Decision Theory
In: Taroni F., Bozza S., Biedermann A. 2020, Decision theory, in: Handbook of Forensic Statistics, Banks D. L., Kafadar K., Kaye D.H., Tackett M. (Eds.), Chapman & Hall/CRC Handbooks of Modern Statistical Methods, Chapter 5, 103–130.
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In: Taroni F., Bozza S., Biedermann A. 2020, Decision theory, in: Handbook of Forensic Statistics, Banks D. L., Kafadar K., Kaye D.H., Tackett M. (Eds.), Chapman & Hall/CRC Handbooks of Modern Statistical Methods, Chapter 5, 103–130.
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Working paper
In: Mathematical social sciences, Band 14, Heft 2, S. 199
In: Oxford Research Encyclopedia of Politics
"Behavioral Decision Theory" published on by Oxford University Press.
In: Reihe Ökonomie 246
Contemporary approaches to decision making describe a decision problem by sets of states and outcomes, and a rich set of acts: functions from states to outcomes over which the decision maker (DM) has preferences. Real problems do not come so equipped. It is often unclear what the state and outcome spaces would be. We present an alternative foundation for decision making, in which the primitive objects of choice are syntactic programs. We show that if the DM's preference relation on objects of choice satisfies appropriate axioms, then we can find states, outcomes, and an embedding of the programs into Savage acts such that preferences can be represented by EU in the Savage framework. A modeler can test for SEU behavior without having access to the subjective states and outcomes. We illustrate the power of our approach by showing that it can represent DMs who are subject to framing effects. -- decision theory ; subjective expected utility ; behavioral anomalies
In: Wiley publications in statistics
In: Synthese: an international journal for epistemology, methodology and philosophy of science, Band 194, Heft 10, S. 4133-4153
ISSN: 1573-0964
In: Mathematical social sciences, Band 4, Heft 2, S. 192-193
In: Cambridge introductions to philosophy
Now revised and updated, this introduction to decision theory is both accessible and comprehensive, covering topics including decision making under ignorance and risk, the foundations of utility theory, the debate over subjective and objective probability, Bayesianism, causal decision theory, game theory, and social choice theory. No mathematical skills are assumed, with all concepts and results explained in non-technical and intuitive as well as more formal ways. There are now over 140 exercises with solutions, along with a glossary of key terms and concepts. This second edition includes a new chapter on risk aversion as well as updated discussions of numerous central ideas, including Newcomb's problem, prisoner's dilemmas, and Arrow's impossibility theorem. The book will appeal particularly to philosophy students but also to readers in a range of disciplines, from computer science and psychology to economics and political science.
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In: The Foundations of Causal Decision Theory, S. 114-145
In: The Foundations of Causal Decision Theory, S. 146-180
In: Springer eBook Collection
The book treats two approaches to decision theory: (1) the normative, purporting to determine how a 'perfectly rational' actor ought to choose among available alternatives; (2) the descriptive, based on observations of how people actually choose in real life and in laboratory experiments. The mathematical tools used in the normative approach range from elementary algebra to matrix and differential equations. Sections on different levels can be studied independently. Special emphasis is made on 'offshoots' of both theories to cognitive psychology, theoretical biology, and philosophy.