DOCSLIB.ORG

Econophysics: from Game Theory and Information Theory to Quantum Mechanics

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Econophysics: from Game Theory and Information Theory to Quantum Mechanics ARTICLE IN PRESS Physica A 348 (2005) 505–543 www.elsevier.com/locate/physa Econophysics: from Game Theory and Information Theory to Quantum Mechanics Edward Jimeneza,b,c,Ã, Douglas Moyad aExperimental Economics, Todo1 Services Inc., Miami, FL 33126, USA bGATE, UMR 5824 CNRS, France cResearch and Development Department, Petroecuador, Paul Rivet E11-134 y 6 de Diciembre, Quito, Ecuador dPhysics Department, Escuela Politecnica Nacional, Ladro´n de Guevara E11-253, Quito, Ecuador Received 11 March 2004; received in revised form 20 September 2004 Available online 27 October 2004 Abstract Rationality is the universal invariant among human behavior, universe physical laws and ordered and complex biological systems. Econophysics isboth the use of physical concepts in Finance and Economics, and the use of Information Economics in Physics. In special, we will show that it is possible to obtain the Quantum Mechanics principles using Information and Game Theory. r 2004 Elsevier B.V. All rights reserved. PACS: 02.30.Cj; 02.50.Le; 03.65.Ta Keywords: Rationality; Optimal laws; Quantum mechanic laws; Energy; Information 1. Introduction At the moment, Information Theory is studied or utilized by multiple perspectives (Economics, Game Theory, Physics, Mathematics, and Computer Science). Our goal ÃCorresponding author. Research and Development Department, Petroecuador, Todo1 Services, Ecuador Experimental Economics, Miami, USA. E-mail address: [email protected] (E. Jimenez). 0378-4371/$ - see front matter r 2004 Elsevier B.V. All rights reserved. doi:10.1016/j.physa.2004.09.029 ARTICLE IN PRESS 506 E. Jimenez, D. Moya / Physica A 348 (2005) 505–543 in this paper is to present the different focuses about information and to select the main ones, to apply them in Economics of Information and Game Theory. Economics and Game Theory1 are interested in the use of information and order state, in order to maximize the utility functions of the rational2 and intelligent3 players, which are part of an interest conflict. The players gather and process information. The information can be perfect4 and complete5 see Refs. [1–4], (Sorin, 1992). On the other hand, Mathematics, Physics and Computer Science are all interested in information representation, entropy (disorder measurement), optim- ality of physical laws and in the living beings’ internal order see Refs. [5–7]. Finally, information is stored, transmitted and processed by physical means. Thus, the concept of information and computation can be formulated not only in the context of Economics, Game Theory and Mathematics, but also in the context of physical theory. Therefore, the study of information ultimately requires experimentation and some multidisciplinary approaches such as the introduction of the Optimality Concept [8]. The Optimality Concept is the essence of the economic and natural sciences [9,10]. Economics introduces the optimality concept (maximum utility and minimum risk) as equivalent of rationality and Physics understands action minimum principle, and maximum entropy (maximum information) as the explanation of nature laws [11,36]. If the two sciences have a common backbone, then they should allow certain analogies and to share other elements such us: equilibrium conditions, evolution, uncertainty measurement and the entropy concept. In this paper, the contributions of Physics (Quantum Information Theory)6 and Mathematics (Classical Information Theory)7 are used in Game Theory and Economics being able to explain mixed 1Game Theory can be defined as the study of mathematical models of conflict and cooperation among intelligent rational decision-makers. 2A decision-maker is rational if he makes decision in pursuit of his own objectives. It is normal to assume that each player’s objective is to maximize the expected utility value of his own payoff. 3A player in the game is intelligent if he knows everything that we know about the game and he can make inferences about the situation that we can make. 4Perfect information means that at each time only one of the players moves, that the game depends only on their choices, they remember the past information (utilities, strategies), and in principle they know all possible futures of the game [1]. 5In games of incomplete information the state of the nature is fixed but not know to all players. In repeated games of incomplete information, the changes in time is each player’s knowledge about the other players’ past actions, which affects his beliefs about the (fixed) state of nature. Games of incomplete information are usually classified according to the nature of the three important elements of the model, namely players and payoffs (within the two-person games: zero-sum games and non-zero-sum games), prior information on the state of the nature (within two-person games: incomplete information on one side and incomplete information on two sides), and signaling structure (full monitoring and state independent signals) [2,3,37]. 