For me, the HoH update is big enough to make a the simulation hypothesis a pretty likely explanation. It also makes it less likely that there are alternative explanations for “HoH seems likely”. See my old post here (probably better to read this comment though).
Imagine a Bayesian model with a variable S=”HoH seems likely” (to us) and 3 variables pointing towards it: “HoH” (prior: 0.001), “simulation” (prior=0.1), and “other wrong but convincing arguments” (prior=0.01). Note that it seems pretty unlikely there will be convincing but wrong arguments a priori (I used 0.01) because we haven’t updated on the outside view yet.
Further, assume that all three causes, if true, are equally likely to cause “HoH seems likely” (say with probability 1, but the probability doesn’t affect the posterior).
Apply Bayes rule: We’ve observed “HoH seems likely”. The denominator in Bayes rule is P(HoH seems likely) ~~ 0.111 (roughly the sum of the three priors because the priors are small). The numerator for each hypothesis H equals 1 * P(H).
Bayes rule gives an equal update (ca 1⁄0.111x = 9x) in favor of every hypothesis, bringing up the probability of “simulation” to nearly 90%.
Note that this probability decreases if we find, or think there are better explanations for “HoH seems likely”. This is plausible but not overwhelmingly likely because we already have a decent explanation with prior 0.1. If we didn’t have one, we would still have a lot of pressure to explain “HoH seems likely”. The existence of the plausible explanation “simulation” with prior 0.1 “explains away” the need for other explanations such as those falling under “wrong but convincing argument”.
This is just an example, feel free to plug in your numbers, or critique the model.
2.
For me, the HoH update is big enough to make a the simulation hypothesis a pretty likely explanation. It also makes it less likely that there are alternative explanations for “HoH seems likely”. See my old post here (probably better to read this comment though).
Imagine a Bayesian model with a variable S=”HoH seems likely” (to us) and 3 variables pointing towards it: “HoH” (prior: 0.001), “simulation” (prior=0.1), and “other wrong but convincing arguments” (prior=0.01). Note that it seems pretty unlikely there will be convincing but wrong arguments a priori (I used 0.01) because we haven’t updated on the outside view yet.
Further, assume that all three causes, if true, are equally likely to cause “HoH seems likely” (say with probability 1, but the probability doesn’t affect the posterior).
Apply Bayes rule: We’ve observed “HoH seems likely”. The denominator in Bayes rule is P(HoH seems likely) ~~ 0.111 (roughly the sum of the three priors because the priors are small). The numerator for each hypothesis H equals 1 * P(H).
Bayes rule gives an equal update (ca 1⁄0.111x = 9x) in favor of every hypothesis, bringing up the probability of “simulation” to nearly 90%.
Note that this probability decreases if we find, or think there are better explanations for “HoH seems likely”. This is plausible but not overwhelmingly likely because we already have a decent explanation with prior 0.1. If we didn’t have one, we would still have a lot of pressure to explain “HoH seems likely”. The existence of the plausible explanation “simulation” with prior 0.1 “explains away” the need for other explanations such as those falling under “wrong but convincing argument”.
This is just an example, feel free to plug in your numbers, or critique the model.