He just can't stop himself, burrowing away (don't much like the idea of a Curry hole, euuuurgh.)
It's quite amusing to watch the contortions he'll go to in order to avoid admitting a mistake. Recall that this started with his novel idea that one could determine the "correctness" of a probabilistic prediction of an event, by whether the event in question actually happens. Eg the prediction "likely to rain tomorrow" is correct if and only if the rain actually falls tomorrow.
While this might sound intuitively appealing, it quickly falls apart under any careful examination (as Doswell and Brooks warn). That is, it leads to conclusions that are obviously nonsensical and/or inconsistent. For example, if we say that a roll of a fair die is likely to come up 1-5, then this statement is correct in the sense of, well, being correct, but Roger's analysis would determine it to have been false if the roll actually turned out to be 6.
Oh, but at this point, rather than admitting that his usage of "correct" made no sense, Roger decided that for some reason his method only applies in truly epistemic cases where probability is a state of belief rather than a long-run property. It's funny that while (dishonestly) accusing me of making the IPCC out to be infallible, he then tries his best to ensure that his personal "correctness" theory is unfalsifiable. But I'm sure he is blind to that irony. Of course, no explanation is forthcoming as to why his theory, if it is useful and valid, should fall flat so quickly when confronted with a simple example. I tried again with a handmade imperfect die which is initially not known to be fair, but for which I still make the same prediction and again throw a 6. In Roger-world the probabilistic prediction is incorrect. However, in this case the long-run frequency of a 6 can subsequently found by experiment, and let's assume it turns out to be 20±0.1%. Was the original probabilistic statement still Roger-incorrect? Answer came there none...
Best of all, entirely unprompted, he came up with an example based on an asteroid falling on Boulder. While he had several times insisted that a prediction at the 90% level should be considered "incorrect" if the event did not occur, he then stated that if I predict that it is 10% probable that an asteroid hits Boulder tomorrow (ie 90% probable that it does not), then my prediction is correct if the asteroid DOES hit! This, he explains, is due to the "baseline expectation" which apparently allows Roger to invert his original definition of "correctness" whenever he feels like it. It's a bit odd that he came up with this new twist completely unprompted, as it blows apart all his previous analysis, but it's not as if his theory made any sense anyway.
Naturally, the actual paper that he co-authored contains no mention of this "baseline expectation".
With his latest post on aleatory and epistemic uncertainty, one might hope that he could have at last been starting to realise that the concept of "correctness" of a probabilistic prediction cannot in general be determined from the occurrence - or otherwise - of the predicted event (the occurrence of an event assigned a probability of zero is of course an exception). But based on the comments, it seems that this insight still eludes him.
It does seem that one infallible guide to "Pielkeian correctness" has emerged, though. If Roger says it, then it is correct, no matter how many impossible or ridiculous contortions and evasions are required to avoid admitting error.
It's quite amusing to watch the contortions he'll go to in order to avoid admitting a mistake. Recall that this started with his novel idea that one could determine the "correctness" of a probabilistic prediction of an event, by whether the event in question actually happens. Eg the prediction "likely to rain tomorrow" is correct if and only if the rain actually falls tomorrow.
While this might sound intuitively appealing, it quickly falls apart under any careful examination (as Doswell and Brooks warn). That is, it leads to conclusions that are obviously nonsensical and/or inconsistent. For example, if we say that a roll of a fair die is likely to come up 1-5, then this statement is correct in the sense of, well, being correct, but Roger's analysis would determine it to have been false if the roll actually turned out to be 6.
Oh, but at this point, rather than admitting that his usage of "correct" made no sense, Roger decided that for some reason his method only applies in truly epistemic cases where probability is a state of belief rather than a long-run property. It's funny that while (dishonestly) accusing me of making the IPCC out to be infallible, he then tries his best to ensure that his personal "correctness" theory is unfalsifiable. But I'm sure he is blind to that irony. Of course, no explanation is forthcoming as to why his theory, if it is useful and valid, should fall flat so quickly when confronted with a simple example. I tried again with a handmade imperfect die which is initially not known to be fair, but for which I still make the same prediction and again throw a 6. In Roger-world the probabilistic prediction is incorrect. However, in this case the long-run frequency of a 6 can subsequently found by experiment, and let's assume it turns out to be 20±0.1%. Was the original probabilistic statement still Roger-incorrect? Answer came there none...
Best of all, entirely unprompted, he came up with an example based on an asteroid falling on Boulder. While he had several times insisted that a prediction at the 90% level should be considered "incorrect" if the event did not occur, he then stated that if I predict that it is 10% probable that an asteroid hits Boulder tomorrow (ie 90% probable that it does not), then my prediction is correct if the asteroid DOES hit! This, he explains, is due to the "baseline expectation" which apparently allows Roger to invert his original definition of "correctness" whenever he feels like it. It's a bit odd that he came up with this new twist completely unprompted, as it blows apart all his previous analysis, but it's not as if his theory made any sense anyway.
Naturally, the actual paper that he co-authored contains no mention of this "baseline expectation".
With his latest post on aleatory and epistemic uncertainty, one might hope that he could have at last been starting to realise that the concept of "correctness" of a probabilistic prediction cannot in general be determined from the occurrence - or otherwise - of the predicted event (the occurrence of an event assigned a probability of zero is of course an exception). But based on the comments, it seems that this insight still eludes him.
It does seem that one infallible guide to "Pielkeian correctness" has emerged, though. If Roger says it, then it is correct, no matter how many impossible or ridiculous contortions and evasions are required to avoid admitting error.
