Metric

From The Jolly Contrarian
Revision as of 09:24, 1 December 2023 by Amwelladmin (talk | contribs)
(diff) ← Older revision | Latest revision (diff) | Newer revision → (diff)
Jump to navigation Jump to search
JC sounds off on Management™
So long, and thanks for all the fish
Index: Click to expand:
Tell me more
Sign up for our newsletter — or just get in touch: for ½ a weekly 🍺 you get to consult JC. Ask about it here.

When everything about a people is for the time growing weak and ineffective, it begins to talk about efficiency. ... Vigorous organisms talk not about their processes, but their aims.

G. K. Chesterton, Heretics

“When a measure becomes a target, it ceases to be a good measure.”

Goodhart’s Law

The stock-in-trade of a middle manager and the management consultant she aspires to become.

Like a key performance indicator, a second-order derivative of actual performance calculated to allow non-experts to make cavalier management decisions, usually to reduce expenditure on — aka make redundant — the person performing that function.

To be contrasted with the ineffable, inarticulable skills that are provided by a subject matter expert.

Goodhart’s law

Not a law of economics or sociology so much as a wry remark — professor Goodhart made it at a symposium in 1975 — that happens to pierce modern management orthodoxy through its heart. Thus it can both spur its own industry of academic work in sociology and systems theory, and at the same time go ignored in the upper tiers of corporate management:

When a measure becomes a target, it ceases to be a good measure.

People are smart and selfish. They will work any target you set to suit themselves. If you tax by number of windows, people will board up their windows.[1]

On dolphins, seagulls and opposable thumbs

Goodhart’s law is universal enough to apply even to dolphins.

An aquarium in Miami is reported to have dealt with the problem of litter and dead seagulls in the main tank by rewarding the resident dolphins for cleaning them up: a fish for each bird or piece of litter.

Before long, the dolphins were observed shredding bits of litter into smaller pieces and claiming multiple fish, and then stockpiling surplus fish, luring seagulls with them, and killing the seagulls.[2]

“We should ” as someone on the podcast remarked, “be grateful dolphins don’t have opposable thumbs.”

Four problems with metrics

One could, and here I am indebted to this excellent resource on Goodhart’s law, break the phenomenon down into four components.

Regressive

Using a single metric as a proxy to measure “multivariate” phenomena that are driven by several factors. Here Simpson’s paradox is not your friend. Much “social justice” — which we define as the wishful, if not wilful, tendency to boil complex socioeconomic phenomena down to simplistic moral propositions that even a dull fifth-former could understand, and only a dull fifth-former would fall for — stumbles into this trap.

Extremal

Many metrics are useful within a range corresponding to “normalcy”: call it “peacetime”, “normal operating conditions”, “business as usual” and similar platitudes — but break down, fail, or even reverse themselves in extremes or unusual cases beyond that range. Using normal distributions of independent events to model non-dependent events with non-linear distributions — like, well, anything that involves human behaviour, such as a market — is especially fraught, because even where, 95% of the time, your metrics work fabulously, that 95% is exactly the range over which it doesn’t matter whether they work or not. This is the time where things are operating as normal, behaving themselves, and not blowing up.

The “use-case” for any metric in the first place, remember, is to warn about risk events. No-one needs a light on the dashboard saying “everything is fine”. A “heightened risk” metric that you can only rely on when there isn’t a heightened risk is a waste of trees.

Causal

Metrics fall for the old “correlation is not the same as causation” chestnut. In recent years, prompted we think by the difference engine’s emergence as the machine of choice for measuring things, we have given up on the idea of proving out causal chains. We are happy enough to rely on correlations. But correlations may be meaningful or spurious, and even where meaningful they give no idea which way the causal arrow flows. It may be true that people often buy ice-cream when they are wearing sunglasses, but handing out complimentary sunglasses will not improve ice-cream sales.

Adversarial

Substitute targeting the desired outcome — a senior tranche in a portfolio of mortgages that will not default in any circumstances — with one that is rated AAA. Hello, global financial crisis.


See also

References

  1. See James C. Scott’s epic Seeing Like a State.
  2. [https://open.spotify.com/episode/3y799K1qGOhqxPUGcqXwx0 Rationally Speaking podcast episode 240.