Publications by Year: 2019

2019
What Makes Foreign Policy Teams Tick: Explaining Variation in Group Performance at Geopolitical Forecasting
Horowitz, Michael, et al. 2019. “What Makes Foreign Policy Teams Tick: Explaining Variation in Group Performance at Geopolitical Forecasting”. The Journal of Politics 81 (4):1388-1404. Publisher's VersionAbstract
When do groups—be they countries, administrations, or other organizations—more or less accurately understand the world around them and assess political choices? Some argue that group decision-making processes often fail due to biases induced by groupthink. Others argue that groups, by aggregating knowledge, are better at analyzing the foreign policy world. To advance knowledge about the intersection of politics and group decision making, this paper draws on evidence from a multiyear geopolitical forecasting tournament with thousands of participants sponsored by the US government. We find that teams outperformed individuals in making accurate geopolitical predictions, with regression discontinuity analysis demonstrating specific teamwork effects. Moreover, structural topic models show that more cooperative teams outperformed less cooperative teams. These results demonstrate that information sharing through groups, cultivating reasoning to hedge against cognitive biases, and ensuring all perspectives are heard can lead to greater success for groups at forecasting and understanding politics.
stm: An R Package for Structural Topic Models
Roberts, Margaret, Brandon Stewart, and Dustin Tingley. 2019. “stm: An R Package for Structural Topic Models”. Journal of Statistical Software 91 (2):1–40. Publisher's VersionAbstract
This paper demonstrates how to use the R package stm for structural topic modeling. The structural topic model allows researchers to flexibly estimate a topic model that includes document-level metadata. Estimation is accomplished through a fast variational approximation. The stm package provides many useful features, including rich ways to explore topics, estimate uncertainty, and visualize quantities of interest.