Artificial Intelligence, Learning And Co

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.

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.