Archive for the 'Mathematics' Category

The Matherati

Published
by
peter
on 2010-08-22
in Computer Science, Creativity, History, Human intelligence, Logic, Mathematics and Obituaries
. Closed

Howard Gardner’s theory of multiple intelligences includes an intelligence he called Logical-Mathematical Intelligence, the ability to reason about numbers, shapes and structure, to think logically and abstractly.   In truth, there are several different capabilities in this broad category of intelligence – being good at pure mathematics does not necessarily make you good at abstraction, and vice versa, and so the set of great mathematicians and the set of great computer programmers, for example, are not identical.

But there is definitely a cast of mind we might call mathmind.   As well as the usual suspects, such as Euclid, Newton and Einstein, there are many others with this cast of mind.  For example, Thomas Harriott (c. 1560-1621), inventor of the less-than symbol, and the first person to draw the  moon with a telescope was one.   Newton’s friend, Nicolas Fatio de Duiller (1664-1753), was another.   In the talented 18th-century family of Charles Burney, whose relatives and children included musicians, dancers, artists, and writers (and an admiral), Charles’ grandson, Alexander d’Arblay (1794-1837), the son of Fanny Burney, was 10th wrangler in the Mathematics Tripos at Cambridge in 1818, and played chess to a high standard.  He was friends with Charles Babbage, also a student at Cambridge at the time, and a member of the Analytical Society which Babbage had co-founded; this was an attempt to modernize the teaching of pure mathematics in Britain by importing the rigor and notation of continental analysis, which d’Arblay had already encountered as a school student in France.

And there are people with mathmind right up to the present day.   The Guardian a year ago carried an obituary, written by a family member, of Joan Burchardt, who was described as follows:

My aunt, Joan Burchardt, who has died aged 91, had a full and interesting life as an aircraft engineer, a teacher of physics and maths, an amateur astronomer, goat farmer and volunteer for Oxfam. If you had heard her talking over the gate of her smallholding near Sherborne, Dorset, you might have thought she was a figure from the past. In fact, if she represented anything, it was the modern, independent-minded energy and intelligence of England. In her 80s she mastered the latest computer software coding.”

Since language and text have dominated modern Western culture these last few centuries, our culture’s histories are mostly written in words.   These histories favor the literate, who naturally tend to write about each other.    Clive James’ book of a lifetime’s reading and thinking, Cultural Amnesia (2007), for instance, lists just 1 musician and 1 film-maker in his 126 profiles, and includes not a single mathematician or scientist.     It is testimony to text’s continuing dominance in our culture, despite our society’s deep-seated, long-standing reliance on sophisticated technology and engineering, that we do not celebrate more the matherati.

FOOTNOTE: The image above shows the equivalence classes of directed homotopy (or, dihomotopy) paths in 2-dimensional spaces with two holes (shown as the black and white boxes). The two diagrams model situations where there are two alternative courses of action (eg, two possible directions) represented respectively by the horizontal and vertical axes.  The paths on each diagram correspond to different choices of interleaving of these two types of actions.  The word directed is used because actions happen in sequence, represented by movement from the lower left of each diagram to the upper right.  The word homotopy refers to paths which can be smoothly deformed into one another without crossing one of the holes.  The upper diagram shows there are just two classes of dihomotopically-equivalent paths from lower-left to upper-right, while the lower diagram (where the holes are positioned differently) has three such dihomotopic equivalence classes.  Of course, depending on the precise definitions of action combinations, the upper diagram may in fact reveal four equivalence classes, if paths that first skirt above the black hole and then beneath the white one (or vice versa) are permitted.  Applications of these ideas occur in concurrency theory in computer science and in theoretical physics.

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AI’s first millenium: prepare to celebrate

Published
by
peter
on 2010-08-20
in AI, Computer Science, History, Logic and Mathematics
. Closed

A search algorithm is a computational procedure (an algorithm) for finding a particular object or objects in a larger collection of objects.    Typically, these algorithms search for objects with desired properties whose identities are otherwise not yet known.   Search algorithms (and search generally) has been an integral part of artificial intelligence and computer science this last half-century, since the first working AI program, designed to play checkers, was written in 1951-2 by Christopher Strachey.    At each round, that program evaluated the alternative board positions that resulted from potential next moves, thereby searching for the “best” next move for that round.

