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Artificial Intelligence and Economic Theory: Skynet in the Market 1st ed. 2017 [Kõva köide]

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  • Formaat: Hardback, 204 pages, kõrgus x laius: 235x155 mm, kaal: 4498 g, 67 Illustrations, black and white; XII, 204 p. 67 illus., 1 Hardback
  • Sari: Advanced Information and Knowledge Processing
  • Ilmumisaeg: 25-Sep-2017
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 3319661035
  • ISBN-13: 9783319661032
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Artificial Intelligence and Economic Theory: Skynet in the Market 1st ed. 2017Suurem pilt
  • Formaat: Hardback, 204 pages, kõrgus x laius: 235x155 mm, kaal: 4498 g, 67 Illustrations, black and white; XII, 204 p. 67 illus., 1 Hardback
  • Sari: Advanced Information and Knowledge Processing
  • Ilmumisaeg: 25-Sep-2017
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 3319661035
  • ISBN-13: 9783319661032
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9783319661032.html
This book theoretically and practically updates major economic ideas such as demand and supply, rational choice and expectations, bounded rationality, behavioral economics, information asymmetry, pricing, efficient market hypothesis, game theory, mechanism design, portfolio theory, causality and financial engineering in the age of significant advances in man-machine systems. The advent of artificial intelligence has changed many disciplines such as engineering, social science and economics. Artificial intelligence is a computational technique which is inspired by natural intelligence concepts such as the swarming of birds, the working of the brain and the pathfinding of the ants.









Artificial Intelligence and Economic Theory: Skynet in the Market analyses the impact of artificial intelligence on economic theories, a subject that has not been studied. It also introduces new economic theories and these are rational counterfactuals and rational opportunity costs. These ideas are applied to diverse areas such as modelling of the stock market, credit scoring, HIV and interstate conflict. Artificial intelligence ideas used in this book include neural networks, particle swarm optimization, simulated annealing, fuzzy logic and genetic algorithms. It, furthermore, explores ideas in causality including Granger as well as the Pearl causality models.
1 Introduction to Man and Machines
1(14)
1.1 Introduction
1(2)
1.2 Economics and Economic Theory
3(6)
1.3 Artificial Intelligence
9(3)
1.4 Conclusion
12(3)
References
13(2)
2 Supply and Demand
15(12)
2.1 Introduction
15(1)
2.2 Scarcity
16(1)
2.3 Utilitarianism
17(1)
2.4 Supply and Demand
18(3)
2.5 Factors Influencing Demand and Supply
21(1)
2.6 Artificial Intelligence (AI) and Demand and Supply
22(2)
2.7 Conclusion
24(3)
References
24(3)
3 Rational Choice and Rational Expectations
27(14)
3.1 Introduction
27(1)
3.2 Adaptive and Rational Expectations
28(2)
3.3 What Is Rational Choice?
30(2)
3.4 Information
32(1)
3.5 Choices
32(1)
3.6 Optimization
33(1)
3.7 Rational Choice
33(2)
3.8 Rational Choice and Opportunity Cost
35(1)
3.9 Rational Choice and Artificial Intelligence
35(1)
3.10 Interstate Conflict and Rational Choice
36(2)
3.11 Conclusion
38(3)
References
38(3)
4 Bounded Rationality
41(10)
4.1 Introduction
41(1)
4.2 Rational Decision Making: A Causal Approach
42(1)
4.3 Rational Decision Making Process
43(1)
4.4 Bounded-Rational Decision Making
43(4)
4.5 Credit Scoring
47(2)
4.6 Conclusions
49(2)
References
49(2)
5 Behavioral Economics
51(12)
5.1 Introduction
51(1)
5.2 Behavioural Economics
52(2)
5.3 Behavioural Economics and Demand and Supply
54(2)
5.4 Behavioural Economics and Rational Expectations
56(1)
5.5 Behavioural Economics and Bounded Rationality
57(1)
5.6 Artificial Intelligence and Behavioural Economics
57(2)
5.7 Moore's Law
59(1)
5.8 Conclusions
60(3)
References
60(3)
6 Information Asymmetry
63(12)
6.1 Introduction
63(3)
6.2 Asymmetric Information
66(2)
6.3 Artificial Intelligence Makes Us Wiser
68(1)
6.4 Asymmetric Information and Market Efficiency
69(2)
6.5 Information Asymmetry and Trading Volumes
71(2)
6.6 Conclusion
73(2)
References
73(2)
7 Game Theory
75(14)
7.1 Introduction
75(1)
7.2 Game-Theory Artefacts
76(5)
7.3 Multi-agent Modelling
81(3)
7.3.1 Complexity Modelling
83(1)
7.3.2 Economics
84(1)
7.3.3 Social Sciences
84(1)
7.4 Intelligent Agents
84(2)
7.5 The Road Ahead
86(1)
7.6 Conclusions
87(2)
References
87(2)
8 Pricing
89(12)
8.1 Introduction
89(1)
8.2 Pricing
90(1)
8.3 Value Theory
90(2)
8.4 Game Theory
92(1)
8.5 Rational Pricing
93(1)
8.6 Capital Asset Pricing Model
93(1)
8.7 Black-Scholes Equation
94(1)
8.8 Demand and Supply
95(3)
8.9 Conclusions
98(3)
References
98(3)
9 Efficient Market Hypothesis
101(10)
9.1 Introduction
101(1)
9.2 Efficient Market Hypothesis
102(1)
9.3 Rational Expectations
103(1)
9.4 Rational Choice
104(1)
9.5 Bounded Rationality
104(1)
9.6 Information Asymmetry
105(1)
9.7 Behavioral Economics
106(1)
9.8 Game Theory
106(1)
9.9 Demand and Supply
107(1)
9.10 Pricing
108(1)
9.11 Artificial Intelligence
109(1)
9.12 Conclusions
109(2)
References
110(1)
10 Mechanism Design
111(14)
10.1 Introduction
111(1)
10.2 The Players
112(2)
10.3 Efficiency and Equilibria
114(1)
10.4 Incentive Compatibility and the Revelation Principle
115(1)
10.5 Goalposts
116(2)
