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Goodhart’s law isn’t as useful as you might think (2023)

140 pointsby yagizdegirmenci7 months ago

24 comments

shadowsun77 months ago
If you are interested in these ideas, you should know that this essay kicks off a series of essays that culminates, a year later, with an examination of the Amazon-style Weekly Business Review:<p><a href="https:&#x2F;&#x2F;commoncog.com&#x2F;becoming-data-driven-first-principles&#x2F;" rel="nofollow">https:&#x2F;&#x2F;commoncog.com&#x2F;becoming-data-driven-first-principles&#x2F;</a><p><a href="https:&#x2F;&#x2F;commoncog.com&#x2F;the-amazon-weekly-business-review&#x2F;" rel="nofollow">https:&#x2F;&#x2F;commoncog.com&#x2F;the-amazon-weekly-business-review&#x2F;</a><p>(It took that long because of a) an NDA, and b) it takes time to put the ideas to practice and understand them, and then teach them to other business operators!)<p>The ideas presented in this particular essay are really attributed to W. Edwards Deming, Donald Wheeler, and Brian Joiner (who created Minitab; ‘Joiner’s Rule’, the variant of Goodhart’s Law that is cited in the link above is attributed to him)<p>Most of these ideas were developed in manufacturing, in the post WW2 period. The Amazon-style WBR merely adapts them for the tech industry.<p>I hope you will enjoy these essays — and better yet, put them to practice. Multiple executives have told me the series of posts have completely changed the way they see and run their businesses.
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ang_cire7 months ago
This doesn&#x27;t really touch on the core of the issue, which is business expectations that don&#x27;t match up with reality.<p>Business leaders like to project success and promise growth that there is no evidence they will or can achieve, and then put it on workers to deliver that, and when there&#x27;s no way to achieve the outcome other than to cheat the numbers, the workers will (and will have to).<p>At some point businesses stopped treating outperforming the previous year&#x27;s quarter as <i>over-delivering</i>, and made it an expectation, regardless of what is actually doable.
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thayne7 months ago
&gt; Let’s demonstrate this by example. Say that you’re working in a widget factory, and management has decided you’re supposed to produce 10,000 widgets per month...<p>It then discusses ways that the factory might cheat to get higher numbers.<p>But it doesn&#x27;t even mention what I suspect the most likely outcome is: they achieve the target by sacrificing something else that isn&#x27;t measured, such as quality of the product (perhaps by shipping defective widgets that should have been discarded, or working faster which results in more defects, or cutting out parts of the process, etc.), or safety of the workers, or making the workers work longer hours, etc.
bachmeier7 months ago
Just a side note that this usage isn&#x27;t really the application Goodhart had in mind. Suppose you&#x27;re running a central bank and you see a variable that can be used to predict inflation. If you&#x27;re doing your job as a central banker optimally, you&#x27;ll prevent inflation whenever that variable moves, and then no matter what happens to the variable, due to central bank policy, inflation is always at the target plus some random quantity and the predictive power disappears.<p>As &quot;Goodhart&#x27;s law&quot; is used here, in contrast, the focus is on side effects of a policy. The goal in this situation is not to make the target useless, as it is if you&#x27;re doing central bank policy correctly.
jjmarr7 months ago
I can confirm this. We&#x27;ve standardized Goodhart&#x27;s law creating a 90-day rotation requirement for KPIs. We found that managers would reuse the same performance indicators with minor variations and put them on sticky notes to make them easier to target.
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godelski7 months ago
Goodhart&#x27;s law is often misunderstood and the author here seems to agree and disagree. Goodhart&#x27;s law is about alignment. That every measure is a proxy for the thing you are actually after and that it doesn&#x27;t perfectly align.<p>Here&#x27;s the thing, there&#x27;s no fixing Goodhart&#x27;s Law. You just can&#x27;t measure anything directly, even measuring with a ruler is a proxy for a meter without infinite precision. This gets much harder as the environment changes under you and metrics&#x27; utility changes with time.<p>That said, much of the advice is good: making it hard to hack and giving people flexibility. It&#x27;s a bit obvious that flexibility is needed if you&#x27;re interpreting Goodhart&#x27;s as &quot;every measure is a proxy&quot;, &quot;no measure is perfectly aligned&quot;, or &quot;every measure can be hacked&quot;
nrnrjrjrj7 months ago
I want to block some time to grok the WBR and XMR charts that Cedric is passionate about (for good reason).<p>I might be wrong but I feel like WBR treats variation (looking at the measure and saying &quot;it has changed&quot;) as a trigger point for investigation rather than conclusion.<p>In that case, lets say you do something silly and measure lines of code committed. Lets also say you told everyone and it will factor into a perforance review and the company is know for stack ranking.<p>You introduce the LOC measure. All employees watch it like a hawk. While working they add useless blocks of code an so on.<p>LOC commited goes up and looks significant on XMR.<p>Option 1: grab champagne, pay exec bonus, congratulate yourself.<p>Option 2: investigate<p>Option 2 is better of course. But it is such a mindset shift. Option 2 lets you see if goodhart happened or not. It lets you actually learn.
