Choosing a PhD program is one of the hardest decisions we can make. For a start, most of us only make this decision once in our lives, and so we have no prior personal experience to go on.

Second, the success or otherwise of a PhD depends a great deal on factors about which we have little advanced knowledge or control, including, for example:

• The relationship between the student and the supervisor. A PhD is usually awarded only after a student has undertaken some original research. In some programs, this must also be significant. The key point here is that the student has to do this, not the supervisor, and not the two of them together. If you have never done research before, then you will have a period of learning. A good supervisor should be helpful, particularly at the beginning, but eventually wean you off his or her help. 
• The relationship between the student and the subject-matter. In formal subjects, such as pure mathematics, research is primarily undertaken in the head of the researcher. In experimental subjects, much of the effort involved in research may be taken up with creating the apparatus or system on which the experiments are conducted. In engineering, much of the effort may be taken up with designing and building the artefact or system which is the object of the research.One of the great features of Artificial Intelligence (AI) at this particular time in its history is that there are not yet established rules and procedures for how research in AI should be undertaken. Hence, people in AI use a mix of: the deductive analysis of formal systems (as in pure mathematics), computational experiment and simulation (as in the physical sciences and computational economics), the creation of artefacts (as in engineering, music or art), personal introspection (reasoning about our own reasoning and behaviours, as in parts of philosophy), reasoning about the reasoning processes of others (as in so-called rational-actor theories in economics, game theory, or political science), social introspection (reasoning about the behaviour of groups with which we are acquainted, as in sociology, social psychology, or the study of organizational behaviour), and reflective narrative and dialog (as in anthropology or business strategy). Some researchers emphasize one approach over others, some use a mix of approaches. Not everyone has the skills or aptitude for each approach. If you attempt a PhD centered on simulation, for example, without good software programming and debugging skills, you will not be playing to your strengths. It may still be possible to complete the PhD, but only at the cost of great personal pain.In my experience, academics are remarkably unwilling to engage in discussion about HOW they do research. I do not know if this is because they fear that talking about their methods will frighten away their muse, or because, like most people in most professions, they do not reflect much on what they do. Of all disciplines, AI ought to have the most self-reflective practitioners, but I have not found this. 
• The relationship between the student and the school. Despite their claims to the contrary, Universities are not at all meritocratic. Having now had personal working experience in Government, in business and in University, I have to say that Universities are the most status-conscious of the three institutions, and the one where good, original ideas from low-status people are given the shortest-shrift, if they are heard at all. So be prepared to be ignored.If you are coming to a PhD straight from undergraduate studies, you will not find many changes in the way you are treated by academic or other staff. However, if you have any prior working experience at all, you will find life as a PhD student a great shock. You may have commanded empires, thousands may have quaked at your words, but this will count for absolutely nothing in a university. You will be treated as if you were a blank piece of paper, to be inscribed on by the faculty, and only rarely will you find anyone interested in what you may have done before enrolling in the PhD. I think part of the reason for this is that most academics — having no experience of the world beyond their walls — think that only their problems contain intellectual challenges, and look down on those in business and Government. How little they know!Related to this is the bias which most academics have for beliefs over actions. Perhaps it is a result of the nature of the modern research university where the culture is primarily a written one, rather than being verbal or tactile; in the main, written outputs (such as books and journal articles) are preferred over non-written outputs (such as developing complex software). Certainly, there are many important activities in modern society requiring great intelligence and advanced skills which are not, and could not be, taught through lectures and reading (for example: playing the piano; forecasting demand for high-tech products; managing software development projects). All of these activities are learnt on the job, not in formal education.Another part of the reason is that most universities, being state-funded or funded by generous endowments, do not face the ever-present threat of extinction which even large companies in most markets face. How else to explain the fact that Universities so often treat their next generation of leaders with apathy, disrespect and cynicism, in ways which no company would survive very long doing.

  A PhD is perhaps the last remnant of a feudal relationship in the modern world. The only way to deal with this, in my opinion, is to maintain your self-respect and self-esteem, despite the insults thrown at you (wittingly or not) by the system. Stand your ground, give no quarter, and believe in yourself.

