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UID:29@lincs.fr
DTSTART;TZID=Europe/Paris:20170118T140000
DTEND;TZID=Europe/Paris:20170118T150000
DTSTAMP:20170313T170808Z
URL:https://www.lincs.fr/events/summarizing-performance-samples-with-extre
 ma-rather-than-averages-a-lesson-from-practitioners/
SUMMARY:Summarizing Performance Samples with Extrema rather than Averages:
 A Lesson from Practitioners
DESCRIPTION:The notion of performance seems crucial to many fields of
 science and engineering. Some scientists are concerned with the performance
 of algorithms or computing hardware\, others evaluate the performance of
 communication channels\, of materials\, of chemicals\, psychologists
 investigate the performances of experimental participants\, etc. Yet\,
 rather surprisingly\, the performance notion is generally used without any
 explicit definition. I propose this: a performance is a quantitative
 behavioral measure that an agent deliberately tries to either minimize or
 maximize. The scope of the performance concept seems impressively large:
 the agent whose behavioral performance is being scored may be a human\, a
 coalition of humans (e.g.\, an enterprise\, an academic institution)\, or
 even a human product (e.g.\, a chemical\, an algorithm\, a market
 share).Performance measures are random variables of a very special sort:
 their distributions are strongly skewed as a direct consequence of the
 extremization (minimization or maximization) pressure that constitutes
 their defining characteristic. Mainstream statistics takes it for granted
 that any distribution needs to be summarized by means of some
 representative central-tendency indicator (e.g.\, an arithmetic mean\, a
 median)\, and so the asymmetry of performance distributions has been
 traditionally considered an unfortunate complication. In fact I will argue
 that when it comes to statistics of performance\, averages become
 essentially irrelevant. The point is easy to make with the example of
 spirometry testing: practitioners of spirometry never compute an average\,
 they retain the best measure of respiratory performance (i.e.\, the sample
 max) and flatly discard all other measures. And they are quite right to do
 so\, as a simple model will help explain. The important general lesson to
 be learned from spirometry is that the better a sample measure of
 performance\, the more valid as an estimate of the capacity of
 performance---the theoretical upper limit whose estimation is in fact the
 goal of most performance testing in experimental science. One likely reason
 why experimenters of many fields have recourse to the measurement of
 performances is because non-extremized behavior tends to be random\,
 whereas performance capacities can abide by quantitative laws. This is easy
 to illustrate with empirical data from human experimental psychology
 (Hick's law\, George Miller's magic number\, Fitts' law). An experimenter
 myself\, I can only conclude with a question to statisticians: i.e.\,
 don’t we need a brand new sort of statistics to fully
 acknowledge and accommodate the rather special nature of all these
 performance measures that these days we encounter everywhere\, not just in
 virtually every sector of society but also in many field of scientific
 research
CATEGORIES:Seminars,Youtube
LOCATION:LINCS Meeting Room 40\, 23\, avenue d'Italie\, Paris\, 75013\,
 France
GEO:48.8283983;2.3568972000000485
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