User:IssaRice/Belief propagation and cognitive biases: Difference between revisions

Revision as of 06:12, 5 September 2018

"Several cognitive biases can be seen as confusion between probabilities and likelihoods, most centrally base-rate neglect." [1]
- confusing p-values with Pr(null hypothesis | data) seems like another instance of this.
- confidence interval vs credible interval also does this flipping about the conditional bar.
I think a polytree graph like $X\rightarrow Z\leftarrow Y$ can illuminate the halo effect/horn effect
Maybe https://en.wikipedia.org/wiki/Berkson%27s_paradox The page even says "The effect is related to the explaining away phenomenon in Bayesian networks."
Fundamental attribution error? The simplified DAG would look like: situational influence → observed action ← personality. And the evidence feeds into the "observed action" node, which propagates upwards to the "situational influence" and "personality" nodes. I think the bias is that the "personality" node gets updated too much. Can belief propagation give insight into this?
This one might be too simple, but the idea of screening off I think can be visualized in a Bayesian network. Not sure where the belief propagation would come in though... Related here are [2]/stereotyping.
Hindsight bias seems like an evidence node misfiring and causing updates in the graph? See also https://www.lesswrong.com/posts/TiDGXt3WrQwtCdDj3/do-we-believe-everything-we-re-told
Buckets error and flinching away from truth: I think you can formulate a probabilistic version of my comment using bayes nets and belief prop. (in that case, there still may or may not be causality involved; i think all you need are the independence relationships.)
- [3] the sour grapes/tolerification seems pretty similar, but the steps go like this: (1) initially, one has $X\implies Y$ stored. (2) the world shows you $X$ in a way that's undeniable (this is contrasted with the buckets error situation, where someone merely asserts/brings to attention $X$ ). (3) one does the modus ponens, obtaining $Y$ . Here, $Y$ is undesirable, but even more undesirable is $X\wedge \neg Y$ , and by (2), we cannot deny $X$ . So we pick the best of the bads and stick with $Y$ .

@@ Line 8: / Line 8: @@
 * Hindsight bias seems like an evidence node misfiring and causing updates in the graph? See also https://www.lesswrong.com/posts/TiDGXt3WrQwtCdDj3/do-we-believe-everything-we-re-told
 * Buckets error and flinching away from truth: I think you can formulate a probabilistic version of [https://www.greaterwrong.com/posts/EEv9JeuY5xfuDDSgF/flinching-away-from-truth-is-often-about-protecting-the/comment/D6WcJW4zpCT5WhG4T my comment] using bayes nets and belief prop. (in that case, there still may or may not be causality involved; i think all you need are the independence relationships.)
+** [http://mindingourway.com/see-the-dark-world/] the sour grapes/tolerification seems pretty similar, but the steps go like this: (1) initially, one has <math>X \implies Y</math> stored. (2) the world shows you <math>X</math> in a way that's undeniable (this is contrasted with the buckets error situation, where someone merely asserts/brings to attention <math>X</math>). (3) one does the modus ponens, obtaining <math>Y</math>. Here, <math>Y</math> is undesirable, but even more undesirable is <math>X \wedge \neg Y</math>, and by (2), we cannot deny <math>X</math>. So we pick the best of the bads and stick with <math>Y</math>.
 ==possibly related==