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Titlebook: Human-Centric Decision-Making Models for Social Sciences; Peijun Guo,Witold Pedrycz Book 2014 Springer-Verlag Berlin Heidelberg 2014 Compu

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發(fā)表于 2025-3-21 16:46:04 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書(shū)目名稱(chēng)Human-Centric Decision-Making Models for Social Sciences
編輯Peijun Guo,Witold Pedrycz
視頻videohttp://file.papertrans.cn/430/429644/429644.mp4
概述Recent research on Human-Centric Decision-Making Models for Social Sciences.Presents Computational Intelligence models for Social Sciences.Written by leading experts in the field.Includes supplementar
叢書(shū)名稱(chēng)Studies in Computational Intelligence
圖書(shū)封面Titlebook: Human-Centric Decision-Making Models for Social Sciences;  Peijun Guo,Witold Pedrycz Book 2014 Springer-Verlag Berlin Heidelberg 2014 Compu
描述.The volume delivers a wealth of effective methods to deal with various types of uncertainty inherently existing in human-centric decision problems. It elaborates on ?comprehensive decision frameworks to handle different decision scenarios, which help use effectively the explicit and tacit knowledge and intuition, model perceptions and preferences in a more human-oriented style..The book presents original approaches and delivers new results on fundamentals and applications related to human-centered decision making approaches to business, economics and social systems. Individual chapters cover multi-criteria (multiattribute) decision making, decision making with prospect theory, decision making with incomplete probabilistic information, granular models of decision making and decision making realized with the use of non-additive measures. New emerging decision theories being presented as along with a wide spectrum of ongoing research make the book valuable to all interested in the field of advanced decision-making. The volume, self-contained in its nature, offers a systematic exposure to the concepts, design methodologies, and detailed algorithms. A prudent balance between the theore
出版日期Book 2014
關(guān)鍵詞Computational Intelligence; Human-Centric Decision-Making Models; Social Sciences
版次1
doihttps://doi.org/10.1007/978-3-642-39307-5
isbn_softcover978-3-662-51069-8
isbn_ebook978-3-642-39307-5Series ISSN 1860-949X Series E-ISSN 1860-9503
issn_series 1860-949X
copyrightSpringer-Verlag Berlin Heidelberg 2014
The information of publication is updating

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Aggregating Imprecise Linguistic Expressions, The procedure we propose is based on distances and it ranks order the alternatives taking into account the linguistic information provided by the agents. The main features and properties of the proposal are analyzed.
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Risk Perception and Ambiguity in a Quantile Cumulative Prospect Theory,e decision-maker’s attitude to consider maximization of entropy as a rule of inference. Finally, it is defined a simplified approach based on the epsilon contamination method of a probability distribution.
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The Logic and Ontology of Assessment of Conditions in Older People,gory theory framework involving term monads both composed with other monads, and as viewed over other categories than just the category of sets. The main question is where uncertainty actually resides, so that they are canonically retrieved rather than amalgamated in ad hoc approaches.
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Book 2014t elaborates on ?comprehensive decision frameworks to handle different decision scenarios, which help use effectively the explicit and tacit knowledge and intuition, model perceptions and preferences in a more human-oriented style..The book presents original approaches and delivers new results on fu
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1860-949X ritten by leading experts in the field.Includes supplementar.The volume delivers a wealth of effective methods to deal with various types of uncertainty inherently existing in human-centric decision problems. It elaborates on ?comprehensive decision frameworks to handle different decision scenarios,
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Decision Making Under Interval Uncertainty (and Beyond),hich of them is preferable. In reality, often, when the alternatives are close, the user is often unable to select one of these alternatives. In this chapter, we show how we can extend the utility-based decision theory to such realistic (interval) cases.
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