June 14, 2008

In a curious recent debate on the nature of just war, the point arose that a just war ought to make every effort to limit bloodshed and ancillary damage. This is an absolutely desirable thing. Let no person reading this post think that I believe otherwise.

The fact of the matter, however, is that what is considered humanly possible in limiting the terror and death of war is neither objective nor absolute. In practice, it usually tends to translate, not into actual minimising of bloodshed, but into a risk equation determining acceptable ratios of 'our' casualties to 'theirs'.

Rules of engagement notwithstanding, it remains unacceptable to most western publics to adopt a full-kill policy, no matter how many domestic military lives might be saved thereby; yet at the same time the perceived value of one of 'our' soldiers' lives is considerably more than that of the local civilians (or else collateral damage would be utterly unacceptable). I suspect the function determining the exact ratio could probably be determined with mathematical precision, if one could find a way to quantitatively measure the domestic perceived "white-hat-ness" of a given war.

To my surprise, I was immediately told that to work out such a ratio was fascist. Specifically, the line was immediately blurred between identification of the ratio and application of the ratio into creating a new right: in this case, the 'right' to a certain level of civilian casualties for every soldier killed.

Statistics and interpretation march in close synch -- but this is not inherent to the statistics themselves, but to the frames or preconception and agenda through which we choose to view those statistics. In this particular case, precisely the same numbers could be used to lead a moral call to reduce or completely eliminate collateral damage. Same number, two opposite interpretations, based on two opposite agendas and leading to two opposite outcomes.

All of which may go to show that anyone can prove or support anything with statistics ... but that it takes some real work to isolate data without trying to prove or support anything with it.

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