## The ordering of authors’ names in academic publications

Alphabetical ordering of authorship of articles in economics journals apparently is the source of two biases.  Einav and Yariv (2006) show that alpha order is biased against authors with later surname initials; the problem is the name that is salient and that readers remember in connection with an article is the first in the sequence, especially when subsequent names disappear in “et al.”  Eninav et al. (1999) show that alpha order biases downward the total quality of research; here the problem is that the alpha order convention blocks a race among authors to attain first place by contributing more.   Although it should be possible to overcome the first bias via random ordering, the second appears much more intractable.

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## Useful bias

I would like to introduce the perhaps, in this forum, heretical notion of useful bias.  By useful bias I mean the deliberate introduction of an error as a means to solving a problem.  The two examples I discuss below are concrete rather than abstract and come from my training as an infantry officer many years ago.  Now technology solves the problems they solved, but the examples may still serve to illustrate the notion.

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## Malatesta Estimator

We frequently encounter competing estimates of politically salient magnitudes. One example would be the number of attendees at the 1995 “Million Man March”.  Obviously, frequently the estimates emanate from biased observers seeking to create or dispel an impression of strength.  Someone interested in generating a more neutral estimate might consider applying what I would call the Malatesta Estimator, which I have named after its formulator, the 14th Century Italian mercenary captain, Galeotto Malatesta of Rimini (d. abt. 1385). His advice was: “Take the mean between the maximum given by the exaggerators, and the minimum by detractors, and deduct a third” (Saunders 2004).  This simplifies into: the sum of the maximum and the minimum, divided by three.  It adjusts for the fact that the minimum is bounded below by zero, while there is no bound on the maximum.  Of course, it only works if the maximum is at least double the minimum.

In the case of the Million Man March, supporters from the Nation of Islam claimed attendance of 1.5 to 2 million.  The Park Service suggested initially that 400,000 had participated.  The Malatesta Estimator therefore yields an estimate of 800,000.  We can calibrate this by comparing it with an estimate by Dr. Farouk El-Baz and his team at the Boston University Remote Sensing Lab.  Dr. El-Baz and his team used samples of 1 meter square pixels from a number of overhead photos to estimate the density per pixel, and then calculated an estimate for the entire area.  Their estimate was 837,000, with 20% error bounds giving a range from 1 million to 670,000.

Saunders, Frances Stonor. 2004. The Devil’s Broker: Seeking Gold, God, and Glory in Fourteenth-Century Italy. (New York: HarperCollins), p. 93.

BU Remote Sensing Lab Press Release: http://www.bu.edu/remotesensing/Research/MMM/MMMnew.html

Accessed 14 December 2006.

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