Sociology

Today is the first day of the “Ways to Protolanguage 3” conference. which takes place on 25–26 May in in Wroc?aw, Poland. The Plenary speakers are Robin Dunbar, Joesp Call, and Peter Gärdenfors Both Hannah and I are at the conference and we’re also live-tweeting about the conference using the hashtag #protolang3 Hannah’s just given her talk Jack J. Wilson, Hannah Little (University of Leeds, UK; Vrije Universiteit Brussel, Belgium) – Emerging languages in esoteric and exoteric niches: evidence from rural sign languages (abstract here) And I’m due tomorrow. Michael Pleyer (Heidelberg University, Germany) - Cooperation and constructions: looking at the evolution of language from a usage-based and construction grammar perspective (abstract here) The Programme can be found here: (Day 1 / Day 2)

Vermont Senator Bernie Sanders knows that the The Food and Drug Administration will not require any special label on foods just because a competitor seeks to market its product differently.And marketing is the only distinction between organic food and traditional food. It's a process and they don't require regular meat to have a big NON-KOSHER label on it either. Kosher food just puts a 'kosher' label on the package and has to obey truth-in-advertising laws. --> read more

As a data analyst and a scientist, Fisher > Neyman, no question. But as a theorist, Fisher came up with ideas that worked just fine in his applications but can fall apart when people try to apply them too generally. Here’s an example that recently came up. Deborah Mayo pointed me to a comment by Stephen Senn on the so-called Fisher and Neyman null hypotheses. In an experiment with n participants (or, as we used to say, subjects or experimental units), the Fisher null hypothesis is that the treatment effect is exactly 0 for every one of the n units, while the Neyman null hypothesis is that the individual treatment effects can be negative or positive but have an average of zero. Senn explains why Neyman’s hypothesis in general makes no sense—the short story is that Fisher’s hypothesis seems relevant in some problems (sometimes we really are studying effects that are zero or close enough for all practical purposes), whereas Neyman’s hypothesis just seems weird (it’s implausible that a bunch of nonzero effects would exactly cancel). And I remember a similar discussion as a student, many years ago, when Rubin talked about that silly Neyman null hypothesis. Thinking about it more, though, I side with Neyman over Fisher, because the interesting problem for me is not testing the null hypothesis, which in nontrivial problems can never be true anyway, but in estimation. And in estimation I am intersted in an average effect, not an effect that is identical across all people. I could imagine a model in which the variance of the treatment effect is proportional to its mean—this would bridge between the Neyman and Fisher ideas—but this is not a model that anyone ever fits. So, just to say it again: if it’s a pure null hypothesis, sure, go with Fisher. But if you’re inverting a family of hypothesis tests to get a confidence interval (something which I’d almost never want to do, but let’s go with this, since that’s the common application of these ideas), I’d go with Neyman, as it omits the implausible requirement that the treatment effect be exactly identical on all items. The post In which I side with Neyman over Fisher appeared first on Statistical Modeling, Causal Inference, and Social Science.

Duplicated images in a research paper have sparked worries that the journal Cell may have been hasty in its peer review process

Dr. Daniel Freeman, professor of clinical psychology at Oxford, has good news if you believe women are more nuts than men: there is a 40% chance you are right.We know that discussing biological differences between men and women is taboo - men and women are no different in any physical way, as former Harvard President Larry Summers will rush to agree these days. But what about in psychological ways? --> read more

Every once in awhile I get a question that I can directly answer from my published research. When that happens it makes me so happy. Here’s an example. Patrick Lam wrote, Suppose one develops a Bayesian model to estimate a parameter theta. Now suppose one wants to evaluate the model via simulation by generating fake data where you know the value of theta and see how well you recover theta with your model, assuming that you use the posterior mean as the estimate. The traditional frequentist way of evaluating it might be to generate many datasets and see how well your estimator performs each time in terms of unbiasedness or mean squared error or something. But given that unbiasedness means nothing to a Bayesian and there is no repeated sampling interpretation in a Bayesian model, how would you suggest one would evaluate a Bayesian model? My reply: I actually have a paper on this! It is by Cook, Gelman, and Rubin. The idea is to draw theta from the prior distribution. You can find the paper in the published papers section on my website. P.S. Although unbiasedness doesn’t mean much to a Bayesian, calibration does. We’re planning on implementing this in Stan at some point. The post Validation of Software for Bayesian Models Using Posterior Quantiles appeared first on Statistical Modeling, Causal Inference, and Social Science.

Social structure is an imposition, but by definition one that ‘should’ be imposed, meaning by the origin of the meaning of “should” in the co-evolution of the social with our language. --> read more

A 12-year study (1999 to 2010) analyzed fatality reductions in bicycle-car collisions to determine the effect of mandatory helmet laws. 16 states had bike helmet laws in the beginning or the study. The researchers identified all relevant fatalities, totaling 1612, in states with and without bike helmet laws. Relevance was determined by adjusting for factors previously associated with rates of motor vehicle fatalities (elderly driver licensure laws, legal blood alcohol limit and household income) and, among those, they found that the adjusted fatality rate was significantly lower in states with helmet laws. On average, 900 people die annually in bicycle-motor vehicle collisions — three quarters of those are from head injuries. --> read more

