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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Categorizing Variants of Goodhart's Law · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1803.04585/</link><description>Collected HN comments and submissions linking this paper.</description><language>en</language><lastBuildDate>Mon, 02 Nov 2026 11:16:22 GMT</lastBuildDate><atom:link href="https://hackerpaper.kaipfister.de/p/1803.04585/feed.xml" rel="self" type="application/rss+xml" /><item><title>Categorizing Variants of Goodhart's Law — HN submission</title><link>https://news.ycombinator.com/item?id=46656464</link><guid isPermaLink="false">urn:hackerpapers:submission:1803.04585:46656464</guid><pubDate>Sat, 17 Jan 2026 09:02:35 GMT</pubDate><description>&lt;p&gt;Categorizing Variants of Goodhart&amp;#x27;s Law&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;1 point; 0 discussion replies on HN. &lt;a href="https://hackerpaper.kaipfister.de/p/1803.04585/"&gt;Paper and source history&lt;/a&gt;. A submission is not an endorsement.&lt;/p&gt;</description><category>cs.AI</category></item><item><title>Categorizing Variants of Goodhart's Law — mentioned by divan</title><link>https://news.ycombinator.com/item?id=39492671</link><guid isPermaLink="false">urn:hackerpapers:mention:1803.04585:39492671</guid><pubDate>Sat, 24 Feb 2024 16:17:30 GMT</pubDate><description>&lt;p&gt;Interesting links in the article and comments, but it seems like two different concepts are mixed here – the nature of bureaucracies and wrong incentives. I struggle to understand how cobra and rat cases are &amp;quot;institutions trying to preserve the problem&amp;quot;. Cobra effect, in particular, is an immensely interesting topic of how rewarding the metrics can distort the system, otherwise known as Goodhart&amp;#x27;s effect. I highly recommend reading these two papers on the subject for anyone interested: - Categorizing Variants of Goodhart&amp;#x27;s Law [1] - Building less-flawed metrics: Understanding and creating…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1803.04585/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.AI</category></item><item><title>Categorizing Variants of Goodhart's Law — mentioned by divan</title><link>https://news.ycombinator.com/item?id=39409415</link><guid isPermaLink="false">urn:hackerpapers:mention:1803.04585:39409415</guid><pubDate>Sat, 17 Feb 2024 13:54:32 GMT</pubDate><description>&lt;p&gt;Must reads: - Categorizing Variants of Goodhart&amp;#x27;s Law [1] - Building less-flawed metrics: Understanding and creating better measurement and incentive systems [2] [1] https://arxiv.org/abs/1803.04585 [2] https://www.sciencedirect.com/science/article/pii/S266638992...&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1803.04585/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.AI</category></item><item><title>Categorizing Variants of Goodhart's Law — mentioned by divan</title><link>https://news.ycombinator.com/item?id=39128358</link><guid isPermaLink="false">urn:hackerpapers:mention:1803.04585:39128358</guid><pubDate>Thu, 25 Jan 2024 11:17:29 GMT</pubDate><description>&lt;p&gt;There is a deeper and largely overlooked problem with sports judging, especially in the sports with artistic component (like gymnastics or figure skating) – Goodhart&amp;#x27;s effects. Goodhart&amp;#x27;s effect stands for &amp;quot;when the metric becomes a target it stops being a good metric&amp;quot;. As soon as you make measurement part of the reward system, it changes the whole system. Take figure skating as an example. Since 2006, ISU has introduced a similar judging system - IJS – that uses points for elements and an increasingly complex code of rules on how to assign those points. Each element has a so-called &amp;quot;base…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1803.04585/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.AI</category></item><item><title>Categorizing Variants of Goodhart's Law — mentioned by divan</title><link>https://news.ycombinator.com/item?id=36154819</link><guid isPermaLink="false">urn:hackerpapers:mention:1803.04585:36154819</guid><pubDate>Thu, 01 Jun 2023 17:52:58 GMT</pubDate><description>&lt;p&gt;Right. Do you have any good recommendations to read on this matter? Here are a couple of good papers on how metrics distort systems and what can be done to mitigate this. [1] [2]. And then a new post by Cedric Chin on practical examples of mitigating Goodhart&amp;#x27;s