About the Editors - Home - Springer978-1-4419-9… ·  · 2017-08-28About the Editors Ahti Salo is ... operations management, logistics and supply chain management, ... Bert held

About the Editors

Ahti Salo is professor of systems analysis at the Systems Analysis Laboratory of theAalto University School of Science. He obtained his M.Sc. and Dr.Tech. degreesat the Helsinki University of Technology in 1987 and 1992, respectively. He hasworked as a senior researcher at the Technical Research Centre of Finland (VTT)and Nokia Research Center, and as a visiting professor at the London BusinessSchool, Universite Paris-Dauphine and the University of Vienna. His researchinterests include portfolio decision analysis, risk management, efficiency analysis,and technology foresight. He has written well over 100 publications, includingpapers in leading journals such as Management Science, Operations Research, andEuropean Journal of Operational Research, and is currently an associate editorof Decision Analysis. He has directed numerous high-impact decision supportprocesses such as the national foresight project FinnSight 2015. In 2010, he becamethe President of the Finnish Operations Research Society, served on the jury ofthe EURO Doctoral Dissertation Award, and began his work as the European andMiddle East Representative on the INFORMS International Activities Committee.

A. Salo et al. (eds.), Portfolio Decision Analysis: Improved Methods for ResourceAllocation, International Series in Operations Research & Management Science 162,DOI 10.1007/978-1-4419-9943-6, © Springer Science+Business Media, LLC 2011

383

384 About the Editors

Jeffrey Keisler is associate professor of Management Science and InformationSystems at the University of Massachusetts Boston. He earned a Ph.D. in decisionsciences and an S.M. in engineering sciences at Harvard as well as an MBA at theUniversity of Chicago and a B.S. from the University of Wisconsin. He has over50 assorted publications including articles in journals such as Decision Analysis,Interfaces, and Risk Analysis. His research interests include decision analysisapplications, portfolio decision analysis, decision processes, value of information,and other decision analytic methods. He was previously a decision analyst atGeneral Motors, Argonne National Laboratory and Strategic Decisions Group. Heis editor of Decision Analysis Today and an associate editor for Interfaces. He isPresident of the Specialty Group on Decision Analysis and Risk within the Societyfor Risk Analysis. Most recently, he became Vice-President/President-Elect of theINFORMS Decision Analysis Society.

Dr. Alec Morton is lecturer in Operational Research in the Department of Man-agement at the London School of Economics and Political Science. He teachescourses in decision analysis, simulation, and statistics, and his research interestsare in the application of decision analysis to planning problems, especially in

About the Editors 385

healthcare; in Multi-criteria Decision Analysis and Multi-objective Optimization;in the normative foundations of health economics, and in games of attack anddefence. He is a graduate of the Universities of Manchester and Strathclyde, andbefore joining the LSE, worked at Singapore Airlines and the National Universityof Singapore.

About the Authors

Mara Airoldi is a Research Officer and PhD candidate in the Department ofManagement at the London School of Economics. Her research interests includeboth methodological and application issues in the area of resource allocation, witha focus on health care policies. Her methodological interests revolve around thenormative foundation of resource allocation, which in the case of health care includethe definition of objectives such as maximizing the health of a definite populationand reducing health inequalities. Mara is particularly interested in bridging the gapbetween normative approaches and practice. To bridge this gap, her applied workaims at developing decision-analytic tools which employ normative principles, buildon evidence and can be used in practice. She has a degree in economics fromBocconi University in Milan where she specialized in Economics of Public Choiceand an M.Sc. in decision science from the LSE.

387A. Salo et al. (eds.), Portfolio Decision Analysis: Improved Methods for ResourceAllocation, International Series in Operations Research & Management Science 162,DOI 10.1007/978-1-4419-9943-6, © Springer Science+Business Media, LLC 2011

388 About the Authors

Gonzalo Arevalo is part-time lecturer of statistics and operations research at the ReyJuan Carlos University, and advisor on International RTD and Innovation fundingmechanisms for the Spanish Ministry of Science and Innovation, based at the CarlosIII Health Institute. Previously, he was Technology Transfer Officer at URJC. He hasparticipated in more than 20 regional, national, and international projects in relationwith RTD and innovation. He is also evaluator on RTD and Innovation Policies forthe Spanish Ministry of Science and Innovation, the Belgian Federal Science PolicyOffice, and the European Commission. Currently, he is working on his Ph.D. thesison decision analysis methods to support innovation, and studying a Master Degreeon Direction & Management of RTD and Innovation. He has recently cofoundedSKITES to apply the methodology proposed in his contribution to this volume.

