Improving Geopolitical Forecasts with Bayesian Networks
This work addresses forecasting accuracy for geopolitical analysts, but it is incremental as it applies existing methods to a specific dataset without major innovations.
This study tackled the problem of improving geopolitical forecast accuracy by comparing Bayesian networks (BNs) to logistic regression and recalibration methods, finding that a recalibrated aggregate achieved the highest accuracy (AUC = 0.985), followed by BNs and then logistic regression.
This study explores how Bayesian networks (BNs) can improve forecast accuracy compared to logistic regression and recalibration and aggregation methods, using data from the Good Judgment Project. Regularized logistic regression models and a baseline recalibrated aggregate were compared to two types of BNs: structure-learned BNs with arcs between predictors, and naive BNs. Four predictor variables were examined: absolute difference from the aggregate, forecast value, days prior to question close, and mean standardized Brier score. Results indicated the recalibrated aggregate achieved the highest accuracy (AUC = 0.985), followed by both types of BNs, then the logistic regression models. Performance of the BNs was likely harmed by reduced information from the discretization process and violation of the assumption of linearity likely harmed the logistic regression models. Future research should explore hybrid approaches combining BNs with logistic regression, examine additional predictor variables, and account for hierarchical data dependencies.