Rational Decision And Causality

Originally published: New York: Cambridge University Press, 1982.

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.

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. Contents:Introduction to Artificial Intelligence based Decision MakingWhat is a Correlation Machine?What is a Causal Machine?Correlation Machines Using Optimization MethodsNeural Networks for Modeling Granger CausalityRubin, Pearl and Granger Causality Models: A Unified ViewCausal, Correlation and Automatic Relevance Determination Machines for Granger CausalityFlexibly-bounded RationalityMarginalization of Irrationality in Decision MakingConclusions and Further Work Readership: Graduate students, researchers and professionals in the field of artificial intelligence. Key Features:It proposes fresh definition of causality and proposes two new theories i.e. flexibly bounded rationality and marginalization of irrationality theory for decision makingIt also applies these techniques to a diverse areas in engineering, political science and biomedical engineeringKeywords:Causality;Correlation;Artificial Intelligence;Rational Decision Making

This book defends the view that any adequate account of rational decision making must take a decision maker's beliefs about causal relations into account. The early chapters of the book introduce the non-specialist to the rudiments of expected utility theory. The major technical advance offered by the book is a 'representation theorem' that shows that both causal decision theory and its main rival, Richard Jeffrey's logic of decision, are both instances of a more general conditional decision theory. The book solves a long-standing problem for Jeffrey's theory by showing for the first time how to obtain a unique utility and probability representation for preferences and judgements of comparative likelihood. The book also contains a major new discussion of what it means to suppose that some event occurs or that some proposition is true. The most complete and robust defence of causal decision theory available.

The papers collected here are, with three exceptions, those presented at a conference on probability and causation held at the University of California at Irvine on July 15-19, 1985. The exceptions are that David Freedman and Abner Shimony were not able to contribute the papers that they presented to this volume, and that Clark Glymour who was not able to attend the conference did contribute a paper. We would like to thank the National Science Foundation and the School of Humanities of the University of California at Irvine for generous support. WILLIAM HARPER University of Western Ontario BRIAN SKYRMS University of California at Irvine Vll INTRODUCTION PART I: DECISIONS AND GAMES Causal notions have recently corne to figure prominently in discussions about rational decision making. Indeed, a relatively influential new approach to theorizing about rational choice has come to be called "causal decision theory". 1 Decision problems such as Newcombe's Problem and some versions of the Prisoner's Dilemma where an act counts as evidence for a desired state even though the agent knows his choice of that act cannot causally influence whether or not the state obtains have motivated causal decision theorists.

Most philosophers agree that causal knowledge is essential to decision-making: agents should choose from the available options those that probably cause the outcomes that they want. This book argues against this theory and in favour of evidential or Bayesian decision theory, which emphasises the symptomatic value of options over their causal role. It examines a variety of settings, including economic theory, quantum mechanics and philosophical thought-experiments, where causal knowledge seems to make a practical difference. The arguments make novel use of machinery from other areas of philosophical inquiry, including first-person epistemology and the free will debate. The book also illustrates the applicability of decision theory itself to questions about the direction of time and the special epistemic status of agents.

This work aims to construct a theory of rational decision making for real, resource-bounded, agents. Such decision making must be based on objective probabilities rather than subjective probabilities, and can't be done by choosing single action with maxmimal expected values.

J. Howard Sobel has long been recognized as an important figure in philosophical discussions of rational decision. He has done much to help formulate the concept of causal decision theory. In this volume of essays Sobel explores the Bayesian idea that rational actions maximize expected values, where an action's expected value is a weighted average of its agent's values for its possible total outcomes. Newcomb's Problem and The Prisoner's Dilemma are discussed, and Allais-type puzzles are viewed from the perspective of causal world Bayesianism. The author establishes principles for distinguishing options in decision problems, and studies ways in which perfectly rational causal maximizers can be capable of resolute choices. Sobel also views critically Gauthier's revisionist ideas about maximizing rationality. This collection will be a desideratum for anyone working in the field of rational choice theory, whether in philosophy, economics, political science, psychology or statistics. Howard Sobel's work in decision theory is certainly among the most important, interesting and challenging that is being done by philosophers.