Guessing and gambling

Scoring methods in multiple-choice tests are usually designed as fair bets, and thus random guesswork yields zero expected return. This causes the undesired result of forcing risk averse test-takers to pay a premium in the sense of letting unmarked answers for which they have partial but not full knowledge. In this note I use a calibrated model of prospect theory [Tversky and Kahneman (1992, 1995))] to compute a fair rule which is also strategically neutral, (i.e. under partial knowledge answering is beneficial for the representative calibrated agent, while under total uncertainty it is not). This rule is remarkably close to an old rule presented in 1969 by Traub et al. in which there is no penalty for wrong answers but omitted answers are rewarded by 1/M if M is the number of possible answers.