Decision making and imperfection

<p>Decision making (DM) is ubiquitous in both natural and artificial systems. The decisions made often differ from those recommended by the axiomatically well-grounded normative Bayesian decision theory, in a large part due to limited cognitive and computational resources of decision makers (e...

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Bibliografiske detaljer
Hovedforfatter: Guy, Tatiana V.
Andre forfattere: Karny, Miroslav (Éditeur intellectuel), Wolpert, David (Éditeur intellectuel), Guy, Tatiana Valentine, 19..- (Directeur de la publication), Kárný, Miroslav, ingénieur (Directeur de la publication), Wolpert, David H., 19..- (Directeur de la publication)
Format: Livre numérique
Sprog:Anglais
Udgivet: Berlin, Heidelberg : Springer Berlin Heidelberg [20..].
Cham : Springer Nature
Udgivelse:1st ed. 2013.
Serier:Studies in Computational Intelligence 474
Online adgang:Accès sur la plateforme de l'éditeur
Accès sur la plateforme Istex
Accès Université d'Orléans
Accès INSA CVL
Kommentar: Archives Springer e-books (Licence nationale)
Archives Springer e-books (Licence nationale)
Autres localisations: Voir dans le Sudoc
Edition sous un autre format:• Decision Making and Imperfection, Texte imprimé, 9783642364051
• Decision Making and Imperfection, Texte imprimé, 9783642446382
• Decision Making and Imperfection, Texte imprimé, 9783642364075
• Decision Making and Imperfection, Texte imprimé, 9783642364051
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245 1 0 |a Decision making and imperfection   |c edited by Tatiana V. Guy, Miroslav Karny, David Wolpert. 
250 |a 1st ed. 2013. 
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260 |a Cham :  |b Springer Nature,  |c [20..]. 
490 0 |a Studies in Computational Intelligence  |v 474  |x 1860-9503 
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505 1 |a Dynamic Bayesian Combination of Multiple Imperfect Classifiers Distributed Decision Making by Categorically-Thinking Agents Automated Preference Elicitation for Decision Making Counter-Factual Reinforcement Learning: How To Model Decision-Makers that Anticipate the Future Effect of Emotion and Personality on Deviation from Purely Ra-tional Decision-Making An Adversarial Risk Analysis Model for an Autonomous Imper-fect Decision Agent 
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506 |a Conditions particulières de réutilisation pour les bénéficiaires des licences nationales. https://www.licencesnationales.fr/springer-nature-ebooks-contrat-licence-ln-2017 
520 |a <p>Decision making (DM) is ubiquitous in both natural and artificial systems. The decisions made often differ from those recommended by the axiomatically well-grounded normative Bayesian decision theory, in a large part due to limited cognitive and computational resources of decision makers (either artificial units or humans). This state of a airs is often described by saying that decision makers are imperfect and exhibit bounded rationality. The neglected influence of emotional state and personality traits is an additional reason why normative theory fails to model human DM process.</p><p> </p><p>The book is a joint effort of the top researchers from different disciplines to identify sources of imperfection and ways how to decrease discrepancies between the prescriptive theory and real-life DM. The contributions consider:</p><p> </p><p>·          how a crowd of imperfect decision makers outperforms experts' decisions;</p><p> </p><p>·          how to decrease decision makers' imperfection by reducing knowledge available;</p><p> </p><p>·          how to decrease imperfection via automated elicitation of DM preferences;</p><p> </p><p>·          a human's limited willingness to master the available decision-support tools as an additional source of imperfection; </p><p><p> </p><p>·          how the decision maker's emotional state influences the rationality;  a DM support of edutainment robot based on its system of values and respecting emotions.</p><p> </p><p>The book will appeal to anyone interested in the challenging topic of DM theory and its applications.</p><p>.</p> 
700 1 |a Karny, Miroslav.  |4 edt 
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700 1 |a Guy, Tatiana Valentine,  |d 19..-  |4 pbd 
700 1 |a Kárný, Miroslav,  |c ingénieur.  |4 pbd 
700 1 |a Wolpert, David H.,  |d 19..-  |4 pbd 
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