On "Managerial Insight"

Professor Chris Tang asked the operations research community to come together and provide a definition of "Insight." In our field, most of the top journals ask authors to consider the potential managerial insights available as a result of their paper. Ostensibly clearly stating the insights will save the reader some effort in figuring out how they can use the results in the paper, and perhaps will also ensure that the paper has real-world implications.

When a fellow PhD student first introduced me to the idea of insight, I understood it to simply be the practical implications of your work. Since then I have learned a lot about how OR is used in practice. It seems that occasionally our models are directly implemented. Most of the time though, there is some reason decision makers would not want to directly implement the output of the model. Maybe the optimization problem is missing some important piece that keeps it from being directly implementable. Maybe the model is far too complex to be worth solving exactly. Or maybe the whole goal of the model is simply to provide the decision maker with a collection of options to decide between.

Given this understanding, I now think of insights as the results that can help get a better understanding of the problem even without actually implementing the model. For illustrations of insight, physics provides some good examples:

• Heavier objects do not fall faster just because they have more mass.
• Doors open more easily when you push near the handle instead of near the hinge.
• Going uphill takes more energy than going downhill.

Behind each of these examples are equations which we may or may not care about in any particular situation. But the insight provided is accessible and useful whether or not we need to make a quantitative decision.

One additional point. While insights may come from the results of optimization models, sometimes simply formulating the model in a clever way can help provide insights. To use an OR example, Michael Trick recently posted on the topic of complete enumeration as an argument for complexity. He points out that just because complete enumeration is one way to find the optimal solution does not mean the underlying problem is hard. In the same way, clever formulations of hard problems can bypass a lot of unnecessary complexity.