https://machinelearning.subwiki.org/w/index.php?action=history&feed=atom&title=User%3AIssaRice%2FBelief_propagation_and_cognitive_biasesUser:IssaRice/Belief propagation and cognitive biases - Revision history2026-09-02T08:16:30ZRevision history for this page on the wikiMediaWiki 1.41.2https://machinelearning.subwiki.org/w/index.php?title=User:IssaRice/Belief_propagation_and_cognitive_biases&diff=3174&oldid=prevIssaRice at 06:06, 11 November 20202020-11-11T06:06:12Z<p></p>
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<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>* "Several cognitive biases can be seen as confusion between probabilities and likelihoods, most centrally base-rate neglect." [https://www.greaterwrong.com/posts/tp4rEtQqRshPavZsr/learn-bayes-nets]</div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>* "Several cognitive biases can be seen as confusion between probabilities and likelihoods, most centrally base-rate neglect." [https://www.greaterwrong.com/posts/tp4rEtQqRshPavZsr/learn-bayes-nets<ins style="font-weight: bold; text-decoration: none;">] [https://lw2.issarice.com/posts/6vMBpZtoRw4ia2JrK/probability-vs-likelihood</ins>]</div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>** confusing p-values with Pr(null hypothesis {{!}} data) seems like another instance of this.</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>** confusing p-values with Pr(null hypothesis {{!}} data) seems like another instance of this.</div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>** confidence interval vs credible interval also does this flipping about the conditional bar.</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>** confidence interval vs credible interval also does this flipping about the conditional bar.</div></td></tr>
</table>IssaRicehttps://machinelearning.subwiki.org/w/index.php?title=User:IssaRice/Belief_propagation_and_cognitive_biases&diff=2607&oldid=prevIssaRice at 01:43, 20 October 20192019-10-20T01:43:09Z<p></p>
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<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>** confusing p-values with Pr(null hypothesis {{!}} data) seems like another instance of this.</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>** confusing p-values with Pr(null hypothesis {{!}} data) seems like another instance of this.</div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>** confidence interval vs credible interval also does this flipping about the conditional bar.</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>** confidence interval vs credible interval also does this flipping about the conditional bar.</div></td></tr>
<tr><td colspan="2" class="diff-side-deleted"></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">** it seems like for some kinds of evidence (like car alarms going off) we automatically take the base rate into account (e.g. i find myself ignoring car alarms because they've never turned out to be real thefts).</ins></div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* "It’s also a useful analogy for aspects of group epistemics, like avoiding double counting as messages pass through the social network." [https://www.greaterwrong.com/posts/tp4rEtQqRshPavZsr/learn-bayes-nets]</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* "It’s also a useful analogy for aspects of group epistemics, like avoiding double counting as messages pass through the social network." [https://www.greaterwrong.com/posts/tp4rEtQqRshPavZsr/learn-bayes-nets]</div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>** it seems like one reason group epistemics is hard is that we can't control the structure of the social graph, i.e. it could be a very dense graph or have unexpected edges in it, which makes accounting for evidence (and avoiding double counting of evidence) difficult. But if we could fully control the social graph and instruct people to message pass the lambdas and pis according to the belief prop algorithm, then it seems like we could do gossip the right way.</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>** it seems like one reason group epistemics is hard is that we can't control the structure of the social graph, i.e. it could be a very dense graph or have unexpected edges in it, which makes accounting for evidence (and avoiding double counting of evidence) difficult. But if we could fully control the social graph and instruct people to message pass the lambdas and pis according to the belief prop algorithm, then it seems like we could do gossip the right way.</div></td></tr>
</table>IssaRicehttps://machinelearning.subwiki.org/w/index.php?title=User:IssaRice/Belief_propagation_and_cognitive_biases&diff=1390&oldid=prevIssaRice at 21:25, 15 January 20192019-01-15T21:25:55Z<p></p>
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<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* Can the ideas in [http://effective-altruism.com/ea/st/beware_surprising_and_suspicious_convergence/ "Beware surprising and suspicious convergence"] be explained in terms of belief propagation/explaining away/causal diagrams/something else in this general area?</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* Can the ideas in [http://effective-altruism.com/ea/st/beware_surprising_and_suspicious_convergence/ "Beware surprising and suspicious convergence"] be explained in terms of belief propagation/explaining away/causal diagrams/something else in this general area?</div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* https://en.wikipedia.org/wiki/Blocking_effect</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* https://en.wikipedia.org/wiki/Blocking_effect</div></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>* 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?</div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>* 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? <ins style="font-weight: bold; text-decoration: none;">Actually I think the problem is that when we observe others, the causal graph we mentally draw looks like "observed action ← personality", but if we're the ones in the situation it looks like "situational influence → observed action". So it's more of a problem of using an incomplete causal graph rather than a problem with propagating updates.