Archive for quant

Nature tidbits [Jan 2025]

Posted in Statistics with tags , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , on April 13, 2025 by xi'an

Entries of these two January issues on

– yet another 100th anniversary!, namely the founding paper of Heisenberg’s quantum mechanics paper in Zeitschrift für Physik, written on the Instagrammable island of Heligoland. With celebrations at the UNESCO in Paris, Anaheim (CA), Kumasi (Ghana), and Salvador de Bahia (Brazil)!;

– some bird species decorating their nest with shed snakeskins, to frighten predators;

– climate predictions for Trump 2.0, albeit the only certainty being it will get worse and worse (and only the beginning of a flow of articles on the Trumpian attacks on science and scientists);

– the curious plight of the open access journal elife loosing its impact factor after getting too open for Clarivate and then seeing submission from China falter;

– the first European cities from 6000 years ago being found in Ukraine (and Romania) within the Cucuteni-Trypillia culture, with apparently an equalitarian structure with no temple or elite, although the lack of written documents (besides beautiful clay figures whose style evolved over the period);

  • an AI tool that interprets spreadsheets (which I would find most useful, given my distaste of said spreadsheets!);

and how to help lab workers facing substance disorders. And break the attached taboos. Which reminded me of a colleague in that situation when I was head of a lab, years and years ago. And of the difficulty of handling the case all by myself…

Also

– a quick report on a Physics Review Letters paper about simulating elections results to spot whether or not margins of victory were properly distributed, having a distribution (for the scaled margins) mostly depending on voter turnout, with a universal shape! The concept behind this analysis is one of universality borrowed from statistical physics. (The fit does not work for the Ethiopian election of 2010 and the Belarus elections during 2004–2019, no wonder!);

– another book review of David Spiegelhalter’s Art of Uncertainty, by Yongyi Min, a statistician from the UN Statistics Division;

  • creating a DNA base for identifying children kidnapped during a conflict, as for the 20,000 Ukrainian children forcibly deported to Russia over the past three years;

  • a substantial survey article on neuromorphic computing (submitted in 2023!), about new forms of computing based on hardware/software co-design;

  • with more technologies to watch in 2025, and another long article (and the cover) on the strong impact of small-scale fisheries on sustainable development.

     

    weapons of math destruction [book review]

    Posted in Books, Kids, pictures, Statistics, University life with tags , , , , , , , , , , , , , , , on December 15, 2016 by xi'an

    wmd As I had read many comments and reviews about this book, including one by Arthur Charpentier, on Freakonometrics, I eventually decided to buy it from my Amazon Associate savings (!). With a strong a priori bias, I am afraid, gathered from reading some excerpts, comments, and the overall advertising about it. And also because the book reminded me of another quantic swan. Not to mention the title. After reading it, I am afraid I cannot tell my ascertainment has changed much.

    “Models are opinions embedded in mathematics.” (p.21)

    The core message of this book is that the use of algorithms and AI methods to evaluate and rank people is unsatisfactory and unfair. From predicting recidivism to fire high school teachers, from rejecting loan applications to enticing the most challenged categories to enlist for for-profit colleges. Which is indeed unsatisfactory and unfair. Just like using the h index and citation ranking for promotion or hiring. (The book mentions the controversial hiring of many adjunct faculty by KAU to boost its ranking.) But this conclusion is not enough of an argument to write a whole book. Or even to blame mathematics for the unfairness: as far as I can tell, mathematics has nothing to do with unfairness. Some analysts crunch numbers, produce a score, and then managers make poor decisions. The use of mathematics throughout the book is thus completely inappropriate, when the author means statistics, machine learning, data mining, predictive algorithms, neural networks, &tc. (OK, there is a small section on Operations Research on p.127, but I figure deep learning can bypass the maths.) Continue reading →