10.6 So What?
118(1)
10.7 Through the Looking Glass
119(2)
10.8 Application to Market Design
121(2)
10.9 Conclusions
123(2)
References
123(2)
11 Portfolio Theory
125(12)
11.1 Introduction
125(3)
11.2 Assumptions and Limitations
128(2)
11.3 The Trading Machine
130(1)
11.4 The Emerging Trading Machine
131(1)
11.5 Intelligence
132(1)
11.6 Evolutionary Programming
133(1)
11.7 Results Analysis
134(1)
11.8 Conclusion
135(2)
References
135(2)
12 Counterfactuals
137(10)
12.1 Introduction
137(1)
12.2 Counterfactuals
138(2)
12.3 Rational Counterfactuals
140(1)
12.4 Counterfactuals and Causality
141(1)
12.5 Counterfactuals and Opportunity Cost
142(1)
12.6 Counterfactuals and Artificial Intelligence
143(1)
12.7 Interstate Conflict
143(2)
12.8 Conclusions
145(2)
References
145(2)
13 Financial Engineering
147(12)
13.1 Introduction
147(1)
13.2 Risk
148(1)
13.3 Stock Markets
149(4)
13.4 Control Systems
153(2)
13.5 Factor Analysis
155(2)
13.6 Conclusions
157(2)
References
157(2)
14 Causality
159(12)
14.1 Introduction
159(1)
14.2 Correlation
160(1)
14.3 Causality
161(4)
14.4 Granger Causality
165(1)
14.5 Pearl Causality
165(3)
14.6 Use of Do-Calculus
168(1)
14.7 Conclusions
169(2)
References
169(2)
15 Future Work
171(10)
15.1 Conclusions
171(4)
15.2 Decision Theory
175(1)
15.3 Developmental Economics
176(1)
15.4 New Economic Theory
177(4)
References
178(3)
Appendix A Multi-layer Perceptron Neural Network 181(4)
Appendix B Particle Swarm Optimization 185(4)
Appendix C Simulated Annealing 189(4)
Appendix D Genetic Algorithms 193(4)
Appendix E Fuzzy Logic 197(4)
Appendix F Granger Causality 201(2)
Index 203
Tshilidzi Marwala is the Vice-Chancellor and Principal of the University of Johannesburg. He was previously the Deputy Vice Chancellor: Research and Internationalization and a Dean of the Faculty of Engineering at the University of Johannesburg. He was previously a full Professor of Electrical Engineering, the Carl and Emily Fuchs Chair of Systems and Control Engineering as well as the SARChI chair of Systems Engineering at the University of the Witwatersrand. Prior to this, he was an executive assistant to the technical director at the South African Breweries. He holds a Bachelor of Science in Mechanical Engineering (magna cum laude) from Case Western Reserve University (USA), a Master of Mechanical Engineering from the University of Pretoria, a PhD in Engineering from Cambridge University and was a post-doctoral research associate at the Imperial College (London). He is a registered professional engineer, a Fellow of TWAS, the World Academy of Sciences, the Academy of Science of South Africa (ASSAf), the African Academy of Sciences and the South African Academy of Engineering. He is a Senior Member of the IEEE (Institute of Electrical and Electronics Engineering) and a distinguished member of the ACM (Association for Computing Machinery). His research interests are multi-disciplinary and they include the theory and application of computational intelligence to engineering, computer science, finance, social science and medicine. He has supervised 47 Masters and 21 PhD students to completion. He has published 9 books (one translated into Mandarin), over 280 papers and holds three international patents. He is an associate editor of the International Journal of Systems Science (Taylor and Francis Publishers). Evan Hurwitz is a South African computer scientist. He obtained his BSc Engineering (Electrical) (2004), his MSc Engineering (2006) from the University of the Witwatersrand and PhD from the University of Johannesburg in 2014 supervized by Tshilidzi Marwala. He is known for his work on teaching a computer how to bluff which was widely covered by the magazine New Scientist. Hurwitz together with Tshilidzi Marwala proposed that there is less level of information asymmetry between two artificial intelligent agents than between two human agents and that the more artificial intelligent there is in the market the less is the volume of trades in the market.