Against Bayesianism – David Deutsch
This is a deeply confused set of arguments.<p>There&#x27;s no fundamental ontological difference between &quot;give me your likelihood ratios between drawing cards [a and b] as the next draw from this deck of cards&quot; and &quot;give me your likelihood ratios between finding and not finding a continent in an unexplored part of the world&quot;.  While what we ultimately care about is the territory, which is fixed, we only have our imperfect map of the world with which to model it.  There is nothing inherently &quot;probabilistic&quot; about a shuffled deck of cards - the cards have a specific configuration; the uncertainy is only present in the observer(s).  Predicting the next draw from a deck of cards is subject to &quot;out of context&quot; model violations the same way that any other prediction over future world states is.<p>The proposed escape hatch seems to be to dodge the question, but of course in reality you are not agnostic over all possible future events that haven&#x27;t been ruled out by being &quot;bad explanations&quot;.  You can say, &quot;well, don&#x27;t make predictions with numbers on them&quot;, but by acting in the world you are making predictions with numbers on them all the time!  Bayesian epistemology doesn&#x27;t claim to be perfect, it claims to be the least inaccurate way of making those predictions and updating on new evidence.  Now, there&#x27;s a separate question of how bounded reasoners (i.e. humans) can best accomplish their goals, and &quot;explicitly run Bayes to decide whether to get out of bed&quot; probably isn&#x27;t it.  But superforecasters are an existence proof that explicit Bayesian calculations are still extremely useful even in highly uncertain domains.<p>Mechanistic explanations are great but even having a mechanistic explanation doesn&#x27;t let you say P(x) = 1.  There can be enormous differences in reasonable mitigations to take between P(x) = 0.99 and 0.999999, depending on context.