The first search algorithm in modern times apparently dates from 1895:  a depth-first search algorithm to solve a maze, due to amateur French mathematician Gaston Tarry (1843-1913).  Now, in a recent paper by logician Wilfrid Hodges, the date for the first search algorithm has been pushed back much further:  to the third decade of the second millenium, the 1020s.  Hodges translates and analyzes a logic text of Persian Islamic philosopher and mathematician, Ibn Sina (aka Avicenna, c. 980 – 1037) on methods for finding a proof of a syllogistic claim when some premises of the syllogism are missing.   Representation of domain knowledge using formal logic and automated reasoning over these logical representations (ie, logic programming) has become a key way in which intelligence is inserted into modern machines;  searching for proofs of claims (“potential theorems”) is how such intelligent machines determine what they know or can deduce.     It is nice to think that theorem-proving is almost 990 years old.

References:

B. Jack Copeland [2000]:  What is Artificial Intelligence?

Wilfrid Hodges [2010]: Ibn Sina on analysis: 1. Proof search. or: abstract state machines as a tool for history of logic.  pp. 354-404, in: A. Blass, N. Dershowitz and W. Reisig (Editors):  Fields of Logic and Computation. Lecture Notes in Computer Science, volume 6300.  Berlin, Germany:  Springer.   A version of the paper is available from Hodges’ website, here.

Gaston Tarry [1895]: La problem des labyrinths. Nouvelles Annales de Mathématiques, 14: 187-190.

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As we once thought

Published
by
peter
on 2010-08-17
in AI, Computer Science, Computer technology, Computing-as-interaction, Forecasting, Logic and Mathematics
. Closed

The Internet, the World-Wide-Web and hypertext were all forecast by Vannevar Bush, in a July 1945 article for The Atlantic, entitled  As We May Think.  Perhaps this is not completely surprising since Bush had a strong influence on WW II and post-war military-industrial technology policy, as Director of the US Government Office of Scientific Research and Development.  Because of his influence, his forecasts may to some extent have been self-fulfilling.

However, his article also predicted automated machine reasoning using both logic programming, the computational use of formal logic, and computational argumentation, the formal representation and manipulation of arguments.  These areas are both now important domains of AI and computer science which developed first in Europe and which still much stronger there than in the USA.   An excerpt:

The scientist, however, is not the only person who manipulates data and examines the world about him by the use of logical processes, although he sometimes preserves this appearance by adopting into the fold anyone who becomes logical, much in the manner in which a British labor leader is elevated to knighthood. Whenever logical processes of thought are employed—that is, whenever thought for a time runs along an accepted groove—there is an opportunity for the machine. Formal logic used to be a keen instrument in the hands of the teacher in his trying of students’ souls. It is readily possible to construct a machine which will manipulate premises in accordance with formal logic, simply by the clever use of relay circuits. Put a set of premises into such a device and turn the crank, and it will readily pass out conclusion after conclusion, all in accordance with logical law, and with no more slips than would be expected of a keyboard adding machine.

Logic can become enormously difficult, and it would undoubtedly be well to produce more assurance in its use. The machines for higher analysis have usually been equation solvers. Ideas are beginning to appear for equation transformers, which will rearrange the relationship expressed by an equation in accordance with strict and rather advanced logic. Progress is inhibited by the exceedingly crude way in which mathematicians express their relationships. They employ a symbolism which grew like Topsy and has little consistency; a strange fact in that most logical field.

A new symbolism, probably positional, must apparently precede the reduction of mathematical transformations to machine processes. Then, on beyond the strict logic of the mathematician, lies the application of logic in everyday affairs. We may some day click off arguments on a machine with the same assurance that we now enter sales on a cash register. But the machine of logic will not look like a cash register, even of the streamlined model.”

