"The quality of our lives depends on the quality of the decisions we make." -Ray Dalio
Average Decision Quality is related to the notions of power-law world, model uncertainty, and the prediction horizon. In a world in which the outcomes of actions:
- Vary widely in utility
- Have unknown causal structure and/or side-effects
- Are hard to predict
You don't know in advance which of your decisions will turn out to be important. Warren Buffet and Ray Dalio both point out that this applies to the upside and the downside. A few key bets made most of the upside difference, while avoiding key mistakes limited the big downsides, but that they were not able to predict these in advance. You therefore want your average decision to be high quality. Decision quality judgment criteria are domain dependent but might generally include things like
- Being well calibrated with regard to evidence
- Having good problem-heuristic fit
- Minimizes loss of option value
- Is robust with regard to reference class uncertainty (i.e. is foxy)
- Takes all stakeholder incentives into account
- Suggests unambiguous next actions
- Is consistent with terminal goals
In motivation systems, it is often recommended to reward/reinforce any behavior that is better than your average behavior. This is in contrast to only reinforcing "model behavior." Only reinforcing days where one sticks to a perfect diet, rather than all days where one ate healthier than average, for example. Reinforcing all behavior that is better than the average behavior tightens feedback loops.
Average Decision Quality might also be thought of as pointing towards the importance of epistemic rigor.