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RE-DESIGN Posts

Antifragility goes to Davos

Published by demetriodor on February 7, 2013

A sample of statements made at the 2013 meeting of the world leaders in Davos (as reported by Felix Salmon): “[We have] removed the tail risk from the euro” Mario Draghi (European Central Bank) ‘There is no tail risk anymore’ Oli Rehn (European Commission) “In Europe, the tail risk has been moved off the table” Zhu Min (IMF) …….. …….. “[R]andomness in the Black Swan domain [fat tails] is intractable…The limit is mathematical, period, and there is no way around it on this planet. What is nonmeasurable and nonpredictable will remain nonmeasurable and nonpredictable…” (Nassim Taleb, Antigragility, p. 138) “While in the past people of rank or status were those and only those who took risks, who had the downside for their actions, and heroes were those who did so for the sake of others, today the exact reverse is taking place. We are witnessing the rise of a new class of inverse heroes, that is, bureaucrats, bankers, Davos-attending members of the I.A.N.D. (International Association of Name Droppers), and academics with too much power and no real downside and/or accountability” (Nassim Taleb, Antigragility, p. 6)

Discretion is Fractal

Published by demetriodor on February 1, 2013

Last week, I made a presentation at the Leiden University conference ‘Political Legitimacy and the Paradox of Regulation’ under the admittedly esoteric title ‘Discretion is Fractal’. Despite the title, my point is actually quite simple: one cannot continue to model, conceptualize and measure (administrative or legal) discretion as a linear phenomenon because of the nested structure of legal norms which exhibits self-similarity at different levels of observation. And, yes, this means that law is fractal, too. In the same way there is no definite answer to the question ‘how long is the coast of Britain‘, there can be no answer to the question which legal code provides for more discretion, unless a common yardstick and level of observation is used (which requires an analytic reconstruction of the structure of the legal norms). The presentation tries to unpack some of the implications of the fractal nature of legal norms and proposes an alternative strategy for measuring discretion. Here is a pdf of the presentation which I hope makes some sense on its own.

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Published by demetriodor on January 30, 2013

After a not-so-short hiatus during which I visited friends and family in Bulgaria, went through a couple of seasonal colds, and got a new workstation up and running (MacBook Pro with W7), I am finally back to the blog. As a warm-up, a bunch of interesting links you might have missed during January: The art of procrastination (surprisingly effective) Why students don’t learn Modeling the spread of rumours (in the digital age) Visualizing the connections between the artists who developed abstract art Coursera starts a free course on Social Network Analysis (with R) Calls for more science-based policy making coming from the UK What happens when you take you econometric results too seriously  What’s like to spend 40 years in the Siberian taiga (if you understand Russian, watch the amazing videos about the story) Coral by Fleix Salazar [via Colossal]

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Published by demetriodor on December 21, 2012

Philip Tetlock on political forecastingInterview with Pearl on causal inferenceBrian Jones on the possibility for change in American gun (safety) policyFights over evidence in medicineThe Mayan Doomsday’s Effects on Survival Rates

New data source for political science researchers

Published by demetriodor on December 19, 2012

Political Data Yearbook Interactive is a new source for data on election results, turnout and government composition for all EU and some non-European countries. It is basically an online version of the yearbooks that ECPR printed as part of the European Journal for Political Research for many years now. The interactive online tool has some (limited) visualization options and can export data in several formats.

Music Network Visualization

Published by demetriodor on December 11, 2012

Note: probably of interest only to the intersection of the readers who are into niche music genres and those interested in network visualization. My music interests have always been rather, hmm…, eclectic. Somehow IDM, ambient, darkwave, triphop, acid jazz, bossa nova, qawali, Mali blues and other more or less obscure genres have managed to happily co-exist in my music collection. The sheer diversity always invited the question whether there is some structure to the collection, or each genre is an island of its own. Sounds like a job for network visualization! Now, there are plenty of music network viz applications on the web. But they don’t show my collection, and just seem unsatisfactory for various reasons. So I decided to craft my own visualization using R and igraph. As a first step I collected for all artists in my last.fm library the artists that the site classifies as similar. So I piggyback on last.fm for the network similarity measures. I also get info on the most-often used tag for the artist and the number of plays it has on the site. The rest is pretty straightforward as can be seen from the code. # Load the igraph and foreign packages (install if needed) require(igraph) require(foreign) lastfm<-read.csv(“http://www.dimiter.eu/Data_files/lastfm_network_ad.csv”, header=T, encoding=”UTF-8″) #Load the dataset lastfm$include<-ifelse(lastfm$Similar %in% lastfm$Artist==T,1,0) #Index the links between artists in the library lastfm.network<-graph.data.frame(lastfm, directed=F) #Import as a graph last.attr<-lastfm[-which(duplicated(lastfm$Artist)),c(5,3,4) ] #Create some attributes V(lastfm.network)[1:106]$listeners<-last.attr[,2] V(lastfm.network)[107:length(V(lastfm.network))]$listeners<-NA V(lastfm.network)[1:106]$tag<-last.attr[,3] V(lastfm.network)[107:length(V(lastfm.network))]$tag<-NA #Attach the attributes to the artist from the library (only) V(lastfm.network)$label.cex$tag<-ifelse(V(lastfm.network)$listeners>1200000, 1.4, (ifelse(V(lastfm.network)$listeners>500000, 1.2, (ifelse(V(lastfm.network)$listeners>100000, 1.1,…

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