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  anchorContext: 'Bayesian:  Are we going there?  I guess we're going there\\.  My good Scientist, I mean that if you offered me either side of an even\\-money bet on whether Plum committed the murder, I'd bet that he didn't do it\\.  But if you offered me a gamble that costs \\$1 if Professor Plum is innocent and pays out \\$5 if he's guilty, I'd cheerfully accept that gamble\\.  We only ran the 2012 US Presidential Election one time, but that doesn't mean that on November 7th you should've refused a \\$10 bet that paid out \\$1000 if Obama won\\.  In general when prediction markets and large liquid betting pools put 60% betting odds on somebody winning the presidency, that outcome tends to happen 60% of the time; they are well\\-calibrated for probabilities in that range\\.  If they were systematically uncalibrated\\-\\-if in general things happened 80% of the time when prediction markets said 60%\\-\\-you could use that fact to pump money out of prediction markets\\.  And your pumping out that money would adjust the prediction\\-market prices until they were well\\-calibrated\\.  If things to which prediction markets assign 70% probability happen around 7 times out of 10, why insist for reasons of ideological purity that the probability statement is meaningless?',
  anchorText: 'Are we going there?  I guess we're going there\\.',
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  text: 'I feel like this paragraph might be a little necessary for someone who haven't read the bayes rule intro, but on the other hand is a bit off-topic in this context and quite distracting, as it raises questions which are not part of this "discussion"; mainly, questions regarding how to approach "one-off" events.\n\nSay, what if I can't quantify the outcome of my decision so nicely like in the case of a bet? What if I need to decide whether to send Miss Scarlet to prison or not based on these likelihood probabilities?',
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