</ins></div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* This one might be too simple, but the idea of [https://wiki.lesswrong.com/wiki/Screening_off screening off] I think can be visualized in a Bayesian network. Not sure where the belief propagation would come in though... Related here are [https://en.wikipedia.org/wiki/Group_attribution_error]/stereotyping.</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* This one might be too simple, but the idea of [https://wiki.lesswrong.com/wiki/Screening_off screening off] I think can be visualized in a Bayesian network. Not sure where the belief propagation would come in though... Related here are [https://en.wikipedia.org/wiki/Group_attribution_error]/stereotyping.</div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* 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</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* 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</div></td></tr>
</table>IssaRicehttps://machinelearning.subwiki.org/w/index.php?title=User:IssaRice/Belief_propagation_and_cognitive_biases&diff=1291&oldid=prevIssaRice at 21:43, 8 January 20192019-01-08T21:43:03Z<p></p>
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<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>:: I think a network for this situation looks like <math>X\to Y\to Z</math> and <math>X\to Z</math>. So it's still a DAG but there is now a loop. Or maybe <math>X \rightarrow Z \leftarrow Y</math> is sufficient, i.e. the update on sourness only happens via the tolerability node.</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>:: I think a network for this situation looks like <math>X\to Y\to Z</math> and <math>X\to Z</math>. So it's still a DAG but there is now a loop. Or maybe <math>X \rightarrow Z \leftarrow Y</math> is sufficient, i.e. the update on sourness only happens via the tolerability node.</div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>:: There is something funny going on at the Z node, I think. Like it is failing to update, and sending the opposite message to Y or something. I'll need to work out the calculation to be sure.</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>:: There is something funny going on at the Z node, I think. Like it is failing to update, and sending the opposite message to Y or something. I'll need to work out the calculation to be sure.</div></td></tr>
<tr><td colspan="2" class="diff-side-deleted"></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">* belief propagation, backpropagation, and back-chained behavior training: is there a connection here? https://www.greaterwrong.com/posts/z79ApTRh7Jhmis2g8/srs-advice/comment/NocMj9ohMpPxQ9jnL</ins></div></td></tr>
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</table>IssaRicehttps://machinelearning.subwiki.org/w/index.php?title=User:IssaRice/Belief_propagation_and_cognitive_biases&diff=1246&oldid=prevIssaRice at 05:05, 4 January 20192019-01-04T05:05:54Z<p></p>
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<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* This one might be too simple, but the idea of [https://wiki.lesswrong.com/wiki/Screening_off screening off] I think can be visualized in a Bayesian network. Not sure where the belief propagation would come in though... Related here are [https://en.wikipedia.org/wiki/Group_attribution_error]/stereotyping.</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* This one might be too simple, but the idea of [https://wiki.lesswrong.com/wiki/Screening_off screening off] I think can be visualized in a Bayesian network. Not sure where the belief propagation would come in though... Related here are [https://en.wikipedia.org/wiki/Group_attribution_error]/stereotyping.</div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* 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</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* 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</div></td></tr>
<tr><td colspan="2" class="diff-side-deleted"></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">* Hindsight bias is also weird because it involves events happening over time.</ins></div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* 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.)</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* 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.)</div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>** [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 (example: X=grapes unreachable, Y=grapes sour). (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 (the world would be better with sweeter grapes!), but even more undesirable is <math>X \wedge \neg Y</math> (i.e. <math>\neg(X\implies Y)</math>, where the grapes are both sweet ''and'' unreachable), and by (2), we cannot deny <math>X</math>. So we pick the best of the undesirable choices and stick with <math>Y</math>.</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>** [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 (example: X=grapes unreachable, Y=grapes sour). (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 (the world would be better with sweeter grapes!), but even more undesirable is <math>X \wedge \neg Y</math> (i.e. <math>\neg(X\implies Y)</math>, where the grapes are both sweet ''and'' unreachable), and by (2), we cannot deny <math>X</math>. So we pick the best of the undesirable choices and stick with <math>Y</math>.</div></td></tr>
</table>IssaRicehttps://machinelearning.subwiki.org/w/index.php?title=User:IssaRice/Belief_propagation_and_cognitive_biases&diff=1245&oldid=prevIssaRice at 04:30, 4 January 20192019-01-04T04:30:20Z<p></p>
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<td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">Revision as of 04:30, 4 January 2019</td>
