The Application of Utility Theory in the Decision-Making of Marketing Risk Management
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Prisoners of Reason Game Theory and Neoliberal Political Economy
C:/ITOOLS/WMS/CUP-NEW/6549131/WORKINGFOLDER/AMADAE/9781107064034PRE.3D iii [1–28] 11.8.2015 9:57PM Prisoners of Reason Game Theory and Neoliberal Political Economy S. M. AMADAE Massachusetts Institute of Technology C:/ITOOLS/WMS/CUP-NEW/6549131/WORKINGFOLDER/AMADAE/9781107064034PRE.3D iv [1–28] 11.8.2015 9:57PM 32 Avenue of the Americas, New York, ny 10013-2473, usa Cambridge University Press is part of the University of Cambridge. It furthers the University’s mission by disseminating knowledge in the pursuit of education, learning, and research at the highest international levels of excellence. www.cambridge.org Information on this title: www.cambridge.org/9781107671195 © S. M. Amadae 2015 This publication is in copyright. Subject to statutory exception and to the provisions of relevant collective licensing agreements, no reproduction of any part may take place without the written permission of Cambridge University Press. First published 2015 Printed in the United States of America A catalog record for this publication is available from the British Library. Library of Congress Cataloging in Publication Data Amadae, S. M., author. Prisoners of reason : game theory and neoliberal political economy / S.M. Amadae. pages cm Includes bibliographical references and index. isbn 978-1-107-06403-4 (hbk. : alk. paper) – isbn 978-1-107-67119-5 (pbk. : alk. paper) 1. Game theory – Political aspects. 2. International relations. 3. Neoliberalism. 4. Social choice – Political aspects. 5. Political science – Philosophy. I. Title. hb144.a43 2015 320.01′5193 – dc23 2015020954 isbn 978-1-107-06403-4 Hardback isbn 978-1-107-67119-5 Paperback Cambridge University Press has no responsibility for the persistence or accuracy of URLs for external or third-party Internet Web sites referred to in this publication and does not guarantee that any content on such Web sites is, or will remain, accurate or appropriate. -
Statistical Decision Theory: Concepts, Methods and Applications
Statistical Decision Theory: Concepts, Methods and Applications (Special topics in Probabilistic Graphical Models) FIRST COMPLETE DRAFT November 30, 2003 Supervisor: Professor J. Rosenthal STA4000Y Anjali Mazumder 950116380 Part I: Decision Theory – Concepts and Methods Part I: DECISION THEORY - Concepts and Methods Decision theory as the name would imply is concerned with the process of making decisions. The extension to statistical decision theory includes decision making in the presence of statistical knowledge which provides some information where there is uncertainty. The elements of decision theory are quite logical and even perhaps intuitive. The classical approach to decision theory facilitates the use of sample information in making inferences about the unknown quantities. Other relevant information includes that of the possible consequences which is quantified by loss and the prior information which arises from statistical investigation. The use of Bayesian analysis in statistical decision theory is natural. Their unification provides a foundational framework for building and solving decision problems. The basic ideas of decision theory and of decision theoretic methods lend themselves to a variety of applications and computational and analytic advances. This initial part of the report introduces the basic elements in (statistical) decision theory and reviews some of the basic concepts of both frequentist statistics and Bayesian analysis. This provides a foundational framework for developing the structure of decision problems. The second section presents the main concepts and key methods involved in decision theory. The last section of Part I extends this to statistical decision theory – that is, decision problems with some statistical knowledge about the unknown quantities. This provides a comprehensive overview of the decision theoretic framework. -
Richard Bradley, Decision Theory with a Human Face
Œconomia History, Methodology, Philosophy 9-1 | 2019 Varia Richard Bradley, Decision Theory with a Human Face Nicolas Gravel Electronic version URL: http://journals.openedition.org/oeconomia/5273 DOI: 10.4000/oeconomia.5273 ISSN: 2269-8450 Publisher Association Œconomia Printed version Date of publication: 1 March 2019 Number of pages: 149-160 ISSN: 2113-5207 Electronic reference Nicolas Gravel, « Richard Bradley, Decision Theory with a Human Face », Œconomia [Online], 9-1 | 2019, Online since 01 March 2019, connection on 29 December 2020. URL : http://journals.openedition.org/ oeconomia/5273 ; DOI : https://doi.org/10.4000/oeconomia.5273 Les contenus d’Œconomia sont mis à disposition selon les termes de la Licence Creative Commons Attribution - Pas d'Utilisation Commerciale - Pas de Modification 4.0 International. | Revue des livres/Book review 149 Comptes rendus / Reviews Richard Bradley, Decision Theory with a Human Face Cambridge: Cambridge University Press, 2017, 335 pages, ISBN 978-110700321-7 Nicolas Gravel∗ The very title of this book, borrowed from Richard Jeffrey’s “Bayesianism with a human face” (Jeffrey, 1983a) is a clear in- dication of its content. Just like its spiritual cousin The Foun- dations of Causal Decision Theory by James M. Joyce(2000), De- cision Theory with a Human Face provides a thoughtful descrip- tion of the current state of development of the Bolker-Jeffrey (BJ) approach to decision-making. While a full-fledged presen- tation of the BJ approach is beyond the scope of this review, it is difficult to appraise the content of Decision Theory with a Hu- man Face without some acquaintance with both the basics of the BJ approach to decision-making and its fitting in the large cor- pus of “conventional” decision theories that have developed in economics, mathematics and psychology since at least the publication of Von von Neumann and Morgenstern(1947). -
