Abstract
Forecasting of geopolitical events is a notoriously
difficult task, with experts failing to significantly
outperform a random baseline across many types
of forecasting events. One successful way to increase the performance of forecasting tasks is to
turn to crowdsourcing: leveraging many forecasts
from non-expert users. Simultaneously, advances
in machine learning have led to models that can
produce reasonable, although not perfect, forecasts for many tasks. Recent efforts have shown
that forecasts can be further improved by “hybridizing” human forecasters: pairing them with
the machine models in an effort to combine the
unique advantages of both. In this demonstration,
we present Synergistic Anticipation of Geopolitical
Events (SAGE), a platform for human/computer interaction that facilitates human reasoning with machine models