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001469495 001__ 1469495
001469495 003__ OCoLC
001469495 005__ 20230803003333.0
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001469495 019__ $$a1381171804
001469495 020__ $$a9783031300851$$q(electronic bk.)
001469495 020__ $$a3031300858$$q(electronic bk.)
001469495 020__ $$z9783031300844
001469495 020__ $$z303130084X
001469495 0247_ $$a10.1007/978-3-031-30085-1$$2doi
001469495 035__ $$aSP(OCoLC)1381457358
001469495 040__ $$aGW5XE$$beng$$erda$$epn$$cGW5XE$$dYDX$$dEBLCP$$dUKAHL$$dYDX$$dOCLCF
001469495 049__ $$aISEA
001469495 050_4 $$aQA76.9.Q36$$bJ83 2023
001469495 08204 $$a001.4/2$$223/eng/20230608
001469495 24500 $$aJudgment in predictive analytics /$$cMatthias Seifert, editor.
001469495 264_1 $$aCham :$$bSpringer,$$c[2023]
001469495 300__ $$a1 online resource (xiv, 313 pages) :$$billustrations (some color).
001469495 336__ $$atext$$btxt$$2rdacontent
001469495 337__ $$acomputer$$bc$$2rdamedia
001469495 338__ $$aonline resource$$bcr$$2rdacarrier
001469495 4901_ $$aInternational series in operations research & management science,$$x2214-7934 ;$$vvolume 343
001469495 500__ $$aIncludes index.
001469495 506__ $$aAccess limited to authorized users.
001469495 520__ $$aThis book highlights research on the behavioral biases affecting judgmental accuracy in judgmental forecasting and showcases the state-of-the-art in judgment-based predictive analytics. In recent years, technological advancements have made it possible to use predictive analytics to exploit highly complex (big) data resources. Consequently, modern forecasting methodologies are based on sophisticated algorithms from the domain of machine learning and deep learning. However, research shows that in the majority of industry contexts, human judgment remains an indispensable component of the managerial forecasting process. This book discusses ways in which decision-makers can address human behavioral issues in judgmental forecasting. The book begins by introducing readers to the notion of human-machine interactions. This includes a look at the necessity of managerial judgment in situations where organizations commonly have algorithmic decision support models at their disposal. The remainder of the book is divided into three parts, with Part I focusing on the role of individual-level judgment in the design and utilization of algorithmic models. The respective chapters cover individual-level biases such as algorithm aversion, model selection criteria, model-judgment aggregation issues and implications for behavioral change. In turn, Part II addresses the role of collective judgments in predictive analytics. The chapters focus on issues related to talent spotting, performance-weighted aggregation, and the wisdom of timely crowds. Part III concludes the book by shedding light on the importance of contextual factors as critical determinants of forecasting performance. Its chapters discuss the usefulness of scenario analysis, the role of external factors in time series forecasting and introduce the idea of mindful organizing as an approach to creating more sustainable forecasting practices in organizations.
001469495 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed June 8, 2023).
001469495 650_0 $$aPredictive analytics.
001469495 655_0 $$aElectronic books.
001469495 7001_ $$aSeifert, Matthias,$$eeditor.
001469495 77608 $$iPrint version: $$z303130084X$$z9783031300844$$w(OCoLC)1372131617
001469495 830_0 $$aInternational series in operations research & management science ;$$v343.$$x2214-7934
001469495 852__ $$bebk
001469495 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-031-30085-1$$zOnline Access$$91397441.1
001469495 909CO $$ooai:library.usi.edu:1469495$$pGLOBAL_SET
001469495 980__ $$aBIB
001469495 980__ $$aEBOOK
001469495 982__ $$aEbook
001469495 983__ $$aOnline
001469495 994__ $$a92$$bISE