@article{1469495,
      recid = {1469495},
      author = {Seifert, Matthias,},
      title = {Judgment in predictive analytics /},
      pages = {1 online resource (xiv, 313 pages) :},
      note = {Includes index.},
      abstract = {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-level biases such as algorithm aversion, model  selection criteria, model-judgment aggregation issues and  implications for behavioral change. In turn, Part II  addresses the role of collective judgments in predictive  analytics. The chapters focus on issues related to talent  spotting, performance-weighted aggregation, and the wisdom  of timely crowds. Part III concludes the book by shedding  light on the importance of contextual factors as critical  determinants of forecasting performance. Its chapters  discuss the usefulness of scenario analysis, the role of  external factors in time series forecasting and introduce  the idea of mindful organizing as an approach to creating  more sustainable forecasting practices in organizations.},
      url = {http://library.usi.edu/record/1469495},
      doi = {https://doi.org/10.1007/978-3-031-30085-1},
}