I think it would be good for you to define precisely what you mean by “consistent” with a model and “sufficiently explanatory” models. Various models will fit the data more or less well according to various measures of goodness of fit. It seems to me that the rigorous approach when faced with two conflicting models is to fit a general one that encompasses both and then perform classic tests (Wald, likelihood ratio, etc.) to pit one against the other. The operative question is whether adding a post-1945 dummy improves the fit more than would be expected by chance. Or is there specific evidence that such a standard procedure would fail in this case?

If the post-1945 dummy is in fact significant, that would of course speak only to whether there was a trend break and would offer no guarantee of continuance.

]]>The authors construct a null hypothesis using a homogeneous Poisson process, in which inter-arrival times are independent and identically distributed, i.e. the process is memoryless; there is no time trend. If the data can be explained (i.e. could have been produced) by this simple memoryless process, then we cannot reject the null hypothesis that there is no time trend.

The data may be consistent with a time-trend-based model at the same time, in the same way that some non-temporal data can be consistent with both a Gaussian distribution and a fat-tailed distribution. In the presence of two sufficiently explanatory but conflicting models, one with no time trend, and the other with time trend, the one with the fewer assumptions must be accepted, i.e. the no-time-trend model. In the same way, it is unreasonable to assume a Gaussian distribution when both a Gaussian distribution and a fat-tailed distribution can explain the observed data (Taleb shows elsewhere how you can fool the Kolmogorov-Smirnov test using a sample from a fat-tailed distribution). This is a way to not be fooled by randomness, i.e. seeing a stronger pattern (time trend), when no pattern (no time trend) would explain the data equally well.

I do not see any inconsistency in this methodology.

]]>Surely, Twitter (or Facebook, Google+ et cetera) can provide lawmakers with significant information about situations around the world, especially when tweets are written by users from distant areas that are not directly monitored.

However, owning a potentially infinite amount of data needs plenty of men, skills and time to analyze them in order to select those truly worthy of being taken into account.

Last but not least, as this article rightly notes, Twitter, just like other social networking sites, has an audience whose size is still too small. In my opinion, it would be vague probability calculation rather than plausible forecasting.

]]>The bulk of the Cirillo and Taleb analysis does not contain any notion of time. It is analogous to fitting a bell curve to all the wars in the last 2000 years, and then concluding that the experience since 1945 is only 1 standard deviation away from the mean. My point is that such a parametric model can be elaborated to allow, say, the mean and/or log standard deviation to depend linearly on a post-1945 dummy. Fitting this model would provide a sharp test of the long peace hypothesis (at least if the sample were restricted to great powers). This is not done.

As I explain, all that is done regarding time patterns amounts to a single graph and a couple of sentences.

I think this line of your is quite on-target: “Bearing that in mind, I think the paper successfully undercuts the most optimistic view about the future of violent conflict—that violent conflict has inexorably and permanently declined—but then I don’t know many people who actually hold that view.” I wish Cirillo and Taleb would state precisely what hypothesis they are challenging, demonstrate with citations who espouses it, then construct a direct statistical test of it.

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