Third, it is very hard to evaluate a decision to undertake a PhD. Because most of us only do one PhD in our lives, we have no control group to compare our PhD with another. Moreover, even after you have finished, and successfully obtained your PhD, you may not be able to tell whether it was a good program or not. It may have been a painful and frustrating exercise, but that may be true of both good and bad programs. The program may produce lots of prize-winning graduates, but that may be feature of the people attracted to enter it, rather than anything they received while doing the PhD.

Deciding to do a PhD and deciding which PhD program to enter are therefore decisions we make and carry-through under great uncertainty. In particular, prior to doing the PhD, you will not be in a position to know what will be your own reactions to the experience, what the possible outcomes will be, or your own valuations of these outcomes. (It is odd that classical decision theory – developed by academic economists – should be so useless for such a common and important decision. Yet another failing of economics!) The first thing you can do is talk to as many people as possible about their experiences as PhD students (both successful and failed), or as PhD supervisors, before you make your decision. Here are some guides which I have found useful, and you may gain something from them:

• David Chapman (Editor) [1988]: How to Do Research at the MIT AI Lab. AI Working Paper 316. MIT. 
• Alan Bundy, Ben du Boulay, Jim Howe and Gordon Plotkin [1985]: The Researcher’s Bible, a guide produced by AI and CS people at Edinburgh University. 
• Some guides produced by the Computer Science Department at Carnegie-Mellon University.

The second thing to do, before you start your PhD, is to list all the challenges you expect to encounter in the course of the program, and to identify possible reactions to these. Most PhD students get depressed at one or more points in their studies, often at the immense amount of reading they feel they have to do. To counter this depression, you need to identify strategies to deal with it, such as tackling some non-reading PhD activity (e.g., building a software simulator) or engaging in something not associated with your PhD (e.g., taking a holiday). Of course, you won’t know in advance all the challenges you are likely to face, nor the best strategies for surmounting or coping with them. But thinking about these in advance of starting forces you to reflect on the path you are embarking on. Thirdly, it is useful in my experience to keep a diary of your experiences, and of your reactions to them, as you proceed through the program. Writing a regular diary forces you to reflect on your experiences, and thereby distances you somewhat from them. I think it the best antidote to depression.

Some general advice I give to PhD students:

It is my belief that one crucial skill which a PhD student should acquire in the course of his or her degree is the ability to identify a feasible research problem. Therefore, I believe very strongly that the supervisor should not choose the problem for the student, but instead allow the student to identify a problem for him or herself. I realize that this is not the usual practice in all academic disciplines, especially mathematics, where the supervisor usually assigns a problem to each student. I think this practice condescending, and inappropriate in computer science and AI.Accordingly, as the problem may only be identified gradually, the precise details of the research may only emerge in the course of the PhD itself. Emergence is a phenomenon with which all reseachers in AI should be familiar. This means that the actual work undertaken during the PhD may appear to repeat on itself, or to diverge in new directions, or appear in other ways to be undirected. Nothing is undirected, if viewed from the right perspective. Part of the task of a PhD is to find the right perspective with which to view the work undertaken.

Research can be very frightening. In formal subjects such as computer science, we are trying to put together a jigsaw puzzle, but a puzzle where we do not know in advance what the picture is on the jigsaw. Also, the pieces are not given to us in advance — we usually have to find them, or even have to construct them ourselves. Moreover, once we complete the puzzle we may discover that the picture it displays is not the one we thought we were constructing. We may even find that our efforts result in a jigsaw without any picture at all. This is indeed scary, and I liken it to finding one’s way across a deep canyon one has never been in before in thick fog. Why do we do it? Well, partly because we imagine the view from the other side of the canyon is so beautiful, partly because we want to be first to reach the other side, and partly because the adrenalin rush as we stumble down and back up the canyon is addictive. Doing a PhD successfully involves finding that source of adrenalin and using it to motivate us through three hard years of mountaineering.

I view the literature search as a survey of a landscape: you want to find what’s in the landscape, and where it is. Most of the survey is simply so you know what’s where, and so that you can find it again, if you need to. Some of the material you will read will turn out to be extremely important to your research topic, but you won’t know this in advance of reading it, and you may not even know it until you are near the end of your PhD. Only when you come to final write-up will you be forced to identify, formally and precisely, what your research is really about, and so ideally your literature search should only be done at the end. But, of course, you need to do it at the beginning in order to know what is where. This tension is an example of activities which appear to be undirected (reading everything more than once), but which in reality are essential.

