Single-Objective Optimization
Combining all of our evaluators into a single measure is an example of Single-Objective optimization.
By doing so, we are asserting that we understand the relationships and relative importance of all the measures on our desired outcome and create a single measure that encodes all individual measures into a single score for each alternative being considered.
While this approach is efficient, it is often an oversimplification that hides the nuances of how the separate evaluations interact.
Multi-Objective Optimization
In the next module, we'll learn about tools that support Multi-Objective optimization -- enabling us to better see the relationships (and potential trade-offs) between the individual measures and find the Pareto-Optimal Frontier of alternatives to consider.
Comparing these Two Approaches
This video from YouTube creator @paretos does a great job of illustrating the differences between these two approaches.