東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Artificial intelligence, learning an...

Venkatachalam, Ragupathy.

FindBook

Google Book

Amazon

博客來

Artificial intelligence, learning and computation in economics and finance

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Artificial intelligence, learning and computation in economics and finance/ edited by Ragupathy Venkatachalam.
其他作者:	Venkatachalam, Ragupathy.
出版者:	Cham :Springer International Publishing : : 2023.,
面頁冊數:	xii, 325 p. :ill., digital ;24 cm.
內容註:	Perspectives from the Development of Agent-based Modelling in Economics and Finance -- Towards a General Model of Financial Markets -- The U-Mart Futures Exchange Experiment and Her Institutional Design Historically Inherited -- A Bottom-Up Framework for Data-Driven Agent-Based Simulations -- Can News Networks and Topics Influence Assets Return and Volatility? -- Causal Inference and Agent-Based Models -- Finding the Human in Their Stories: Some Thoughts on Digital Humanities Tools -- Interdependence Overcomes the Limitations of Rational Theories of Collective Behavior: The Productivity of Patents by Nations -- Sand Castles and Financial Systems -- Estimation of Agent-Based Models via Approximate Bayesian Computation -- Unravelling Aspects of Decision Making Under Uncertainty -- Logic and Epistemology in Behavioral Economics -- Aggregate Investor Attention and Bitcoin Return: The Machine Learning Approach -- Information and Market Power: An Experimental Investigation into the Hayek Hypothesis -- Algorithmically Learning, Creatively and Intelligently to Play Games -- A Simonian Formalistic Perspective on Collaborative, Distributed Invention -- Modified Sraffan Schemes and Algorithmic Rational Agents.
Contained By:	Springer Nature eBook
標題:	Economics - Data processing. -
電子資源:	https://doi.org/10.1007/978-3-031-15294-8
ISBN:	9783031152948

Artificial intelligence, learning and computation in economics and finance
Artificial intelligence, learning and computation in economics and finance[electronic resource] /edited by Ragupathy Venkatachalam. - Cham :Springer International Publishing :2023. - xii, 325 p. :ill., digital ;24 cm. - Understanding complex systems,1860-0840. - Understanding complex systems..

Perspectives from the Development of Agent-based Modelling in Economics and Finance -- Towards a General Model of Financial Markets -- The U-Mart Futures Exchange Experiment and Her Institutional Design Historically Inherited -- A Bottom-Up Framework for Data-Driven Agent-Based Simulations -- Can News Networks and Topics Influence Assets Return and Volatility? -- Causal Inference and Agent-Based Models -- Finding the Human in Their Stories: Some Thoughts on Digital Humanities Tools -- Interdependence Overcomes the Limitations of Rational Theories of Collective Behavior: The Productivity of Patents by Nations -- Sand Castles and Financial Systems -- Estimation of Agent-Based Models via Approximate Bayesian Computation -- Unravelling Aspects of Decision Making Under Uncertainty -- Logic and Epistemology in Behavioral Economics -- Aggregate Investor Attention and Bitcoin Return: The Machine Learning Approach -- Information and Market Power: An Experimental Investigation into the Hayek Hypothesis -- Algorithmically Learning, Creatively and Intelligently to Play Games -- A Simonian Formalistic Perspective on Collaborative, Distributed Invention -- Modified Sraffan Schemes and Algorithmic Rational Agents.

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.

ISBN: 9783031152948

Standard No.: 10.1007/978-3-031-15294-8doiSubjects--Topical Terms:

782280
Economics
--Data processing.

LC Class. No.: HB143.5 / .A77 2023

Dewey Class. No.: 330.028563

Artificial intelligence, learning and computation in economics and finance
LDR:03618nmm a2200337 a 4500 001 2316255
003 DE-He213
005 20230215173434.0
006 m d
007 cr nn 008maaau
008 230902s2023 sz s 0 eng d
020 $a 9783031152948 $q (electronic bk.)
020 $a 9783031152931 $q (paper)
024 7 $a 10.1007/978-3-031-15294-8 $2 doi
035 $a 978-3-031-15294-8
040 $a GP $c GP
041 0 $a eng
050 4 $a HB143.5 $b .A77 2023
072 7 $a KC $2 bicssc
072 7 $a SCI000000 $2 bisacsh
072 7 $a KC $2 thema
082 0 4 $a 330.028563 $2 23
090 $a HB143.5 $b .A791 2023
245 0 0 $a Artificial intelligence, learning and computation in economics and finance $h [electronic resource] / $c edited by Ragupathy Venkatachalam.
260 $a Cham : $c 2023. $b Springer International Publishing : $b Imprint: Springer,
300 $a xii, 325 p. : $b ill., digital ; $c 24 cm.
490 1 $a Understanding complex systems, $x 1860-0840
505 0 $a Perspectives from the Development of Agent-based Modelling in Economics and Finance -- Towards a General Model of Financial Markets -- The U-Mart Futures Exchange Experiment and Her Institutional Design Historically Inherited -- A Bottom-Up Framework for Data-Driven Agent-Based Simulations -- Can News Networks and Topics Influence Assets Return and Volatility? -- Causal Inference and Agent-Based Models -- Finding the Human in Their Stories: Some Thoughts on Digital Humanities Tools -- Interdependence Overcomes the Limitations of Rational Theories of Collective Behavior: The Productivity of Patents by Nations -- Sand Castles and Financial Systems -- Estimation of Agent-Based Models via Approximate Bayesian Computation -- Unravelling Aspects of Decision Making Under Uncertainty -- Logic and Epistemology in Behavioral Economics -- Aggregate Investor Attention and Bitcoin Return: The Machine Learning Approach -- Information and Market Power: An Experimental Investigation into the Hayek Hypothesis -- Algorithmically Learning, Creatively and Intelligently to Play Games -- A Simonian Formalistic Perspective on Collaborative, Distributed Invention -- Modified Sraffan Schemes and Algorithmic Rational Agents.
520 $a 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.
650 0 $a Economics $x Data processing. $3 782280
650 0 $a Artificial intelligence $x Financial applications. $3 3493836
650 1 4 $a Economics. $3 517137
650 2 4 $a Theoretical, Mathematical and Computational Physics. $3 1066859
650 2 4 $a Computer Science. $3 626642
650 2 4 $a Applied Dynamical Systems. $3 3538870
650 2 4 $a Computer Engineering and Networks. $3 3538504
650 2 4 $a Mathematics in Business, Economics and Finance. $3 3538573
700 1 $a Venkatachalam, Ragupathy. $3 3629392
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
830 0 $a Understanding complex systems. $3 1568162
856 4 0 $u https://doi.org/10.1007/978-3-031-15294-8
950 $a Mathematics and Statistics (SpringerNature-11649)