用于众包地缘政治事件预测的深度神经排名
Deep Neural Ranking for Crowdsourced Geopolitical Event Forecasting
摘要：
有许多“群体智慧”效应的例子，其中大量的参与者赋予了对群体集体判断的信心。但是，当人群的规模有限时，我们如何形成一个综合判断呢？谁的判断，我们包括，我们给予最重？本文认为这个问题的背景下，地缘政治事件预测，志愿者分析师被询问，让他们的专业知识，信心，并预测一个事件的结果。我们开发了一个预测聚合模型，它集成了关于一个问题的主题信息，关于一对预测者的元数据，以及他们在深连体神经网络中的预测，该神经网络决定哪些预测者的预测更有可能接近正确的响应。预测者的排名是从成对预测者比较的锦标赛中得出的，排名用于创建聚合预测。初步结果发现，由我们的深连体网络模型排名的最佳预测者的聚合预测始终击败典型的聚合技术的Brier得分。
英文摘要：
There are many examples of 'wisdom of the crowd' effects in which the large number of participants imparts confidence in the collective judgment of the crowd. But how do we form an aggregated judgment when the size of the crowd is limited? Whose judgments do we include, and whose do we accord the most weight? This paper considers this problem in the context of geopolitical event forecasting, where volunteer analysts are queried to give their expertise, confidence, and predictions about the outcome of an event. We develop a forecast aggregation model that integrates topical information about a question, meta-data about a pair of forecasters, and their predictions in a deep siamese neural network that decides which forecasters' predictions are more likely to be close to the correct response. A ranking of the forecasters is induced from a tournament of pair-wise forecaster comparisons, with the ranking used to create an aggregate forecast. Preliminary results find the aggregate prediction of the best forecasters ranked by our deep siamese network model consistently beats typical aggregation techniques by Brier score.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