Edinburgh sociologist, Donald MacKenzie, wrote a nice history and sociology of logic programming and the use of logic of computer science, Mechanizing Proof: Computing, Risk, and Trust.  The only flaw of this fascinating book is an apparent misunderstanding throughout that theorem-proving by machines  refers only to proving (or not) of theorems in mathematics.    Rather, theorem-proving in AI refers to proving claims in any domain of knowledge represented by a formal, logical language.    Medical expert systems, for example, may use theorem-proving techniques to infer the presence of a particular disease in a patient; the claims being proved (or not) are theorems of the formal language representing the domain, not necessarily mathematical theorems.

References:

Donald MacKenzie [2001]:  Mechanizing Proof: Computing, Risk, and Trust (2001).  Cambridge, MA, USA:  MIT Press.

Vannevar Bush [1945]:  As we may think.  The Atlantic, July 1945.

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Precision as the enemy of knowledge

Published
by
peter
on 2010-08-09
in Africa, Computer Science, Economics and Mathematics
. Closed

I have posted previously about the different ways in which knowledge may be represented.  A key learning of the discipline of Artificial Intelligence in its short life thus far is that not all representations are equal.    Indeed, more precise representations may provide less information, as in this example from cartography (from a profile of economist Paul Krugman):

Again, as in his [Krugman's] trade theory, it was not so much his idea [that regional ecomomic specializations were essentially due to historical accidents] that was significant as the translation of the idea into a mathematical language.  “I explained this basic idea” – of economic geography – “to a non-economist friend,” Krugman wrote, “who replied in some dismay, ‘Isn’t that pretty obvious?’  And of course it is.”  Yet, becaue it had not been well modelled, the idea had been disregarded by economists for years.  Krugman began to realize that in the previous few decades economic knowledge that had not been translated into [tractable analytical mathematical] models had been effectively lost, because economists didn’t know what to do with it.  His friend Craig Murphy, a political scientist at Wellesley, had a collection of antique maps of Africa, and he told Krugman that a similar thing had happened in cartography.  Sixteenth century maps of Africa were misleading in all kinds of ways, but they contained quite a bit of information about the continent’s interior – the River Niger, Timbuktu.  Two centuries later, mapmaking had become more accurate, but the interior of Africa had become a blank.  As standards for what counted as a mappable fact rose, knowledge that didn’t meet those standards – secondhand travellers’ reports, guesses hazarded without compasses or sextants – was discarded and lost.  Eventually, the higher standards paid off – by the nineteenth century the maps were filled in again – but for a while the sharpening of technique caused loss as well as gain. ” (page 45)

Reference:

Larissa MacFarquhar [2010]:  The deflationist:  How Paul Krugman found politics.  The New Yorker, 2010-03-01, pp. 38-49.




Symphonic Form

Published
by
peter
on 2010-08-03
in Argumentation, Mathematics and Music
. Closed
Composer and musicologist Kyle Gann has an interesting post citing David Fanning’s quotation of Russian musicologist Mark Aranovsky’s classification of the movements of the typical symphony, a classification which runs as follows:
  • Movement #1:  Homo agens: man acting, or in conflict (Allegro)
  • Movement #2: Homo sapiens: man thinking (Adagio)
  • Movement #3:  Homo ludens: man playing (Scherzo), and
  • Movement #4:  Homo communis: man in the community (Allegro)
This makes immense sense, and provides a neat explanation of the structure of symphonic form.  Many of my long-standing questions are answered with this classification.    Why normally 4 movements?  Why is the first one normally louder and faster and more serious than the next two?  And why does the first movement often seem more like an ending movement than a beginning one?   In other words, why is the climax to the first movement so often more impressive and more compelling than that for the other movements?  Why is there usually a middle movement that is noticeably less serious than the outer movements?  Why is the last movement often in rondo form?  Why do some composers (eg, Mozart, Mendelssohn) include a fugue in their last movements?  Why do some composers include a song to brotherly love  (Beethoven) or a hymn (Mendelssohn)  in their last movements?
Of great relevance here is that the German word for movement (of a musical work) is Satz, meaning “sentence”.  In the German art-musical tradition, a musical work first makes some claim or states some musical position, and then (in Sonata form) argues the case for that claim by exploring the musical consequences of the theme (or themes), or of its  component musical parts, before returning to a re-statement of the initial claim (theme) at the end of the movement.  In this tradition, the theme, being a claim which is developed, does not have to be very interesting or melodious in itself, since its purpose is not to please the ear but to announce a position.   Beethoven, for example, was notorious for not writing good melodies:  his most famous theme, that of the first movement of the 5th Symphony, has just 4 notes, of which 3 are identical and are repeated together.  But he was a superb developer, perhaps one of the best, of themes, even of such apparently insignificant ones as this one.
The distinction between writing good melodies and developing them well strikes me as very similar to that between problem-solving and theory-building mathematicians - both these cases essentially involve a difference between exploring and exploiting.



