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Artificial Intelligence, Learning and Computation in Economics and Finance

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Table of Contents

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    Book Overview
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    Chapter 1 Computational Thinking in Economics and Finance: Introductory Remarks
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    Chapter 2 Uncomputabilities, Games, Axioms, Proofs and Artificial Intelligence
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    Chapter 3 Logic and Epistemology in Behavioral Economics
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    Chapter 4 Agent-Based Computational Economics: Overview and Brief History
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    Chapter 5 Sequential Monte Carlo Squared for Agent-Based Models
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    Chapter 6 Toward a General Model of Financial Markets
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    Chapter 7 Sand Castles and Financial Systems: A Sandpile Metaphor
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    Chapter 8 A Systematic Review of Investor Attention: Measurements, Implications, and Future Directions
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    Chapter 9 What is the Market? The Essential Teachings from an AI Market Experiment
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    Chapter 10 Market Power and the Hayek Hypothesis: An Experimental Investigation
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    Chapter 11 Counterfactual Thinking and Causal Mediation: An Application to Female Labour Force Participation in India
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    Chapter 12 When Supply and Demand do NOT Meet: Sraffa’s Critique of Economic Theory Restated
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    Chapter 13 Visualizing the Roles of Frequent Terms in LTEs Following Two Economic Crisis Trigger Articles
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    Chapter 14 Design and Performance Metrics for Autonomous Human-Machine Teams and Systems (A-HMT-S)
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    Chapter 15 Thirty-Five Years of Computational Economics
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    Chapter 16 Extending Herbert Simon's “Science of Design”: the Role of Collaboration and Users in Development of Technically Advanced Systems
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Title
Artificial Intelligence, Learning and Computation in Economics and Finance
Published by
Springer International Publishing, February 2023
DOI 10.1007/978-3-031-15294-8
ISBNs
978-3-03-115293-1, 978-3-03-115294-8
Editors

Venkatachalam, Ragupathy

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