwards from the solution and goals – solving each intermediary step – until the full solution path is revealed.

If it seems simple – that’s because it is. But the power of this problem solving approach is that it can take even the most complex challenges and break it down into manageable pieces.

Thanks for reading, I hope you found this helpful!