Sir Andrew Wiles smacks down unethical use of mathematics for profit
My buddy Jordan Ellenberg sent me this link to an article which covered Sir Andrew Wiles’ comments at a the opening of the Andrew Wiles Building, a housing complex for math nerds in Oxford. From the article:
Wiles claimed that the abuse of mathematics during the global financial meltdown in 2009, particularly by banks’ manipulation of complex derivatives, had tarnished his chosen subject’s reputation.
He explained that scientists used to worry about the ethical repercussions of their work and that mathematics research, which used to be removed from day-to-day life, has diverged “towards goals that you might not believe in”.
At one point Wiles said the following, which is music to my ears coming from a powerful mathematician:
One has to be aware now that mathematics can be misused and that we have to protect its good name.
Two things.
First, maybe I should invite Wiles to be on my panel of mathematicians for investigating public math models. I originally thought this should be run under the auspices of a society such as the AMS but after talking to some people I’ve given up on that and just want it to be independent.
Second, the Andrew Wiles building was evidently paid for primarily by Landon Clay, who also founded the Clay Institute and was the CEO of Eaton Vance, which an investment management firm which provides its clients with wealth management tools and advice. I’m wondering if that kind of mathematical tool was in Wiles’ mind when he made his speech, and if so, how it went over. Certainly in my experience, wealth management tools are definitely in the “weapons of math destruction” toolbox.
mathbabe 
Er, remember nuclear weapons? The use of math has the same moral conundrums of any science-based technology. Maybe the more realistic way to approach this is to stress the moral/political education of the people being trained in STEM fields (mathematicians are fairly liberal to progressive, engineers not as much). Put another, the issue is as much democracy as it is the application of any particular technology.
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From what I saw, the manipulation in the derivative markets in 2008 and 2009 was more legal (taking advantage of document terms and conditions, and lobbying) than mathematical. There weren’t many traders who understood the guts of the models anyway, they only saw the outputs.
I definitely agree with your “weapons of math destruction” comment, but I think the models are used to justify the risk after the fact, rather than to analyze risk properly ahead of time.
If someone is using math to stretch the truth when they are selling something the incentives are more to blame as the math is, at least in my mind. It would seem strange to me, for example, that someone would look at the financial crisis and think Ito’s lemma had been tarnished rather than thinking the reputation of Black-Scholes had been tarnished.
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Yay for AW!
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We all know that Sir Andrew digs deep, but this isn’t even deep. There is no way he made those comments irrespective of the building’s financial support.
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Thank for sharing this. It is helpful.
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This shows once more, if it was ever needed, that expertise, experience of even genius in one domain doesn’t translate into the same brilliance in other domains. This is particularly true when the domain of expertise is math and the other domain is by essence “social”.
The Mathbabe is of course a shining exception to this heuristic rule !
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Heuristic rule? Oh no. This is downright first law of thermodynamics, conservation of energy stuff. It’s a mystery Wiles even retains the ability to speak!
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Abuse of mathematics occurs not only to banking. It is also rampant in the manipulation of big data in a growing number of sectors. Insurance, government, retail, etc. I encounter the issues in the context of conceptual and semantic modeling, at the foundation of mathematics.
The assumptions that are baked in into the formal representations of concepts such as “pensioner”, “family with children”, “income” etc. can be far removed from the semantics that people would intuitively associate with these concepts.
At the very least poorly validated formal representations lead to hairy boundary cases, simply due to the inherent variability in large data sets. Companies that want to secure a profit strive to maximise the gap between formal representation and the intuitive understanding of the words used to denote the elements in the formal representation. In government the gap manifests itself in the difference between legislation, which in itself may contain poor representations and subtle ambiguities, and the implementation in data structures and software.
Formal manipulation of “big data” and machine learning techniques can then be used to engineer an outcome that suits a specific group of stakeholders.
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