My friend John Cook asked me an interesting question recently:

If you had a room full of people with a graduate degree in [operations research], what things would nearly everyone in the room know?

Operations research is notoriously hard to define. According to the Institute for Operations Research and Management Science, “In a nutshell, operations research (O.R.) is the discipline of applying advanced analytical methods to help make better decisions.” I suspect graduate programs spend most of their time teaching those “analytical methods,” i.e. mathematical and computational techniques for modeling and solving problems related to decisions. Examples include Received exception: linear programming, nonlinear programming, integer programming, dynamic programming, stochastic programming, stochastic models, queueing theory, game theory, and simulation. The course requirements for OR PhD students at my university provide an upper bound for this problem: the only courses everyone must take are linear programming, nonlinear programming, and stochastic modeling. Some topics are surprisingly optional; in particular: simulation, statistics, integer programming/combinatorial optimization. John suggests that statistics PhD programs are similar. Topics diverge rather quickly after first year courses. Are all graduate programs like this? Is this a necessary evil (or evil at all)?