Three minutes of freedom

Published
by
peter
on 2010-08-01
in Argumentation, Heroes, History, Intelligence, Literature, Mathematics, Poetry and Politics
. Closed

Jane Gregory, speaking in 2004, on the necessary conditions for a public sphere:

To qualify as a public, a group of people needs four characteristics. First, it should be open to all and any: there are no entry qualifications. Secondly, the people must come together freely. But it is not enough to simply hang out – sheep do that. The third characteristic is common action. Sheep sometimes all point in the same direction and eat grass, but they still do not qualify as a public, because they lack the fourth characteristic, which is speech. To qualify as a public, a group must be made up of people who have come together freely, and their common action is determined through speech: that is, through discussion, the group determines a course of action which it then follows. When this happens, it creates a public sphere.

There is no public sphere in a totalitarian regime – for there, there is insufficient freedom of action; and difference is not tolerated. So there are strong links between the idea of a public sphere and democracy.”

I would add that most totalitarian states often force their citizens to participate in public events, thus violating a basic human right not to associate and not to listen.

I am reminded of a moment of courage on 25 August 1968, when seven Soviet citizens staged a brave public protest at Lobnoye Mesto in Red Square, Moscow, at the military invasion of Czechoslovakia by forces of the Warsaw Pact.   The seven (and one baby) were:  Konstantin Babitsky (mathematician and linguist), Larisa Bogoraz (linguist, then married to Yuli Daniel), Vadim Delone (also written “Delaunay”, language student and poet), Vladimir Dremlyuga (construction worker), Victor Fainberg (mathematician), Natalia Gorbanevskaya (poet, with baby), and Pavel Litvinov (mathematics teacher, and grandson of Stalin’s foreign minister, Maxim Litvinov).  The protest lasted only long enough for the 7 adults to unwrap banners and to surprise onlookers.  The protesters were soon set-upon and beaten by “bystanders” – plain clothes police, male and female – who  then bundled them into vehicles of the state security organs.  Ms Gorbanevskaya and baby were later released, and Fainberg declared insane and sent to an asylum.

The other five faced trial later in 1968, and were each found guilty.   They were sent either to internal exile or to prison (Delone and Dremlyuga) for 1-3 years; Dremlyuga was given additional time while in prison, and ended up serving 6 years.  At his trial, Delone said that the prison sentence of almost three years was worth the “three minutes of freedom” he had experienced during the protest.

Delone (born 1947) was a member of a prominent intellectual family descended from a French doctor who had stayed in Russia after Napoleon’s defeat.   Delone was the great-grandson of a professor of physics, Nikolai Borisovich Delone, grandson of a more prominent mathematician, Boris Delaunay (1890-1980), and son of physicist Nikolai Delone (1926-2008).   One of B. Delaunay’s students was Aleksandr D. Alexandrov (1912-1999), founder of the Leningrad School of Geometry (which studies the differential geometry of curvature in manifolds, and the geometry of space-time).  Vadim Delone lived with Alexandrov when, serving out a one-year suspended sentence which required him to leave Moscow, he studied at university in Novosibirsk, Siberia.   At some risk to his own academic career, Alexandrov twice bravely visited Vadim Delone while he was in prison.