law in Amazon. Really good read: [3] But generally, I feel like not many people or organizations care about this. The non-profit sports world is conservative and slow, organizations and corporations are also often slow, and there some psychological/social effects linked to the metrics-that-don&amp;#x27;t-work-anymore. --- - [1] Categorizing…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1803.04585/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.AI</category></item><item><title>Categorizing Variants of Goodhart's Law — mentioned by divan</title><link>https://news.ycombinator.com/item?id=35840207</link><guid isPermaLink="false">urn:hackerpapers:mention:1803.04585:35840207</guid><pubDate>Sat, 06 May 2023 11:15:33 GMT</pubDate><description>&lt;p&gt;Exams and sports competitions are a particularly nasty example of Campbell&amp;#x27;s Law (or Goodheart&amp;#x27;s Law) – collapsing the correlation between metric and the true goal. Exams, for example, are supposed to measure how much students progressed. &amp;quot;True goal&amp;quot; - learn some knowledge or master some skill. &amp;quot;Metric&amp;quot; is the quiz/tests/exam result. If you try to design an exam for any new skill or knowledge, you&amp;#x27;ll come up with a pretty good metric. But soon, people will start ignoring the true goal and focus on the metric. Instead of &amp;quot;measuring&amp;quot; how well they learned the skill, your metric measures &amp;quot;how…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1803.04585/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.AI</category></item><item><title>Categorizing Variants of Goodhart's Law — mentioned by davidmanheim</title><link>https://news.ycombinator.com/item?id=28897153</link><guid isPermaLink="false">urn:hackerpapers:mention:1803.04585:28897153</guid><pubDate>Sun, 17 Oct 2021 15:28:44 GMT</pubDate><description>&lt;p&gt;Others pointed out that it&amp;#x27;s hard to measure subjective factors well. I&amp;#x27;d point to a different issue - if you have a metric for, say, wellbeing which is correlated with what you actually care about, putting pressure on other parts of the system - like maximizing engagement - will systemically warp those metrics to be less accurate. For an extensive discussion of how this can happen, see: https://arxiv.org/abs/1803.04585&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1803.04585/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.AI</category></item><item><title>Categorizing Variants of Goodhart's Law — mentioned by pikwip</title><link>https://news.ycombinator.com/item?id=28566223</link><guid isPermaLink="false">urn:hackerpapers:mention:1803.04585:28566223</guid><pubDate>Fri, 17 Sep 2021 14:58:28 GMT</pubDate><description>&lt;p&gt;Here&amp;#x27;s a interesting paper I found that attempts to categorize the mechanisms by which Goodhart&amp;#x27;s Law operates in the real world. The variants are separated into Causal and Non-causal mechanisms. https://arxiv.org/abs/1803.04585&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1803.04585/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.AI</category></item><item><title>Categorizing Variants of Goodhart's Law — mentioned by davidmanheim</title><link>https://news.ycombinator.com/item?id=17321644</link><guid isPermaLink="false">urn:hackerpapers:mention:1803.04585:17321644</guid><pubDate>Fri, 15 Jun 2018 17:27:28 GMT</pubDate><description>&lt;p&gt;That&amp;#x27;s often helpful, but not sufficient in general. See the dynamics discussed in my article here; https://arxiv.org/abs/1803.04585&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1803.04585/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.AI</category></item><item><title>Categorizing Variants of Goodhart's Law — mentioned by davidmanheim</title><link>https://news.ycombinator.com/item?id=17321623</link><guid isPermaLink="false">urn:hackerpapers:mention:1803.04585:17321623</guid><pubDate>Fri, 15 Jun 2018 17:25:36 GMT</pubDate><description>&lt;p&gt;This is probably where I should mention my two longer articles that discuss this in depth; https://www.ribbonfarm.com/2016/06/09/goodharts-law-and-why-... https://www.ribbonfarm.com/2016/09/29/soft-bias-of-underspec... As well as my more recent paper categorizing the different ways it occurs; https://arxiv.org/abs/1803.04585&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1803.04585/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.AI</category></item></channel></rss>
