Marwala - Causality, Correlation, and Artificial Intelligence for Rational Decision Making

Causality, Correlation and
Artificial Intelligence for
Rational Decision Making

Causality, Correlation and
Artificial Intelligence for
Rational Decision Making

Tshilidzi Marwala

University of Johannesburg, South Africa

Published by

World Scientific Publishing Co. Pte. Ltd.

5 Toh Tuck Link, Singapore 596224

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Library of Congress Cataloging-in-Publication Data

Marwala, Tshilidzi, 1971

Causality, correlation, and artificial intelligence for rational decision making / by Tshilidzi Marwala (University of Johannesburg, South Africa).

pages cm

Includes bibliographical references and index.

ISBN 978-9814630863 (alk. paper)

1. Decision making. 2. Artificial intelligence. I. Title.

T57.95.M375 2015

006.301'51--dc23

2014027464

British Library Cataloguing-in-Publication Data

A catalogue record for this book is available from the British Library.

Copyright 2015 by World Scientific Publishing Co. Pte. Ltd.

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Preface

Causality, correlation and artificial intelligence for rational decision making covers five significant areas of intellectual inquiry which are causality, correlation, artificial intelligence, rationality, and decision making. It gives new insights on causality and correlations machines and proposes that these form as a fundamental unit for rational decision making.

Causality has been a subject of study for many years and has often been confused with correlation. Human intuition has evolved such that it has learned to identify causality through correlation. This book proposes a model that claims that within any causality model is buried a correlation machine.

This study uses artificial intelligence to build correlation and causality machines. Artificial intelligence methods that are used are the multi-layer perceptron (MLP), radial basis function (RBF), fuzzy inference systems, support vector machines, genetic algorithms (GAs), and simulated annealing (SA). The concepts of causality, correlation and artificial intelligence are then applied in interstate conflict, condition monitoring and several biomedical applications.

This book introduces the concept of flexibly-bounded rationality and uses the correlation and causal machines to implement this. Flexibly-bounded rationality is a special case of bounded rationality.

Furthermore, this book introduces the theory of the marginalization of irrationality for decision making and applies this to the problem of breast cancer diagnosis.

This book is intended for philosophers, computer scientists, engineers, and other interested parties at both undergraduate and graduate levels as well as practitioners.

Acknowledgments

I would like to thank the following institutions for contributing toward the writing of this book: University of Cambridge, University of Pretoria, and University of Johannesburg.

I also would like to thank my students for their assistance in developing this manuscript more particularly Fholisani Mashegana.

I dedicate this book to the schools that gave me the foundation to always seek excellence in everything I do and these are: Mbilwi Secondary School, Case Western Reserve University, University of Pretoria, University of Cambridge (St. Johns College), and Imperial College (London). I thank the Stellenbosch Institute of Advanced Study (STIAS) for the financial support as well as accommodation during the writing of this book.

This book is dedicated to the following people: Nhlonipho Khathutshelo, Lwazi Thendo, Mbali Denga Marwala as well as Dr. Jabulile Vuyiswa Manana.

Professor Tshilidzi Marwala, Ph.D.
Johannesburg
1 February 2014

Contents

Abstract.In this chapter, decision making which is based on causality, correlation and artificial intelligence is introduced. The concept of correlation is introduced and is applied to build a correlation function. Furthermore, it relates causality to correlation and uses it to build a causal function. Artificial intelligence methods are introduced and these include neural networks, particle swarm optimization, genetic algorithm and simulated annealing. It describes decision making and outlines the rest of the book.

The problem of understanding causality has pre-occupied philosophers for a very long time. From the times of Plato and Aristotle and even before that period, mankind has been asking why and how they are on this planet and various answers to these questions involve causality. The very idea that a higher being, or a great architect of the universe created, mankind is squarely premised on the principle of causality. The significance of understanding causality to satisfy human curiosity and enhance the functioning of society is enormous. As an example, the medical field is based on the premise that some medicines cause healing of diseases. To link specific medicines to the curing of diseases requires a good understanding of causality and as long as this understanding is not complete the results of clinical studies will always be sub-optimal. One example to illustrate this, is the avocado pear that was thought to be dangerous because the causal relation between avocado pear consumption and cholesterol was poorly understood but today it is considered good for ones cholesterol.

There is an old Tshivenda saying that states: Tsho bebiwaho tsho fa (Marwala, 2013a). What this expression signifies is that there is a perfect correlation between birth and death and those who are born will necessarily die. Many have simplified and misinterpreted this allegory to purport that it alludes to the fact that birth causes death. In fact, there is an information flow from birth to death but this is not causal information. It might be that the actual causal relation for this case was between a road accident and death, therefore, the road accident caused death. This example illustrates a very simple explanation of what a cause is and what a correlation is. On trying to understand causality, it is vital to understand the principle of correlation because within any causal model lies a correlation function.

In this section, we explore the concept of correlation, what it is and what it can be used for. To illustrate this, we invoke the relationship between obesity and heart attack. In this example, the two variables x indicating a persons weight and y indicating whether they had a heart attack. In this case, variable x is positively correlated to variable