Delone’s wife, Irina Belgorodkaya, was also active in dissident circles, being arrested both in 1969 and again in 1973, and was sentenced to prison terms each time.  She was the daughter of a senior KGB official.  After his release in 1971 and hers in 1975, Delone and his wife emigrated to France in 1975, and he continued to write poetry.   In 1983, at the age of just 35, he died of cardiac arrest.   Given his youth, and the long lives of his father and grandfather, one has to wonder if this event was the dark work of an organ of Soviet state security.  According to then-KGB Chairman Yuri Andropov’s report to the Central Committee of the CPSU on the Moscow Seven’s protest in September 1968, Delone was the key link between the community of dissident poets and writers on the one hand, and that of mathematicians and physicists on the other.    Andropov even alleges that physicist Andrei Sakharov’s support for dissident activities was due to Delone’s personal persuasion, and that Delone lived from a so-called private fund, money from voluntary tithes paid by writers and scientists to support dissidents.   (Sharing of incomes in this way sounds suspiciously like socialism, which the state in the USSR always determined to maintain a monopoly of.)  That Andropov reported on this protest to the Central Committee, and less than a month after the event, indicates the seriousness with which this particular group of dissidents was viewed by the authorities.  That the childen of the nomenklatura, the intelligentsia, and even the KGB should be involved in these activities no doubt added to the concern.  If the KGB actually believed the statements Andropov made about Delone to the Central Committee, they would certainly have strong motivation to arrange his early death.

Several of the Moscow Seven were honoured in August 2008 by the Government of the Czech Republic, but as far as I am aware, no honour or recognition has yet been given them by the Soviet or Russian Governments.   Although my gesture will likely have little impact on the world, I salute their courage here.

I have translated a poem of Delone’s here.

References:

M. V. Ammosov [2009]:  Nikolai Borisovich Delone in my Life.  Laser Physics, 19 (8): 1488-1490.

Yuri Andropov [1968]: The Demonstration in Red Square Against the Warsaw Pact Invasion of Czechoslovakia. Report to the Central Committee of the CPSU, 1968-09-20.

Jane Gregory [2004]:  Subtle signs that divide the public from the private.  The Independent, 2004-05-20.




Bayesian statistics

Published
by
peter
on 2010-07-08
in AI, Decision theory, Mathematics and Uncertainty
. Closed

One of the mysteries to anyone trained in the frequentist hypothesis-testing paradigm of statistics, as I was, and still adhering to it, as I do, is how Bayesian approaches seemed to have taken the academy by storm.   One wonders, first, how a theory based – and based explicitly – on a measure of uncertainty defined in terms of subjective personal beliefs, could be considered even for a moment for an inter-subjective (ie, social) activity such as Science.    One wonders, second, how a theory justified by appeals to such socially-constructed, culturally-specific, and readily-contestable activities as gambling (ie, so-called Dutch-book arguments) could be taken seriously as the basis for an activity (Science) aiming for, and claiming to achieve, universal validity.   One wonders, third, how the fact that such justifications, even if gambling presents no moral, philosophical or other qualms,  require infinite sequences of gambles is not a little troubling for all of us living in this finite world.  (You tell me you are certain to beat me if we play an infinite sequence of gambles? Then, let me tell you, that I have a religion promising eternal life that may interest you in turn.)

One wonders, fourthly, where are recorded all the prior distributions of beliefs which this theory requires investigators to articulate before doing research.  Surely someone must be writing them down, so that we consumers of science can know that our researchers are honest, and hold them to potential account.   That there is such a disconnect between what Bayesian theorists say researchers do and what those researchers demonstrably do should trouble anyone contemplating a choice of statistical paradigms, surely.   Finally, one wonders how a theory that requires non-zero probabilities be allocated to models of which the investigators have not yet heard or even which no one has yet articulated, for those models to be tested, passes muster at the statistical methodology corral.

To my mind, Bayesianism is a theory from some other world – infinite gambles, imagined prior distributions, models that disregard time or requirements for constructability,  unrealistic abstractions from actual scientific practice – not from our own.

So, how could the Bayesians make as much headway as they have these last six decades? Perhaps it is due to an inherent pragmatism of statisticians – using whatever techniques work, without much regard as to their underlying philosophy or incoherence therein.  Or perhaps the battle between the two schools of thought has simply been asymmetric:  the Bayesians being more determined to prevail (in my personal experience, to the point of cultism and personal vitriol) than the adherents of frequentism.  Greg Wilson’s 2001 PhD thesis explored this question, although without finding definitive answers.