Dr. Nikolaos Argyris is an LSE Fellow in Operational Research in the Departmentof Management at the London School of Economics and Political Science, wherehe teaches courses in Optimization Modelling. His research interests includeMulti-Objective Optimization, Interactive Multi-Criteria Decision Making, UtilityTheory, and Efficiency Analysis. He has been involved in applications interfacingOperational Research methods for efficiency analysis of schools in England as wellresource allocation in the English National Health Service. Nikolaos holds a Ph.D.in Operational Research from the LSE.

About the Authors 389

Dr. Roger Chapman Burk is an associate professor in the Department of SystemsEngineering at the United States Military Academy at West Point. He teachescourses in operations research, decision analysis, and systems engineering. Hisresearch interests include R&D portfolio optimization and modelling of space andunmanned aircraft systems. He retired from the Air Force in 1995 and workedin industry for 5 years before coming to West Point. He has an M.S. in SpaceOperations from the Air Force Institute of Technology and a Ph.D. in OperationsResearch from the University of North Carolina at Chapel Hill.

Zeger Degraeve first studied at the Universities of Ghent and Leuven in Belgiumand then gained his PhD from the Graduate School of Business at the University ofChicago. He was Professor of Management Science in the Department of AppliedEconomics at the Katholieke Universiteit Leuven in Belgium, before joiningLondon Business School in 1999. Since March 2002 he has served as AssociateDean of the Executive MBA and Executive MBA-Global Programmes, and he hasalso served as Deputy Dean (Programmes) and a member of the School’s Manage-ment Committee. He established London Business School’s new centre in Dubai

390 About the Authors

and in July 2008, he was appointed the inaugural Sheikh Mohammed bin Rashid AlMaktoum Professor of Innovation at London Business School. Degraeve providesconsultancy support across a range of issues including: operations management,logistics and supply chain management, environmental planning and purchasingstrategy development. Recent consultancy clients include the European Com-mission, Andersen Business Consulting, National Economic Research Associates(NERA) and McKinsey and Co. Degraeve’s research has won several awards,including: the Association of the European Operational Research Societies’ (EURO)Prize for Best Applied Paper and the Chairman’s Award for the Best AppliedContributed Paper by INFORMS (The Institute For Operations Research andManagement Sciences). He has published over 50 articles on decision making,optimization, scenario analysis and risk assessment for leading journals, including:Management Science, Operations Research, INFORMS Journal on Computing,European Journal of Operational Research, Interfaces and Harvard BusinessReview.

Bert De Reyck is professor and chair of the Department of Management Science andInnovation at University College London (UCL). He is also an adjunct professor atLondon Business School. Before joining UCL, Bert held positions at the KelloggSchool of Management at Northwestern University and the Rotterdam School ofManagement. Bert’s research and teaching activities focus on strategic decisionmaking, project management, project portfolio management, and risk management.Among others, Bert has worked with Pfizer, Merck, Roche, Novartis, Sanofi-Aventis, Shire, Unilever, PwC, Dunlop Aerospace, Eaton Aerospace, EuroControl,Lloyds, the New York Transit Authority, and Regal Petroleum.

About the Authors 391

Dr. Barbara Fasolo is tenured lecturer in decision sciences, in the Department ofManagement, at the London School of Economics and an expert in behaviouraldecision science. An economist by training (B.Sc. Bocconi University, Milan), shethen took an M.Sc. in decision sciences from the LSE and a Ph.D. in psychologyfrom the University of Colorado at Boulder. Prior to joining the LSE, she workedfor 2 years as a post-doctoral researcher at the Adaptive Behaviour and CognitionGroup of the Max Planck Institute of Human Development in Berlin, Germany. Dr.Fasolo’s research focuses on decision behaviour and implications for policy making,managerial practice, and personal development.

