Comments on: Redefining the Gap 11, Results

By: tdaxp » Blog Archive » Redefining the Gap 4, First Geopolitical Theories

Sat, 09 Aug 2008 13:14:48 +0000

[...] the Gap 9. Methods and Operationalizations Redefining the Gap 10. Limitations and Conclusion Redefining the Gap 11. Results Redefining the Gap 12. Bibliography Redefining the Gap 13. Appendix: Computer Code Redefining the [...]

By: purpleslog

purpleslog — Wed, 30 Nov -0001 00:00:00 +0000

So, what does this do for the old-core-newcore-gap map? Is AfroIslam a beter model? Whatdoes that say about central and osuth Americ (definetly not core states). Coming Anarchy has a post on ungoverned spaces ( http://www.cominganarchy.com/archives/2006/05/20/neo-medievalism-ii/ ) which I am thinking of as mini-gaps.

Maybe the proper mapping is a set of mini-gap networks and mini-core-networks that are strongly intra-connected, but only weakly inter-connected.

By: Dan tdaxp

Dan tdaxp — Wed, 30 Nov -0001 00:00:00 +0000

Matt,

Thank you VERY MUCH. Your comment was extremely informative. I apologize for the delay in getting this reply to you (check the blogs for reasons for that ;-) )

Instead of the ternary variable in the El Nino years study, did you use something else? (I agree with your general comments on statistics, by the way)

I liked your discussion of normalize anamoly. I got my method from the UNHDI, which uses that method in scaling its numbers.

Purpleslog,

Same apology for the lateness of the reply.

Among other things, it makes the Bush administration's focus on Iran instead of North Korea make more sense. [1] Focus on Barnett's model, with its inclusive Gap, and the "tailbone of the Cold War" is obviously the gravest threat. Limit the really bad countries to AfroIslamiyya, however, and the implication is that the a second Islamic bomb is the worse future.

[1] http://www.thomaspmbarnett.com/weblog/archives2/003287.html

By: Matt R

Matt R — Wed, 30 Nov -0001 00:00:00 +0000

Dan,
First off, GREAT work. Putting numbers behind a global-scale theory is hard work. I am currently a grad student in meteorology, and being an inexact science; we live and die by the pseudo-math of stats. We do also have an appreciation for complex, non-linear systems with possible perturbations (Typhoons/Nor'easters, etc)
I would have to raise an issue I have with the analysis. You use distinct values for variables (0, 1, or 2) that are discrete in time and space, and then apply the method of correlations to analyze them. Correlations are truly meant for variables that are continuous in time and/or space (2D correlations are REALLY tricky). Using binary/ternary values can really mess with the correlation coefficient results. I was shot down on a project where I assigned El-Nino years a value of "1", La-Nina years a "-1" and neutral years a "0", so I have gone down this road.
When you described earlier how you standardized your results (For each state, it's value will be calculated by taking the difference between that state's value and the lowest state's value, divided by the difference between the highest state's value and the lowest state's value.), I thought that to be quite an odd way of doing things.
A better route may be to use the more widely used of creating a normalized anomaly for each states value (Take the current value, subtract the sample mean, and then divide by the standard deviation of each). This results in a distribution of values with a mean of 0 and a variance of 1. The zero line could then be used to create the dividing line between "haves" and "have-nots" of each variable. Maybe those states whose brutality [or whichever index you choose] is 1.5 stddev below the mean could be quantified as REALLY brutal.
From a data standpoint, can you obtain these same indices for each country for a few years? This would create a time series for each country.
From a methods standpoint, if you can get that data, each country would then be an ensemble member, and you could use other methods that combine both time and space (EOFs which have been used in population studies since the 1930s).
A multivariate regression might also be a good method, being that you have multiple inputs (brutal, nasty, poor, sol, short) trying to "predict" one thing on the left. This would take more work as there really isn’t a value that could be considered ground truth that your research would be trying to find a way to predict.
One problem introduced into all of this is that most statistics are suited for continuous data, not variables with fixed values
Again, I must compliment you. This is great work that will yield great benefits.

By: TM Lutas

TM Lutas — Wed, 30 Nov -0001 00:00:00 +0000

I would view the brutality index with suspicion. It strikes me as something akin to trying to discover the arsonists by their proximity to fires. Yes, you'll get the arsonists, but you'll also get the firefighters caught up in the same net. Is the US scored as more brutal for its incursion into Somalia to avoid mass starvation? A group of allies bands together to defend one another and by your measure, they are as equally brutal as the aggressors in the war. That just doesn't make sense. Somebody needs a better measure of brutality.

By: Dan tdaxp

Dan tdaxp — Wed, 30 Nov -0001 00:00:00 +0000

TM,

I agree with your criticism. You are right that America scores as very brutal -- indeed, as the most brutal state in the world, with a scaled score of 0.0 (0 being the worst. The only country close is Iraq, with a brutality of .58.

This is a limitation of the data. Tom defined brutality based on where the wars are, but the University of Maryland's ICBP (which I took for his "current conflicts database") lists them based on combatants.

It would be interesting to see a "corrected" brutality score. However, that AfroIslam still has apositive correlation even with this complication means that Gap is very, very brutal.