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A problem with probabilites in many worlds

  1. Jun 4, 2013 #1
    In many worlds, all the results with probability>0 happens. But the predictive power of a theory is based in that events with very low probabilities no matter ( the possible ocurrence is `[but a constant]) around the averaged valued by the exponential of shannon entropy, that in the case of gaussians distributions, it is the typical standard deviation) Without this limitation, we could flip a coin 100 times and obtain 100 faces, probability is not 0 is 1/2^(100). What is the Everet III solve of this problem? I read his thesis but not mention to it. HOw is this trouble solved??
     
  2. jcsd
  3. Jun 4, 2013 #2
    That can happen in classically too. The probability of flipping 100 heads is always ##(1/2)^{100}##, it doesn't become 0 in classical probability.

    In any case, there is currently no agreement on how our familiar understanding of probability is recovered from MWI, which has lead to some of the variants of MWI. Probably the simplest proposal, which doesn't add any additional major assumptions, is the ignorance after measurement interpretation. It still is not agreed if you can derive the Born rule, or if it must be postulated that the Born probability of a world constitutes its 'measure of existence'. Whichever it is, the reality of every day life is that you are not aware of the outcome of a measurement instantaneously when it is performed. By the time you become aware of the measurement outcome (if you ever become aware of it), the worlds have split and you are ignorant of which world you are in. Hence, if I do 100 quantum mechanical coin flips (e.g. Stern-Gerlach trials) while you are in the next room, and then walk over and ask you, "Which world are you in?" the Born rule-motivated ignorance-based probabilistic answer is, "Very likely not the world corresponding to 100 'spin ups' in a row."

    There are other proposals under various names like Many Minds and so on, but in addition to the Born rule, additional structure has to be postulated.
     
    Last edited: Jun 4, 2013
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