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<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>** confusing p-values with Pr(null hypothesis {{!}} data) seems like another instance of this.</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>** confusing p-values with Pr(null hypothesis {{!}} data) seems like another instance of this.</div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>** confidence interval vs credible interval also does this flipping about the conditional bar.</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>** confidence interval vs credible interval also does this flipping about the conditional bar.</div></td></tr>
<tr><td colspan="2" class="diff-side-deleted"></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">* "It’s also a useful analogy for aspects of group epistemics, like avoiding double counting as messages pass through the social network." [https://www.greaterwrong.com/posts/tp4rEtQqRshPavZsr/learn-bayes-nets]</ins></div></td></tr>
<tr><td colspan="2" class="diff-side-deleted"></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">** it seems like one reason group epistemics is hard is that we can't control the structure of the social graph, i.e. it could be a very dense graph or have unexpected edges in it, which makes accounting for evidence (and avoiding double counting of evidence) difficult. But if we could fully control the social graph and instruct people to message pass the lambdas and pis according to the belief prop algorithm, then it seems like we could do gossip the right way.</ins></div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* https://www.readthesequences.com/Fake-Causality discusses belief propagation and its relation to fake explanations, e.g. "Alas, human beings do not use a rigorous algorithm for updating belief networks. We learn about parent nodes from observing children, and predict child nodes from beliefs about parents. But we don’t keep rigorously separate books for the backward-message and forward-message. We just remember that phlogiston is hot, which ''causes'' fire to be hot. So it seems like phlogiston theory predicts the hotness of fire. Or, worse, it just feels like phlogiston ''makes the fire hot''." and "Speaking of “hindsight bias” is just the nontechnical way of saying that humans do not rigorously separate forward and backward messages, allowing forward messages to be contaminated by backward ones."</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* https://www.readthesequences.com/Fake-Causality discusses belief propagation and its relation to fake explanations, e.g. "Alas, human beings do not use a rigorous algorithm for updating belief networks. We learn about parent nodes from observing children, and predict child nodes from beliefs about parents. But we don’t keep rigorously separate books for the backward-message and forward-message. We just remember that phlogiston is hot, which ''causes'' fire to be hot. So it seems like phlogiston theory predicts the hotness of fire. Or, worse, it just feels like phlogiston ''makes the fire hot''." and "Speaking of “hindsight bias” is just the nontechnical way of saying that humans do not rigorously separate forward and backward messages, allowing forward messages to be contaminated by backward ones."</div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* Also look at the general structure of rationalizations, and see if belief propagation can help us understand them.</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* Also look at the general structure of rationalizations, and see if belief propagation can help us understand them.</div></td></tr>
</table>IssaRicehttps://machinelearning.subwiki.org/w/index.php?title=User:IssaRice/Belief_propagation_and_cognitive_biases&diff=808&oldid=prevIssaRice at 02:11, 13 October 20182018-10-13T02:11:51Z<p></p>
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<td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">Revision as of 02:11, 13 October 2018</td>
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<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>** confusing p-values with Pr(null hypothesis {{!}} data) seems like another instance of this.</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>** confusing p-values with Pr(null hypothesis {{!}} data) seems like another instance of this.</div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>** confidence interval vs credible interval also does this flipping about the conditional bar.</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>** confidence interval vs credible interval also does this flipping about the conditional bar.</div></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>* https://www.readthesequences.com/Fake-Causality discusses belief propagation and its relation to fake explanations, e.g. "Alas, human beings do not use a rigorous algorithm for updating belief networks. We learn about parent nodes from observing children, and predict child nodes from beliefs about parents. But we don’t keep rigorously separate books for the backward-message and forward-message. We just remember that phlogiston is hot, which ''causes'' fire to be hot. So it seems like phlogiston theory predicts the hotness of fire. Or, worse, it just feels like phlogiston ''makes the fire hot''." and "Speaking of “hindsight bias” is just the nontechnical way of saying that humans do not rigorously separate forward and backward messages, allowing forward messages to be contaminated by backward ones. "</div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>* https://www.readthesequences.com/Fake-Causality discusses belief propagation and its relation to fake explanations, e.g. "Alas, human beings do not use a rigorous algorithm for updating belief networks. We learn about parent nodes from observing children, and predict child nodes from beliefs about parents. But we don’t keep rigorously separate books for the backward-message and forward-message. We just remember that phlogiston is hot, which ''causes'' fire to be hot. So it seems like phlogiston theory predicts the hotness of fire. Or, worse, it just feels like phlogiston ''makes the fire hot''." and "Speaking of “hindsight bias” is just the nontechnical way of saying that humans do not rigorously separate forward and backward messages, allowing forward messages to be contaminated by backward ones."</div></td></tr>
