DOCSLIB.ORG

9<HTLDTH=Hebaaa>

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
9<HTLDTH=Hebaaa> 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. comprehensive guide for practitioners who wish mate full reality. Hirotugu Akaike found a link For the second edition the book has been updated to understand, construct, and analyze intelligent between K-L information (a cornerstone of infor- and extensively revised. Several new aspects have systems for decision support based on probabilistic mation theory) and the maximized log-likelihood been included, among others a chapter on credit networks. Intended primarily for practitioners, this (a cornerstone of mathematical statistics). This risk management. book does not require sophisticated mathematical combination has become the basis for a new para- skills or deep understanding of the underlying digm in model based inference. The text advocates Features theory and methods nor does it discuss alterna- formal inference from all the hypotheses/models in 7 Ideal basis for lectures, seminars, and crash tive technologies for reasoning under uncertainty. the a priori set - multimodel inference. courses on statistical applications in finance The theory and methods presented are illustrated 7 Interactive approach using statistical software through more than 140 examples, and exercises Features are included for the reader to check his/her level of 7 Very broad applicability, very science-based, Contents understanding. and practical 7 Very powerful – the concept Option Pricing.- Statistical Model of Financial of formal “strength of evidence” 7 Simple to Time Series.- Selected Financial Applications. Features use and understand 7 Emphasis on science 7 Comprehensive introduction to probabilistic philosophy, not just “data analysis” Fields of interest networks 7 Written specifically for practitioners Statistics for Business/Economics/Mathematical of applied artificial intelligence 7 Complete guide Contents Finance/Insurance; Quantitative Finance; Finance / to understand, construct, and analyze probabilistic Introduction-science hypotheses and science Banking networks philosophy.- Data and models.- Information theory and entropy.- Quantifying the evidence Target groups Contents about science hypotheses.- Multimodel inference.- Students in financial engineering, statistics, math- Introduction.- Networks.- Probabilities.- Probabi- Advanced topics.- Summary. ematical finance, and financial econometrics listic Networks.- Solving Probabilistic Networks.- Eliciting the Model.- Modeling Techniques.- Data- Fields of interest Type of publication Driven Modeling.- Conflict Analysis.- Sensitivity Statistics for Life Sciences, Medicine, Health Graduate/Advanced undergraduate textbook Analysis.- Value of Information Analysis. Sciences; Environmental Monitoring/Analysis; Ecology Fields of interest Statistics and Computing/Statistics Programs; Target groups Probability and Statistics in Computer Science Graduate students, scientists Target groups Type of publication Practitioners Graduate/Advanced undergraduate textbook Type of publication Monograph Due January 2008 Due December 2007 Due January 2008 2008. Approx. 345 p. (Information Science and Statistics) 2008. Approx. 190 p. 8 illus. Softcover 2nd ed. 2008. XXII, 495 p. (Universitext) Softcover Hardcover 7 approx. € 30,80 | £23.50 7 approx. € 69,95 | £54.00 7 approx. € 61,60 | £47.50 9<HTLDTH=heahdh>ISBN 978-0-387-74073-7 9<HTOFPA=hgcgja>ISBN 978-3-540-76269-0 9<HTLDTH=hebaaa>ISBN 978-0-387-74100-0 50 Statistics Springer News 11/12/2007 springer.com/booksellers S. Konishi, Kyushu University, Fukuoka, Japan; F. Liese, Universität Rostock, Germany; K. Miescke, J. Shao, University of Wisconsin, Madison, WI, USA G. Kitagawa, The Institute of Statistical Mathematics, University of Illinois at Chicago, IL, USA Tokyo, Japan Mathematical Statistics Statistical Decision Theory Information Criteria and Estimation, Testing, and Selection Statistical Modeling This graduate textbook covers topics in statistical theory essential for graduate students preparing This monograph is written for advanced graduate for work on a Ph.D. degree in statistics. The first The Akaike information criterion (AIC) derived as students, Ph.D. students, and researchers in math- chapter provides a quick overview of concepts an estimator of the Kullback-Leibler information ematical statistics and decision theory. All major and results in measure-theoretic probability discrepancy provides a useful tool for evaluating topics are introduced on a fairly elementary level theory that are useful in statistics. The second statistical models, and numerous successful and then developed gradually to higher levels. The chapter introduces some fundamental concepts in applications of the AIC have been reported in book is self-contained as it provides full proofs, statistical decision theory and inference. Chapters various fields of natural sciences, social sciences worked-out examples, and problems. It can be used 3-7 contain detailed studies on some important and engineering. as a basis for graduate courses, seminars, Ph.D. topics: unbiased estimation, parametric