I've always considered Goodhart's Law to be "of the people," but the article offers only top down solutions. What is the solution when the decision-makers have subjected you to a Goodhartable system of incentives and don't have enough awareness, vision, or humility themselves to change the system as described in the article? Well, you play the game or get beat by those who do. Right up until the point when you all lose.<p>I agree that if you're the one in control, complaining about Goodhart's Law is a sort of learned helplessness. However for those not in control -- and this goes as well for scenarios with diffuse control such as large crowds and democracy -- Goodhart's Law is useful, in the way that naming any systemic ailment is useful.<p>Perhaps we can talk about the 'hard problem' of Goodhart's Law to separate it from the problem solved in the article. Does anyone have good advice for those living inside the hard problem?
What presumption! The author doesn't know how useful I find Goodhart's Law!<p>Seriously though, Goodhart's Law means that the world is more complex in a way that you may not have realized. Saying it is wrong, and that the world is more complex than you may have realized is just expanding it.<p>Every model is wrong, some models are useful after all.<p>> "Well, let’s think about the weight control example. Losing weight is a process with two well known inputs: calories in (what you eat), and calories out (what you burn through exercise). This means that the primary difficulty of hitting a weight loss goal is to figure out how your body responds to different types of exercise or different types of foods, and how these new habits might fit into your daily life (this assumes you’re disciplined enough to stick to those habits in the first place, which, well, you know)."<p>Oh the irony! If you want to lose fat and keep it off, you need to understand and address HUNGER[0]. Just because you understand part of the system, doesn't mean you understand the whole system.<p>If one part of the process or system is obvious and easy to measure, we will measure it and talk about it. Thus we talk about weight and BMI, not Body Fat Percent and Lean Mass. We talk about calories and measuring food intake and not the body system that drives us to maintain our energy reserves.<p>Hunger is hard to understand, impossible to measure, and impossible to compare between people at scale. Calories are relatively easy to measure, but they are only INPUTS to the system, not an understanding of the system.<p>0. Basically every obese person has lost a significant amount of weight at least once in their life. Losing weight is not a mystery. Book recommendations:<p>guyenet hungry brain <a href="https://www.amazon.com/Hungry-Brain-Outsmarting-Instincts-Overeat/dp/125008119X" rel="nofollow">https://www.amazon.com/Hungry-Brain-Outsmarting-Instincts-Ov...</a><p>pontzer burn <a href="https://www.amazon.com/Burn-Research-Really-Calories-Healthy/dp/0525541527" rel="nofollow">https://www.amazon.com/Burn-Research-Really-Calories-Healthy...</a>
Picking on calories in/out is problematic if you think that is just two variables. Easily listed problematic parts, there are several ways to get "calories out," "calories in" have an impact on energy levels, and "hunger" and "satiation" are impacted by more than just "calories in or out." Though, as stated, the biggest gotcha is going to be that, unless you are doing extreme exercise, that will not be the dominate way that you are expending calories. Such that the criticism of the idea is flawed from the start. That is not what in/out means.<p>For the rest? I don't buy it. For starters the WBR process can easily get downright toxic from my view. Too much time spent preparing the metrics without nearly enough time understanding. There are literally teams of people whose job is to present numbers up and feel like they are somehow masters of the system. Not at all healthy for the boots on the ground doing work. (Ironically, this is a manifestation of Goodhart's Law. When the metrics were being used by the boots on the ground, they were great. As soon as you get a middle layer...)<p>Which, is the point of the "law." There are no evergreen policies or metrics that can lead to improvement. Sucks, as you can't even really take this in a meta direction. Constantly changing metrics and process is a bad idea when you are tracking how often you do that.
While it sounds simple in the article, I think in reality the difference between "Distorting the system" and "Improving the system" is often not very clear. Even looking at the example of a WBR metric that evolved over time[1], the final version incentivizes product behavior that may run counter to the idea of Amazon as an "everything store", such as down-ranking items that are a better match for what the customer is looking for because they aren't immediately available or removing a long tail of products altogether. Is that a distortion or an improvement?<p>[1]:<p>- number of detail pages, which we refined to<p>- number of detail page views (you don’t get credit for a new detail page if customers don’t view it), which then became<p>- the percentage of detail page views where the products were in stock (you don’t get credit if you add items but can’t keep them in stock), which was ultimately finalized as<p>- the percentage of detail page views where the products were in stock and immediately ready for two-day shipping, which ended up being called Fast Track In Stock.
In any corporation many, if not most managers, directors and VPs are not measured by revenue impact. Not they don't have a revenue impact but that they aren't measured by it because the contribution is indirect. In those cases, I notice that the 4 most common places for distorting or cheating the system aren't in the article--budget, head count (hiring), performance reviews and space allocation. And the gaming can come from below by inflating budget requests, head count and the like--empire building, or from above by underinvesting in or cutting critical functions where the revenue impact is indirect.
