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	<title>Some stuff &#187; disaster</title>
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	<description>here.</description>
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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>
		<dc:creator>admin</dc:creator>
				<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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		<title>tax forms must be designed by idiots</title>
		<link>https://blog.yhuang.org/?p=71</link>
		<comments>https://blog.yhuang.org/?p=71#comments</comments>
		<pubDate>Sat, 07 Apr 2007 08:59:19 +0000</pubDate>
		<dc:creator>admin</dc:creator>
				<category><![CDATA[Uncategorized]]></category>
		<category><![CDATA[answer]]></category>
		<category><![CDATA[argh]]></category>
		<category><![CDATA[code]]></category>
		<category><![CDATA[disaster]]></category>
		<category><![CDATA[logic]]></category>
		<category><![CDATA[non residents]]></category>
		<category><![CDATA[reverse-engineering]]></category>
		<category><![CDATA[value calculations]]></category>

		<guid isPermaLink="false">http://scripts.mit.edu/~zong/wpress/?p=71</guid>
		<description><![CDATA[CA income tax form is the worst. MA is only slightly better. The federal one is a disaster but at least I&#8217;m used to it. These things require reverse-engineering the spagetti code behind the instructions in order to see the actual calculations, which are all fairly simple. And yet, there is no logic to the [...]]]></description>
			<content:encoded><![CDATA[<p>CA income tax form is the worst.<br />
MA is only slightly better.<br />
The federal one is a disaster but at least I&#8217;m used to it.<br />
These things require reverse-engineering the spagetti code behind the instructions in order to see the actual calculations, which are all fairly simple. And yet, there is no logic to the instructions, like why the apportioning of income for non-residents need to be calculated multiple times, or why rate and value calculations are interleaved in random order, or why two forms that should give the same answer, don&#8217;t&#8230; Argh!</p>
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