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springer.com/booksellers Springer News 11/12/2007 Statistics 49 D. R. Anderson, Colorado State University, Fort J. Franke, W. Härdle, C. M. Hafner U. B. Kjaerulff, Aalborg University, Aalborg, Denmark; Collins, CO, USA A. L. Madsen, HUGIN Expert A/S, Aalborg, Denmark Statistics of Financial Markets Model Based Inference in the An Introduction Bayesian Networks and Life Sciences Influence Diagrams: A Guide A Primer on Evidence to Construction and Analysis Statistics of Financial Markets offers a vivid yet concise introduction to the growing field of statis- The abstract concept of “information” can be tical applications in finance. The reader will learn Probabilistic networks, also known as Bayesian quantified and this has led to many important the basic methods to evaluate option contracts, to networks and influence diagrams, have become advances in the analysis of data in the empirical analyse financial time series, to select portfolios one of the most promising technologies in the area sciences. This text focuses on a science philosophy and manage risks making realistic assumptions of of applied artificial intelligence, offering intui- based on “multiple working hypotheses” and statis- the market behaviour. tive, efficient, and reliable methods for diagnosis, tical models to represent them. The fundamental The focus is both on fundamentals of math- prediction, decision making, classification, trou- science question relates to the empirical evidence ematical finance and financial time series analysis bleshooting, and data mining under uncertainty. for hypotheses in this set - a formal strength of and on applications to given problems of financial Bayesian Networks and Influence Diagrams: A evidence. Kullback-Leibler information is the markets, making the book the ideal basis for Guide to Construction and Analysis provides a information lost when a model is used to approxi- lectures, seminars and crash courses on the topic. -
1947–1967 a Conversation with Howard Raiffa
Statistical Science 2008, Vol. 23, No. 1, 136–149 DOI: 10.1214/088342307000000104 c Institute of Mathematical Statistics, 2008 The Early Statistical Years: 1947–1967 A Conversation with Howard Raiffa Stephen E. Fienberg Abstract. Howard Raiffa earned his bachelor’s degree in mathematics, his master’s degree in statistics and his Ph.D. in mathematics at the University of Michigan. Since 1957, Raiffa has been a member of the faculty at Harvard University, where he is now the Frank P. Ramsey Chair in Managerial Economics (Emeritus) in the Graduate School of Business Administration and the Kennedy School of Government. A pioneer in the creation of the field known as decision analysis, his re- search interests span statistical decision theory, game theory, behavioral decision theory, risk analysis and negotiation analysis. Raiffa has su- pervised more than 90 doctoral dissertations and written 11 books. His new book is Negotiation Analysis: The Science and Art of Collaborative Decision Making. Another book, Smart Choices, co-authored with his former doctoral students John Hammond and Ralph Keeney, was the CPR (formerly known as the Center for Public Resources) Institute for Dispute Resolution Book of the Year in 1998. Raiffa helped to create the International Institute for Applied Systems Analysis and he later became its first Director, serving in that capacity from 1972 to 1975. His many honors and awards include the Distinguished Contribution Award from the Society of Risk Analysis; the Frank P. Ramsey Medal for outstanding contributions to the field of decision analysis from the Operations Research Society of America; and the Melamed Prize from the University of Chicago Business School for The Art and Science of Negotiation. -
Theories of International Relations* Ole R. Holsti
Theories of International Relations* Ole R. Holsti Universities and professional associations usually are organized in ways that tend to separate scholars in adjoining disciplines and perhaps even to promote stereotypes of each other and their scholarly endeavors. The seemingly natural areas of scholarly convergence between diplomatic historians and political scientists who focus on international relations have been underexploited, but there are also some signs that this may be changing. These include recent essays suggesting ways in which the two disciplines can contribute to each other; a number of prizewinning dissertations, later turned into books, by political scientists that effectively combine political science theories and historical materials; collaborative efforts among scholars in the two disciplines; interdisciplinary journals such as International Security that provide an outlet for historians and political scientists with common interests; and creation of a new section, “International History and Politics,” within the American Political Science Association.1 *The author has greatly benefited from helpful comments on earlier versions of this essay by Peter Feaver, Alexander George, Joseph Grieco, Michael Hogan, Kal Holsti, Bob Keohane, Timothy Lomperis, Roy Melbourne, James Rosenau, and Andrew Scott, and also from reading 1 K. J. Holsti, The Dividing Discipline: Hegemony and Diversity in International Theory (London, 1985). This essay is an effort to contribute further to an exchange of ideas between the two disciplines by describing some of the theories, approaches, and "models" political scientists have used in their research on international relations during recent decades. A brief essay cannot do justice to the entire range of theoretical approaches that may be found in the current literature, but perhaps those described here, when combined with citations of some representative works, will provide diplomatic historians with a useful, if sketchy, map showing some of the more prominent landmarks in a neighboring discipline. -
