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	<title>Some stuff &#187; Avoid</title>
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		<title>how to summarize a long tail?</title>
		<link>https://blog.yhuang.org/?p=406</link>
		<comments>https://blog.yhuang.org/?p=406#comments</comments>
		<pubDate>Sun, 01 May 2011 11:36:29 +0000</pubDate>
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				<category><![CDATA[Uncategorized]]></category>
		<category><![CDATA[Avoid]]></category>
		<category><![CDATA[big earthquake]]></category>
		<category><![CDATA[catastrophic disaster]]></category>
		<category><![CDATA[disaster]]></category>
		<category><![CDATA[disaster weather]]></category>
		<category><![CDATA[law of large numbers]]></category>
		<category><![CDATA[Natural]]></category>
		<category><![CDATA[number]]></category>
		<category><![CDATA[Weather]]></category>
		<category><![CDATA[weather disasters]]></category>

		<guid isPermaLink="false">http://scripts.mit.edu/~zong/wpress/?p=406</guid>
		<description><![CDATA[Where to Live to Avoid a Natural Disaster Weather disasters and quakes: who’s most at risk? The analysis below, by Sperling’s Best Places, a publisher of city rankings, is an attempt to assess a combination of those risks in 379 American metro areas &#8230; and take(s) into account the relative infrequency of quakes, compared with [...]]]></description>
			<content:encoded><![CDATA[<blockquote><p>Where to Live to Avoid a Natural Disaster<br />
Weather disasters and quakes: who’s most at risk? The analysis below, by Sperling’s Best Places, a publisher of city rankings, is an attempt to assess a combination of those risks in 379 American metro areas &#8230; and take(s) into account the relative infrequency of quakes, compared with weather events and floods.</p></blockquote>
<p><img src="wp-content/uploads/images/01safe-custom1.gif" alt="http://graphics8.nytimes.com/images/2011/05/01/weekinreview/01safe/01safe-custom1.gif" width="600" /></p>
<p>I don&#8217;t know what exact metric they used here but it seems to be more or less expected value of &#8220;disaster points&#8221; accumulated during a unit period, for the lack of a better description. Here is the problem with these expected value based metrics, trying to summarize very different distributions: the variance matters! One catastrophic disaster isn&#8217;t quite the same as several smaller disasters costing the same number of disaster points. Yet &#8220;maximizing&#8221; survival using this map, humanity moves to the West Coast then becomes extinct in the next big earthquake. Even imposing a convex utility function on the distribution isn&#8217;t entirely satisfying. When it comes to decision making, tail risk is in a different bucket than central risk. Somehow an important aspect of the decision making process isn&#8217;t captured by &#8220;soft&#8221; metrics.<br />
<span id="more-406"></span><br />
But let&#8217;s go back a bit. The reason why distributions are summarized by a single number is often based on the notion of repeated experiments such that the law of large numbers kicks in. For a single sample path, repetition is not relevant for truly rare events, so expected value and other metrics that depend on repetition should be discarded immediately. It isn&#8217;t clear what a reasonable replacement is though. If something like a simple survival threshold were more appropriate (let&#8217;s say you could survive a two sigma lifetime event and no larger), people certainly don&#8217;t behave as such, since there would be no solution: there is always a catastrophic risk, say, of the universe ending. It may be necessary to treat three different severity regimes separately: soft metrics for the &#8220;mundane&#8221; and multiply occurring central risks, thresholding for the &#8220;rare&#8221; but &#8220;statistically identifiable&#8221; tail risks, and disregard for the &#8220;never occurred and unknown&#8221; acts of god.</p>
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