estimation, One of the main objectives of this book is to programs, self-studies, and as a reference book. nonparametric estimation, hypothesis testing, and provide comprehensive explanations of the The authors present a rigorous account of the confidence sets. A large number of exercises in concepts and derivations of the AIC and related concepts and a broad treatment of the major each chapter provide not only practice problems criteria, including Schwarz’s Bayesian informa- results of classical finite sample size decision for students, but also many additional results. tion criterion (BIC), together with a wide range of theory and modern asymptotic decision theory. practical examples of model selection and evalu- Highlights are systematic applications to the fields Contents ation criteria. A secondary objective is to provide of parameter estimation, testing hypotheses, and Probability Theory.- Fundamentals of Statistics.- a theoretical basis for the analysis and extension selection of populations. Unbiased Estimation.- Estimation in Parametric of information criteria via a statistical functional Models.- Estimation in Nonparametric Models.- approach. Features Hypothesis Tests.- Confidence Sets. 7 Presents the main ideas of decision theory in an Features organized, balanced, and mathematically rigorous Field of interest 7 With the development of modeling techniques, manner, while observing statistical relevance Statistical Theory and Methods it has been required to construct model selection criteria, relaxing the assumptions imposed AIC Contents Target groups and BIC Statistical models.- Tests in models with mono- Graduate students tonicity properties.- Statistical decision theory.- Contents Comparison of models, reduction by sufficiency.- Type of publication Concept of statistical modeling.- Statistical Invariant statistical decision models.- Large sample Graduate/Advanced undergraduate textbook models.- Information criterion.- Statistical approximations of models and decisions.- Estima- modeling by AIC.- Generalized information crite- tion.- Testing.- Selection. rion GIC.- Statistical modeling by GIC.- Theoret- ical development and asymptotic properties of the Fields of interest GIC.- Bootstrap information criterion.- Bayesian Statistical Theory and Methods information criteria.- Various model evaluation criteria. Target groups Graduate students, researchers Fields of interest Statistical Theory and Methods; ; Probability and Type of publication Statistics in Computer Science Monograph Target groups Researchers Type of publication Monograph Due December 2007 Available Due November 2007 2008. Approx. 695 p. (Springer Series in Statistics) 2nd ed. 2003. Corr. 4th printing 2008. XVI, 591 p. 2008. XII, 276 p. (Springer Series in Statistics) Hardcover Hardcover (Springer Texts in Statistics) Hardcover 7 € 64,95 | £50.00 7 approx. € 69,95 | £54.00 7 € 79,95 | £61.50 9<HTLDTH=hbiigg>ISBN 978-0-387-71886-6 9<HTLDTH=hdbjdd>ISBN 978-0-387-73193-3 9<HTLDTH=jfdicd>ISBN 978-0-387-95382-3.
Load more

Recommended publications
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • 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).
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • 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
    [Show full text]
  • 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.
    [Show full text]
  • Nber Working Paper Series Decision Theoretic
    NBER WORKING PAPER SERIES DECISION THEORETIC APPROACHES TO EXPERIMENT DESIGN AND EXTERNAL VALIDITY Abhijit Banerjee Sylvain Chassang Erik Snowberg Working Paper 22167 http://www.nber.org/papers/w22167 NATIONAL BUREAU OF ECONOMIC RESEARCH 1050 Massachusetts Avenue Cambridge, MA 02138 April 2016 We thank Esther Duflo for her leadership on the handbook, and for extensive comments on earlier drafts. Chassang and Snowberg gratefully acknowledge the support of NSF grant SES-1156154. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research. NBER working papers are circulated for discussion and comment purposes. They have not been peer-reviewed or been subject to the review by the NBER Board of Directors that accompanies official NBER publications. © 2016 by Abhijit Banerjee, Sylvain Chassang, and Erik Snowberg. All rights reserved. Short sections of text, not to exceed two paragraphs, may be quoted without explicit permission provided that full credit, including © notice, is given to the source. Decision Theoretic Approaches to Experiment Design and External Validity Abhijit Banerjee, Sylvain Chassang, and Erik Snowberg NBER Working Paper No. 22167 April 2016 JEL No. B23,C9,O1 ABSTRACT A modern, decision-theoretic framework can help clarify important practical questions of experimental design. Building on our recent work, this chapter begins by summarizing our framework for understanding the goals of experimenters, and applying this to re-randomization. We then use this framework to shed light on questions related to experimental registries, pre- analysis plans, and most importantly, external validity. Our framework implies that even when large samples can be collected, external decision-making remains inherently subjective.