6Quantum Information Theory is fundamentally richer than Classical Information Theory, because quantum mechanics includes so many more elementary classes of static and dynamic resources—not only does it support all the familiar classical types, but there are entirely new classes such as the static resource of entanglement (correlated equilibria) to make life even more interesting that it is classically. 7Classical Information Theory is mostly concerned with the problems of sending Classical Information- letters in an alphabet, speech, strings of bits—over communications channels which operate within the laws of classical physics. ARTICLE IN PRESS E. Jimenez, D. Moya / Physica A 348 (2005) 505–543 507 strategy Nash’s equilibrium using Shannon’s entropy [12–15]. According to [16, p. 11] P ‘‘quantities of the form H ¼À pi log pi play a central role in information theory as measures of information, choice and uncertainty. The form of H will be recognized as that entropy as defined in certain formulations of statistical mechanics where pi is the probability of a system being in cell i of its phase space; ...’’ In Quantum Information Theory, the correlated equilibria in two-player games means that the associated probabilities of each-player strategies are functions of a correlation matrix. Entanglement, according to the Austrian physicist Erwin Schro¨ dinger, which is the essence of Quantum Mechanics, has been known for long time now to be the source of a number of paradoxical and counterintuitive phenomena. Of those, the most remarkable one is the usually called non-locality which is at the heart of the Einstein–Podolsky–Rosen paradox (ERP) see Ref. [17, p. 12]. Einstein et al. [18] which consider a quantum system consisting of two particles separated long distance. ‘‘ERP suggests that measurement on particle 1 cannot have any actual influence on particle 2(locality condition); thus the property of particle 2 must be independent of the measurement performed on particle 1.’’ The experiments verified that two particles in the ERP case are always part of one quantum system and thus measurement on one particle changes the possible predictions that can be made for the whole system and therefore for the other particle [5]. It is evident that physical and mathematical theories have had a lot of utility for the economic sciences, but it is necessary to highlight that Information Theory and Economics also contribute to the explanation of Quantum Mechanics laws. Will the strict incorporation of Classic and Quantum Information Theory elements allow the development of Economics and Game Theory? The definitive answer is yes. Economics has carried out its own developments around information theory; especially it has demonstrated both that the asymmetry of information causes errors in the definition of a optimal negotiation and that the assumption of perfect markets is untenable in the presence of asymmetric information see Refs. [19,20]. The asymmetry of the information according to the formalism of Game Theory can have two causes: incomplete information and imperfect information. As we will see in the development of this paper, Information Economics does not even incorporate in a formal way neither elements of Classical Information Theory nor Quantum Information concepts. The creators of Information Theory are Shannon and von Newmann see Ref. [15, Chapter 11]. Shannon the creator of Classical Information Theory introduces the entropy as the heart of your theory, endowing it of a probabilistic characteristics. On (footnote continued) ‘‘The key concept of Classical Information Theory is the Shannon Entropy Theory. Suppose we learn the value of a random variable X. The Shannon Entropy of X quantifies how much information we gain, on average, when we learn the value of X: An alternative view is that the entropy of X measures the amount of uncertainty about X before we learn its value. These two views are complementary; we can view the entropy either as a measure of our uncertainty before we learn the value of X; or as measure of how much information we have gained after we learn the value of X: ‘‘[15, p. 500]. ARTICLE IN PRESS 508 E. Jimenez, D. Moya / Physica A 348 (2005) 505–543 the other hand, von Newmann also creator of Game Theory, uses the probabilistic elements take into account by Shannon but defines a new mathematical formulation of entropy using the density matrix of Quantum Mechanics. Both entropy formulations developed by Shannon and von Newmann, respectively, permit us to model pure states (strategies) and mixed states (mixed strategies). In Eisert et al. [21,35], they not only give a physical model of quantum strategies but also express the idea of identifying moves using quantum operations and quantum properties. This approach appears to be fruitful in at least two ways. On one hand, several recently proposed quantum information application theories can already be conceived as competitive situations, where several factors which have opposing motives interact. These parts may apply quantum operations using a bipartite quantum system. On the other hand, generalizing decision theory in the domain of quantum probabilities seems interesting, as the roots of game theory are partly rooted in probability theory.
Load more

Recommended publications
  • When Does Behavioural Economics Really Matter?
