Overview
- Includes studies focusing on modern learning/AI approaches
- Gathers contributions broadly related to inference in the context of machine learning tools
- Highlights the role of cognition and learning in economic theory
Part of the book series: Understanding Complex Systems (UCS)
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Table of contents (16 chapters)
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Front Matter
Keywords
About this book
This book presents frontier research on the use of computational methods to model complex interactions in economics and finance. Artificial Intelligence, Machine Learning and simulations offer effective means of analyzing and learning from large as well as new types of data. These computational tools have permeated various subfields of economics, finance, and also across different schools of economic thought. Through 16 chapters written by pioneers in economics, finance, computer science, psychology, complexity and statistics/econometrics, the book introduces their original research and presents the findings they have yielded.
Theoretical and empirical studies featured in this book draw on a variety of approaches such as agent-based modeling, numerical simulations, computable economics, as well as employing tools from artificial intelligence and machine learning algorithms. The use of computational approaches to perform counterfactual thought experiments are also introduced, which help transcend the limits posed by traditional mathematical and statistical tools.
The book also includes discussions on methodology, epistemology, history and issues concerning prediction, validation, and inference, all of which have become pertinent with the increasing use of computational approaches in economic analysis.
Editors and Affiliations
About the editor
Dr. Ragupathy Venkatachalam is a Senior Lecturer in Economics at the Institute of Management Studies, Goldsmiths, University of London. He obtained his Ph.D. from the University of Trento, Italy. He has previously taught economics at the Centre for Development Studies (India) and worked as a research fellow at the Artificial Intelligence Economics Research Center at the National Chengchi University (Taiwan). He serves as the co-editor of Economia Politica [Journal of Analytical and Institutional Economics]. His broad research areas include computable economics, economic dynamics, causal inference, discrimination and history of economic thought. He has published several peer-reviewed journal articles, book chapters and edited special issues on these areas. His research focuses on the algorithmic models of theorizing both at the micro- and macro-levels.
Bibliographic Information
Book Title: Artificial Intelligence, Learning and Computation in Economics and Finance
Editors: Ragupathy Venkatachalam
Series Title: Understanding Complex Systems
DOI: https://doi.org/10.1007/978-3-031-15294-8
Publisher: Springer Cham
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023
Hardcover ISBN: 978-3-031-15293-1Published: 16 February 2023
Softcover ISBN: 978-3-031-15296-2Published: 16 February 2024
eBook ISBN: 978-3-031-15294-8Published: 15 February 2023
Series ISSN: 1860-0832
Series E-ISSN: 1860-0840
Edition Number: 1
Number of Pages: XII, 325
Number of Illustrations: 14 b/w illustrations, 75 illustrations in colour
Topics: Science, Humanities and Social Sciences, multidisciplinary, Theoretical, Mathematical and Computational Physics, Computer Science, general, Complexity, Computer Systems Organization and Communication Networks, Applications of Mathematics