Comment by perfect-blue
6 hours ago
Pareto efficiency is a welfare economics concept. In game theory, the closest you can get to that is a Nash equilibrium.
6 hours ago
Pareto efficiency is a welfare economics concept. In game theory, the closest you can get to that is a Nash equilibrium.
Pareto optimal is definitely a core concept in game theory. It says that no other vector beats it in every dimension (or at least as good in all but one, and better in at least one).
I wish more business / product people understood this concept. When a product has been refined enough to approach Pareto optimality (at least on the dimensions the product is easily measured), it's all too common for people to chase improvements to one metric at a time, and when that runs out, switch to another metric. This results in going in circles (make metric A go up-up-up, forcing metric B down-down-down, then make B go up-up-up while forcing A to go down-down-down - it's worse than this because multiple dimensions go up/down together, making it harder to spot). Sometimes these cycles are over a period of quarters or years, making it even harder to spot because cycles are slower than employee attrition.
This is not independent of Goodhart's Law[1]. I've seen entire product orgs, on a very mature product (i.e., nearing the Pareto frontier for the metrics that are tracked), assign one metric per PM and tie PM comp to their individual metric improving. Then PMs wheel and deal away good features because "don't ship your thing that hurts my metric and I won't ship my thing that hurts yours" - and that's completely rational given the incentives. Of course the best wheelers-and-dealers get the money/promotions. So the games escalate ("you didn't deal last time, so it's going to cost you more this time"). Eventually negative politics explode and it's all just a reality TV show. Meanwhile engineers who don't have an inside view of what's going on are left wondering why PMs appear to be acting insane with ship/no-ship decisions.
If more people understood Pareto optimality and Goodhart's Law, even at a surface level, I think being "data driven" would be a much better thing.
[1] Goodhart's Law: when a measure becomes a target, it ceases to be a good measure
Cybernetics has devolved into KPI metrics with accelerationism as a treat.
Apparently documents from Google's antitrust case revealed the search algorithm was adjusted to give worse results in order to force the KPI for AdSense to drive quarterly earnings reports.
> “I care more about revenue that the average person but think we can all agree that for all of our teams trying to live in high cost areas another $[redacted] in stock price loss will not be great for morale, not to mention the huge impact on our sales team.
> “I don’t want the message to be ‘we’re doing this thing because the Ads team needs revenue.’ That’s a very negative message.
> But my question to you is – based on above – what do we think is the best decision for Google overall?
> …Are there other ranking tweaks we can push out quickly?” - Dischler
Anil Sabharwal, the Chrome executive: > “1…we were able to get launch approval to rollout two changes (entity suggest and tail suggest) that increase queries by [redacted]% and [redacted]% respectively.
> 2. We are going to immediately start experiments to improve search ranking in the omnibox (more search results and nudging search to the top).”
[1] https://www.searchenginejournal.com/google-execs-scheme-to-i...