A meta-problem is the choice of which problem, or group of problems, you want to solve in order to achieve the outcome you want. For any given outcome, there are usually many problems you could tackle that would help you get there. The key features of a meta-problem are a clear statement of your goals and the problems you could choose to pursue those goals.

The process is problem-agnostic. You can use it on any size or type of dilemma, from the relatively trivial to the urgently existential, from local to global, and in any role from personal through business and professional, to political. On this site I aim to explain the idea to a wide audience, but in my professional life I use the meta-problem process to help businesses solve complex data analytics problems (you can learn more at https://www.analyticsstrategiesllc.com).

The only way to know you are focusing on the “right” problem is to identify and compare the alternatives (including the “do nothing” option). The meta-problem approach is a framework for discussing those comparisons, and the solution to the meta-problem is the selection of the best problem.

Describing a meta-problem starts with a list of goals. There will usually be many problems that you could choose to solve to get to your goals. Often, there is a group of problems that are closely related, so that solving one could help you make progress with the others. A meta-problem will define that set of “high yield” problems.

That’s highly unlikely, and can lead you to waste a lot of time, money and other resources pursuing dead-ends. People invariably have criteria they are unaware of at the beginning of the process. In business, this often emerges when they try to ask a (rigid and rules-driven) computer system what to do, and find that either it is unable to produce the results they need, or it produces nonsense. Most of the time your choices will also depend at least a little on some unknowns about the future.

It’s based on a combination of ideas from my work in Operations Research (OR), a field that bridges engineering, mathematics, and business. It’s used by organizations to solve resource allocation problems and optimize the use of those resources. We use math models of systems and decisions to help businesses clients understand how different choices they could make would impact outcomes they care about. You can learn more about my business consulting work on https://www.analyticsstrategiesllc.com