Try not to be depressed by all the reading in front of you at the beginning. If you persist through this, then by about 18 months or so after you start, you will awake one morning to find you now know what is important to your topic and what not. You will then find you need to do very little reading until you come near the end.

Finally, it seems customary in guidebooks for PhDs to have some statement about this being the best experience of one’s life, or about research being a noble and elevated calling. I think such statements are misleading. PhDs are a feudal anachronism, an example of Karl Marx’s definition of tradition being the accumulated errors of past generations. They are required in order to get a job as an academic, or as a researcher in many advanced research labs. They serve no purpose that I can see which would not be served by other, less humbling and less psychologically-intrusive means of learning how to do research. The best you can hope for, in my experience, is to find a supervisor and a topic with whom you are sympatico, and try your best to get the damn thing over with as soon as possible. Real life in a real world awaits you, after all.

If you have any comments on these notes, I would very much welcome hearing from you.

From the perspective of the national economy and technological progress, these proposals are extremely misguided, and should be opposed by us all.    They demonstrate a profound ignorance of where important ideas come from, of when and where and how they are applied, and of where they end up.  In particular, they demonstrate great ignorance of the multi-disciplinary nature of most socio-economically-impactful research.

One example will demonstrate this vividly.  As more human activities move online, more tasks can be automated or semi-automated.    To enable this, autonomous computers and other machines need to be able to communicate with one using shared languages and protocols, and thus much research effort in Computer Science and Artificial Intelligence these last three decades has focused on designing languages and protocols for computer-to-computer communications.  These protocols are used in various computer systems already and are likely to be used in future-generation mobile communications and e-commerce systems. 

Despite its deep technological nature, research in this area draws fundamentally on past research and ideas from the Humanities, including: 

• Speech Act Theory in the Philosophy of Language (ideas due originally to Adolf Reinach 1913, John Austin 1955, John Searle 1969 and Jurgen Habermas 1981, among others)
• Formal Logic (George Boole 1854, Clarence Lewis 1910, Ludwig Wittgenstein 1922, Alfred Tarski 1933, Saul Kripke 1959, Jaakko Hintikka 1962, etc), and
• Argumentation Theory (Aristotle c. 350 BC, Stephen Toulmin 1958, Charles Hamblin 1970, etc). 

Assessment of the impacts of research over five years is laughable when Aristotle’s work on rhetoric has taken 2300 years to find technological application.   Even Boole’s algebra took 84 years from its creation to its application in the design of electronic circuits (by Claude Shannon in 1938).  None of the humanities scholars responsible were doing their research to promote technologies for computer interaction or to support e-commerce, and most would not have even understood what these terms mean.  Of the people I have listed, only John Searle (who contributed to the theory of AI), and Charles Hamblin (who created one of the first computer languages, GEORGE, and who made major contributions to the architecture of early computers, including invention of the memory stack), had any direct connection to computing.   Only Hamblin was afforded an obituary by a computer journal (Allen 1985).

None of the applications of these ideas to computer science were predicted, or even predictable.  If we do not fund pure research across all academic disciplines without regard to its potential socio-economic impacts, we risk destroying the very source of the ideas upon which our modern society and our technological progress depend. 

Reference:

M. W. Allen [1985]: “Charles Hamblin (1922-1985)”. The Australian Computer Journal, 17(4): 194-195.

Technorati Tags: RAE

Now, science tells us that appetite – and hence obesity – may also be a function of one’s genes, as this article in tomorrow’s SMH reports. 

In the primordial ooze of Internet content several hundred million seconds ago (1993), fewer than 100 Web sites inhabited the planet. Early clans of information seekers hunted for data among the far larger populations of text-only Gopher sites and FTP file-sharing servers. This was the world in the years before Google.

Even in this primitive Internet world, the need for more accessible interfaces to growing data collections had already been recognized. The National Science Foundation led the multi-agency Digital Library Initiative (DLI) that, in 1994, made its first six awards. One of those awards supported a Stanford University project led by professors Hector Garcia-Molina and Terry Winograd.

None of the early DLI proposals – submitted before the World Wide Web experienced its Cambrian explosion – explicitly included research into the Web. However, by the time DLI funding began, the information landscape had changed.

