Criteria for Selecting Measures of Plant Information with Application to Nuclear Reactors

Criteria for selecting from alternative measures of plant information have been derived by information theory and the expected-value model of decision theory. Two criteria derived by information theory are minimum " uncertainty" of judgment of plant states, and maximum "surprise" of human operators. Two criteria derived by decision theory are maximum expected reward to operators, and maximum ratio of the maximum and next-to-maximum expected rewards. These four criteria have been evaluated experimentally in application to human monitoring problems of three "plants": an abstract laboratory exercise, a small research reactor, and a conventional nuclear power plant (high-fidelity training simulator). The results show that the criteria derived by decision theory are superior to the others from the viewpoint of correct rate of human subjects' judgment.