    [Show full text]
  • Publics and Markets. What's Wrong with Neoliberalism?
    12 Publics and Markets. What’s Wrong with Neoliberalism? Clive Barnett THEORIZING NEOLIBERALISM AS A egoists, despite much evidence that they POLITICAL PROJECT don’t. Critics of neoliberalism tend to assume that increasingly people do act like this, but Neoliberalism has become a key object of they think that they ought not to. For critics, analysis in human geography in the last this is what’s wrong with neoliberalism. And decade. Although the words ‘neoliberal’ and it is precisely this evaluation that suggests ‘neoliberalism’ have been around for a long that there is something wrong with how while, it is only since the end of the 1990s neoliberalism is theorized in critical human that they have taken on the aura of grand geography. theoretical terms. Neoliberalism emerges as In critical human geography, neoliberal- an object of conceptual and empirical reflec- ism refers in the first instance to a family tion in the process of restoring to view a of ideas associated with the revival of sense of political agency to processes previ- economic liberalism in the mid-twentieth ously dubbed globalization (Hay, 2002). century. This is taken to include the school of This chapter examines the way in which Austrian economics associated with Ludwig neoliberalism is conceptualized in human von Mises, Friedrich von Hayek and Joseph geography. It argues that, in theorizing Schumpeter, characterized by a strong com- neoliberalism as ‘a political project’, critical mitment to methodological individualism, an human geographers have ended up repro- antipathy towards centralized state planning, ducing the same problem that they ascribe commitment to principles of private property, to the ideas they take to be driving forces and a distinctive anti-rationalist epistemo- behind contemporary transformations: they logy; and the so-called Chicago School of reduce the social to a residual effect of more economists, also associated with Hayek but fundamental political-economic rationalities.
    [Show full text]
  • Decision Theory
    Decision Theory Philipp Koehn 9 April 2019 Philipp Koehn Artificial Intelligence: Decision Theory 9 April 2019 Outline 1 ● Rational preferences ● Utilities ● Multiattribute utilities ● Decision networks ● Value of information ● Sequential decision problems ● Value iteration ● Policy iteration Philipp Koehn Artificial Intelligence: Decision Theory 9 April 2019 2 preferences Philipp Koehn Artificial Intelligence: Decision Theory 9 April 2019 Preferences 3 ● An agent chooses among prizes( A, B, etc.) ● Notation: A ≻ BA preferred to B A ∼ B indifference between A and B ≻ A ∼ BB not preferred to A ● Lottery L = [p; A; (1 − p);B], i.e., situations with uncertain prizes Philipp Koehn Artificial Intelligence: Decision Theory 9 April 2019 Rational Preferences 4 ● Idea: preferences of a rational agent must obey constraints ● Rational preferences Ô⇒ behavior describable as maximization of expected utility ● Constraints: Orderability (A ≻ B) ∨ (B ≻ A) ∨ (A ∼ B) Transitivity (A ≻ B) ∧ (B ≻ C) Ô⇒ (A ≻ C) Continuity A ≻ B ≻ C Ô⇒ ∃ p [p; A; 1 − p; C] ∼ B Substitutability A ∼ B Ô⇒ [p; A; 1 − p; C] ∼ [p; B; 1 − p; C] Monotonicity ≻ A ≻ B Ô⇒ (p ≥ q ⇔ [p; A; 1 − p; B] ∼ [q; A; 1 − q; B]) Philipp Koehn Artificial Intelligence: Decision Theory 9 April 2019 Rational Preferences 5 ● Violating the constraints leads to self-evident irrationality ● For example: an agent with intransitive preferences can be induced to give away all its money ● If B ≻ C, then an agent who has C would pay (say) 1 cent to get B ● If A ≻ B, then an agent who has B would pay (say) 1 cent
    [Show full text]