In 1994, some of the first Web search tools crawled out of the Internet sea. Two Stanford students started Yahoo!, a manually constructed “table of contents” for Web sites. Other early search engines emerged, such as Lycos and WebCrawler, and began automatically indexing Web pages, focusing on keyword-based techniques to rank search results.

Around the same time, one of the graduate students funded under the NSF-supported DLI project at Stanford took an interest in the Web as a “collection.” The student was Larry Page.

Page uncovered the missing links, so to speak, in Web page ranking. His evolutionary leap was to recognize that the act of linking one page to another required conscious effort, which in turn was evidence of human judgment about the link’s destination. Individually, each link was a simple but effective tool. But collectively, millions of these links provided a key adaptation for the natural selection of search results.

Page was soon joined by Sergey Brin, another Stanford graduate student working on the DLI project. (Brin was supported by an NSF Graduate Student Fellowship.) Together, Page and Brin constructed an ambitious prototype in their Stanford student offices. The equipment for the prototype, called BackRub, was funded by the DLI project and other industrial contributions.

The prototype used well-established technology to crawl from page to page by following links. However, in addition to compiling a standard text index, the prototype also mapped out a vast family tree that reflected the Web links among pages.

To calculate rankings from this family tree, the pair developed the PageRank method. In short, the method ranks a particular Web page highly if many other highly ranked Web pages link to it. Those other page’s rankings, in turn, depend on the pages that link to them. Such logic could spiral out of control, but PageRank eventually stops because, as a rule, the more distantly related a page is, the less it contributes to the final rank of its descendants.

Page and Brin wrote an initial paper on their ideas and the theoretical underpinnings of PageRank and tested the fitness of the ranking approach on live Web data — initially a test set of 24 million pages. PageRank survives as one of the main components of today’s Google search service.

By late 1997, as the Dot-Com Era began to flourish, the BackRub approach proved to be sound, expandable and popular. By the end of the Early DLI Age in 1998, Page and Brin obtained funding that allowed them to move their growing hardware facility from the Stanford campus into a friend’s garage and to incorporate Google, Inc.

Technorati Tags: DARPA, Google, National Science Foundation

I have argued before (and here) that many (perhaps even most) religious adherents have personal encounters which they perceive to be of the divine.   These experiences, despite being widespread (in apparently all human societies, and seemingly across all human history) have so far proven not to be objectively replicable, which therefore invalidates them as evidence for scientific claims.  But this does not invalidate them as evidence for personal beliefs and actions.   Indeed, quite the contrary.   If I prefer the taste of drinking coffee to the taste of drinking tea, it is because of my personal subjective experiences of the two liquids; there is nothing that an explicitly-socially-negotiated activity such as science should have to say on the matter of my personal preferences or tastes.    To argue otherwise is to misunderstand the nature of science as an activity as well as to intrude on my autonomy as a human person.  (In other words, if Richard Dawkins chooses not to believe in a non-material realm, fine.  But he has no moral right to tell other people what they may believe or practice, let alone to assert that they should ignore or discount their own personal experiences in deference to his.)

All the writers here are victims of the bias in our western intellectual culture these last three centuries, a bias which is long-run consequence of the European religious wars of the 17th century.  This bias favours beliefs over actions, favours propositions over other representations of knowledge, and (inter alia) favours written accounts over other forms of communication.  There are many ways of knowing besides beliefs, and other forms of knowledge representation besides propositions.  For example:

