↑ comment by fubarobfusco ·
2015-12-20T21:08:13.070Z · LW(p) · GW(p)
Well, it depends on what you mean by "rationality". Here's something I posted in 2014, slightly revised:
If not rationality, then what?
LW presents epistemic and instrumental rationality as practical advice for humans, based closely on the mathematical model of Bayesian probability. This advice can be summed up in two maxims:
- Obtain a better model of the world by updating on the evidence of things unpredicted by your current model.
- Succeed at your given goals by using your (constantly updating) model to predict which actions will maximize success.
Or, alternately: Having correct beliefs is useful for humans achieving goals in the world, because correct beliefs enable correct predictions, and correct predictions enable goal-accomplishing actions. And the way to have correct beliefs is to update your beliefs when their predictions fail.
We can call these the rules of Bayes' world, the world in which updating and prediction are effective at accomplishing human goals. But Bayes' world is not the only imaginable world. What if we deny each of these premises and see what we get? Other than Bayes' world, which other worlds might we be living in?
To be clear, I'm not talking about alternatives to Bayesian probability as a mathematical or engineering tool. I'm talking about imaginable worlds in which Bayesian probability is not a good model for human knowledge and action.
Suppose that making correct predictions does not enable goal-accomplishing actions. We might call this Cassandra's world, the world of tragedy — in which those people who know best what the future will bring, are most incapable of doing anything about it.
In the world of heroic myth, it is not oracles (good predictors) but rather heroes and villains (strong-willed people) who create change in the world. Heroes and villains are people who possess great virtue or vice — strong-willed tendencies to face difficult challenges, or to do what would repulse others. Oracles possess the truth to arbitrary precision, but they accomplish nothing by it. Heroes and villains come to their predicted triumphs or fates not by believing and making use of prediction, but by ignoring or defying it.
Suppose that the path to success is not to update your model of the world, so much as to update your model of your self and goals. The facts of the external world are relatively close to our priors; not much updating is needed there — but our goals are not known to us initially. In fact, we may be thoroughly deceived about what our goals are, or what satisfying them would look like.
We might consider this to be Buddha's world, the world of contemplation — in which understanding the nature of the self is substantially more important to success than understanding the external world. In this world, when we choose actions that are unsatisfactory, it isn't so much because we are acting on faulty beliefs about the external world, but because we are pursuing goals that are illusory or empty of satisfaction.
There are other models as well, that could be extrapolated from denying other premises (explicit or implicit) of Bayes' world. Each of these models should relate prediction, action, and goals in different ways: We might imagine Lovecraft's world (knowledge causes suffering), Qoheleth's world (maybe similar to Buddha's), Job's world, or Nietzsche's world.
Each of these models of the world — Bayes' world, Cassandra's world, Buddha's world, and the others — does predict different outcomes. If we start out thinking that we are in Bayes' world, what evidence might suggest that we are actually in one of the others?
Replies from: Bryan-san
↑ comment by Bryan-san ·
2015-12-21T20:08:43.262Z · LW(p) · GW(p)
This is a perspective I hadn't seen mentioned before and helps me understand why a friend of mine gives low value to the goal-oriented rationality material I've mentioned to him.
Thank you very much for this post!
Replies from: fubarobfusco
↑ comment by fubarobfusco ·
2015-12-21T20:49:28.522Z · LW(p) · GW(p)
It's worth noting that, from what I can tell at least (having not actually taken their courses), quite a bit of CFAR "rationality" training seems to deal with issues arising not directly from Bayesian math, but from characteristics of human minds and society.