The Foundations of Behavioral Economic Analysis (PDF)
Volume VI: Behavioral Models of Learning
(Sprache: Englisch)
This sixth volume of The Foundations of Behavioral Economic Analysis covers behavioral models of learning. It is an essential guide for advanced undergraduate and postgraduate students seeking a concise and focused text on this important subject, and...
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This sixth volume of The Foundations of Behavioral Economic Analysis covers behavioral models of learning. It is an essential guide for advanced undergraduate and postgraduate students seeking a concise and focused text on this important subject, and examines evolutionary game theory, models of learning, and stochastic social dynamics.
This updated extract from Dhami's leading textbook allows the reader to pursue subsections of this vast and rapidly growing field and to tailor their reading to their specific interests in behavioral economics.
This updated extract from Dhami's leading textbook allows the reader to pursue subsections of this vast and rapidly growing field and to tailor their reading to their specific interests in behavioral economics.
Autoren-Porträt von Sanjit Dhami
Sanjit Dhami is Professor of Economics at the University of Leicester. He studied at the Delhi School of Economics and the University of Toronto for his Masters, MPhil, and PhD degrees in economics. He has previously taught at the Universities of Toronto, Essex, and Newcastle. His research has mainly focused on behavioral economic theory and its applications. He has published on the axiomatic foundations of the various components of prospect theory, behavioral political economy using other-regarding preferences, behavioral time preferences, foundations of behavioral game theory, and applications in tax evasion, stochastic dominance concepts under other-regarding preferences, and in behavioral law and economics.
Bibliographische Angaben
- Autor: Sanjit Dhami
- 2019, 320 Seiten, Englisch
- Verlag: Oxford University Press
- ISBN-10: 0192595164
- ISBN-13: 9780192595164
- Erscheinungsdatum: 16.12.2019
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- Größe: 4.82 MB
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Englisch
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