Comment author: 30 June 2014 07:54:13PM *  6 points [-]

Not everything that takes place in an author's fiction is indicative of something they support.

This, however, is a recurring theme in Eliezer's work. I don't think I fully grok the motivations (though I could hazard a guess or two), but it's definitely not just HJPEV's supervillain fetish talking.

Comment author: 01 July 2014 05:43:34AM 13 points [-]

Agreed, it's also Eliezer's super-villain fetish thing.

Comment author: 27 June 2014 07:00:16AM 5 points [-]

Consequently the actual annual income distribution and the power law distribution cannot actually be the same distribution; they have different support.

Every actual population differs from a parameterised mathematical function with few parameters, and for pretty much anything you can measure, if the mathematical distribution has infinite support, there will be some reason that the population cannot. But the question to ask is not, are they different, but, does the difference make a difference?

The way to answer this question is to repeat the analysis in the paper Eugine cited using a truncated power law. The bounds must be placed at the limits of what is possible, not at the accidental maximum and minimum values observed in the current population, as the point here is that the population is not fully exploring the tails.

I have not done this, but I did once do a simulation for the Cauchy distribution (which has no mean), finding empirically the standard deviation of the mean of samples of size N. Each individual set of N values has a mean, but they will be wildly different for different samples. Increasing N does not reduce the effect for any practical value of N (and I did this in Matlab, which is optimised for fast number-crunching on arrays). This is completely different from what happens for sample means drawn from distributions with finite mean and variance, whose means converge with increasing N to the population mean.

For my experiment with the Cauchy distribution, not a single one of my samples had to be rejected due to exceeding the limits of finite precision arithmetic. The absence of infinite tails from the samples made no difference to the experimental results, even though it is the presence of those infinite tails that gives the Cauchy distribution its lack of moments.

This may look like a paradox. You have two distributions, the Cauchy distribution and its truncation at 1e50 or wherever. The former has no moments, and the latter does. Yet the empirical behaviour of samples drawn from the latter agrees with mathematical analysis of the former, even though in the latter case the standard deviation of the sample mean must converge with increasing sample size to zero, and in the former case it remains infinite.

The resolution of this paradox lies in the fact that as the variance of a distribution that has a finite variance becomes larger and larger, the rate of convergence of sample means becomes slower and slower. For the Cauchy distribution truncated at +/- X and a sample size of N, for large X and N the variance of the sample mean is proportional to X/N. If we take the limit of this as X goes to infinity, we get infinity, independent of N. If we take the limit as N goes to infinity we get zero, independent of X. The behaviour found when both X and N are finite will depend on which is bigger. When X is very large, even the entire population (conceived as a sample from an underlying data-generation process) may not give a good estimate of the distribution mean.

Taleb and Douady's point is that for a power law distribution, wealth owned by the top 1% is subject to this phenomenon. A larger population will explore more of the tail of the distribution, and unlike the normal distribution, the tail is fat enough to give a different value for the statistic. The "true" distribution does not have to actually have infinite support, for the entire population of a country to be insufficient to explore the tails.

The authors draw the implication that as both population and technological development grow, the top 1% will be found to have larger proportions of the wealth, not because of any change in the mechanisms of society to favour them, but because more of the sample space is being explored. "So examining times series, we can easily get a historical illusion of rise in wealth concentration when it has been there all along." (Presumably one could quantify the effect and correct for it.)

A possibility that the paper does not raise is that instead of calculating the actual wealth held by the actual top 1%, you could estimate the Gini coefficient from the whole population, and calculate a theoretical 1% wealth. This may be substantially more. The authors suggest that Pareto's empirical observation of the 80/20 rule, which implies 53% wealth held by the top 1%, might actually correspond to a figure of 70%.

This could be spun in opposite ways. If you want to boom freedom and boo levellers, you can point to this and say there's always more room at the top. If you want to boom equality and boo the rich, you can say that the true situation is even worse that the 1% figure says, indeed that the figure is a systematic underestimate, a piece of evil propaganda used by the rich to conceal the true extent of the inequality inherent in the system.

Comment author: 01 July 2014 12:50:19AM 2 points [-]

A possibility that the paper does not raise is that instead of calculating the actual wealth held by the actual top 1%, you could estimate the Gini coefficient from the whole population, and calculate a theoretical 1% wealth.

Taleb would probably object on the grounds that the above will lead misleading results if the population is actually composed of a supper position of several distinct populations with different Gini coefficients.

Comment author: 30 June 2014 06:31:01AM *  0 points [-]

Or moving from conspiracy land, big budget cuts to climate research starting in 2009 might have something to do with it.

