There exists a 6-sided die that is weighted such that one of the 6 numbers has a 50% chance to come up and all the other numbers have a 1 in 10 chance. Nobody knows for certain which number the die is biased in favor of, but some people have had a chance to roll the die and see the result.
You get a chance to roll the die exactly once, with nobody else watching. It comes up 6. Running a quick Bayes's Theorem calculation, you now think there's a 50% chance that the die is biased in favor of 6 and a 10% chance for the numbers 1 through 5.
You then discover that there's a prediction market about the die. The prediction market says there's a 50% chance that "3" is the number the die is biased in favor of, and each other number is given 10% probability.
How do you update based on what you've learned? Do you make any bets?
I think I know the answer for this toy problem, but I'm not sure if I'm right or how it generalizes to real life...
Let's assume prediction markets are efficient and you didn't already possess any relevant information that you weren't trading on beforehand. Then you should treat the market odds as a prior and your die roll as evidence, in exactly the way you always do Bayesian updates. In this case, that means it looks like that gives you a posterior probability of 5/14 each for the die being weighted in favor of 3 or 6, and 1/14 for each of the other possibilities. Contrary to what other commenters were saying, it doesn't matter what information led to the market odds under these assumptions.