Jose Rui Figueira is full professor at Nancy’s School of Mines, Nancy, Franceand researcher at LORIA laboratory and associate member at CEG-IST, Centerfor Management Studies of Instituto Superior Tecnico. Professor Figueira taughtand did research also at University of Evora, University of Coimbra, and TechnicalUniversity of Lisbon. He obtained his Ph.D. in 1996 in the field of OperationsResearch from University of Paris-Dauphine and his HDR at the same University

392 About the Authors

in 2009 in the field of Multiple Criteria in Operations Research and DecisionAiding. Professor Figueira’s current research interests are in decision analysis,integer programming, network flows, and multiple criteria decision aiding. Hecurrently serves as editor of the Newsletter of the European Working Group onMultiple Criteria Decision Aiding and one of the coordinators of this group. He iscurrently the book editor section of the European Journal of Operational Researchand associate editor of Journal of Multi-Criteria Decision Analysis and of theInternational Journal of Multi-Criteria Decision Making. He was recently electedas a member of the Executive Committee of the International Society for MCDM.

Dr. L. Alberto Franco is associate professor of Operational Research and Systemsat Warwick Business School, United Kingdom. He has a background in civilengineering, and holds an M.Sc. in Operational Research from Lancaster Universityand a Ph.D. in Operational Research from the London School of Economics andPolitical Science. At Warwick he lectures on managing complexity and decisionmaking at postgraduate level, and his main research interests focus on understandingthe role and impact of facilitated dialogue and analysis on the effectiveness ofmanagement teams. He has extensive practical experience in the use of problemstructuring and decision analysis approaches to support strategic issue management,value-focused thinking, and inter-organisational collaboration in the private, public,and non-for profit sectors.

About the Authors 393

Johannes Gettinger is a research assistant at the Vienna University of Technology,Austria. He holds a master’s degree in international business administration fromhis studies at the University of Vienna and the University of Bologna. His currentresearch is funded by the Austrian Science Fund (FWF). The reserach focus is onconflict resolution, in particular electronically mediated negotiation, negotiation aswell as decision support systems, and post-settlement negotiations.

Janne Gustafsson, Dr.Sc. (Tech.), is a Portfolio Manager at Ilmarinen, a Finnishpension fund, focusing on hedge fund style investments. Prior to joining Ilmarinen,Dr. Gustafsson worked 6 years at Cheyne Capital Management, a London-basedhedge fund, which he joined from Monte Paschi Asset Management in Milan.Dr. Gustafsson has studied at Helsinki University of Technology in Finland, now apart of Aalto University, and at the London Business School. His research interestsinclude valuation of private investment opportunities, asset management, portfolioselection models, and decision making under risk and uncertainty. Some key partsof Dr. Gustafsson’s research have been published in Operations Research, a leadingacademic journal.

394 About the Authors

Elmar Kiesling is a junior researcher and lecturer at the University of Vienna, Aus-tria. He holds a master’s degree in international business administration and pursuesa Ph.D. in management at the University of Vienna. His research interests includemulti-criteria decision support systems, agent-based modelling, and informationsecurity management.

Don N. Kleinmuntz, Ph.D., is cofounder at Strata Decision Technology LLC (SDT).He has been named a Fellow of the Institute for Operations Research and theManagement Sciences (INFORMS, 2007) and of the Association for PsychologicalScience (1997). Don is an active member of INFORMS, and served as President in2009. Dr. Kleinmuntz was a founding editor-in-chief of Decision Analysis (2001–2006) and an associate editor of Management Science (1995–2001).

About the Authors 395

Jack Kloeber is a retired US Army Lieutenant Colonel with a Ph.D. in decisionanalysis and years of experience in R&D portfolio management, decision analysis,modelling and simulation, technology selection, and strategy development. Jacktaught mathematics at West Point for 3 years and graduate level decision analysis,systems simulation, and technology selection for 6 years at the Air Force Instituteof Technology. His work has supported decisions for various superfund sites withinthe Department of Energy, technology organizations within the Department ofDefense, and many global pharmaceutical developers and manufacturers. Jack ledthe Portfolio Management group for Bristol-Myers Squibb in the early 2000sand, more recently led the Portfolio Management group for J&J PharmaceuticalServices – coordinating the portfolio management efforts across multiple R&Dand marketing operating companies. He received his Ph.D. in economic decisionanalysis from Georgia Institute of Technology and Masters in Industrial Engineeringfrom Lehigh University. Jack is currently a senior partner at Kromite LLC.

