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Re: [Synoptic-L] Re: PWRWSIS: piecemeal and cumulative solutions to the SynopticProblem

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  • David Gentile
    ... Every judgment is based on partial evidence, unless you happen to be God. We never have *all* possible relevant data, so we can never be completely sure of
    Message 1 of 1 , Apr 27, 2002
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      Zeba writes:

      > Ok. But is not drawing conclusions with a reduced bank of evidence a
      > little like asking a jury to make an judgment on partial evidence? What
      > value is a judgment based on partial evidence?

      Every judgment is based on partial evidence, unless you happen to be God.
      We never have *all* possible relevant data, so we can never be completely
      sure of any conclusion. Some are very very probable, of course.

      In the case of our problem, all I can state is the conclusion based on a
      certain type of quantifiable evidence. Other evidence could be weighted
      against it, but given that the other evidence is hard to quantify, or
      objectively express, that would be quite difficult. Its also unlikely that
      it would count agaist it, since the quantifiable evidence is strong.

      > Thanks for this. I know I started it by drawing the analogy of flipping
      > coins, but it did occur to me afterwards that the the analogy is not that
      > helpful. Redactors are not working in random ways; as they approach a
      > text, their decisions will not be random, but probably within a set a
      > editorial preferences. Now whether we can assess those preferences is
      > another matter, but it does stand that they are not mindless "coins."

      My wife is not mindless, but I can estimate a probability that she will come
      home in the next hour. The idea of estimating an apriori probability is
      actually called "Bayesian probability". I've never seen much of a
      distinction, but it is a different from that classical probability that is
      normally taught. The difference in the concepts is a bit of an issue for
      some people.

      An example of applying it would be like this:

      Suppose I think there is a 30% chance it will rain tomorrow, even though I
      have no idea of what the whether is today. Just based on how often it
      generally rains, I can assign a probability. If I look outside, and it is
      raining, I might adjust the odds to 50% chance of rain tomorrow.

      Dave Gentile
      Riverside, Illinois
      M.S. Physics
      Ph.D. Management Science candidate




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