I think this article has great advice when the metric is well chosen.<p>But that's the hard part, choosing a metric that matters, and that's the majority what goodhart's law is about. Too many things in business cannot be quantified, so you measure things adjacent and hope the correlate, and often that correlation is just wishful thinking.<p>Take for example, how do you measure productivity of developers?<p>Lines of code? Number pull requests? Story points completed? Tickets closed?<p>All of those measure....something...but they don't measure productivity directly. A smart engineer who spends a few extra days on a design that comes up with a new plan that cuts out half the work fails by all metrics I've listed above, but could easily be the most productive developer just by cutting out work.<p>Business is hard to measure directly. It's way too easy to penalize your best people by over focusing on quantifiable metrics.
This is a nice refinement to the discussion.
There is an element of truth to Goodhart's law, but it is certainly not a law and it is maybe not even a default condition. As per the author, the work of Demming clearly shows that measuring tolerances has a good effect on quality. I personally even seen Goodhart's law (mis)applied to discussions of machine learning pipelines, so clearly there are glaring exceptions to the utility of metrics.
Site is not loading for me (maybe hugged to death).<p>Archive: <a href="https://web.archive.org/web/20230303122025/https://commoncog.com/goodharts-law-not-useful/" rel="nofollow">https://web.archive.org/web/20230303122025/https://commoncog...</a>
This is such a compelling article on Goodharts law and interesting to see how Amazon conducts their Weekly Business Reviews. I'm wondering if there's a rebuttal to this piece.
Correlation is not causality.<p>If you base your incentives on something that's only <i>correlated</i> with what you want (for example, paying programmers per line of code) you get screwy results (say, lots of manually unrolled loops).
> [Goodhart's Law] is descriptive; it tells you of the existence of a phenomenon, but it doesn’t tell you what to do about it or how to solve it.<p>This is the difference between an adage and an essay. And I will say just thunking about the existence of Goodhart's Law whenever you design a system is all you really need. Any time you make a metric, put yourself in the shoes of the people who will be judged by the metric, and try to game it as hard as you can. Ask others to try to game it. And if the cheating you discover is manageable, use it. If not, get rid of it.
I mostly disagree with this.<p>Yes, there are ways to be really careful with targets. But at a societal or institutional level, you don’t get that kind of control.<p>I’ve written about the difficulties a bit here:<p><a href="https://superbowl.substack.com/p/beware-the-variable-maximizers" rel="nofollow">https://superbowl.substack.com/p/beware-the-variable-maximiz...</a>
I can't seem to reach the original URL; this seems to work:<p><a href="https://web.archive.org/web/20230303122025/https://commoncog.com/goodharts-law-not-useful/" rel="nofollow">https://web.archive.org/web/20230303122025/https://commoncog...</a>
Goodhart's law is extremely useful for illustrating the issues which come from bad KPIs and raising awareness of the natural psychology that people will work to game those measures if incentivized to do so.
Campbell's law is not simply a "social variant" of Goodhart's law. The two have a subtle but crucial difference.<p>To paraphrase a DonaldTrumpism, Goodhart's law says "Sounds good, doesn't work".<p>Whereas Campbell's law adds "Worse still, it'll certainly backfire", and makes that the focus.<p>Incidentally, the other "law" alluded to in the text, also has a name:<p>> The (Lance) Armstrong Principle:<p>"If you push people to promise more than they can deliver, they’re motivated to cheat."<p>A more general rephrasing: if you create / hold people accountable (e.g. by forcing a promise) to unreasonable expectations of performance that cannot be delivered there will be a motivation to cut corners, e.g. cheat.<p>(Popularized by Andrew Gelman; I first encountered this in an article by friend/colleague Nick Yeung, about the bad side of forcing phd students to publish, also an interesting article in its own right: <a href="https://www.nature.com/articles/s41562-019-0685-4" rel="nofollow">https://www.nature.com/articles/s41562-019-0685-4</a> )
Although this is an insightful article with many good points, I am disappointed that the author didn't mention a more fundamental problem with metrics: that the very framing of business that leads to a focus on metrics is toxic to responsible business practices.<p>The author talks a lot about listening to the voice of the process. How about listening to the voice of people? For instance, people who are concerned that we're losing our ethics and our vision and our sense of meaning in our work?<p>The moment someone suggests a metric, my reaction is not to think about causes and effects but rather "what is motivating you to think that dwelling on that metric, OR ANY METRIC, will lead to happiness?<p>Framing and philosophy precedes any possible notion of improvement. Start there. If that leads you to measurement, so be it... Or you can be like Amazon: a rich company that is famous for its rapaciousness and exploitative behavior.