Dr. Juuso Liesio is a teaching research scientist in the Systems Analysis Laboratoryat Aalto University. His research interests include portfolio decision analysis, robustresource allocation models and decision-support systems. In these fields, he has

396 About the Authors

developed novel modelling approaches, algorithms and computer software, andcollaborated with several companies and public organizations in applying portfoliodecision support models in practice. He teaches courses in decision analysis andoptimization.

Dr. Gilberto Montibeller is tenured lecturer in management sciences in theDepartment of Management, at the London School of Economics. With a first degreein electrical engineering (UFSC, Brazil, 1993), he started his career as an executiveat British and American Tobacco. Moving back to the academia, he was awardeda masters degree (UFSC, 1996) and a Ph.D. in production engineering (UFSC,2000). He then continued his studies as a post-doctoral fellow in managementscience at the University of Strathclyde. Dr Montibeller is area editor of the Journalof Multi-Criteria Decision Analysis. His research interests encompass strategicdecision making, decision making under multiple objectives and uncertainty, andstructuring of decision analytic models. He was awarded the Wiley Prize in AppliedDecision Analysis, for his research on combining scenario planning and multi-criteria decision analysis. An expert on decision analysis, Dr. Montibeller hasextensive experience in applying it during the past 15 years, consulting to bothprivate and public organisations in Europe and South America.

About the Authors 397

Vincent Mousseau obtained a Ph.D. in computer science/operations research at theUniversity of Paris-Dauphine in 1993, and went on to complete his HDR in 2003.He has served as an assistant professor at the University of Paris-Dauphine from1994 to 2008, and he has since served as Professor at Ecole Centrale Paris in theIndustrial Engineering laboratory. Within the Industrial Engineering laboratory, heheads the research team “Decision Aid for Production/Distribution.” His researchis in the field of operations research and decision-making aids. More specifically,his research focuses on preference modelling and elicitation. He is very activein this research community and, for instance, organized in 2010 an internationaldoctoral school in multiple criteria decision Aid (http://www.gi.ecp.fr/mcda-ss).Professor Mousseau has published many articles in international journals such asEuropean Journal of Operational Research, Computers and OR, Annals of OR,Decision Support Systems, Naval Research Logistics, and Information Sciences. Heis involved in the Decision Deck project (http://www.decisiondeck.org) as presidentof the consortium.

398 About the Authors

Dr. Gregory S. Parnell is a professor of systems engineering at the United StatesMilitary Academy at West Point. He teaches decision analysis, systems engineering,and operations research. Dr. Parnell is also Chairman of the Board and a seniorprincipal with Innovative Decisions Inc., a leading decision analysis consulting firm.His research and consulting involve strategic planning, research and development(R&D) portfolio analysis, and resource allocation decision making. He has served asPresident of the Decision Analysis Society of Institute for Operations Research andManagement Science and President of the Military Operations Research Society.Dr. Parnell received his Ph.D. from Stanford University.

Larry Phillips is a visiting professor at the London School of Economics andPolitical Science, the Director of the Decision Capability Group in the LSE’sDepartment of Management and Director of Facilitations Ltd. He specializes inhelping decision makers to analyse complex issues involving uncertainty, risk, andmultiple, conflicting objectives and often works with groups of key players usinga problem-solving process called decision conferencing. He previously worked atBrunel University and has a Ph.D. in psychology from the University of Michigan,where his adviser was Ward Edwards, and a B.Sc. in engineering from Cornell.

About the Authors 399

In 2005, he was awarded the Ramsey Medal of the Decision Analysis Society ofINFORMS, in recognition of his distinguished contributions to the field of decisionanalysis.

