Causality, correlation, and artificial intelligence for rational decision making /
Causality has been a subject of study for a long time. Often causality is confused with correlation. Human intuition has evolved such that it has learned to identify causality through correlation. In this book, four main themes are considered and these are causality, correlation, artificial intellig...
Saved in:
| Main Author: | |
|---|---|
| Format: | Electronic eBook |
| Language: | English |
| Published: |
New Jersey :
World Scientific,
[2015]
|
| Subjects: | |
| Online Access: | CONNECT |
MARC
| LEADER | 00000cam a2200000 i 4500 | ||
|---|---|---|---|
| 001 | mig00005352084 | ||
| 006 | m o d | ||
| 007 | cr cnu---unuuu | ||
| 008 | 150216s2015 nju ob 001 0 eng d | ||
| 005 | 20240618153521.7 | ||
| 020 | |a 9789814630870 |q (electronic bk.) | ||
| 020 | |a 981463087X |q (electronic bk.) | ||
| 020 | |z 9789814630863 | ||
| 020 | |z 9814630861 | ||
| 035 | |a 1WRLDSHRocn903489366 | ||
| 035 | |a (OCoLC)903489366 | ||
| 040 | |a N$T |b eng |e rda |e pn |c N$T |d OCLCF |d YDXCP |d IDEBK |d EBLCP |d DEBSZ |d OCLCQ |d DA$ |d AGLDB |d OCLCQ |d VTS |d CEF |d WYU |d STF |d UKAHL |d OCLCQ |d OCLCO |d OCLCQ |d OCLCO |d OCLCL | ||
| 049 | |a TXMM | ||
| 050 | 4 | |a T57.95 |b .M375 2015eb | |
| 082 | 0 | 4 | |a 006.301/51 |2 23 |
| 100 | 1 | |a Marwala, Tshilidzi, |d 1971- |1 https://id.oclc.org/worldcat/entity/E39PBJth3Vv4ywFWv9rdrkjHmd | |
| 245 | 1 | 0 | |a Causality, correlation, and artificial intelligence for rational decision making / |c by Tshilidzi Marwala (University of Johannesburg, South Africa). |
| 264 | 1 | |a New Jersey : |b World Scientific, |c [2015] | |
| 300 | |a 1 online resource | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 347 | |a data file |2 rda | ||
| 504 | |a Includes bibliographical references and index. | ||
| 588 | 0 | |a Print version record. | |
| 505 | 0 | |a 1. Introduction to artificial intelligence based decision making. 1.1. Introduction. 1.2. Correlation. 1.3. Causality. 1.4. Introduction to artificial intelligence. 1.5. Rational decision making. 1.6. Summary and outline of the book. 1.7. Conclusions -- 2. What is a correlation machine? 2.1. Introduction. 2.2. Correlation machines. 2.3. Genetic algorithm. 2.4. Multi-layer perceptron. 2.5. Experimental comparison. 2.6. Conclusions -- 3. What is a causal machine? 3.1. Introduction. 3.2. Induction, deduction, and abduction. 3.3. What is causality? 3.4. Multi-layer perceptron causal machine. 3.5. Radial basis function causal machine. 3.6. Fuzzy inference system causal machine. 3.7. Conclusions | |
| 505 | 0 | |a 4. Correlation machines using optimization methods. 4.1. Introduction. 4.2. Multi-layer perceptron neural network. 4.3. Missing data estimation technique. 4.4. Genetic algorithms. 4.5. Particle swarm optimization. 4.6. Simulated annealing. 4.7. Missing data estimation: Case studies. 4.8. Conclusions -- 5. Neural networks for modeling Granger causality. 5.1. Introduction. 5.2. Granger causality. 5.3. Multi-layer perceptron for Granger causality. 5.4. RBF for Granger causality. 5.5. Example: Mackey-Glass system. 5.6. Conclusions -- 6. Rubin, Pearl and Granger causality models: A unified view. 6.1. Introduction. 6.2. Neyman-Rubin causal model. 6.3. Pearl causality. 6.4. Granger causality. 6.5. Comparison: Neyman-Rubin, Pearl and Granger causality. 6.6. Conclusions | |
| 505 | 0 | |a 7. Causal, correlation and automatic relevance determination machines for Granger causality. 7.1. Introduction. 7.2. Causal machine to Granger causality. 7.3. Correlation machine to Granger causality. 7.4. Automatic relevance determination for Granger causality. 7.5. Experimental investigation: Mackey-Glass time-delay differential equation. 7.6. Conclusions -- 8. Flexibly-bounded rationality. 8.1. Introduction. 8.2. Rational decision making: A causal approach. 8.3. Rational decision making process. 8.4. Bounded-rational decision making. 8.5. Flexibly-bounded rational decision making. 8.6. Experimental investigations. 8.7. Conclusions | |
| 505 | 0 | |a 9. Marginalization of irrationality in decision making. 9.1. Introduction. 9.2. Rational decision making. 9.3. What is irrationality? 9.4. Marginalization of irrationality theory. 9.5. Irrational decision making and the theory of marginalization of irrationality in decision making. 9.6. Application of the marginalization of irrationality theory for breast cancer diagnosis. 9.7. Conclusions -- 10. Conclusions and further work. 10.1. Introduction. 10.2. Way forward. | |
| 520 | |a Causality has been a subject of study for a long time. Often causality is confused with correlation. Human intuition has evolved such that it has learned to identify causality through correlation. In this book, four main themes are considered and these are causality, correlation, artificial intelligence and decision making. A correlation machine is defined and built using multi-layer perceptron network, principal component analysis, Gaussian Mixture models, genetic algorithms, expectation maximization technique, simulated annealing and particle swarm optimization. Furthermore, a causal machine is defined and built using multi-layer perceptron, radial basis function, Bayesian statistics and Hybrid Monte Carlo methods. Both these machines are used to build a Granger non-linear causality model. In addition, the Neyman-Rubin, Pearl and Granger causal models are studied and are unified. The automatic relevance determination is also applied to extend Granger causality framework to the non-linear domain. The concept of rational decision making is studied, and the theory of flexibly-bounded rationality is used to extend the theory of bounded rationality within the principle of the indivisibility of rationality. The theory of the marginalization of irrationality for decision making is also introduced to deal with satisficing within irrational conditions. The methods proposed are applied in biomedical engineering, condition monitoring and for modelling interstate conflict. | ||
| 500 | |a EBSCO eBook Academic Comprehensive Collection North America |5 TMurS | ||
| 650 | 0 | |a Decision making. | |
| 650 | 0 | |a Artificial intelligence. | |
| 730 | 0 | |a WORLDSHARE SUB RECORDS | |
| 776 | 0 | 8 | |i Print version: |a Marwala, Tshilidzi, 1971- |t Causality, correlation, and artificial intelligence for rational decision making |z 9789814630863 |w (DLC) 2014027464 |w (OCoLC)883836377 |
| 856 | 4 | 0 | |u https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=956047&authtype=ip,sso&custid=s4672406 |z CONNECT |3 eBooks on EBSCOhost |t 0 |
| 907 | |a 4623812 |b 05-26-21 |c 06-30-20 | ||
| 949 | |a ho0 | ||
| 994 | |a 92 |b TXM | ||
| 998 | |a wi |d z | ||
| 999 | f | f | |s e8f7a668-49fb-4dfc-9bdf-3bcb83ce1275 |i 6912cd9d-9388-4085-971e-329fbbcecc56 |t 0 |
| 952 | f | f | |a Middle Tennessee State University |b Main |c James E. Walker Library |d Electronic Resources |t 0 |e T57.95 .M375 2015eb |h Library of Congress classification |
