In a few days, I give a “public lecture” as part of the requirements for my Hood Fellowship that the University of Auckland gave me (I would have visited here anyway – New Zealand is wonderful – but the Fellowship was a very nice addition). Rather than trot out a version of my “sports scheduling” talk, I am giving an overview of OR talk. It is really quite a challenge. These overview talks can be quite a snooze for those in the audience who know OR, so I am thinking hard about the role OR has in the world, and the challenges the field faces. The “Science of Better” initiative of INFORMS has been very helpful in this regard.
One aspect I am exploring is the appropriate role of OR in decision making. While optimizers like me tend to be a bit impatient with this (“The best answer is this! Stop arguing with me and do it!!), the real-world is full of stuff that doesn’t appear in our models. In many (most?) applications, OR is an adjunct to decision making, not a replacement. Slate has an interesting article about this in the context of chess-playing programs that I may try to work into my talk.
Apologies in advance: the next few posts may be me trying to work through my thoughts in preparation for the talk.
As far as I can tell, the best way in which OR can help is to keep us on that production possibilities frontier (or get us there). Which point on that PPF we get at is a much messier matter, one better left to politics, morality and chance.
Technological efficiency vs. allocative efficiency, and so on.
Bringing linear programming to the masses (of firms) via simple software packages (more generic and widespread than at this time) could prove to be quite a quasi-free lunch. The same is probably true for econometric/formal forecasting methods.
Actually, a relocation of focus from academia (high-minded theoretical speculation) to the private sector (quick-and-dirty optimization of business processes) could have a huge impact in terms of welfare and so on.