  We initiate the study of incentive-compatible forecasting competitions in
which multiple forecasters make predictions about one or more events and
compete for a single prize. We have two objectives: (1) to incentivize
forecasters to report truthfully, so that forecasts are informative and
forecasters need not spend any cognitive effort strategizing about reports, and
(2) to award the prize to the most accurate forecaster. Proper scoring rules
incentivize truthful reporting if all forecasters are paid according to their
scores. However, incentives become distorted if only the best-scoring
forecaster wins a prize, since forecasters can often increase their probability
of having the highest score by reporting more extreme beliefs. In this paper,
we introduce two novel forecasting competition mechanisms. Our first mechanism
is dominant strategy incentive compatible and guaranteed to select the most
accurate forecaster with probability higher than any other forecaster.
Moreover, we show that in the standard single-event, two-forecaster setting and
under mild technical conditions, no other incentive-compatible mechanism
selects the most accurate forecaster with higher probability. Our second
mechanism is incentive compatible when forecasters' beliefs are such that
information about one event does not lead to a belief update on the other
events, and it selects the best forecaster with probability approaching 1 as
the number of events grows. Our mechanisms are easy to implement and can be
generalized to the related problems of outputting a ranking over forecasters
and hiring a forecaster with high accuracy on future events.