• Know-What: We may know what objects there are in the world, what features they have, and what their relationships are to each other.  Such knowledge may be expressed in the form of propositions – statements which are true (or not) of the portion of reality they purport to describe.  On the other hand, not all human communicative utterances are statements with truth values (perhaps, indeed, most utterances are not), and a great part of the meaning of utterances is unrelated to their truth or falsity relative to the world.  Arguably, the mainstream of pure mathematics since Mario Pieri’s and David Hilbert’s work in creating and studying formal axiom systems in the 1890s has been unrelated to truth or falsity.    As Eagleton is trying to argue, many religious actions may be communicative utterances with profound meanings and highly-impactful consequences not associated with their truth values.  And in situations of extreme uncertainty, where the very objects in the domain are unknown, let alone their attributes and relationships, I am not convinced it makes any sense at all to talk of propositional representation of knowledge of the domain.
• Know-How:  We may be able  to perform some activity, or exercise some skill, without our knowledge being expressed, or even expressible, in propositional form.  All of us can breathe, most of us can walk down a staircase, some of us can even hit a 99-mile-per-hour cricket ball over the boundary or improvise jazz on a piano or successfully manage a large software development team, yet every single one of us would be hard-put to express any of these abilities in propositional form.  Indeed, many of us would be hard part to even describe the actions involved  in these activities:  try saying exactly what basic actions, in what precise order, you undertake to walk down stairs, for instance. (And as any sportsman or musician can tell you, the whole point of achieving mastery of some physical skill is to put it out of conscious awareness – to be such a master of the skill that one does not think consciously about it and cannot describe it verbally.)    In any case, representation of  know-how knowledge may be much better undertaken with collections of actions or commands, rather than with propositions, as in computer protocols (such as HTTP).
• Know-Where: We may know our geographic position, relative for instance to the sun or to the directions of the compass, without even necessarily being aware that we have this knowledge, let alone being able to put it into propositional form.   Yet we may use this knowledge even without being aware that we have it.   In an earlier post, I reported on experiments in Australian aboriginal communities which demonstrated that some people maintain a sense of their relative geographic position all the time, and use this knowledge without conscious awareness, in the same way that we all breathe or most of us walk down stairs.   One could easily imagine non-propositional representation of know-where knowledge by means (for instance) of co-ordinates in some manifold, or a directed arrow (a vector) always pointing from the knower to some other point, such as the sun or the north pole.
• Know-Who (or perhaps, in  deference to Huxley, Know-That):  We may know that some entity exists merely by being in its presence.   We may know this without use of any of our so-called “five senses“, but through some other means.  The Sufi school of Illuminationism, founded by Shahab al-Din Suhrawardi (1155-1191), is an example of a mystical tradition based on such knowledge.    Anyone who has been in love or felt the love of another person will have had something like this knowledge.

Wittgenstein famously said, “Whereof one cannot speak, thereof one must remain silent.“  He was certainly not claiming that there is nothing about which one cannot speak.

Reference:

Aldous Huxley [1944]: The Perennial Philosophy.  London, UK:  Chatto and Windus.

Mehdi Amin Razavi [1996]: Suhrawardi and the School of Illumination.  London, UK:  Routledge.

Technorati Tags: Terry Eagleton, Richard Dawkins, Sufi, Illuminationism, Suhrawardi

Asking for a theory of intentions and intentionality does not a creationist one make, despite the vitriol often deployed by supporters of evolution.  One non-creationist evolutionary biologist who has long been a critic of this absence of a subtle theory of intentionality in biology is J. Scott Turner, whose theories are derived from homeostasis he has observed in natural ecologies.   I previously discussed some of his ideas here.

References:

Alfred Gell [1998]:  Art and Agency:  An Anthropological Theory.  Oxford, UK: Clarendon Press.

J. Scott Turner [2007]: The Tinkerer’s Accomplice: How Design Emerges from Life Itself. Cambridge, MA, USA:  Harvard University Press.

Technorati Tags: intentionality, homeostasis, Alfred Gell, Scott Turner

To express my gratitude, I thought I would list some of the people whose ideas have influenced me, either directly through their lectures, or indirectly through their books and other writings.   In the second category, I have not included those whose ideas have come to me mediated through the books or lectures of others, which therefore excludes many mathematicians whose work has influenced me (in particular:  Newton, Leibniz, Cauchy, Weierstrauss, Cantor, Frege, Poincare, Hilbert, Lebesque, Godel, and Kolmogorov).  I have also not included the many writers of poetry, fiction, history and biography whose work has had great impact on me.  These two categories also exclude people whose intellectual influence has been manifest in non-verbal forms, such as through visual arts or music, or via working together, since those categories need posts of their own.

