“there are multiple problems with (probability x utility) calculations” DOES imply that discarding this approach might be helpful.
Agreed with the caveat—if and only if the alternative approach can mimic most of the benefits that this approach brings.
I’m not aware of any other decision theories that really can come close to rigorously defining decision making, so until those are developed, it makes sense to try and create patches to what we already have.
You’re optimizing for the wrong thing. “Matching reality” is a much more useful criterion than “rigorous”.
It’s easy to match reality when you’re non-rigorous. You just describe how you make decisions in plain language, and you have a decision making criterion.
But, when your decisions become very complicated (what startup should I start and why)) , turns out that vague explanation isn’t much help. This is when you need rigor.
It’s easy to match reality when you’re non-rigorous
Not if you want to make forecasts (= good decisions for the future).
But, when your decisions become very complicated (what startup should I start and why)) , turns out that vague explanation isn’t much help. This is when you need rigor.
That’s when you need to avoid simplistic models which will lead you astray. Your criterion is still the best forecast. Given the high level of uncertainty and noise I am not at all convinced that the more rigor you can bring, the better.
Agreed with the caveat—if and only if the alternative approach can mimic most of the benefits that this approach brings.
I’m not aware of any other decision theories that really can come close to rigorously defining decision making, so until those are developed, it makes sense to try and create patches to what we already have.
You’re optimizing for the wrong thing. “Matching reality” is a much more useful criterion than “rigorous”.
You can get very rigorous about spherical cows in vacuum.
It’s easy to match reality when you’re non-rigorous. You just describe how you make decisions in plain language, and you have a decision making criterion.
But, when your decisions become very complicated (what startup should I start and why)) , turns out that vague explanation isn’t much help. This is when you need rigor.
Not if you want to make forecasts (= good decisions for the future).
That’s when you need to avoid simplistic models which will lead you astray. Your criterion is still the best forecast. Given the high level of uncertainty and noise I am not at all convinced that the more rigor you can bring, the better.
Then that’s where we disagree.