用贝叶斯网络提升地缘政治预测准确率，效果优于逻辑回归。
Improving Geopolitical Forecasts with Bayesian Networks
- 构建两类贝叶斯网络，利用多个预测因子建模关系
- 校准聚合结果准确率最高（AUC=0.985），贝叶斯网络次之
- 适合关注预测模型改进与地缘政治分析的研究者
本研究探讨了贝叶斯网络（BNs）在地缘政治预测中相比逻辑回归及校准聚合方法的性能表现，数据来自“好判断项目”（Good Judgment Project）。对比了正则化逻辑回归模型与基准校准聚合结果，以及两类贝叶斯网络：基于结构学习的有向边网络和朴素贝叶斯网络。考察了四个预测变量：与聚合值的绝对差异、预测值、距问题关闭的天数、平均标准化布里尔得分。结果显示，校准聚合的准确率最高（AUC=0.985），两种贝叶斯网络紧随其后，逻辑回归模型表现最差。贝叶斯网络性能可能受离散化过程的信息损失影响，而逻辑回归模型则可能因线性假设违背而受损。未来研究应探索贝叶斯网络与逻辑回归的混合方法，引入更多预测变量，并考虑层级数据依赖关系。
原文摘要 · Abstract (English)
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.
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