RE-DESIGN

David Graeber’s ‘Debt’ will shake your world

Published by demetriodor on March 7, 2012

David Graeber’s ‘Debt: The First 5,000 Year‘ is easily the most thought-provoking, insightful, erudite and provocative book I have read over the last few years. While you can disagree with particular arguments or resist certain conclusions, it will shake your most fundamental assumptions about social life. After reading the book, you will never see money, credit, war, debt, slavery, states, religion, capitalism, finance, economics, anthropology, presents, hierarchy, and history in the same way again. Don’t be fooled by the title (and the horrendous cover) – this book is nothing less than a reconstruction of world history in the grand traditions of Toynbee, Spengler, Jaspers, and Braudel. Debt plays center stage but one learns just as much about the genesis of the state, the origin of money, the history of slavery and the meaning of gifts. The approach of the book not only spans history, anthropology, social science and philosophy but switches effortlessly between the empirical and the normative, the theoretical and the metaphysical. Which is actually, my major problem with the book. The prose is so convincing and the erudition of the author so deep that one has to be constantly on the alert to separate the evidence from the opinion, the analysis from the speculation, the social critique from the dispassionate search for scientific truth (I suspect Graeber wouldn’t really agree that these can be separated anyways). Personally, I found the demolition with the help of anthropological evidence of the ‘foundational myth of the discipline of economics’ – the…

Creating Data Maps

Published by demetriodor on March 5, 2012

There are several online tools for data visualization including IBM’s ManyEyes and Google’s Chart Tools. For a recent post on the other blog to which I contribute I wanted to map the distribution of a variable on a geographical map of Europe. I decided that’s a good opportunity to try a site called Target Map which promises free, high-quality, customizable data maps. The result of my efforts can be seen below: The link to the map is here. Altogether, I can’t say that I am too happy with the mapping utility. My main quibble is that there are no default color palettes that translate well continuous variables into color hues. By default, the program offers highly contrasting color choices for the different categories but ones that don’t suggest the ranking of categories. And I couldn’t find an easy way to customize the color palette. Data entry is OK, although once you select Europe as the geographical scope of your data, you can’t have any values for Turkey, for example, even if you try to supply them manually. Altogether, Target Map might be useful for some very small and inconsequential projects but for serious staff one should bite the bullet and get familiar with R’s map utilities (something I have been planning to do for a while).

Satan in Academia

Published by demetriodor on February 29, 2012

Republican Presidential hopeful Rick Santorum in 2008: “Where did Satan start? The place where he was, in my mind, the most successful and first — first successful was in academia. He understood pride of smart people. He attacked them at their weakest. They were in fact smarter than everybody else and could come up with something new and different — pursue new truths, deny the existence of truth, play with it because they’re smart. And so academia a long time ago fell.” More at Inside Higher Ed

Explanation and the quest for ‘significant’ relationships. Part II

Published by demetriodor on February 22, 2012

In Part I I argue that the search and discovery of statistically significant relationships does not amount to explanation and is often misplaced in the social sciences because the variables which are purported to have effects on the outcome cannot be manipulated. Just to make sure that my message is not misinterpreted – I am not arguing for a fixation on maximizing R-squared and other measures of model fit in statistical work, instead of the current focus on the size and significance of individual coefficients. R-squared has been rightly criticized as a standard of how good a model is** (see for example here). But I am not aware of any other measure or standard that can convincingly compare the explanatory potential of different models in different contexts. Predictive success might be one way to go, but prediction is altogether something else than explanation. I don’t expect much to change in the future with regard to the problem I outlined. In practice, all one could hope for is some clarity on the part of the researchers whether their objective is to explain (account for) or find significant effects. The standards for evaluating progress towards the former objective (model fit, predictive success, ‘coverage’ in the QCA sense) should be different than the standards for the latter (statistical & practical significance and the practical possibility to manipulate the exogenous variables). Take the so-called garbage-can regressions, for example. These are models with tens of variables all of which are interpreted causally if they reach the magic…

Explanation and the quest for ‘significant’ relationships. Part I

Published by demetriodor on February 17, 2012

The ultimate goal of social science is causal explanation*. The actual goal of most academic research is to discover significant relationships between variables. The two goals are supposed to be strongly related – by discovering (the) significant effects of exogenous (independent) variables, one accounts for the outcome of interest. In fact, the working assumption of the empiricist paradigm of social science research is that the two goals are essentially the same – explanation is the sum of the significant effects that we have discovered. Just look at what all the academic articles with ‘explanation’, ‘determinants’, and ’causes’ in their titles do – they report significant effects, or associations, between variables. The problem is that explanation and collecting significant associations are not the same. Of course they are not. The point is obvious to all uninitiated into the quantitative empiricist tradition of doing research, but seems to be lost to many of its practitioners. We could have discovered a significant determinant of X, and still be miles (or even light-years) away from a convincing explanation of why and when X occurs. This is not because of the difficulties of causal identification – we could have satisfied all conditions for causal inference from observational data, but the problem still stays. And it would not go away after we pay attention (as we should) to the fact that statistical significance is not the same as practical significance. Even the discovery of convincingly-identified causal effects, large enough to be of practical rather than only statistical significance, does not amount to explanation. A successful explanation needs to account for…

Google tries to find the funniest videos

Published by demetriodor on February 15, 2012

Following my recent post on the project which tries to explain why some video clips go viral, here is a report on Google’s efforts to find the funniest videos: You’d think the reasons for something being funny were beyond the reach of science – but Google’s brain-box researchers have managed to come up with a formula for working out which YouTube video clips are the funniest. The Google researcher behind the project is quoted saying: ‘If a user uses an “loooooool” vs an “loool”, does it mean they were more amused? We designed features to quantify the degree of emphasis on words associated with amusement in viewer comments.’ Other factors taken into account are tags, descriptions, and ‘whether audible laughter can be heard in the background‘. Ultimately, the algorithm gives a ranking of the funniest videos (with No No No No Cat on top, since you asked). Now I usually have high respect for all things Google, but this ‘research’ at first appeared to be a total piece of junk. Of course, it turned out that it is just the way it is reported by the Daily Mail (cited above), New Scientist and countless other more or less reputable outlets. Google’s new algorithm does not provide a normative ranking of the funniest videos ever based on some objective criteria; it is a predictive score about the video’s comedic potential. Google trained the algorithm on a bunch of videos (it’s unclear from the original source what the external ‘fun’ measure used for the…

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