Teachers & lecturers I have had who have influenced my thinking include:  Leo Birsen (1902-1992), Sr. Claver Butler RSM (d. 2009), Burgess Cameron, Sr. Clare Castle RSM,  John Coates, Dot Crowe, James Cutt, Bro. Clive Davis FMS, Tom Donaldson (1945-2006), Sol Encel, Richard Gill, Rachel Harland, Chip Heathcote, Alec Hope (1907-2000),  John Hutchinson, Marg Keetles, Joe Lynch, Robert Marks, John McBurney (1932-1998), David Midgley, Terry O’Neill, Jim Penberthy* (1917-1999), Malcolm Rennie (1940-1980), John Roberts, Gisela Soares, Brian Stacey (1946-1996), Frank Torpie, Myrtle Torrens (1909-1984), Neil Trudinger, David Urquhart-Jones, Frederick Wedd (1890-1972), Gary Whale, Ted Wheelwright (1921-2007), and John Woods.

People whose writings have influenced my thinking include: John Baez, Ole Barndorff-Nielsen, Charlotte Joko Beck, Johan van Bentham, Mark Evan Bonds, John Cage (1912-1992), Albert Camus (1913-1960), John Miller Chernoff, Sam Eilenberg (1913-1998), Paul Feyerabend (1924-1994), Kyle Gann, Alfred Gell (1945-1997), Herb Gintis, Jurgen Habermas, Charles Hamblin (1922-1985), Vaclav Havel,  Jaakko Hintikka, Eric von Hippel, Wilfrid Hodges, Christmas Humphreys (1901-1983), Jon Kabat-Zinn, Herman Kahn (1922-1983), Maynard Keynes (1883-1946), Paul Krugman, Imre Lakatos (1922-1974), Trevor Leggett (1914-2000), George Leonard (1923-2010), Brad de Long, Donald MacKenzie,  Saunders Mac Lane, Karl Marx (1818-1883), Grant McCracken, Henry Mintzberg, Philip Mirowski, Michel de Montaigne (1533-1592), Michael Porter, Charles Reich, Jean-Francois Revel (1924-2006), Bertrand Russell (1872-1970), Pierre Ryckmans (Simon Leys), Oliver Sacks, George Shackle (1903-1992), Cosma Shalizi, Raymond Smullyan, Rory Stewart, Anne Sweeney (d. 2007), Nassim Taleb, Stephen Toulmin (1922-2009), Scott Turner, Roy Weintraub, Geoffrey Vickers (1894-1982), and Richard Wilson.

FOOTNOTES: 

* Which makes me a grand-pupil of Nadia Boulanger (1887-1979).

** Of course, this being the World-Wide-Web, I need to explicitly say that nothing in what I have written here should be taken to mean that I agree with anything in particular which any of the people mentioned here have said or written.

His [theological investigation] was a voyage of personal discovery; even the Principia required Halley’s exertions as a midwife to bring them to light.  Newton might share his religious opinions with other members of the remnant, as he did in his letters to Locke, but he worried about the consequences of their wider dissemination:  ‘I was of opinion my papers had lain still & am sorry to heare there is news about them.  Let me entreat you to stop their translation & impression so soon as  you can for I designe to suppress them.‘  Newton’s concern may have reflected fear of being discovered to hold unorthodox  opinions, but it was also the product of religious motives.  Not everyone could be expected to comprehend ‘strong meat’, which was  intended for personal consumption, and which might be wasted on others.” (p.299)

His [Newton's] theology pervaded his alchemy, in his analysis of the Emerald Tablets of Hermes Trismegistus, and in turn  his alchemy suggested to him how matter might be understood physically.  A true understanding of the uses of language enabled  Newton to introduce astronomical calculation into his chronological writings, and to complete his mathematical arguments with theological references:

[pagebreak]
. . . .  

Mathematics was God’s language; the language of the prophets communicated God’s purposes and ‘times’ to men.  Newton felt it was his duty to understand and to reconcile the two, to decipher the hieroglyphs which corrupted religion and learning had obscured.   The problems of mathematics ended in the solutions of divine majesty, and mathematical language solved the theological problem of describing Newton’s Arian interpretation of the relations within the Trinity:

. . . 

Newton’s natural philosophical and theological discoveries removed the obscurities from divine language, in the books of nature and of scripture.  In the life of the true believer, the two could not be separated.  But most had to be content with the milk for babes, because Newton’s own language was beyond them.” (pp. 300-301).

Reference:

Scott Mandelbrote [1993]:  ‘A dute of the greatest moment’: Isaac Newton and the writing of biblical criticism. British Journal of the History of Science, 26:  281-302.