I’ve been blogging a lot lately about plagiarism (sorry, Bob!), and one thing that’s been bugging me is, why does it bother me so much. Part of the story is simple: much of my reputation comes from the words I write, so I bristle at any attempt to devalue words. I feel the same way about plagiarism that a rich person would feel about counterfeiting: Don’t debase my currency! But it’s more than that. After discussing this a bit with Thomas Basbøll, I realized that I’m bothered by the way that plagiarism interferes with the transmission of information: Much has been written on the ethics of plagiarism. One aspect that has received less notice is plagiarism’s role in corrupting our ability to learn from data: We propose that plagiarism is a statistical crime. It involves the hiding of important information regarding the source and context of the copied work in its original form. Such information can dramatically alter the statistical inferences made about the work. In statistics, throwing away data is a no-no. From a classical perspective, inferences are determined by the sampling process: point estimates, confidence intervals and hypothesis tests all require knowledge of (or assumptions about) the probability distribution of the observed data. In a Bayesian analysis, it is necessary to include in the model all variables that are relevant to the data-collection process. In either case, we are generally led to faulty inferences if we are given data from urn A and told they came from urn B. A statistical perspective on plagiarism might seem relevant only to cases in which raw data are unceremoniously and secretively transferred from one urn to another. But statistical consequences also result from plagiarism of a very different kind of material: stories. To underestimate the importance of contextual information, even when it does not concern numbers, is dangerous. Here’s our full article (which has just appeared in the American Scientist). It features two of the recurring characters from this blog. Here’s our conclusion: Scholars in fields ranging from psychology to history to computer science have recognized that stories are part of how people understand the world. As statisticians, we can consider reasoning from stories as a form of approximate inference. From this perspective, statistical principles should provide some approximate guidance about the potential biases and precision of such inferences. One key principle is not to throw away information and, if discarding data is for some reason necessary, to describe as clearly as possible the mechanism by which the relevant information was excluded. Plagiarism violates both these rules and, as such, is a violation of statistical ethics, beyond any other considerations of moral behavior. P.S. I’m more interested in scientific plagiarism than the legal or literary variety, but this 2004 news article by Daniel Hemel and Lauren Schuker (which I found by googling *laurence tribe plagiarism*) is full of good quotes. Here’s my favorite part: Tribe’s mea culpa comes just three weeks after another prominent Harvard faculty member—Climenko Professor of Law Charles J. Ogletree—publicly apologized for copying six paragraphs almost word-for-word from a Yale scholar in a recent book, All Deliberate Speed. Last fall, Frankfurter Professor of Law Alan M. Dershowitz also battled plagiarism charges. And in 2002, Harvard Overseer Doris Kearns Goodwin admitted that she had accidently copied passages from another scholar in her bestseller The Fitzgeralds and the Kennedys. University President Lawrence H. Summers told The Crimson in an interview last week—before the allegations against Tribe surfaced—that he did not see “a big trend” of plagiarism problems at the Law School as a result of the charges against Ogletree and Dershowitz, but indicated that a third case would change his mind. “If you had a third one, then I would have said, okay

Once again, cultural evolution, and the problem of memes: What are they? Where are they? What do they do? While the general case does interest me, culture is so various that it is impossible to think about it directly. One has to think about specific cases. As details are important, I want to choose a fairly specific case, that of jazz in mid-20th-Century America. I want you to imagine that you’re in a jazz club in, say, Philadelphia, in, say, mid-October of 1952. It’s 1:30 in the morning, and the tune is Charlie Parker’s “Dexterity.” The piano player counts it off–ah one, ah two, one two three four… But we’re getting ahead of ourselves. We need a little conceptual equipment before considering the example. It’s the conceptual equipment that’s in question. Make no mistake, the concept of memes is conceptual equipment, and it’s confused and confusing. Roles in Cultural Selection Genes and phenotypes play certain roles in a more or less standard account of biological evolution. The phenotype interacts with the environment, where it either succeeds or fails at reproduction, depending on the “fit” between its traits and that environment. Where the phenotype is successful at reproduction, it is the genes which are said to carry heredity from one generation to the next. In one very widespread account genes are said to be replicators. That is to say, replication is the role they play in evolutionary change. Here’s what Peter Godfrey-Smith has to say about that (The Replicator in Retrospect, Biology and Philosophy 15 (2000): 403-423.): In The Selfish Gene (1976), Richard Dawkins had argued that individual genes must be seen as the units of selection in evolutionary processes within sexual populations. This is primarily because the other possible candidates, notably whole organisms and groups, do not “replicate.” Organisms and groups are ephemeral, like clouds in the sky or dust storms in the desert. Only a replicator, which can figure in selective processes over many generations, can be a unit of selection. At the same time Dawkins coined the term “meme” to name entities filling the replicator role in cultural evolution. Later on he used the term “vehicle” to designate the entity that interacts with the environment. In biological evolution it is phenotypes that are the vehicles. In cultural evolution, well, that’s a matter of some dispute. And that more general dispute–what are the roles in cultural evolution and what kinds of things occupy them?–is what interests me. However, I don’t particularly like the term “vehicle.” As Godfrey-Smith has noted, following others, it is a gene-centric term, characterizing what entities do from the so-called “gene’s eye” perspective. I’d prefer a more neutral perspective and so will use a term coined by Richard Hull, “interactor.” Here are definitions as Godfrey-Smith gives them: Replicator: an entity that passes on its structure largely intact in successive replications. Interactor: an entity that interacts as a cohesive whole with its environment in such a way that this interaction causes replication to be differential. We need one more role, that of beneficiary, as defined by Elisabeth Lloyd. “The beneficiary, for Lloyd, is the entity that ‘ultimately benefits’ from a process of evolution by selection” (Godfrey-Smith, see also Lloyd’s treatment of Units and Levels of Selection HERE). Godfrey-Smith goes on to suggest that “that most of the language of … ‘ultimate benefit’ in this context is merely metaphorical.” That may be so, however, I find it useful, at least heuristically. Consider for example gene-culture coevolution school of thinking, which offers a technically sophisticated treatment of cultural change. In that tradition it is biological organisms, mostly humans, but also chimpanzees, songbirds and some other animal species, that occupy the beneficiary role. But that is not the case in any version of memetics, where it is the memes that are the beneficiaries. In fact, part of the app