Simple, under dubiously ethical and physically possible conditions, you turn their internal world model into a formal bayesian network, and for every possible physical and mathematical observation and outcome, do the above calculation. Sum, print, idle.
It's impossible in practise, but only like, four line formal definition.
How do you measure someone whose internal world model is not isomorphic to one formal Bayesian network (for example, someone who is completely certain of something)? Should it be the case that someone whose world model contains fewer possible observations has a major advantage in being closer to the truth?
Note also that a perfect Bayesian will score lower than some gamblers using this scheme. Betting everything on black does better than a fair distribution almost half the time.
Another month has passed and here is a new rationality quotes thread. The usual rules are: