Increasing Users' Confidence in Uncertain Data by Aggregating Data from Multiple Sources

論文URL:http://dl.acm.org/citation.cfm?doid=3025453.3025998

論文アブストラクト:We often base our decisions on uncertain data - for instance, when consulting the weather forecast before deciding what to wear. Due to their uncertainty, such forecasts can differ by provider. To make an informed decision, many people compare several forecasts, which is a time-consuming and cumbersome task. To facilitate comparison, we identified three aggregation mechanisms for forecasts: manual comparison and two mechanisms of computational aggregation. In a survey, we compared the mechanisms using different representations. We then developed a weather application to evaluate the most promising candidates in a real-world study. Our results show that aggregation increases users' confidence in uncertain data, independent of the type of representation. Further, we find that for daily events, users prefer to use computationally aggregated forecasts. However, for high-stakes events, they prefer manual comparison. We discuss how our findings inform the design of improved interfaces for comparison of uncertain data, including non-weather purposes.

日本語のまとめ:

「日本語のまとめ」はツイッターに投稿する予定です。ツイッターでは110文字程度まで表示可能です。それ以降はツイッターに投稿する際にはざっくり削除されます。ウェブサイト上では削除されずに残りますが、一方であまり長いとまとめの意味がなくなるので、110字程度でお願いします。修正したい場合には、再度この画面から登録してください。一番最後に登録したものが採用されます。

(110文字)