    When does behavioural economics really matter? Ian McAuley, University of Canberra and Centre for Policy Development (www.cpd.org.au) Paper to accompany presentation to Behavioural Economics stream at Australian Economic Forum, August 2010. Summary Behavioural economics integrates the formal study of psychology, including social psychology, into economics. Its empirical base helps policy makers in understanding how economic actors behave in response to incentives in market transactions and in response to policy interventions. This paper commences with a short description of how behavioural economics fits into the general discipline of economics. The next section outlines the development of behavioural economics, including its development from considerations of individual psychology into the fields of neurology, social psychology and anthropology. It covers developments in general terms; there are excellent and by now well-known detailed descriptions of the specific findings of behavioural economics. The final section examines seven contemporary public policy issues with suggestions on how behavioural economics may help develop sound policy. In some cases Australian policy advisers are already using the findings of behavioural economics to advantage. It matters most of the time In public policy there is nothing novel about behavioural economics, but for a long time it has tended to be ignored in formal texts. Like Molière’s Monsieur Jourdain who was surprised to find he had been speaking prose all his life, economists have long been guided by implicit knowledge of behavioural economics, particularly in macroeconomics. Keynes, for example, understood perfectly the “money illusion” – people’s tendency to think of money in nominal rather than real terms – in his solution to unemployment.
    [Show full text]
  • The Process of Inflation Expectations' Formation
    The Process of Inflation Expectations’ * Formation Anna Loleyt** Ilya Gurov*** Bank of Russia July 2010 Abstract The aim of the investigation is to classify and systematize groups of economic agents with different types of inflation expectations in information economy. Particularly it’s found out that it is not feasible to exclude the possibility of current signals perception by economic agents. The analysis has also shown that there is an uncertainty in economy when authorities redeem monetary policy promises, but their action wouldn’t influence on average inflation expectations of economic agents. The investigation results testify the flat existence of agents in economy which are characterizing with rational, quasi-adaptive (including adaptive) and also arbitral inflation expectations. Keywords: information economy, information signal, information perception, agent belief in information, inflation expectations, quasi-adaptive expectations, arbitral expectations. *Acknowledgments: The first authors gratefully acknowledge support through the Bank of Russia. Especially we would like to thank Mr. Alexey V. Ulyukaev for helpful research assistance and Mrs. Nadezhda Yu. Ivanova for strong support. We are also grateful to Mr. Sergey S. Studnikov for his comments. **General Economic Department, Bank of Russia, 12 Neglinnaya Street, Moscow, 107016 Russia Faculty of Economics, Lomonosov Moscow State University, Moscow, Russia Email: [email protected] ***General Economic Department, Bank of Russia, 12 Neglinnaya Street, Moscow, 107016 Russia Faculty of Economics, Lomonosov Moscow State University, Moscow, Russia Email: [email protected] Abbreviations: a variety of information signals. W an element of a variety of information signals that is an information signal. w economic agents. x q a number of signals.
    [Show full text]
  • 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]
  • The Chilean Experience with Price-Cap Regulation [With Comments] Author(S): Rafael Di Tella, Alexander Dyck, Alexander Galetovic and William W
    Cost Reductions, Cost Padding, and Stock Market Prices: The Chilean Experience with Price-Cap Regulation [with Comments] Author(s): Rafael Di Tella, Alexander Dyck, Alexander Galetovic and William W. Hogan Source: Economía, Vol. 8, No. 2 (Spring, 2008), pp. 155-196 Published by: Brookings Institution Press Stable URL: https://www.jstor.org/stable/20065527 Accessed: 22-11-2019 16:55 UTC JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact [email protected]. Your use of the JSTOR archive indicates your acceptance of the Terms & Conditions of Use, available at https://about.jstor.org/terms Brookings Institution Press is collaborating with JSTOR to digitize, preserve and extend access to Economía This content downloaded from 206.253.207.235 on Fri, 22 Nov 2019 16:55:23 UTC All use subject to https://about.jstor.org/terms RAFAEL DI TELLA ALEXANDER DYCK Cost Reductions, Cost Padding, and Stock Market Prices: The Chilean Experience with Price-Cap Regulation Every four years, you feel you are going to war. Alejandro Jadresic, former Minister of Energy of Chile The flaws of traditional rate-of-return regulation are well known. They include a lack of incentives to reduce costs, as well as a tendency for firms to choose the wrong mix of inputs and to misreport costs in order to inflate the revenues allowed by the regulator.
    [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]
  • 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.