Now,  Andrew Gelman and the indefatigable Cosma Shalizi have written a superb paper, entitled “Philosophy and the practice of Bayesian statistics”.  Their paper presents another possible reason for the rise of Bayesian methods:  that Bayesianism, when used in actual practice, is most often a form of hypothesis-testing, and thus not as untethered to reality as the pure theory would suggest.  Their abstract:

A substantial school in the philosophy of science identifies Bayesian inference with inductive inference and even rationality as such, and seems to be strengthened by the rise and practical success of Bayesian statistics. We argue that the most successful forms of Bayesian statistics do not actually support that particular philosophy but rather accord much better with sophisticated forms of hypothetico-deductivism. We examine the actual role played by prior distributions in Bayesian models, and the crucial aspects of model checking and model revision, which fall outside the scope of Bayesian confirmation theory. We draw on the literature on the consistency of Bayesian updating and also on our experience of applied work in social science.

Clarity about these matters should benefit not just philosophy of science, but also statistical practice. At best, the inductivist view has encouraged researchers to fit and compare models without checking them; at worst, theorists have actively discouraged practitioners from performing model checking because it does not fit into their framework.

References:

Andrew Gelman and Cosma Rohilla Shalizi [2010]:  Philosophy and the practice of Bayesian statistics.  Available from Arxiv.  Blog post here.

Gregory D. Wilson [2001]:   Articulation Theory and Disciplinary Change:  Unpacking the Bayesian-Frequentist Paradigm Conflict in Statistical Science.  PhD Thesis,  Rhetoric and Professional Communication Programme, New Mexico State University.  Las Cruces, NM, USA.  July 2001.

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The cultures of mathematics education

Published
by
peter
on 2010-07-05
in Culture and Mathematics
. Closed

I posted recently about the macho culture of pure mathematics, and the undue focus that school mathematics education has on problem-solving and competitive games.

I have just encountered an undated essay, “The Two Cultures of Mathematics”, by Fields Medallist Timothy Gowers, currently Rouse Ball Professor of Mathematics at Cambridge.    Gowers identifies two broad types of research pure mathematicians:  problem-solvers and theory-builders.  He cites Paul Erdos as an example of the former (as I did in my earlier post), and Michael Atiyah as an example of the latter.   What I find interesting is that Gowers believes the the profession as a whole currently favours theory-builders over problem-solvers.  And domains of mathematics where theory-building is currently more important (such as Geometry and Algebraic Topology) are favoured over domains of mathematics where problem-solving is currently more important (such as Combinatorics and Graph Theory).

I agree with Gowers here, and wonder, then, why the teaching of mathematics at school still predominantly favours problem-solving over theory-building activities, despite a century of Hilbertian and Bourbakian axiomatics.   Is it because problem-solving was the predominant mode of British  mathematics in the 19th century (under the pernicious influence of the Cambridge Mathematics Tripos, which retarded pure mathematics in the Anglophone world for a century) and school educators are slow to catch-on with later trends?  Or, is it because the people designing and implementing school mathematics curricula are people out of sympathy with, and/or not competent at, theory-building?  Certainly, if your over-riding mantra for school education is instrumental relevance than the teaching of abstract mathematical theories may be hard to justify  (as indeed is the teaching of music or art or ancient Greek).   This perhaps explains how I could learn lots of tricks for elementary arithmetic in day-time classes at primary school, but only discover the rigorous beauty of Euclid’s geometry in special after-school lessons from a sympathetic fifth-grade teacher (Frank Torpie).

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The glass bead game of mathematical economics

Published
by
peter
on 2010-07-01
in Decision theory, Economics and Mathematics
. Closed

Over at the economics blog, A Fine Theorem, there is a post about economic modelling.