David Rıos Insua is a member of the Royal Academy of Sciences, Spain. Previously,he has researched and/or taught at Manchester University, Leeds University, DukeUniversity, Purdue University, the University of Paris-Dauphine, CNR-IMATI(Italy), SAMSI (USA), and IIASA (Austria). He has published 15 books and90 refereed papers in his areas of interest which include decision analysis, riskanalysis, Bayesian inference and group decision making, and their applications towater management, energy planning, security and ICT. He currently leads the RiskAnalysis research program of the region of Madrid and formerly led the NationalScience Foundation program on Risk Analysis, Extreme Events, and DecisionTheory at SAMSI. He is scientific advisor of AIsoy Robotics which developsdecision theoretic-based emotional robots and recently cofounded SKITES to applythe methodology proposed in his contribution to this volume.

400 About the Authors

Julie Stal-Le Cardinal is an associate professor of the Ecole Centrale Paris.Her teaching activities concern the domains of innovation, project management,leadership and personal development, and design methodology. In parallel to theseactivities, Julie Stal-Le Cardinal exercises an activity of council in project manage-ment. Her research activities and national and international scientific collaborationsconcern knowledge and decisions in design, actors’ choices, and collaborative de-cisions in projects as well as organization and company modeling with applicationsin healthcare.

Jeff Stonebraker is currently an assistant professor at North Carolina State Uni-versity’s Poole College of Management with research and teaching interests indecision analysis, commercial forecasting/modelling, and hemophilia. His researchis published in Operations Research, Interfaces, IEEE Transactions on EngineeringManagement, and Haemophilia. Prior to this academic post, Jeff was a decisionanalyst in the biotechnology and pharmaceutical industries at Bayer BiologicalProducts and GlaxoSmithKline and a consultant with Applied Decision Analysis.Jeff is a retired US Air Force Major with two tours of duty at the Air Force Academyteaching decision analysis, operations research, and mathematics. Jeff received the

About the Authors 401

Ph.D. degree in management science from Arizona State University, the M.S. degreein engineering-economic systems from Stanford University, and the B.S. degree inelectrical engineering from the University of South Florida.

Dr. Christian Stummer holds the chair of innovation and technology management atBielefeld University in Germany. His research interests comprise (1) multiobjectiveportfolio analysis, the provision of proper (interactive) decision support systemsas well as their application to various decision problems ranging from R&Dmanagement to IT (security) management or healthcare management and (2) agent-based simulations of innovation diffusion processes. Before his current appointmenthe served as an associate professor at the University of Vienna, as the head of aresearch group at the Electronic Commerce Competence Center in Vienna and as avisiting professor at the University of Texas at San Antonio.

M.Sc. Antti Toppila is a Doctoral Student in the Systems Analysis Laboratory atthe Aalto University School of Science. His research interests are in analysingvalue of information in portfolio selection problems, modelling of uncertainties withincomplete information, and applications of efficiency analysis in risk managementand portfolio selection problems. He obtained his M.Sc. degree from the HelsinkiUniversity of Technology in 2009, and he is currently working on his DoctoralDissertation.

402 About the Authors

Rudolf Vetschera is a professor of organization and planning at the School ofBusiness, Economics and Statistics, University of Vienna, Austria. He holds aPh.D. in economics and social sciences from the University of Vienna, Austria.Before his current position, he was full professor of Business Administration at theUniversity of Konstanz, Germany. He has published three books and more than 80papers in reviewed journals and collective volumes. His main research area is in theintersection of organization, decision theory, and information systems, in particularnegotiations, decisions under incomplete information, and the impact of informationtechnology on decision making and organizations.

Detlof von Winterfeldt is the director of the International Institute for AppliedSystems Analysis (IIASA) in Laxenburg, Austria. He is on leave from the Universityof Southern California (USC), where he is a Professor of Industrial and SystemsEngineering and a Professor of Public Policy and Management. Concurrentlywith his term at IIASA, he is visiting the London School of Economics andPolitical Science as a Centennial Professor in the Management Science Groupof the Department of Management. For the past 30 years, he has been active inteaching, research, university administration, and consulting. He has taught coursesin statistics, decision analysis, risk analysis, systems analysis, research design,and behavioural decision research. His research interests are in the foundation

About the Authors 403

and practice of decision and risk analysis as applied to the areas of technologydevelopment, environmental risks, natural hazards, and terrorism. In 2000, hereceived the Ramsey Medal for distinguished contributions to decision analysis fromthe Decision Analysis Society of INFORMS. In 2009, he received the Gold Medalfrom the International Society for Multi-criteria Decision Making for advancing thefield.

