Learning Prioritization

A tool for deciding which uncertainties are worth reducing

If you’re trying to make a decision under uncertainty, the most important question often goes unasked: is it worth investing in learning before making my choice?

Combining two other tools from this site and an idea from reliability engineering, we can create an engineering model of which uncertainty to prioritize.

Suppose we have several sources of uncertainty. For each one, you can also choose to reduce your uncertainty by learning.

Figure 1: The five unknowns have individual learning curves showing how an investment in resources will reduce the unknown. One example has no uncertainty reduction, while the other four vary.

Next, we look at how the uncertainty impacts your decisions. We start to see that some uncertainties will be cheaper to reduce, while also having a bigger impact on our decision depending on the unknown.

Figure 2: The five sources of unknowns also will have different impacts on the best decision. As the uncertainty is reduced, we may find ourselves on a new leaf of the decision tree where the unknowns no longer matter.

However, decisions and uncertainties don’t happen in a vacuum. While in this illustration there are 5 separate sources of uncertainty, we’re often making a single decision. In reliability engineering you can connect these subsystems and see how the overall system behaves. Sometimes the uncertainties and decisions stack up, like a chain. In those cases, you look for the “weakest link” to see where to focus your learning.

Figure 3: The series of unknowns, which together will lead to an outcome based on our decisions and how much uncertainty we choose to learn. We would probably want to invest in the unknown with the steepest learning curve first, as long as it impacts the decision.

Other times, learning about the unknown could set you down a different path. In reliability engineering Figure 4 shows a top path where unknowns 2 and 3 are said to be “in parallel” compared to unknowns 4 and 5.

Figure 4: There are unknowns in parallel, depending on unknown #1. We will end up caring about unknown 1, 2, and 3 OR 1, 4, and 5.

This provides a model for seeing which uncertainty to prioritize reducing in order to make the best decision possible.

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