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Titlebook: Judgment in Predictive Analytics; Matthias Seifert Book 2023 The Editor(s) (if applicable) and The Author(s), under exclusive license to S

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發(fā)表于 2025-3-21 16:48:23 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Judgment in Predictive Analytics
編輯Matthias Seifert
視頻videohttp://file.papertrans.cn/502/501390/501390.mp4
概述Highlights the current state-of-the-art of judgment-based predictive analytics.Describes and explains the uniqueness of behavioral issues in forecasting.Covers various methodological approaches includ
叢書名稱International Series in Operations Research & Management Science
圖書封面Titlebook: Judgment in Predictive Analytics;  Matthias Seifert Book 2023 The Editor(s) (if applicable) and The Author(s), under exclusive license to S
描述.This book highlights research on the behavioral biases affecting judgmental accuracy in judgmental forecasting and showcases the state-of-the-art in judgment-based predictive analytics. In recent years, technological advancements have made it possible to use predictive analytics to exploit highly complex (big) data resources. Consequently, modern forecasting methodologies are based on sophisticated algorithms from the domain of machine learning and deep learning. However, research shows that in the majority of industry contexts, human judgment remains an indispensable component of the managerial forecasting process. This book discusses ways in which decision-makers can address human behavioral issues in judgmental forecasting.?.The book begins by introducing readers to the notion of human-machine interactions. This includes a look at the necessity of managerial judgment in situations where organizations commonly have algorithmic decision support models at their disposal.The remainder of the book is divided into three parts, with Part I focusing on the role of individual-level judgment in the design and utilization of algorithmic models. The respective chapters cover individual-lev
出版日期Book 2023
關鍵詞Judgmental Forecasting; Forecasting; Managerial Forecasting; Judgmental Accuracy; Judgment Aggregation; A
版次1
doihttps://doi.org/10.1007/978-3-031-30085-1
isbn_softcover978-3-031-30087-5
isbn_ebook978-3-031-30085-1Series ISSN 0884-8289 Series E-ISSN 2214-7934
issn_series 0884-8289
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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沙發(fā)
發(fā)表于 2025-3-21 21:40:47 | 只看該作者
Beyond Algorithm Aversion in Human-Machine Decision-Makingte and the augmented decision-making literature before defining algorithm aversion, its implications, and its antecedents. Finally, we conclude with proposals to improve methods and metrics to help guide the development of human-machine decision-making.
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發(fā)表于 2025-3-22 03:37:55 | 只看該作者
Book 2023judgment-based predictive analytics. In recent years, technological advancements have made it possible to use predictive analytics to exploit highly complex (big) data resources. Consequently, modern forecasting methodologies are based on sophisticated algorithms from the domain of machine learning
地板
發(fā)表于 2025-3-22 05:37:06 | 只看該作者
Subjective Decisions in Developing Augmented Intelligencedecision types we encountered, their importance and how they influenced the overall development outcome. Our discussion aims to make a contribution on the subjective nature of many decisions underpinning the development of intelligent augmentation.
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發(fā)表于 2025-3-22 11:42:34 | 只看該作者
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發(fā)表于 2025-3-22 16:42:53 | 只看該作者
Judgmental Interventions and Behavioral Changethe experts’ adjustments increase in size once they are informed that a subsequent adjustment takes place; one that essentially halves the expert adjustment. In other words, participants in our experiment try to mitigate for that further adjustment and retain the ownership of the final forecasts.
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發(fā)表于 2025-3-22 19:22:35 | 只看該作者
978-3-031-30087-5The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
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發(fā)表于 2025-3-23 00:37:58 | 只看該作者
Judgment in Predictive Analytics978-3-031-30085-1Series ISSN 0884-8289 Series E-ISSN 2214-7934
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