Jun Zheng is currently a Ph.D. Candidate under the supervision of Professor VincentMousseau in Laboratoire Genie Industriel (LGI) at Ecole Centrale Paris (ECP),France. Her research interests mainly focus on preference elicitation and preferencemodelling in multiple criteria decision aid. The objective of her Ph.D. thesis isto implement elicitation tools in order to carry out the multiple criteria decisionaid process, to improve the solution of a large class of problems in operationsmanagement. She is financially supported by the State Scholarship Fund of ChinaScholarship Council.

Index

AAbsolute deviation (AV), 82, 95Absolute number of reversals, 198Across criteria weight, 365, 370–372Across criteria weights, 59Acute vs. preventive interventions, 377–378Admissible portfolios, 189, 193, 198, 203–206Adversaries, 334–335, 345AHP. See Analytic hierarchy processAlternatives, 30, 32, 34–37, 39, 42–43, 47, 48Analysis of variance (ANOVA), 137, 139–144Analytical process, 132, 133, 135, 136, 139,

144, 145Analytic hierarchy process (AHP), 10, 338Anchoring argument, 156–158, 162ANOVA. See Analysis of varianceArbitration, 177, 179–180Aspiration level, 206AV. See Absolute deviation

BBalance, 36, 39, 139, 145, 146Balance constraints, 214, 222, 231, 232BBP. See Breakeven buying priceBCG matrix, 10Behavioral/Behavioural research, 22, 150, 155Benchmarks, 136Benefit-cost curve, 61, 373Biases, 149, 150, 152–163Borderline project, 244Bounding, 34, 35, 39Breakeven buying price (BBP), 80, 84, 85, 87,

88, 103Breakeven selling price (BSP), 80, 84–88,

102–104

Budget, 34, 35, 37, 39, 42, 43, 47Budget constraint, 82, 83, 93–94, 99, 151, 163,

243

CCAGR. See Combined average growth rateCannibalization effects, 243Capital asset pricing model (CAPM), 81–83,

96, 102Capital budgeting, 7, 8, 20, 243, 256Category limits, 225, 226Causal maps, 264CCA. See Contingent claims analysisCI. See Core indexClass solution, 65–69, 71Clinical proof of concept (CPC), 295, 308, 329Coefficient of variation (CV), 136, 137,

139–144Cognitive factors, 150Collaborative decision analysis, 168, 171, 177,

182Combined average growth rate (CAGR), 304,

319, 320Commercialization phase, 244Commercial potential, 132–145Commercial scenario, 136–145Compensatory strategy, 192Concave piecewise linear additive form, 117Concordance, 225, 226Consensus-oriented approach, 244Consistency check, 58Constraint logic programming, 178Constructive view of preference, 108Contingent claims analysis (CCA), 86, 87, 89,

101, 102

405A. Salo et al. (eds.), Portfolio Decision Analysis: Improved Methods for ResourceAllocation, International Series in Operations Research & Management Science 162,DOI 10.1007/978-1-4419-9943-6, © Springer Science+Business Media, LLC 2011

406 Index

Contingent portfolio programming (CPP), 81,90, 101

Convergence, 188, 203, 204, 206, 207Core index (CI), 250Core project, 244Cost-benefit analysis, 11Cost of analysis, 31, 42, 46–48Cost reduction, 296CPC. See Clinical proof of conceptCPP. See Contingent portfolio programmingCriterion function, 109, 110, 115Criterion weight, 224, 225, 227Critical project, 268Culture, 42CV. See Coefficient of variation

DDAAG. See Decision analysis affinity

groupDALY. See Disability-adjusted life yearData envelopment analysis (DEA),

338, 340Decision aid, 9Decisional conflict, 192, 193, 197, 199, 201,

202, 206Decision analysis, 3–24Decision analysis affinity group (DAAG), 12Decision conference, 9, 11, 17, 18, 55, 59–61,

71–75, 347Decision confidence, 191, 193Decision consistency constraint, 93, 94, 100Decision explorer, 264, 269Decision hierarchy, 34Decision maker (DM), 214, 216, 218–223,