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tqi7 months ago
&gt; I immediately glommed onto this list as a more useful formulation than Goodhart’s Law. Joiner’s list suggests a number of solutions: &gt; Make it difficult to distort the system. &gt; Make it difficult to distort the data, and<p>If companies knew how to make it difficult to distort the system&#x2F;data, don&#x27;t you think they would have done it already? This feels like telling a person learning a new language that they should try to sound more fluent.
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osigurdson7 months ago
Goodharts law basically states that false proxies are game-able. The solution is to stop wasting time on tracking false proxies. Instead, roll up your sleeves and do something.
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lmm7 months ago
&gt; It can’t be that you want your org to run without numbers. And it can’t be that you eschew quantitative goals forever!<p>Can&#x27;t it? Amazon may be an exception, but most of the time running without numbers or quantitative goals seems to work better than having them.
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James_K7 months ago
In short, describe the actions you want people to take rather than the results you think those actions should achieve. Or perhaps more fundamentally, you should know what people are doing and you won&#x27;t know that when you are only looking at an opaque metric.
deeviant7 months ago
I&#x27;m still not convinced. Goodhart’s Law is rooted in human behavior—once people know what’s being measured, they’ll optimize for that, often distorting the system or the data to hit targets. The article&#x27;s solution boils down to “just do it right” by refining metrics and improving systems, but that’s easier said than done. It ignores the fact that people will always game metrics if their rewards depend on them. Plus, it conflates data-driven decision-making with performance evaluation, which are very different. The psychology behind Goodhart’s Law isn’t solved by more metrics tweaking.
thenobsta7 months ago
This doesn&#x27;t feel well elucidated, but I&#x27;ve been thinking about Goodhart&#x27;s law in other area&#x27;s of life -- e.g. Owning a home is cool and can enable some cool things. However, when home ownership becomes the goal, it&#x27;s becomes easy to disregard a lot of life giving things in pursuit of owning a home.<p>This seems to pop up in a lot of areas and I find myself asking is X thing a thing I really desire or is it something that is a natural side effect of some other processes.
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ssivark7 months ago
What I find striking is the emphasis on tracking and managing <i>inputs</i> (to develop a healthy causal model for management). This is in contrast with advice that&#x27;s commonly passed around (ostensibly attributable to OKRs, High Output Management, etc -- though I haven&#x27;t carefully read the original sources), with heavy emphasis on the output but eschew focusing on input metrics. While it avoids the failure mode of focusing on causally irrelevant metrics, it also assumes that the right causal model has already been discovered and there&#x27;s no learning process here. This impedes the development of an agile organization that is learning constantly (growth mindset) instead promoting a &quot;fixed&quot; mindset where people&#x2F;teams can either execute well or not, and the only control lever is hiring&#x2F;firing&#x2F;promoting those who seem to &quot;get it&quot;. Fantastic and thought-provoking article!
lamename7 months ago
This is all well and good, but unfortunately depends on the people pushing for the metric&#x2F;system to give a shit about what the metric is supposed to improve. There are still far too many that prefer to slap 1 or 2 careless metrics on an entire team, optimize until they&#x27;re promoted, then leave the company worse off.
