A few months ago, I received a puzzling advertising for this book, Randomness through Computation, and I eventually ordered it, despite getting a rather negative impression from reading the chapter written by Tomasso Toffoli… The book as a whole is definitely perplexing (even when correcting for this initial bias) and I would not recommend it to readers interested in simulation, in computational statistics or even in the philosophy of randomness. My overall feeling is indeed that, while there are genuinely informative and innovative chapters in this book, some chapters read more like newspeak than scientific material (mixing the Second Law of Thermodynamics, Gödel’s incompleteness theorem, quantum physics, and NP completeness within the same sentence) and do not provide a useful entry on the issue of randomness. Hence, the book is not contributing in a significant manner to my understanding of the notion. (This post also appeared on the Statistics Forum.) Continue reading
Archive for Ockham’s razor
Randomness through computation
Posted in Books, Statistics, University life with tags A Search for Certainty, Alan Sokal, Alan Turing, Andrei Kolmogorov, Apple II, Benford's Law, computation, Hector Zenil, Kurt Gödel, Martin-Löf, Ockham's razor, pseudo-random generator, randomness, statistical tests, Wolfram Research on June 22, 2011 by xi'anEvidence and evolution (3)
Posted in Books, Statistics with tags "intelligent" design, Bayesian model choice, book review, creationism, Elliott Sober, evidence, Ockham's razor, philosophy of sciences 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
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 "intelligent" design, Akaike's criterion, Alan Templeton, Bayesian model choice, book review, creationism, Elliott Sober, evidence, Karl Popper, Ockham's razor, philosophy of sciences, René Descartes 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.
