Time Predictions: Understanding and Avoiding Unrealism in Project Planning and Everyday Life

Predicting the time needed to complete a project, task or daily activity can be difficult and people frequently underestimate how long an activity will take. This book sheds light on why and when this happens, what we should do to avoid it and how to give more realistic time predictions. It describe...

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Detaylı Bibliyografya
Yazar: Torleif Halkjelsvik (auth)
Diğer Yazarlar: Magne Jørgensen (auth)
Materyal Türü: Elektronik Kitap Bölümü
Dil:İngilizce
Baskı/Yayın Bilgisi: Springer Nature 2018
Seri Bilgileri:Simula SpringerBriefs on Computing
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Online Erişim:DOAB: download the publication
DOAB: description of the publication
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Özet:Predicting the time needed to complete a project, task or daily activity can be difficult and people frequently underestimate how long an activity will take. This book sheds light on why and when this happens, what we should do to avoid it and how to give more realistic time predictions. It describes methods for predicting time usage in situations with high uncertainty, explains why two plus two is usually more than four in time prediction contexts, reports on research on time prediction biases, and summarizes the evidence in support of different time prediction methods and principles. Based on a comprehensive review of the research, it is the first book summarizing what we know about judgment-based time predictions. Large parts of the book are directed toward people wishing to achieve better time predictions in their professional life, such as project managers, graphic designers, architects, engineers, film producers, consultants, software developers, or anyone else in need of realistic time usage predictions. It is also of benefit to those with a general interest in judgment and decision-making or those who want to improve their ability to predict and plan ahead in daily life.
Fiziksel Özellikler:1 electronic resource (110 p.)
ISBN:/doi.org/10.1007/978-3-319-74953-2
9783319749532
9783319749525
Erişim:Open Access