My first comment is that the poster misunderstands the axiomatic method in pure mathematics.  It is not the case that “axioms are by assumption true”.  Truth is a bivariant relationship between some language or symbolic expression and the world.  Pure mathematicians using axiomatic methods make no assumptions about the relationship between their symbolic expressions of interest and the world.   Rather they deduce consequences from the axioms, as if those axioms were true, but without assuming that they are.    How do I know they do not assume their axioms to be true?  Because mathematicians often work with competing, mutually-inconsistent, sets of axioms, for example when they consider both Euclidean and non-Euclidean geometries, or when looking at systems which assume the Axiom of Choice and systems which do not.   Indeed, one could view parts of the meta-mathematical theory called Model Theory as being the formal and deductive exploration of multiple, competing sets of axioms.

On the question of economic modeling, the blogger presents the views of Gerard Debreu on why the abstract mathematicization of economics is something to be desired.   One should also point out the very great dangers of this research program, some of which we are suffering now.  The first is that people – both academic researchers and others – can become so intoxicated with the pleasures of mathematical modeling that they mistake the axioms and the models for reality itself.  Arguably the widespread adoption of financial models assuming independent and normally-distributed errors was the main cause of the Global Financial Crisis of 2008, where the errors of complex derivative trades (such as credit default swaps) were neither independent nor as thin-tailed as Normal distributions are.  The GFC led, inexorably, to the Great Recession we are all in now.

Secondly, considered only as a research program, this approach has serious flaws.  If you were planning to construct a realistic model of human economic behaviour in all its diversity and splendour, it would be very odd to start by modeling only that one very particular, and indeed pathological, type of behaviour examplified by homo economicus, so-called rational economic man.   Acting with with infinite mental processing resources and time, with perfect knowledge of the external world, with perfect knowledge of his own capabilities, his own goals, own preferences, and indeed own internal knowledge, with perfect foresight or, if not, then with perfect knowledge of a measure of uncertainty overlaid on a pre-specified sigma-algebra of events, and completely unencumbered with any concern for others, with any knowledge of history, or with any emotions, homo economicus is nowhere to be found on any omnibus to Clapham.  Starting economic theory with such a creature of fiction would be like building a general theory of human personality from a study only of convicted serial killers awaiting execution, or like articulating a general theory of evolution using only a hand-book of British birds.   Homo economicus is not where any reasonable researcher interested in modeling the real world would start from in creating a theory of economic man.

And, even if this starting point were not on its very face ridiculous, the fact that economic systems are complex adaptive systems should give economists great pause.   Such systems are, typically, not continuously dependent on their initial conditions, meaning that a small change in input parameters can result in a large change in output values.   In other words, you could have a model of economic man which was arbitrarily close to, but not identical with, homo economicus, and yet see wildly different behaviours between the two.  Simply removing the assumption of infinite mental processing resources creates a very different economic actor from the assumed one, and consequently very different properties at the level of economic systems.  Faced with such overwhelming non-continuity (and non-linearity), a naive person might expect economists to be humble about making predictions or giving advice to anyone living outside their models.   Instead, we get an entire profession labeling those human behaviours which their models cannot explain as “irrational”.

My anger at The Great Wen of mathematical economics arises because of the immorality this discipline evinces:   such significant and rare mathematical skills deployed, not to help alleviate suffering or to make the world a better place (as those outside Economics might expect the discipline to aspire to), but to explore the deductive consequences of abstract formal systems, systems neither descriptive of any reality, nor even always implementable in a virtual world.

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Vale: Martin Gardner: Defending the honor of the human mind!

Published
by
peter
on 2010-05-25
in Mathematics
. Closed

The death has just occurred of Martin Gardner (1914-2010), for 25 years (1956-1981) the writer of the superb Mathematical Games column of Scientific American.   I remember eagerly seeking each new copy of SciAm in my local public library to read Gardner’s column each month,  and devouring all of his books that I could find.  His articles interested me despite my general contempt for games and competitions, and for ad hoc approaches to mathematical reasoning.

Scientific American’s tribute page is here, and here is a just-posted transcript of a February 1979 conversation between Gardner and other mathematicians.   This transcript contains a wonderful statement by mathematician Stan Ulam:

In fact, you know, yesterday Ron Graham gave a marvelous, really interesting lecture about some esoteric question; and I was wondering during it, Well, the question sounds very complicated, why devote so much ingenuity? Then I remember what, I think, Fourier or Laplace wrote: That mathematics—one reason for its being—is to defend the honor of the human mind.”

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