P.S. Since you started this sub-thread and are clearly still following it, are you going to retract your claims that CRU predicted "no more snow in Britain" or that Hansen predicted Manhattan would be underwater by now? Or are you just going to re-introduce those snippets in a future conversation, and hope no-one checks?

Comment author: 01 July 2014 12:32:13AM 6 points [-]

Since you started this sub-thread and are clearly still following it, are you going to retract your claims that CRU predicted "no more snow in Britain" or that Hansen predicted Manhattan would be underwater by now?

I was going from memory, now that I've tracked down the actual links I'd modify the claims what was actually said, i.e., snowfalls becoming exceedingly rare and the West Side Highway being underwater.

Comment author: 29 June 2014 11:43:12PM *  0 points [-]

Seems like a bad proxy to me. Is snowfall really that hard a metric to find...?

Presumably not, though since I'm not making up Met Office evidence (and don't have time to do my own analysis) I can only comment on the graphs which they themselves chose to plot in 2009. Snowfall was not one of those graphs (whereas it was in 2006).

However, the graphs of mean winter temperature, maximum winter temperature, and minimum winter temperature all point to the same trend as the air frost and heating-degree-day graphs. It would be surprising if numbers of days of snowfall were moving against that trend.

Comment author: 30 June 2014 03:21:40AM -1 points [-]

I can only comment on the graphs which they themselves chose to plot in 2009. Snowfall was not one of those graphs (whereas it was in 2006).

Interesting. I wonder why they're no longer plotting some trends. Maybe because it's too hard to fit them into their preferred narrative.

Comment author: 30 June 2014 02:12:35AM 1 point [-]

Having a top-level domain doesn't make an entity a country. Lots of indisputably non-countries have top-level domains. Nobody thinks the Bailiwick of Guernsey is a country, and yet .gg exists.

Comment author: 30 June 2014 03:18:35AM 4 points [-]

Nobody thinks the Bailiwick of Guernsey is a country, and yet .gg exists.

Well, it's sufficiently independent of the UK to function as a tax haven. It's definitely one of those entities that's on the fuzzy boundary between country and non-country, along with Hong Kong and (in a slightly different way) Dubai.

Comment author: [deleted] 28 June 2014 04:29:36PM 1 point [-]

Same applies to (say) Hong Kong and yet I can't recall anyone calling Hong Kong a country.

In response to comment by [deleted] on Open thread, 9-15 June 2014
Comment author: 29 June 2014 11:19:34PM 3 points [-]

I can't recall anyone calling Hong Kong a country.

Well ICANN for starters.

Comment author: 28 June 2014 04:14:57AM *  2 points [-]

The explanation, that I buy, is that we no longer try to promote good citizenship and good living, and so unsurprisingly people answer the call of the short term, to their long term detriment.

This makes sense if we assume marriage is causal for class. i.e. the people who don't heed the call of the short term and do marry have better outcomes and end up higher class. Choosing marriages naturally sorts people into class, by this model.

Liberals would tell a story where things are reversed and class is causal of the pathology- they would say the economic changes that have occurred for the last few decades have increased 'economic uncertainty' for the lower class (for some measure of uncertainty.) which has lead to marital stress and divorce. Its also worth pointing out that in the lower classes divorce is usually less costly for the man (the wife is more likely to be working at a similar paying job, the man has less stuff to lose)

Personally, I found the book Red Families/Blue Families pushed me away from the first explanation and toward the second (full disclosure, this is part of a larger trend of me growing increasing liberal over the last decade and a half or so.)

Comment author: 29 June 2014 11:12:39PM 5 points [-]

Liberals would tell a story where things are reversed and class is causal of the pathology- they would say the economic changes that have occurred for the last few decades have increased 'economic uncertainty' for the lower class (for some measure of uncertainty.) which has lead to marital stress and divorce.

There were many historical periods with much much greater economic uncertainty, they also had higher marriage rates.

Comment author: 29 June 2014 07:50:19AM *  5 points [-]

The only example of a successful prediction in your article is a rise in "mean surface temperature" which as I mentioned in the grand-parent is not hard to fudge

Your evidence that the weights used to calculate mean surface temperature are fudged in favor of global warming is a link to the "VERY ARTIFICIAL correction" in the CRU code. But that correction was not applied to global mean surface temperature data. It was applied to historical tree-ring data in order to account for the discrepancy between recent temperatures calculated using tree-ring data and recent temperatures calculated using other means known to be more reliable.

Uncorrected, the tree ring data suggests a decline in temperatures beginning around 1940 and continuing to the present. We have plenty of evidence that this is not in fact correct from actual thermometer-based records, so the correction was applied as a proxy for the unknown cause of this recent divergence. Now this does perhaps "hide" the fact that tree-ring records are not trustworthy (although CRU published papers explicitly mentioning this supposedly hidden fact), but it does not show that actual thermometer-based temperature records are being artificially tampered with to produce global warming.

