Portfolio Decision Analysis Improved Methods for Resource Allocation
Winner of the 2013 Decision Analysis Publication Award
Portfolio Decision Analysis: Improved Methods for Resource Allocation provides an extensive, up-to-date coverage of decision analytic methods which help firms and public organizations allocate resour
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Series Editor Frederick S. Hillier Stanford University, CA, USA Special Editorial Consultant Camille C. Price Stephen F. Austin State University, TX, USA
For further volumes: http://www.springer.com/series/6161
Ahti Salo Jeffrey Keisler Alec Morton Editors
Portfolio Decision Analysis Improved Methods for Resource Allocation
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Editors Ahti Salo Aalto University School of Science Systems Analysis Laboratory PO Box 11100 00076 Aalto Finland [email protected]
Jeffrey Keisler Department of Management Science and Information Systems University of Massachusetts, Boston Morrissey Boulevard 100 Boston, MA 02125 USA [email protected]
Alec Morton Department of Management London School of Economics Houghton Street WC2A 2AE London United Kingdom [email protected]
ISSN 0884-8289 ISBN 978-1-4419-9942-9 e-ISBN 978-1-4419-9943-6 DOI 10.1007/978-1-4419-9943-6 Springer New York Dordrecht Heidelberg London Library of Congress Control Number: 2011932226 © Springer Science+Business Media, LLC 2011 All rights reserved. This work may not be translated or copied in whole or in part without the written permission of the publisher (Springer Science+Business Media, LLC, 233 Spring Street, New York, NY 10013, USA), except for brief excerpts in connection with reviews or scholarly analysis. Use in connection with any form of information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed is forbidden. The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights. Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com)
Foreword
Resource allocation problems are ubiquitous in business and government organizations. Stated quite simply, we typically have more good ideas for projects and programs than funds, capacity, or time to pursue them. These projects and programs require significant initial investments in the present, with the anticipation of future benefits. This necessitates balancing the promised return on investment against the risk that the benefits do not materialize. An added complication is that organizations often have complex and poorly articulated objectives and lack a consistent methodology for determining how well alternative investments measure up against those objectives. The field of decision analysis (or DA) has recognized the ubiquity of resource allocation problems for three or four decades. The uncertainty of future benefits clearly calls for application of standard approaches for modelling uncertainties – such as decision trees, influence diagrams, and Monte Carlo simulation, and for using utility functions to model decision makers’ risk preference. The problem of many objectives is an opportunity to apply multi-attribute utility or value models – or similar techniques – to decompose a difficult multidim
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