226–229, 231–234Decision-making style, 193, 194, 202Decision portfolio, 29–50Decision quality, 29–50, 132, 133Decision tree, 9, 11, 13, 339, 345, 346, 353,

355Demonstrable benefits argument, 156, 159–162Dialog decision process, 347, 354Disability-adjusted life year (DALY), 361, 379Disability-free life expectancy, 375Discounted cash flow analysis, 79Discovery-driven planning, 175Disease modelling, 375Dissensus-oriented approach, 244DM. See Decision makerDrug development, 132–145Drug development stages, 133–140,

142–145

EEDC. See Expected development costEDR. See Expected downside riskEfficient frontier, 315EFQM. See European Foundation Quality

ManagementEIRR. See Expected internal rate of returnElectre Tri, 214, 216, 223–228, 232, 233, 239Empirical data, 132, 146ENPV. See Expected net present valueEnterprise information, 133, 136–140Equalisation argument, 156–157, 162Equity (software), 58European Foundation Quality Management

(EFQM), 170EUT. See Expected utility theoryExpected development cost (EDC), 295, 300,

316Expected downside risk (EDR), 96Expected internal rate of return (EIRR), 295,

306, 308–310Expected net present value (ENPV), 10,

295–302, 308, 310, 316, 323Expected sales, 246, 248–253Expected utility theory (EUT), 82, 86Expected value maximization, 82, 83Exterior project, 244

FFacilitated modeling, 264Facilitated PDA, 276Failure, 38Financial portfolio optimization, 6–7First-of-class solution, 67, 71Flaw of averages, 145Formal framework, 150–152Frame, 30–35, 48, 49Fundamental objectives, 366Funding level, 243, 248, 251, 252

GGDS. See Group decision supportGeneralized cost effectiveness analysis

(GCEA), 361–363, 374, 378Go/No-Go decision, 292Good death, the, 378Graph theory, 338Group constraints, 214, 228–229, 232Group decision support (GDS), 168–171, 183Group Explorer, 264, 269Group level criteria, 220

Index 407

HHaircut, 277Health economics, 361, 363, 376Health economists, 360, 361Health inequalities, 359, 367, 368, 372,

375–376Heatmap, 188–195, 199, 202–207Historical data, 133, 135, 136Horizon, 54–56, 69, 71, 73Humanitarian vs. realist perspective, 360

IImplementation, 30, 32, 36, 38, 39Imprecise specification of preferences, 216Incomplete information, 17, 21–22Incrementalism, 261–263Indifference threshold, 223Influence diagram, 9, 339Information, 29, 30, 32, 34, 36–43, 46, 48Information asymmetry, 244Innovation management, 167–183, 244Innovation management platform, 169–170Institutional drivers, 146Interacting objectives, 37Interaction mechanism, 188, 196Interaction variable, 253Interactive heatmap, 188, 189, 192Interactive parallel coordinate plot, 188, 189,

192Interactive visualization, 187–207Interim progress review (IPR), 346Internal rate of return (IRR), 175Inverse optimization problem, 85Isopreference line, 111, 112

JJustifiability, 156–160

KKnapsack problem, 109, 178, 268, 270, 271,

279

LLevels of decision quality, 38–39Life expectancy, 375Linear additive model, 111, 112Local government, 259–279Local government budgetary process,

260Logical synthesis, 37–39

Long range goals, 303–304Lower semi-absolute deviation (LSAD), 95,

96, 100

MMajority level, 224, 226, 227Managerial flexibility, 79, 81, 89, 101Marginal analysis, 7, 18Marginal value, 151Market environment, 137, 138MAUT. See Multi-attribute utility theoryMAVT. See Multi-attribute value theoryMCDA. See Multicriteria decision analysisMean objectives, 366Mean-risk preference models, 82, 83Military applications, 333–355Minimum internal rate of return, (MIRR), 295Minimum requirement argument, 156,

158–159, 161, 162MODA. See Multiobjective decision analysisMomentum portfolio, 30Monte Carlo simulation, 251, 254Motivational factors, 150Multi-attribute utility theory (MAUT), 9, 10,

