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.

Date

2019

Authors

Fred Morstatter, Aram Galstyan, Gleb Satyukov, Daniel Benjamin, Andres Abeliuk, Mehrnoosh Mirtaheri, KSM Tozammel Hossain, Pedro Szekely, Emilio Ferrara, Akira Matsui, Mark Steyvers, Stephen Bennet, David Budescu, Mark Himmelstein, Michael Ward, Andreas Beger, Michele Catasta, Jure Leskovec, Pavel Atanasov, Regina Joseph, Rajiv Sethi, Ali Abbas

Conference

28th International Joint Conference on Artificial Intelligence, IJCAI 2019

Pages

6557-6559

Publisher

International Joint Conferences on Artificial Intelligence