Improving Monetary Policy Models by Christopher A. Sims, Princeton
Improving Monetary Policy Models by Christopher A. Sims, Princeton University and NBER CEPS Working Paper No. 128 May 2006 IMPROVING MONETARY POLICY MODELS CHRISTOPHER A. SIMS ABSTRACT. If macroeconomic models are to be useful in policy-making, where un- certainty is pervasive, the models must be treated as probability models, whether formally or informally. Use of explicit probability models allows us to learn sys- tematically from past mistakes, to integrate model-based uncertainty with uncertain subjective judgment, and to bind data-bassed forecasting together with theory-based projection of policy effects. Yet in the last few decades policy models at central banks have steadily shed any claims to being believable probability models of the data to which they are fit. Here we describe the current state of policy modeling, suggest some reasons why we have reached this state, and assess some promising directions for future progress. I. WHY DO WE NEED PROBABILITY MODELS? Fifty years ago most economists thought that Tinbergen’s original approach to macro-modeling, which consisted of fitting many equations by single-equation OLS Date: May 26, 2006. °c 2006 by Christopher A. Sims. This material may be reproduced for educational and research purposes so long as the copies are not sold, even to recover costs, the document is not altered, and this copyright notice is included in the copies. Research for this paper was supported by NSF grant SES 0350686 and by Princeton’s Center for Economic Policy Studies. This paper was presented at a December 2-3, 2005 conference at the Board of Governors of the Federal Reserve and may appear in a conference volume or journal special issue. -
Introduction to Decision Theory: Econ 260 Spring, 2016
Introduction to Decision Theory: Econ 260 Spring, 2016 1/7. Instructor Details: Sumantra Sen Email: [email protected] OH: TBA TA: Ross Askanazi OH of TA: TBA 2/7. Website: courseweb.library.upenn.edu (Canvas Course site) 3/7. Overview: This course will provide an introduction to Decision Theory. The goal of the course is to make the student think rigorously about decision-making in contexts of objective and subjective uncertainty. The heart of the class is delving deeply into the axiomatic tradition of Decision Theory in Economics, and covering the classic theorems of De Finetti, Von Neumann & Morgenstern, and Savage and reevaluating them in light of recent research in behavioral economics and Neuro-economics. Time permitting; we will study other extensions (dynamic models and variants of other static models) as well, with the discussion anchored by comparison and reference to these classic models. There is no required text for the class, as there is no suitable one for undergraduates that covers the material we plan to do. We will reply on lecture notes and references (see below). 4/7. Policies of the Economics Department, including course pre-requisites and academic integrity Issues may be found at: http://economics.sas.upenn.edu/undergraduate-program/course- information/guidelines/policies 5/7. Grades: There will be two non-cumulative in-class midterms (20% each) during the course and one cumulative final (40%). The midterms will be on Feb 11 (will be graded and returned by Feb 18) and April 5th. The final is on May 6th. If exams are missed, documentable reasons of the validity of such actions must be provided within a reasonable period of time and the instructor will have the choice of pro-rating your grade over the missed exam rather than deliver a make-up. -
Evidence-Based Decisions: the Role of Decision Analysis 11 Dawn Dowding and Carl Thompson
Evidence-based decisions: the role of decision analysis 11 Dawn Dowding and Carl Thompson CHAPTER CONTENTS Introduction 173 Conclusion 192 What is decision analysis? 174 Exercises 192 The stages in a decision analysis 174 Resources 194 Benefits and limitations of decision Sources 194 analysis 191 References 195 KEY ISSUES What is a decision analysis? How to undertake a decision analysis: ○ structuring a question ○ designing a decision tree ○ extracting probabilities ○ gathering utilities ○ combining information on probability and utility in the process of ‘rolling back’ a decision tree The strengths and weaknesses of decision analysis INTRODUCTION The components of an evidence-based decision include the results of research, a patient’s values or preferences for particular choices, an aware- ness of available resources, and the experience and expertise of the decision maker (DiCenso et al 1998). Having made it this far in the text, you will have grasped that making evidence-based decisions in practice can sometimes be difficult; requiring the use of complex, often incomplete information, and with uncertain decision outcomes (Hunink et al 2001, Tavakoli et al 2000). Paying attention to and integrating the volume of information in practice can often seem overwhelming. Nurses, like all human beings, have limited capacity to manage all of the different components of a complex decision at the same time (Hunink et al 2001, Sarasin 2001). Decision analysis is one approach that can help systematically combine the elements required for an evidence-based decision. 173 11 WHAT IS DECISION ANALYSIS? Decision analysis is based on the normative expected utility theory (EUT; see Chapters 2 and 5; Elwyn et al 2001, Tavakoli et al 2000, Thornton 1996). -