    [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]
  • Behavioral Law & Economics and Consumer Financ
    Consumer Financial Protection Bureau Behavioral Economics Symposium Panel 2: Behavioral Law & Economics and Consumer Financial Protection The Role of Behavioral Economics in Consumer Protection Policy: Reflections of a Consumer Economist Janis K. Pappalardo, Ph.D.1 Assistant Director, Division of Consumer Protection Bureau of Economics, Federal Trade Commission Washington, DC September 19, 2019 1 The views expressed are those of the author and may not reflect the views of the Federal Trade Commission or any individual Commissioner. This statement draws from my prior work. I thank Jason Chen and Scott Syms for research assistance and many colleagues who have contributed to my understanding of this topic over the years, but I am responsible for any errors. Background on my Perspective Let me tell you a bit about my experience to shed light on my perspective. I joined the Federal Trade Commission’s Bureau of Economics, Division of Consumer Protection in 1986 immediately after obtaining a Ph.D. from Cornell University with a major field in consumer economics and minor fields in industrial organization and statistics. As a staff economist, I analyzed consumer protection legal and policy matters related to unfair or deceptive practices, provided expert declarations for litigation, and conducted research on information regulation. I have published work in the American Economic Review: Papers & Proceedings, Journal of Consumer Affairs, Antitrust Law Journal, Review of Industrial Organization, and Journal of Public Policy and Marketing, from which I received two outstanding article awards. I serve on the editorial review boards of the Journal of Consumer Affairs and the Journal of Public Policy and Marketing, and I am co-editing a symposium on the economics of consumer protection for Economic Inquiry.
    [Show full text]
  • Recent Applications of Economic Theory in Information Technology Research *
    Deciskm Support Systems 8 (1992) 365-386 365 North-Holland Recent applications of economic theory in Information Technology research * J. Yannis Bakos 1. Introduction Unit'ersity ~ff California, lrt'ine, CA, USA Recently there has been heightened interest Chris F. Kemerer on the part of both academicians and practition- ers concerning the economics of Information Massachusetts Institute ~ Technology., Cambridge, MA, USA Technology (IT), where IT is defined as systems for providing context-sensitive data [9]. In part, Academicians and practitioners are becoming increasingly interested in the economics of Information Technology (IT). this interest stems from the increasingly large In part, this interest stems from the increased role that IT role that IT plays in the strategic thinking of most now plays in the strategic thinking of most large organizations, large organizations, and in the significant dollar and from the significant dollar costs expended by these orga- costs now expended by organizations on IT. For nizations on IT. Naturally enough, researchers are turning to example, Kriebel notes that roughly 50% of new economics as a reference discipline in their attempt to answer questions concerning both the value added by IT and the true capital investment by major US corporations is in cost of providing IT resources. This increased interest in the IT [92]. As organizations have improved their economics of IT is manifested in the application of a number of aspects of economic theory in recent information systems J. Yannis Bakos is an assistant pro- research, leading to results that have appeared in a wide fessor of management at the Gradu- variety of publication outlets This article reviews this work ate School of Management, Univer- and provides a systematic categorization as a first step in sity of California, Irvine.
    [Show full text]
  • Economics Chair: Marian Manic Sai Mamunuru Halefom Belay Rosie Mueller Jan P
    Economics Chair: Marian Manic Sai Mamunuru Halefom Belay Rosie Mueller Jan P. Crouter Jason Ralston Denise Hazlett Economics is the study of how people and societies choose to use scarce resources in the production of goods and services, and of the distribution of these goods and services among individuals and groups in society. The economics major requires coursework in economics and mathematics. A student who enters Whitman with no prior college-level work in either of these areas would need to complete math 125 and complete at least 35 credits in economics. Learning Goals: Upon graduation, a student will be able to demonstrate: Major-Specific Areas of Knowledge o Students should have an understanding of how economics can be used to explain and interpret a) the behavior of agents (for example, firms and households) and the markets or settings in which they interact, and b) the structure and performance of national and global economies. Students should also be able to evaluate the structure, internal consistency and logic of economic models and the role of assumptions in economic arguments. Communication o Students should be able to communicate effectively in written, spoken, graphical, and quantitative form about specific economic issues. Critical Reasoning o Students should be able to apply economic analysis to evaluate everyday problems and policy proposals and to assess the assumptions, reasoning and evidence contained in an economic argument. Quantitative Analysis o Students should grasp the mathematical logic of standard macroeconomic and microeconomic models. o Students should know how to use empirical evidence to evaluate an economic argument (including the collection of relevant data for empirical analysis, statistical analysis, and interpretation of the results of the analysis) and how to understand empirical analyses of others.
    [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]