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008			230602s2023 sz | s |||| 0|eng d
020			_a9783031300851
024	7		_a10.1007/978-3-031-30085-1 _2doi
040			_aES-VaUEC _bspa _cES-VaUEC
050	0	4	_aT57.6-.97 _b2024
245	0	0	_aJudgment in Predictive Analytics _cedited by Matthias Seifert
250			_a1st ed 2023
264		1	_aCham _c2023 _bSpringer International Publishing
300	0	0	_a1 recurso en línea
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
490	0		_aInternational Series in Operations Research & Management Science, _x2214-7934 ; _v343
520			_aThis 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.
830		0	_aInternational Series in Operations Research & Management Science, _x2214-7934 ; _v343
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-30085-1 _zAcceso a este recurso digital (usuarios Universidad Europea de Valencia)
912			_aZDB-2-BUM
912			_aZDB-2-SXBM
942			_2lcc _cLE
988			_aSpringer_Business_2023
999			_c238675 _d238675