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Let's imagine that we ran it as a simulation and we ran it a million times. The two people would have a different distribution of results. If you ignore the int

Let's imagine that we ran it as a simulation and we ran it a million times. The two people would have a different distribution of results. If you ignore the intention, you ignore reality as if that intention were not a part of it. Do you not notice that your inference is less accurate using this line of reasoning? Does that not suggest that it's simply wrong?

usgroup 3y ago
This is well put. Coincidentally in the example the results are the same , but they need not be. given repeated experiments with the same intentions one may expect different distributions. However, one could just move the argument up a level and manufacture a case of different intentions leading to the same distributions and then ask the same question.
- kgwgk 3y ago
  > Coincidentally in the example the results are the same , but they need not be. The questions is whether we should draw different conclusions when the results are the same. I don’t think that anyone has any issues with drawing different conclusions when the results are different!
- lalaithion 3y ago
  Imagine you have a machine that rolls a d20 and lies if the die comes up 1-19, and tells the truth on a 20. Should you trust this machine usually? No. But if you can _see that the die comes up 20_ then you should trust it. The fact that it sometimes might lie doesn't mean that you should distrust the machine if you can see that in this case it's telling the truth.
lalaithion 3y ago
What do you mean by 'results'? They would not have different distributions of results on their first die roll. They would have different distributions of results on their reported die roll. If I am looking at their first die roll, the fact that they would have different reported die rolls doesn't matter!
lalaithion 3y ago
Here’s another example: Say you have a lazy researcher. They flip a coin, and if it comes up heads, they do the experiment. If it comes up tails, they just write down a random number. If you _only get access_ to the final number, then you should discount what they wrote down – it’s 50% likely to be fake. If you do 1,000,000 simulations of this, it’s useless 50% of the time. But if you know the result of the coin flip, it doesn’t matter whether they would have generated a nonsense number in a different timeline, or that they’re not reliably accurate. _You know_ they’re reliably accurate in _this case_, so you can trust their data.

usgroup 3y ago

This is well put. Coincidentally in the example the results are the same , but they need not be. given repeated experiments with the same intentions one may expect different distributions. However, one could just move the argument up a level and manufacture a case of different intentions leading to the same distributions and then ask the same question.

kgwgk 3y ago
> Coincidentally in the example the results are the same , but they need not be. The questions is whether we should draw different conclusions when the results are the same. I don’t think that anyone has any issues with drawing different conclusions when the results are different!
lalaithion 3y ago
Imagine you have a machine that rolls a d20 and lies if the die comes up 1-19, and tells the truth on a 20. Should you trust this machine usually? No. But if you can _see that the die comes up 20_ then you should trust it. The fact that it sometimes might lie doesn't mean that you should distrust the machine if you can see that in this case it's telling the truth.

lalaithion 3y ago

What do you mean by 'results'? They would not have different distributions of results on their first die roll. They would have different distributions of results on their reported die roll. If I am looking at their first die roll, the fact that they would have different reported die rolls doesn't matter!

lalaithion 3y ago

Here’s another example: Say you have a lazy researcher. They flip a coin, and if it comes up heads, they do the experiment. If it comes up tails, they just write down a random number. If you _only get access_ to the final number, then you should discount what they wrote down – it’s 50% likely to be fake. If you do 1,000,000 simulations of this, it’s useless 50% of the time. But if you know the result of the coin flip, it doesn’t matter whether they would have generated a nonsense number in a different timeline, or that they’re not reliably accurate. _You know_ they’re reliably accurate in _this case_, so you can trust their data.