Causality Correlation And Artificial Intelligence For Rational Decision Making
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| Author | : Tshilidzi Marwala |
| Publisher | : World Scientific |
| Total Pages | : 207 |
| Release | : 2015-01-02 |
| Genre | : Computers |
| ISBN | : 9814630888 |
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.
| Author | : Tshilidzi Marwala |
| Publisher | : Springer |
| Total Pages | : 178 |
| Release | : 2014-10-20 |
| Genre | : Computers |
| ISBN | : 3319114247 |
Develops insights into solving complex problems in engineering, biomedical sciences, social science and economics based on artificial intelligence. Some of the problems studied are in interstate conflict, credit scoring, breast cancer diagnosis, condition monitoring, wine testing, image processing and optical character recognition. The author discusses and applies the concept of flexibly-bounded rationality which prescribes that the bounds in Nobel Laureate Herbert Simon’s bounded rationality theory are flexible due to advanced signal processing techniques, Moore’s Law and artificial intelligence. Artificial Intelligence Techniques for Rational Decision Making examines and defines the concepts of causal and correlation machines and applies the transmission theory of causality as a defining factor that distinguishes causality from correlation. It develops the theory of rational counterfactuals which are defined as counterfactuals that are intended to maximize the attainment of a particular goal within the context of a bounded rational decision making process. Furthermore, it studies four methods for dealing with irrelevant information in decision making: Theory of the marginalization of irrelevant information Principal component analysis Independent component analysis Automatic relevance determination method In addition it studies the concept of group decision making and various ways of effecting group decision making within the context of artificial intelligence. Rich in methods of artificial intelligence including rough sets, neural networks, support vector machines, genetic algorithms, particle swarm optimization, simulated annealing, incremental learning and fuzzy networks, this book will be welcomed by researchers and students working in these areas.
| Author | : James M. Joyce |
| Publisher | : Cambridge University Press |
| Total Pages | : 300 |
| Release | : 1999-04-13 |
| Genre | : Computers |
| ISBN | : 9780521641647 |
The book also contains a major new discussion of what it means to suppose that some event occurs or that some proposition is true.
| Author | : Jordi Vallverdú |
| Publisher | : Springer Nature |
| Total Pages | : 110 |
| Release | : |
| Genre | : |
| ISBN | : 9819731879 |
| Author | : Ellery Eells |
| Publisher | : Cambridge University Press |
| Total Pages | : 229 |
| Release | : 2016-08-26 |
| Genre | : Science |
| ISBN | : 1316558908 |
First published in 1982, Ellery Eells' original work on rational decision making had extensive implications for probability theorists, economists, statisticians and psychologists concerned with decision making and the employment of Bayesian principles. His analysis of the philosophical and psychological significance of Bayesian decision theories, causal decision theories and Newcomb's paradox continues to be influential in philosophy of science. His book is now revived for a new generation of readers and presented in a fresh twenty-first-century series livery, including a specially commissioned preface written by Brian Skyrms, illuminating its continuing importance and relevance to philosophical enquiry.
| Author | : Michael J. Pazzani |
| Publisher | : Psychology Press |
| Total Pages | : 294 |
| Release | : 2014-02-25 |
| Genre | : Psychology |
| ISBN | : 1317783913 |
This book presents a theory of learning new causal relationships by making use of perceived regularities in the environment, general knowledge of causality, and existing causal knowledge. Integrating ideas from the psychology of causation and machine learning, the author introduces a new learning procedure called theory-driven learning that uses abstract knowledge of causality to guide the induction process. Known as OCCAM, the system uses theory-driven learning when new experiences conform to common patterns of causal relationships, empirical learning to learn from novel experiences, and explanation-based learning when there is sufficient existing knowledge to explain why a new outcome occurred. Together these learning methods construct a hierarchical organized memory of causal relationships. As such, OCCAM is the first learning system with the ability to acquire, via empirical learning, the background knowledge required for explanation-based learning. Please note: This program runs on common lisp.
| Author | : Jiuyong Li |
| Publisher | : Springer |
| Total Pages | : 87 |
| Release | : 2015-03-02 |
| Genre | : Computers |
| ISBN | : 3319144332 |
This brief presents four practical methods to effectively explore causal relationships, which are often used for explanation, prediction and decision making in medicine, epidemiology, biology, economics, physics and social sciences. The first two methods apply conditional independence tests for causal discovery. The last two methods employ association rule mining for efficient causal hypothesis generation, and a partial association test and retrospective cohort study for validating the hypotheses. All four methods are innovative and effective in identifying potential causal relationships around a given target, and each has its own strength and weakness. For each method, a software tool is provided along with examples demonstrating its use. Practical Approaches to Causal Relationship Exploration is designed for researchers and practitioners working in the areas of artificial intelligence, machine learning, data mining, and biomedical research. The material also benefits advanced students interested in causal relationship discovery.
| Author | : Tshilidzi Marwala |
| Publisher | : Springer |
| Total Pages | : 206 |
| Release | : 2017-09-18 |
| Genre | : Computers |
| ISBN | : 3319661043 |
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.
| Author | : A. Emanuel Robinson |
| Publisher | : |
| Total Pages | : |
| Release | : 2006 |
| Genre | : Causation |
| ISBN | : |
Decision-making is something we do every day, and is a broad research area that impacts disciplines spanning from economics to philosophy to psychology. The question of rational behavior has been of particular interest (Colman, 2003). A specific area of decision-making where rationality has been investigated is game theory, which deals with the interactions of two or more opponents in a competitive situation (e.g., von Neumann & Morgenstern, 1944). The dominant theoretical perspective in this area claims individuals try to maximize expected utility when making decisions (e.g., Luce & Raiffa, 1957). An alternative theory has been put forth to better explain experimental deviations from utility theory and rationality. Causal decision theory is based on the assumption that individuals incorporate causal knowledge in decisions, while trying to maximize causal utility (e.g., Sloman, 2005). The present study delineated these theoretical approaches as strategies that can be utilized in game theoretic situations (based on a strategy choice perspective in deductive reasoning developed by Robinson & Hertzog, 2005). The role of causal models, strategy choice, and temporal assumptions were investigated. In both experiments, there was support for causal decision theory and the primary prediction that a direct causal model leads to more cooperation in competitive situations. Conversely, those individuals that were given (or assumed) a common cause model chose to cooperate less. Qualitative coding and strategy self-reports aligned with these findings and according to predictions. These differences in cooperation based on causal models also held across items for the same participant. Finally, causal information superseded temporal cues in affecting behavior.
| Author | : Bo Xing |
| Publisher | : CRC Press |
| Total Pages | : 512 |
| Release | : 2018-12-07 |
| Genre | : Business & Economics |
| ISBN | : 1351265075 |
The book focuses on smart computing for crowdfunding usage, looking at the crowdfunding landscape, e.g., reward-, donation-, equity-, P2P-based and the crowdfunding ecosystem, e.g., regulator, asker, backer, investor, and operator. The increased complexity of fund raising scenario, driven by the broad economic environment as well as the need for using alternative funding sources, has sparked research in smart computing techniques. Covering a wide range of detailed topics, the authors of this book offer an outstanding overview of the current state of the art; providing deep insights into smart computing methods, tools, and their applications in crowdfunding; exploring the importance of smart analysis, prediction, and decision-making within the fintech industry. This book is intended to be an authoritative and valuable resource for professional practitioners and researchers alike, as well as finance engineering, and computer science students who are interested in crowdfunding and other emerging fintech topics.