So when an historian writes the following it is very plausible, at least to people aware of Newton’s religious beliefs and interests:

Isaac Newton (1642-1727) was also enamored of Egyptian wisdom, as we shall see in the next chapter, even if it is not clear that he accepted the Hermetic tradition.  It was essential for his theory of gravitation to have an accurate measure of the world’s circumference, and for that he needed to calculate exactly a single degree of latitude.  Newton was convinced that there was no need to send a team of surveyors to plot distances on the ground, as the French were doing.  It was rather easier to determine the exact length of an Egyptian cubit, which ancient authors insisted was directly related to a degree of latitude.   This information could be obtained from the dimensions of the Great Pyramid, which was always believed (perhaps rightly) to enshrine perfect units of length, area and volume, as well as pi.  Sadly, the results of the Pyramid experiment did not fit Newton’s calculations, but, instead of scrapping the theory, the great scientist blamed the surveyors instead.   As luck would have it, the French astronomer Jean Picard (1620-82) succeeded in 1671 in measuring perfectly a degree of latitude in Sweden, so Newton could prove his theory of gravitation without the Egyptians.” (Katz 2005, page 31)

The reference the author David Katz cites for this story is Shalev 2002.  But consulting that reference, we do not see mentioned the story Katz relates here.  Instead, we find this sentence (on page 574): 

As Robert Palter has argued in his critique on Bernal’s  Black Athena, there is no evidence to show that Newton related his interest in the Egyptian cubit to his physics and geodesy.”

with the reference being to Palter 1993, pages 245 and following.

What is going on here?  Has Katz mistakenly cited the wrong source for the story above, something easy enough to do in academic writing?   Perhaps Katz could tell us.  I hope it is a simple mistake in citation, and not something more sinister.

References:

David S. Katz [2005]: The Occult Tradition:  From the Renaissance to the Present Day.  (London, UK: Jonathan Cape).

Robert Palter [1993]:  Black Athena, Afro-Centrism, and the history of science. History of Science, 31: 227-287.

Zur Shalev [2002]:  Measurer of all things: John Greaves (1602-1652), the Great Pyramid and early modern metrology. Journal of the History of Ideas, 63: 555-575.

Technorati Tags: Isaac Newton

An Argument from Ignorance:

FROM: There is no evidence that BSE in cattle causes CJD in humans.
CONCLUDE:  BSE in cattle does not cause CJD in humans.

An Argument from Analogy:

FROM:  BSE is similar to scrapie in certain respects.
AND: Scrapie has not transmitted to humans.
CONCLUDE:   BSE will not transmit to humans.

Cummings argues that such arguments were justified for science policy, since the two presumptive conclusions adopted acted to guide the direction and prioritisation of subsequent scientific research efforts.  These presumptive conclusions did so despite both being defeasible, and despite, in fact, both being subsequently defeated by the scientific research they invoked.   This is a very interesting viewpoint, with much to commend it as a way to construe (and to reconstrue) the dynamics of scientific epistemology using argumentation.  It would be nice to combine such an approach with Marcello Pera’s 3-person model of scientific progress (Pera 1994), the persons being:  the Investigator, the Scientific Community, and Nature.

Some might be tempted to also believe that these arguments were justified in public health policy terms – for example,  in calming a nervous public over fears regarding possible BSE in humans.   However, because British public policy makers did in fact do just this and because the presumptive conclusions were subsequently defeated (ie, shown to be false), the long-term effect has been to make the great British public extremely suspicious of any similar official pronouncements.   The rise in parents refusing the triple MMR vaccine for their children is a direct consequence of the false assurances we were given by British health ministers about the safety of eating beef.   An argumentation-based  theory of dynamic epistemology in public policy would therefore need to include some game theory.    There’s also a close connection to be made to the analysis of the effects of propaganda and counter-propaganda (as in George 1959), and of intelligence and counter-intelligence.

References:

Louise Cummings [2009]: Emerging infectious diseases: coping with uncertainty.  Argumentation, 23 (2): 171-188.

Alexander L. George [1959]: Propaganda Analysis:  A Study of Inferences Made from Nazi Propaganda in World War II.  (Evanston, IL, USA: Row, Peterson and Company).

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