on intelligent design…

Posted in Books, Kids, Travel with tags , , , , , , , on August 19, 2014 by xi'an

In connection with Dawkins’ The God delusion, which review is soon to appear on the ‘Og, a poster at an exhibit on evolution in the Harvard Museum of Natural History, which illustrates one of Dawkins’ points on scientific agosticism. Namely, that refusing to take a stand on the logical and philosophical opposition between science and religion(s) is not a scientific position. The last sentence in the poster is thus worse than unnecessary…

les sciences face aux créationnismes [book review]

Posted in Books with tags , , , , , , on March 9, 2014 by xi'an

I spotted this small book during my last visit to CBGP in Montpellier, and borrowed it from the local librarian. It is written (in French) by Guillaume Lecointre, who is professor of Biology at the Muséum National d’Histoire Naturelle in Paris, specialised in population evolution and philogenies. The book is published by Editions Quae, a scientific editor supported by four founding French institutes (CIRAD, IFREMER, INRA and IRSTEA), hence no wonder I would spot it in an INRA lab. The theme of the book is not to argue against creationism and intelligent design theories, but rather to analyse how the debates between scientists—interestingly this term scientist sounds much more like a cult in English than the French noun scientifique— and creationists are conducted and to suggest how they should be conducted. While there are redundancies in the text, I found the overall argumentation quite convincing, with the driving lines that creationists are bypassing the rules of scientific investigation and exchange to bring the debate at a philosophical or ideological level foreign to science definition. Lecointre deconstructs the elements put forward in such debates, from replacing the incompleteness of the scientific knowledge and the temporary nature of scientific theories with a total relativism, to engaging scientific supporters from scientific fields not directly related with the theory of evolution, to confusing methodological materialism with philosophical materialism and more fundamentally to imply that science and scientific theories must have a moral or ideological content, and to posturing as anti-establishment and anti-dogmatic free minds… I also liked the points that (a) what really drives the proponents of intelligent design is a refusal of randomness in the evolution, without any global or cosmic purpose; (b) scientists are very ill-prepared to debate with creationists, because the later do not follow a scientific reasoning; (c) journalists are most often contributing to the confusion by picking out-of-their-field “experts” and encouraging the relativity argument. Hence a reasonable recommendation to abstain from oral debates and to stick to pointing out the complete absence of scientific methodology in creationists’ arguments. (Obviously, readers of Alan Sokal’s Beyond the Hoax will be familiar most of the arguments produced in les sciences face aux créationnismes.)

Philosophy of Science, a very short introduction (and review)

Posted in Books, Kids, Statistics, Travel with tags , , , , , , , , , , , on November 3, 2013 by xi'an

When visiting the bookstore on the campus of the University of Warwick two weeks ago, I spotted this book, Philosophy of Science, a very short introduction, by Samir Okasha, and the “bargain” offer of getting two books for £10 enticed me to buy it along with a Friedrich Nietzsche, a very short introduction… (Maybe with the irrational hope that my daughter would take a look at those for her philosophy course this year!)

Popper’s attempt to show that science can get by without induction does not succeed.” (p.23)

Since this is [unsusrprisingly!] a very short introduction, I did not get much added value from the book. Nonetheless, it was an easy read for short trips in the metro and short waits here and there. And would be a good [very short] introduction to any one newly interested in the philosophy of sciences. The first chapter tries to define what science is, with reference to the authority of Popper (and a mere mention of Wittgenstein), and concludes that there is no clear-cut demarcation between science and pseudo-science. (Mathematics apparently does not constitute a science: “Physics is the most fundamental science of all”, p.55) I would have liked to see the quote from Friedrich Nietzsche

“It is perhaps just dawning on five or six minds that physics, too, is only an interpretation and exegesis of the world (to suit us, if I may say so!) and not a world-explanation.”

in Beyond Good and Evil. as it illustrates the main point of the chapter and maybe the book that scientific theories can never be proven true, Plus, it is often misinterpreted as a anti-science statement by Nietzsche. (Plus, it links both books I bought!) Continue reading

Evidence and evolution (5)

Posted in Books, Statistics with tags , , , , , , , , , , , , on April 29, 2010 by xi'an

“Tout étant fait pour une fi n, tout est nécessairement pour la meilleure fi n. Remarquez bien que les nez ont été faits pour porter des lunettes, aussi avons-nous des lunettes.” Voltaire, Candide, Chapitre 1.

