Reasoning under Uncertainty: Some Monte Carlo Results
  A series of monte carlo studies were performed to compare the behavior of
some alternative procedures for reasoning under uncertainty. The behavior of
several Bayesian, linear model and default reasoning procedures were examined
in the context of increasing levels of calibration error. The most interesting
result is that Bayesian procedures tended to output more extreme posterior
belief values (posterior beliefs near 0.0 or 1.0) than other techniques, but
the linear models were relatively less likely to output strong support for an
erroneous conclusion. Also, accounting for the probabilistic dependencies
between evidence items was important for both Bayesian and linear updating
procedures.
