Venkatachalam, Ragupathy, ed. 2023. Artificial Intelligence, Learning and Computation in Economics and Finance. Cham, Switzerland: Springer. ISBN 9783031152931 [Edited Book]

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Abstract or Description

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.

Item Type:	Edited Book
Keywords:	Agent-based modelling, Computational behavioral economics, Agent-Based Scarf economics, Agent-Based macroeconomic model, Complex economic dynamics, algorithmic game theory, Behavioral Economics, Algorithmic Rational Agents, Herbert Simon
Departments, Centres and Research Units:	Institute of Management Studies Institute of Management Studies > Structural Economic Analysis
Date:	19 March 2023
Item ID:	32975
Date Deposited:	05 Jan 2023 17:20
Last Modified:	26 Feb 2024 13:24
URI:	https://research.gold.ac.uk/id/eprint/32975

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