<tr><td colspan="2" class="diff-side-deleted"></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">* Also look at the general structure of rationalizations, and see if belief propagation can help us understand them.</ins></div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* I think a polytree graph like <math>X\rightarrow Z \leftarrow Y</math> can illuminate the halo effect/horn effect</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* I think a polytree graph like <math>X\rightarrow Z \leftarrow Y</math> can illuminate the halo effect/horn effect</div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* 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."</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* 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."</div></td></tr>
</table>IssaRicehttps://machinelearning.subwiki.org/w/index.php?title=User:IssaRice/Belief_propagation_and_cognitive_biases&diff=807&oldid=prevIssaRice: /* possibly related */2018-10-13T02:10:48Z<p><span dir="auto"><span class="autocomment">possibly related</span></span></p>
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<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* http://agentyduck.blogspot.com/2014/02/lobs-theorem-cured-my-social-anxiety.html (lol)</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* http://agentyduck.blogspot.com/2014/02/lobs-theorem-cured-my-social-anxiety.html (lol)</div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* https://briantomasik.com/collection-quick-observations-2018/#Distractors_revert_you_to_your_priors</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* https://briantomasik.com/collection-quick-observations-2018/#Distractors_revert_you_to_your_priors</div></td></tr>
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</table>IssaRicehttps://machinelearning.subwiki.org/w/index.php?title=User:IssaRice/Belief_propagation_and_cognitive_biases&diff=806&oldid=prevIssaRice: /* possibly related */2018-10-13T01:00:55Z<p><span dir="auto"><span class="autocomment">possibly related</span></span></p>
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<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* https://www.gwern.net/Sunk-cost</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* https://www.gwern.net/Sunk-cost</div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* http://agentyduck.blogspot.com/2014/02/lobs-theorem-cured-my-social-anxiety.html (lol)</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* http://agentyduck.blogspot.com/2014/02/lobs-theorem-cured-my-social-anxiety.html (lol)</div></td></tr>
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</table>IssaRicehttps://machinelearning.subwiki.org/w/index.php?title=User:IssaRice/Belief_propagation_and_cognitive_biases&diff=805&oldid=prevIssaRice at 21:33, 9 October 20182018-10-09T21:33:28Z<p></p>
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<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>** confusing p-values with Pr(null hypothesis {{!}} data) seems like another instance of this.</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>** confusing p-values with Pr(null hypothesis {{!}} data) seems like another instance of this.</div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>** confidence interval vs credible interval also does this flipping about the conditional bar.</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>** confidence interval vs credible interval also does this flipping about the conditional bar.</div></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>* https://www.readthesequences.com/Fake-Causality "Alas, human beings do not use a rigorous algorithm for updating belief networks. We learn about parent nodes from observing children, and predict child nodes from beliefs about parents. But we don’t keep rigorously separate books for the backward-message and forward-message. We just remember that phlogiston is hot, which ''causes'' fire to be hot. So it seems like phlogiston theory predicts the hotness of fire. Or, worse, it just feels like phlogiston ''makes the fire hot''."</div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>* https://www.readthesequences.com/Fake-Causality <ins style="font-weight: bold; text-decoration: none;">discusses belief propagation and its relation to fake explanations, e.g. </ins>"Alas, human beings do not use a rigorous algorithm for updating belief networks. We learn about parent nodes from observing children, and predict child nodes from beliefs about parents. But we don’t keep rigorously separate books for the backward-message and forward-message. We just remember that phlogiston is hot, which ''causes'' fire to be hot. So it seems like phlogiston theory predicts the hotness of fire. Or, worse, it just feels like phlogiston ''makes the fire hot''<ins style="font-weight: bold; text-decoration: none;">." and "Speaking of “hindsight bias” is just the nontechnical way of saying that humans do not rigorously separate forward and backward messages, allowing forward messages to be contaminated by backward ones</ins>. "</div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* I think a polytree graph like <math>X\rightarrow Z \leftarrow Y</math> can illuminate the halo effect/horn effect</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* I think a polytree graph like <math>X\rightarrow Z \leftarrow Y</math> can illuminate the halo effect/horn effect</div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* 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."</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* 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."</div></td></tr>
</table>IssaRice