利用群体智慧聚合预测意见，提升复杂事件预判准确性
Wisdom of the Crowds in Forecasting: Forecast Summarization for Supporting Future Event Prediction
- 构建新数据模型表示个体预测陈述，支持群体意见整合
- 系统梳理现有研究与数据集，揭示群体预测的潜力与局限
- 适合关注群体智能与未来事件预测的研究者与应用开发者
未来事件预测（FEP）在多个领域具有重要应用价值。尽管传统方法如模拟、预测建模和时间序列分析取得一定成效，但在复杂事件预测中因难以捕捉事件的语义信息而可靠性不足。一种替代路径是汇聚并整合集体对未来事件的看法，因为综合观点可能更准确地估计事件发生概率。本文系统整理了基于群体智慧支持未来事件预测的研究与框架，讨论了当前挑战、可用数据集以及改进方向与未来研究前景，并提出一种新型数据模型以表征个体预测陈述。
原文摘要 · Abstract (English)
Future Event Prediction (FEP) is an essential activity whose demand and application range across multiple domains. While traditional methods like simulations, predictive and time-series forecasting have demonstrated promising outcomes, their application in forecasting complex events is not entirely reliable due to the inability of numerical data to accurately capture the semantic information related to events. One forecasting way is to gather and aggregate collective opinions on the future to make predictions as cumulative perspectives carry the potential to help estimating the likelihood of upcoming events. In this work, we organize the existing research and frameworks that aim to support future event prediction based on crowd wisdom through aggregating individual forecasts. We discuss the challenges involved, available datasets, as well as the scope of improvement and future research directions for this task. We also introduce a novel data model to represent individual forecast statements.
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