Many metrics has a correlate[1], examples:
- g and income and lifespan
- height and talent at basketball
- faster tennis serves and likelihood of winning
This correlation holds true for most cases, except for extreme outliers. The wealthiest people in the world does not have the highest IQ, the tallest person in the world is not the best basketball player and the fastest tennis server is not the #1 player.
The trend seems to be that even when two factors are correlated, their tails diverge
This is tail end divergence, and awareness of it is important for all agents optimizing (consciously or unconsciously) against a metric. For example, under most circumstances optimizing for capital, happiness and life affirmation leads to similar results. But at the tail ends, which can mean after many cycles, after a long time, or extreme optimization, these metrics lead to widely different outcomes. Optimizing for Nietzschean life affirmation versus hedons will likely lead to different attitudes toward suffering – and different life outcomes.
Personally, I’m trying to become more aware of what I’m optimizing for. Yes, for lower levels of wealth there is a correlation between increased income and happiness – but that curve is asymptotic.