Setting a 6 a.m. alarm is not waking at 6: Goodhart's law for personal goals
Goodhart's law: a measure stops measuring once it becomes a target. Where the wording came from, how solid the cobra story is, and how to pick a goal metric.
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The easier a goal is to measure, the faster it stops being the goal. Choose “alarm set for 6 a.m.” as your scorecard and by Thursday the scorecard is perfect and the 6 a.m. person is still asleep. That pattern has a name, Goodhart’s law, and a common statement of it: when a measure becomes a target, it ceases to be a good measure. The measure was a stand-in for the outcome. Once you are paid, praised or streak-rewarded for the stand-in, you deliver the stand-in, by the cheapest route you can find.
The useful question is therefore not “what should I measure?” but “what is the cheapest way to fake this, and who would notice?”
Who said it first, Goodhart or Strathern?
Charles Goodhart, a British economist, wrote in a 1975 paper on UK monetary policy, “Problems of Monetary Management: The UK Experience”, that any observed statistical regularity will tend to collapse once pressure is placed upon it for control purposes. The paper was presented at a Reserve Bank of Australia conference and printed in Papers in Monetary Economics the same year. It is a remark about money supply data and says nothing yet about people in general.
The sentence everyone quotes comes from Marilyn Strathern, a Cambridge anthropologist, in “‘Improving ratings’: audit in the British University system” (European Review, 1997), which grew out of a lecture at Girton College. She wrote that when a measure becomes a target, it ceases to be a good measure, and the subject was universities being rated rather than interest rates. The name “Goodhart’s law” is generally traced to the accounting historian Keith Hoskin, whom Strathern cites; sources disagree on the year of his work, so treat that attribution as secondhand.
So the quotable form is 1997, the idea is 1975, and the move from money to schools to your morning was made by other people.
How well sourced is the cobra story?
Thinly, and the thinness is itself a lesson about trusting a good example.
The usual telling: a British governor in Delhi puts a bounty on cobras, residents start breeding them, the governor cancels the bounty, the breeders release their stock, and the city ends up with more cobras. The name “cobra effect” is credited to the German economist Horst Siebert, whose book Der Kobra-Effekt (Deutsche Verlags-Anstalt, Stuttgart, 2001; some catalogues list a 2002 edition) uses it as a metaphor for incentives that backfire in firms and states.
The 2012 Freakonomics Radio episode “The Cobra Effect” (episode 96, aired October 10, 2012) is where many English speakers met the story. There it is told by Vikas Mehrotra, a finance professor at the University of Alberta, who cites no document for it. The episode’s own producers went looking and found something else: Michael Vann, a historian at Sacramento State, had read French colonial archives on Hanoi and found a daily tally of rats killed, including 20,114 on June 12, 1902. A health official there later found rat farms where tails were cut off and the animals set loose. Vann’s verdict on air: “I’m pushing for the rat effect, in lieu of the cobra effect.” The episode also covers Fort Benning, Georgia, which in 2007 paid $40 per feral-pig tail; the biologist Rob Holtfreder found the pig population growing anyway, though the episode calls the evidence murky because nobody knew how many tails came from elsewhere.
My count: Hanoi has archives behind it, Fort Benning has a biologist and murky numbers, and Delhi has a plausible anecdote. No primary record for Delhi has surfaced. The Hanoi case already has its own breakdown in what a rat-tail bounty teaches an alarm app, so this post stays with the general rule rather than retelling it.
Why does a measure break once you aim at it?
Two separate failures hide under one name. The Manheim and Garrabrant taxonomy (2018, “Categorizing Variants of Goodhart’s Law”) splits the law into four kinds: regressional, extremal, causal and adversarial. Two matter for goals.
The first is the weak proxy. Even before anyone cheats, a stand-in only correlates with the thing it stands for. Pick the top scorers on a noisy measure and you partly select for the noise.
The second is the adversarial kind, which is the cobra. Once someone gains from the number, they treat it as the thing to optimise. A real case with a paper trail: in September 2016 the US Consumer Financial Protection Bureau fined Wells Fargo $100 million over accounts opened without customers’ consent. By the bank’s own analysis, employees had opened more than two million deposit and credit card accounts that may not have been authorized, and the bank had fired 5,300 employees involved. The Bureau tied the behaviour to sales targets and compensation incentives. “Accounts per customer” was a fine measure until it was a quota.
In a personal goal you play both roles, the one who sets the number and the one who benefits from it looking good. That makes the adversary very well informed.
What does a faked wake-up look like?
Take the goal “wake at 6”. There are five readings of it, each harder to fake than the last.
| What you count | Cheapest fake | Works while still asleep? |
|---|---|---|
| Alarm set for 6:00 | Set it, ignore it | Yes |
| Alarm rang | Phone rings in the next room | Yes |
| Alarm dismissed | Swipe with eyes shut | Often |
| Tracker says you woke | Move your arm | Yes |
| Someone else sees you upright at 6:05 and again at 6:20 | Stand up, then return to bed after the first check | No, for the first reading |
Wearables add a caution here rather than a cure. A tracker is a second proxy, and sleep trackers measure movement and heart signals rather than the sleep or the waking you wanted. The same logic holds for a streak counter, which can turn into the goal itself, a drift that can cost more than a missed day.
What makes a metric hard to game?
Four properties, in rough order of how much they buy you.
- Closeness to the outcome. “Alarm set” is a plan. “Upright at 6:05” is much closer to waking.
- Observation by someone else. A number only you can write is a number you can edit. A witness changes who controls the record, which is the point of the evidence tiers laid out in the proof problem.
- A cost to the fake that exceeds the cost of the real thing. If standing up costs less effort than staging a convincing fake, standing up wins.
- A second reading at a time you did not choose. One check can be beaten once. Two readings with a gap between them need the person to actually be awake.
No metric survives all four forever. The goal is a metric where beating it takes more effort than meeting it, for long enough that the behaviour sets in.
Where does an app fit?
DontSnooze applies the second property by asking for a short video of you after the alarm, so the record is made by a camera and seen by your friends instead of ticked by you. If the video never arrives, a random photo from your own camera roll goes to them instead. It is one tool with one soft spot: you can still record the video from your pillow and go back under. That is why the second reading in the list above matters, and why an app is a starting measure for you to tighten, not a finished one.
The most reliable change is smaller than any app. Try swapping “wake at 6” with “be visible, standing, to a person at 6:05, and again at 6:20”, for a week, and compare it with what the old number would have reported.
The first reading, at least, is covered at dontsnooze.io.