A Note on States and Acts in Theories of Decision Under Uncertainty
A Note on States and Acts in Theories of Decision under Uncertainty Burkhard C. Schipper∗ February 19, 2016 Abstract The purpose of this note is to discuss some problems with the notion of states and acts in theories of decision under uncertainty. We argue that any single-person decision problem under uncertainty can be cast in a canonical state space in which states describe the consequence of every action. Casting decision problems in the canonical state space opens up the analysis to the question of how decisions depend on the state space. It also makes transparent that the set of all acts is problematic because it must contain incoherent acts that assign to some state a consequence that is inconsistent with the internal structure of that state. We discuss three ap- proaches for dealing with incoherent acts, each unsatisfactory in that it limits what can be meaningfully revealed about beliefs with choice experiments. Keywords: Decision theory, State spaces, States of nature, Acts, Subjective expected utility, Ambiguity, Ellsberg paradox. JEL-Classifications: D80, D81. 1 States in Theories of Decision under Uncertainty In many theories of decision under uncertainty such as Subjective Expected Utility (Sav- age 1954, Anscombe and Aumann, 1963), Choquet expected utility (Schmeidler, 1989), Maxmin Expected Utility (Gilboa and Schmeidler, 1989) etc., states of nature are used to list possible resolutions of uncertainty. A decision maker is uncertain which action would yield which consequences. A state is \a description of the world so complete that, if true and known, the consequences of every action would be known" (Arrow, 1971, p. -
Decision Theory: a Brief Introduction
Decision Theory A Brief Introduction 1994-08-19 Minor revisions 2005-08-23 Sven Ove Hansson Department of Philosophy and the History of Technology Royal Institute of Technology (KTH) Stockholm 1 Contents Preface ..........................................................................................................4 1. What is decision theory? ..........................................................................5 1.1 Theoretical questions about decisions .........................................5 1.2 A truly interdisciplinary subject...................................................6 1.3 Normative and descriptive theories..............................................6 1.4 Outline of the following chapters.................................................8 2. Decision processes....................................................................................9 2.1 Condorcet .....................................................................................9 2.2 Modern sequential models ...........................................................9 2.3 Non-sequential models.................................................................10 2.4 The phases of practical decisions – and of decision theory.........12 3. Deciding and valuing................................................................................13 3.1 Relations and numbers .................................................................13 3.2 The comparative value terms .......................................................14 3.3 Completeness ...............................................................................16 -
The Role of Models and Probabilities in the Monetary Policy Process
1017-01 BPEA/Sims 12/30/02 14:48 Page 1 CHRISTOPHER A. SIMS Princeton University The Role of Models and Probabilities in the Monetary Policy Process This is a paper on the way data relate to decisionmaking in central banks. One component of the paper is based on a series of interviews with staff members and a few policy committee members of four central banks: the Swedish Riksbank, the European Central Bank (ECB), the Bank of England, and the U.S. Federal Reserve. These interviews focused on the policy process and sought to determine how forecasts were made, how uncertainty was characterized and handled, and what role formal economic models played in the process at each central bank. In each of the four central banks, “subjective” forecasting, based on data analysis by sectoral “experts,” plays an important role. At the Federal Reserve, a seventeen-year record of model-based forecasts can be com- pared with a longer record of subjective forecasts, and a second compo- nent of this paper is an analysis of these records. Two of the central banks—the Riksbank and the Bank of England— have explicit inflation-targeting policies that require them to set quantita- tive targets for inflation and to publish, several times a year, their forecasts of inflation. A third component of the paper discusses the effects of such a policy regime on the policy process and on the role of models within it. The large models in use in central banks today grew out of a first generation of large models that were thought to be founded on the statisti- cal theory of simultaneous-equations models.