I am now done with my review of Sober’s Evidence and Evolution: The Logic Behind the Science, Posting about each chapter along the way helped me a lot to write down the review over the past few days. Its conclusion is that

Evidence and Evolution is very well-written, with hardly any typo (the unbiasedness property of AIC is stated at the bottom of page 101 with the expectation symbol E on the wrong side of the equation, Figure 3.8c is used instead of Figure 3.7c on page 204, Figure 4.7 is used instead of Figure 4.8 on page 293, Simon Tavaré’s name is always spelled Taveré, vaules rather than values is repeated four times on page 339). The style is sometimes too light and often too verbose, with an abundance of analogies that I regard as sidetracking, but this makes for an easier reading (except for the sentence “the key to answering the second question is that the observation that X = 1 and Y = 1 produces stronger evidence favoring CA over SA the lower the probability is that the ancestors postulated by the two hypotheses were in state 1”, on page 314, that still eludes me!). As detailed in this review, I have points of contentions with the philosophical views about testing in Evidence and Evolution as well as about the methods exposed therein, but this does not detract from the appeal of reading the book. (The lack of completely worked out statistical hypotheses in realistic settings remains the major issue in my criticism of the book.) While the criticisms of the Bayesian paradigm are often shallow (like the one on page 97 ridiculing Bayesians drawing inference based on a single observation), there is nothing fundamentally wrong with the statistical foundations of the book. I therefore repeat my earlier recommendation in favour of Evidence and Evolution, Chapters 1 and (paradoxically) 5 being the easier entries. Obviously, readers familiar with Sober’s earlier papers and books will most likely find a huge overlap with those but others will gather Sober’s viewpoints on the notion of testing hypotheses in a (mostly) unified perspective.

And, as illustrated by the above quote, I found the sentence from Voltaire’s Candide I wanted to include. Of course, this 12 page review may be overly long for the journal it was intended for, Human Genetics, in which case I will have to find another outlet for the current arXived version. But I enjoyed reading this book with a pencil and gathered enough remarks along the way to fill those twelve pages.

Evidence and evolution (3)

Posted in Books, Statistics with tags , , , , , , , on April 17, 2010 by xi'an

“To test a theory, you need to test it against alternatives.” (E&E, p.190)

After a gruesome (!) trek through Chapter 3 of Sober’s Evidence and Evolution: The Logic Behind the Science, I am now done with this chapter entitled “Natural selection”. The chapter is difficult to read (for someone like me) in that it seems overly repetitive, using somehow obvious arguments while missing clearcut conclusions and directions. This bend must be due to the philosophical priorities of the author but, despite opposing Brownian motion to Ornstein-Uhlenbeck processes at the beginning of the chapter —which would make for a neat parametric model comparison setting—, there is no quantitative argument nor illustration found in this third chapter that would relate to statistics. This is unfortunate as the questions of interest (testing for natural selection versus pure drift or versus phylogenetic inertia or yet for tree structure in phylogenics) could clearly be conducted at a numerical level as well, through the AIC factor or through a Bayesian alternative. The aspects I found most interesting in this chapter may therefore be deemed as marginalia by most readers, namely (a) the discussion that the outcome of a test should at all depend on the modelling assumptions (the author seems to doubt this, hence relegating Bayesian techniques to their dust-gathering shelves!), and (b) the point that parsimony is not a criterion per se.

“Data! Data! Data!’ he cried impatiently, I cannot make bricks without clay!” (Sherlock Holmes, The adventure of the copper beeches)

About the first point, the philosophical stance of the author is not completely foolproof in that he concedes that testing hypotheses without accounting for the alternative is not acceptable. My impression is that he looks at the problem from a purely dichotomous perspective, the hypothesis or [exclusive OR] the alternative being true. This is a bit caricatural as he integrates the issue of calibrating parameters under the different hypotheses, but there is a sort of logical discrepancy lurking in the background of the argument. Again working out a fully Bayesian analysis of a philogenic tree say would have clarified the issue immensely! And rejecting Bayesianism (sic!) because “there is no objective basis for producing an answer” (p.239) is a wee limited on the epistemological side! Even though I understand that the book is not trying to debate about the support for a specific evolutionary hypothesis but rather about the methods used to test such hypotheses and the logic behind these, completely worked-out example would have made my appreciation (and maybe other readers’) of Sober’s points much easier. And, again, I fail to see who could take benefits from reading this chapter. A biologist will most likely integrate the arguments and illustrations provided by Sober but could leave the chapter with a feeling of frustration at the apparent lack of conclusion. (As a statistician, I fail to understand how the likelihoods repeatedly mentioned by Sober can be computed because they never involve any parameter.)

“Parsimony does not provide a justification for ignoring the data.” (E&E, p.250)

Since I am interested in general by the negative impact of the “Ockham’s razor” argument, I find the warning signals about parsimony (given in the last third of the chapter) more palatable. Parsimony being an ill-defined concept, especially from a statistical perspective —where even the dimension of the parameter space is debatable—, no model selection is acceptable if only based on this argument.

“Instead of evaluating hypotheses in terms of how probable they say the data are, we evaluate them by estimating how accurately they’ll predict new data when fitted to old.” (E&E, p.229)

The chapter also addresses the distinction between hypothesis testing and model selection as paramount —a point I subscribed to for a long while before being convinced of the opposite by Peter Green and Jean-Michel Marin—, but I cannot get to the core of this argument. It seems Sober sees model selection through the predictive performances of the models under comparison, if the above quote is representative of his thesis. (Overall, I find the style of the chapter slightly uneven, as if the fact that some sections are adapted from earlier papers would make for different levels of depth.)