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mark-r7 months ago
Reminds me of one of my favorite Dilbert cartoons: <a href="https:&#x2F;&#x2F;www.reddit.com&#x2F;r&#x2F;ProgrammerHumor&#x2F;comments&#x2F;k5hka0&#x2F;bug_free_programs_a_dilbert_classic&#x2F;" rel="nofollow">https:&#x2F;&#x2F;www.reddit.com&#x2F;r&#x2F;ProgrammerHumor&#x2F;comments&#x2F;k5hka0&#x2F;bug...</a>
yarg7 months ago
Goodhart&#x27;s law can diagnose an issue, but it prescribes no solutions.<p>However, it&#x27;s still better to recognise a problem, so you can at least look into ways of improving the situation.
satisfice7 months ago
&quot;...when you’re incentivising organisational behaviour with metrics, there are really only three ways people might respond: 1) they might improve the system, 2) they might distort the system, or 3) they might distort the data.&quot;<p>This is wrong, and the wrongness of it undermines the whole piece, I think:<p>- A fourth way people respond is to oppose the choice of target and&#x2F;or metric; to question its value and lobby to change it.<p>- A fifth way people respond is to oppose the whole idea of incentives on the basis of metrics (perhaps by citing Goodhart&#x27;s Law... which is a use of Goodhart&#x27;s Law).<p>Goodhart&#x27;s Law is useful not just because it reminds us that making a metric a target may incentivize behavior that makes THAT metric a poor indicator of a good system, but also because choosing ANY metric as a target changes everyone&#x27;s relationship with ALL metrics-- it spells the end of inquiry and the beginning of what might be called compliance anxiety.
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skmurphy7 months ago
There is a very good essay in the first comment by &quot;Roger&quot; dated Jan-2023, reproduced below. Skip the primary essay and work from this:<p>&quot;I really appreciated this piece, as designing good metrics is a problem I think about in my day job a lot. My approach to thinking about this is similar in a lot of ways, but my thought process for getting there is different enough that I wanted to throw it out there as food for thought.<p>One school of thought 9<a href="https:&#x2F;&#x2F;www.simplilearn.com&#x2F;tutorials&#x2F;itil-tutorial&#x2F;measurement-in-itil-malc" rel="nofollow">https:&#x2F;&#x2F;www.simplilearn.com&#x2F;tutorials&#x2F;itil-tutorial&#x2F;measurem...</a>) I have trained in is that metrics are useful to people in 4 ways:<p><pre><code> 1. Direct activities to achieve goals 2. Intervene in trends that are having negative impacts 3. Justify that a particular course of action is warranted 4. Validate that a decision that was made was warranted </code></pre> My interpretation of Goodhart’s Law has always centered more around duration of metrics for these purposes. The chief warning is that regardless of the metric used, sooner or later it will become useless as a decision aid. I often work with people who think about metrics as a “do it right the first time, so you won’t have to ever worry about it again”. This is the wrong mentality, and Goodhart’s Law is a useful way to reach many folks with this mindset.<p>The implication is that the goal is not to find the “right” metrics, but to instead find the most useful metrics to support the decisions that are most critical at the moment. After all, once you pick a metric, 1 of 3 things will happen:<p><pre><code> 1. The metric will improve until it reaches a point where you are not improving it anymore, at which point it provides no more new information. 2. The metric doesn’t improve at all, which means you’ve picked something you aren’t capable of influencing and is therefore useless. 3. The metric gets worse, which means there is feedback that swamps whatever you are doing to improve it. </code></pre> Thus, if we are using metrics to improve decision making, we’re always going to need to replace metrics with new ones relevant to our goals. If we are going to have to do that anyway, we might as well be regularly assessing our metrics for ones that serve our purposes more effectively. Thus, a regular cadence of reviewing the metrics used, deprecating ones that are no longer useful, and introducing new metrics that are relevant to the decisions now at hand, is crucial for ongoing success.<p>One other important point to make is that for many people, the purpose of metrics is not to make things better. It is instead to show that they are doing a good job and that to persuade others to do what they want. Metrics that show this are useful, and those that don’t are not. In this case, of course, a metric may indeed be useful “forever” if it serves these ends. The implication is that some level of psychological safety is needed for metric use to be more aligned with supporting the mission and less aligned with making people look good.&quot;
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rc_mob7 months ago
its not a &quot;law&quot; of course. should not be called a law
stoperaticless7 months ago
tl;dr version:<p>- Use not one, but many metrics (article mentioned 600)<p>- Recognize that some metrics you control directly (input metrics) and others you want to but can’t (output metrics).<p>- Constantly refine metrics and your causal model between inputs and outputs. (Article mentions weekly 60-90min reviews)<p>Edit: crucial part, all consumers of these metrics (all leadership) is in this.
bediger40007 months ago
Seems like the headline should be:<p>Is Goodhart&#x27;s Law as useful as you think?
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stonethrowaway7 months ago
What does “Law” mean in this case?
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aunwick7 months ago
I suspect next months article will be pay for performance as measured by lines of code and production issues. Prepare for a 10x increase in code base and zero production changes until after bonuses hit the bank.
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