Tuesday, 25 November 2008

Defining O.R.

The journal that I edit, International Abstracts in O.R. (IAOR), aims to index and abstract the worldwide academic literature of O.R. and Management Science. This means that I (and others) need to make binary decisions about papers; is this one "O.R/M.S." or not?

So what rules to follow? I have several. There are about 40 journals which are abstracted cover-to-cover. These are the journals published by one or more national O.R. societies, such as Operations Research, Management Science, Journal of the Operational Research Society, 4OR and ORiON. There are others which are clearly primary journals in O.R. such as Omega and Health Care Management Science Dealing with these is, so to speak, easy. Then there are about a hundred which regularly include O.R. related papers. Then, I have a long list of journals which have, at one time or another, provided one or more abstracts for IAOR. Some of these abstracts have been found by serendipity, others by researchers citing them in papers in the principal journals. But I need to decide that binary question in each case; is this O.R.? I look at the content (as described in the title and the abstract). As I do, I ask myself what the paper is about. If it is a paper about theory, is the theory directly concerned with a modelling tool (not, I stress, necessarily a mathematical tool) from the suite of techniques used in O.R.. If so, I say yes. If the theory is less directly relevant, I speculate about whether it is close to a technique that is used in O.R.. Practical papers are considered with the question: is this about a decision-making problem? Is there something that a decision-maker could learn from? Is this paper about a problem in practice where I would expect an O.R. person to be involved?

These may appear naive heuristics, but they work. And in my reading, I can add further questions. When I taught in a unit devoted to statistics and operational research, I often used the (crude) distinction that statistics was concerned with looking at what had happened, and O.R. with modelling what might happen, to answer the questions "What if?" and/or "What's best?"

There are still fuzzy edges, at the interfaces with other disciplines. Engineering problems, economic models, psychology of decision-making ... all pose classification uncertainties. But all of these point to the universality of O.R. in the world today.

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