It seems to me that ESR misrepresents this fact (although perhaps he was unaware of it) when he characterizes the "correction" as being applied to "Northern Hemisphere temperatures and reconstructions", with no mention of tree rings.

And I am very skeptical that temperature records over a very recent decade (the basis for the article I linked) have had significant external weighting applied to them to "fudge the results". The problem of changing station locations may necessitate differential weighting over longer time frames, but just from 2002 to 2011? I don't believe you. If you have any evidence suggesting that this is what is going on, I'm interested to see it.

The rest of said article reads like an attempt to (preemptively?) explain away failed predictions.

It doesn't read that way to me.

And yet for some reason all said predictions fail in the same direction.

Probably due to politically motivated reasoning. I'm not denying that climate change activists often make exaggerated and unwise predictions about the impact of climate change, especially in the popular media. I am denying your claim that the predictive record of climate science is entirely negative. There are climate models that have done pretty well, at least when it comes to global trends.

Comment author: 29 June 2014 02:37:52PM -1 points [-]

Here is the article I linked to above. Note that it implies a different conclusion about recent temperature trends. Do you have any evidence for preferring your letter to the editor over the article Eric discusses besides it confirming your pre-existing belief?

The rest of said article reads like an attempt to (preemptively?) explain away failed predictions.

It doesn't read that way to me.

Have you even read the article you linked to? Here are the first four sentences:

Early climate forecasts are often claimed to have overestimated recent warming. However, their evaluation is challenging for two reasons. First, only a small number of independent forecasts have been made. And second, an independent test of a forecast of the decadal response to external climate forcing requires observations taken over at least one and a half decades from the last observations used to make the forecast, because internally generated climate fluctuations can persist for several years.

Comment author: 27 June 2014 08:27:38PM 1 point [-]

The set {Yemen, Oman, Somalia, Dubai} is "wrong", for the same reason that {plane, train, boat, driver's-seat-of-car} is

Again, I disagree; it's a useful set for practical purposes

There is an ambiguity here, but if what you are claiming to disagree with is the analogy to {plane, train, boat, driver's-seat-of-car} (as opposed to merely the "wrongness" of either), then you genuinely do not have a good understanding of, or are stubbornly refusing to acknowledge, the relevant political geography, and I would suspect you of having heard of Dubai before you had heard of the UAE (probably as a result of journalists' ignorance), and anchoring on this fact.

But I can't be sure to what extent we really have differing models of how the world works, as opposed to at least one of us going out of our way to signal something (willingness to disregard official politics in your case, familiarity with the Middle East in mine).

Comment author: 28 June 2014 03:44:20AM 0 points [-]

But I can't be sure to what extent we really have differing models of how the world works, as opposed to at least one of us going out of our way to signal something (willingness to disregard official politics in your case, familiarity with the Middle East in mine).

If your goal was to signal your familiarity with the Middle East, you've utterly failed since it appears you didn't know how the UAE was organized. You come across as one of those people who memorizes lists of countries and capitals and possibly shapes but has no idea how the map does (or does not) correspond to facts on the ground.

Comment author: 26 June 2014 03:55:59AM *  2 points [-]

If I understand Taleb correctly, his objection is that if X's distribution's upper tail tends to a power law with small enough (negated) exponent α, then sample proportions of X going to the distribution's top end are inconsistent under aggregation, and suffer a bias that decreases with sample size. And since the Gini coefficient is such a measure, it has these problems.

However, Taleb & Douady give me the impression that the quantitative effect of these problems is substantial only when α is appreciably less than 2. (The sole graphical example for which T&D mention a specific α, their figure 1, uses α = 1.1). But I have a hard time seeing how α can really be that small for income & wealth, because that'd imply mean income & mean wealth aren't well-defined in the population, which must be false because no one actually has, or is earning, infinitely many dollars or euros.

[Edit after E_N's response: changed "a bias that rises with sample size" to "a bias that decreases with sample size", I got that the wrong way round.]

Comment author: 27 June 2014 01:36:27AM 0 points [-]

But I have a hard time seeing how α can really be that small for income & wealth, because that'd imply mean income & mean wealth aren't well-defined in the population,

Um no. They're not well defined over the distribution, they will certainly be well defined over a finite population.

which must be false because no one actually has, or is earning, infinitely many dollars or euros.

You seem to be confused about how distributions with infinite means work. Here's a good exercise: get some coins and flip them to obtain data in a St. Petersburg distribution notice that even though the distribution has infinite mean all your data points are still finite (and quite small).

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