Statistically speaking, this chapter also has a difficulty with the continuity assumption. To make this point more precise, I notice there is a long discussion about reaching the optimum configuration (for polar bear fur length) under the SPD hypothesis, but I think evolution happens in discontinuous moves. The case about the local minimum in Section 3.4 is characteristic of this difficulty as a “valley” on a “fitness curve” that in essence takes three possible values over the three different types of eye designs does not really constitute a bottleneck in the optimisation process. Similarly, the temporal structure of the statistical models in Sections 3.3 and 3.5 is never mentioned, even though it needs to be defined for the tests to take place. The past versus current convergence to stationarity or equilibrium and hence to optimality under the SPD hypothesis is an issue (are we there yet?!) and so is the definition of time in the very simple 2×2 Markov chain example… And given a 2×2 contingency table like

$\begin{matrix} &\text{fixed} &\text{polymorphic}\\ \text{synonymous} &17 &42 \\ \text{nonsynonymous} &7 &2\\ \end{matrix}$

testing for independence between both factors is a standard among the standards: I thus fail to understand the lengthy and inconclusive discussion of pp.240-243.

Evidence and evolution (2)

Posted in Books, Statistics with tags , , , , , , , , , , , on April 9, 2010 by xi'an

“When dealing with natural things we will, then, never derive any explanations from the purpose which God or nature may have had in view when creating them and we shall entirely banish from our philosophy the search for final causes. For we should not be so arrogant as to suppose that we can share in God’s plans.” René Descartes, Les Principes de la Philosophie, Livre I, 28

I have now read the second chapter of the book Evidence and Evolution: The Logic Behind the Science by Elliott Sober. The very chapter which title is “Intelligent design”… As posted earlier, I was loath to get into this chapter for fear of being dragged into a nonsensical debate. In fact, the chapter is written from a purely philosophical/logical perspective, while I was looking for statistical arguments given the tenor of the first chapter (reviewing the differences between Bayesians, likelihoodists (sic!), and frequentists). There is therefore very little I can contribute to the debate, being no philosopher of science. I find the introduction of the chapter interesting in that it relates the creationism /”intelligent design” thesis to a long philosophical tradition (witness the above quote from Descartes) rather than to the current political debate about “teaching” creationism in US and UK schools. The disputation of older theses like Paley’s watch is however taking most of the chapter which is disappointing in my humble opinion. In a sense, Sober mostly states the obvious when arguing that when gods or other supernatural beings enter the picture, they can explain for any observed fact with the highest likelihood while being unable to predict any fact not yet observed. I would have prefered to see hard scientific facts and the use of statistical evidence, even of the AIC sort! The call to Popper’s testability does not bring further arguments because Sober also defends the thesis that even the theory of “intelligent” design is falsifiable… In Section 2.19 about model selection, the comparison between a single parameter model and a one million parameter model hints at Ockham’s razor, but Sober misses the point about a  major aspect of Bayesian analysis, which is that by the virtue of hyperpriors and hyperparameters, observations about one group of parameters also brings information about another group of parameters when those are related via a hyperprior (as in small area estimation). Given that the author never discusses the use of priors over the model parameters and uses instead pluggin estimates, he does not take advantage of the marginal posterior dependence between the different groups of parameters.

Evidence and evolution

Posted in Books, Statistics with tags , , , , , , , on April 1, 2010 by xi'an

I have received the book Evidence and Evolution: The Logic Behind the Science by Elliott Sober to review. The book is written by a philosopher of science who has worked on the notion of evidence, in the statistical meaning of the word. I am currently reading the first chapter which is fairly well written and which presents a reasonable picture on the different perspectives (Bayesian, likelihood, frequentist) used for hypothesis testing and model choice. Akaike’s information criterion is a wee too much promoted but that’s the author’s choice after all. However I just came yesterday upon a section where Sober reproduces the error central to Templeton’s thesis and discussed on the Og a few days ago. He indeed states that “the simpler model cannot have the higher prior probability—a point that Popper (1959) emphasized.” And he insists further that there is no reason for thinking that

$P(\theta=0) > P(\theta>0)$

is true (page 84). (The measure-theoretic objections raised earlier obviously apply there as well.) It must thus be more of a common misconception among philosophers of science than I previously thought….

As described on the backcover, the purpose of the book is

“How should the concept of evidence be understood? And how does the concept of evidence apply to the controversy about creationism as well as to work in evolutionary biology about natural selection and common ancestry? In this rich and wide-ranging book, Elliott Sober investigates general questions about probability and evidence and shows how the answers he develops to those questions apply to the specifics of evolutionary biology. Drawing on a set of fascinating examples, he analyzes whether claims about intelligent design are untestable; whether they are discredited by the fact that many adaptations are imperfect; how evidence bears on whether present species trace back to common ancestors; how hypotheses about natural selection can be tested, and many other issues. His book will interest all readers who want to understand philosophical questions about evidence and evolution, as they arise both in Darwin’s work and in contemporary biological research.”

Sober applies these concepts of evidence to some versions of creationism… I am obviously reluctant to go through this second chapter about creationism as there is no use in arguing about the existence of gods in a book about science, but I am still curious to see how Sober analyses this issue.