353Multi-attribute value theory (MAVT), 9, 10Multicriteria decision analysis (MCDA), 54,

55, 57, 58, 71–74Multicriteria portfolio decision analysis, 361,

363–367, 378Multicriteria sorting, 213–239Multiobjective decision analysis (MODA),

313, 325Multiobjective programme, 109Multiperiod project valuation, 90–101Multiple objective value analysis, 337–339

NNatural monopoly, 246NDA. See New drug applicationNegotiation, 177, 179, 180, 182, 183Net present value (NPV), 80, 135, 137, 138,

141–144, 174, 175, 177, 291, 295, 296,298, 299, 301, 306, 308, 309, 319, 323,331

Network markets, 247New drug application (NDA), 286–287New Public Management, 262Nominal group technique, 172Non-compensatory strategy, 191, 192Nondiscordance, 225, 226Nondominated portfolio, 244NPV. See Net present value

408 Index

OOpportunity selling price (OSP), 88, 89Optimization, 29, 33, 37, 39, 40, 49Options pricing, 79, 80Organizational learning, 23Organizational objectives, 335OSP. See Opportunity selling priceOutranking approaches, 9Outranking methods, 216

PParallel coordinate plot, 188, 189, 192, 193,

206Pareto analysis, 338, 340Pareto frontier, 189Partition dependence, 152–154, 161Payback period, 175PDA. See Portfolio decision analysis, 3–24PDQ. See Portfolio decision quality, 290, 291Peak year revenue, 300Perceived accuracy, 193, 197, 199, 201, 206Perceived decisional conflict, 197Perceived ease of use, 194, 197, 199Perceived effort, 190, 193, 197, 199, 202Perceived usefulness, 190, 193, 197, 199, 201,

206Performance objectives, 135Pessimistic assignment rule, 223Petrochemicals, 334–336Pharmaceutical industry, 134Pharmaceuticals, 293, 294, 328, 334–336PI. See Productivity indexPOC. See Proof of conceptPoint estimate, 145Portfolio balance, 301–302, 317–319, 330Portfolio decision analysis (PDA), 3–24Portfolio decision quality (PDQ), 290, 291Portfolio decision review, 336Portfolio information, 139, 140Portfolio management, 131–147, 285–288,

292, 294–295, 299, 303, 307, 308, 317,321, 325, 327–330

Portfolio tradeoff, 315Postsettlement, 180Potentially optimal portfolio, 250, 251Preference disaggregation, 115, 117Preference elicitation, 108, 206Preference intensity, 150Preference programming, 243, 256Preference threshold, 223Primary Care Trust, 360, 364–366, 369,

375–379Prioritisation triangle, 63

Priority index, 57Probability of technical and regulatory success

(PTRS), 296, 297, 300, 327Problem complexity, 193, 194, 196, 202, 203,

205, 206Problem structuring methodologies, 215Productivity index (PI), 295, 298, 300, 306,

308, 310Product portfolio management, 12Product profile, 137, 138Program budgeting, 7, 18Program budgeting and marginal

analysis(PBMA), 362, 363, 374,378

Project dependencies, 344Project filtering, 174–176Project interactions, 8, 245, 354Project level information, 132, 133, 135–137Project selection, 7–8, 10, 34, 176–181Project valuation, 79, 81, 90–101Proof of concept (POC), 295, 308, 323, 325,

329Psychometric constructs, 198PTRS. See Probability of technical and

regulatory successPublic health, 360, 362, 364, 365, 374, 375Public value, 260, 273–275, 278, 279

QQuality-adjusted life year (QALY), 360, 361,

368Qualitative value model development, 349–351

RRank reversal, 10, 21Real options, 81, 89, 101Required capacity, 286Research and development (R&D), 241–256,

294–299Research and development (R&D) investment,

132–134Resource allocation, 149–153, 156–161, 163,

259–279Resource constraints, 109, 110Resource function, 110, 115Risk constraint, 80, 84, 90, 93–96, 100Risk measures, 7Risk reduction, 295, 329Risk-return matrix, 11Robust portfolio modeling (RPM), 244Royal Navy, 53–75R–W–W method, 175

Index 409

SScope insensitivity, 152, 154–155, 161Scoping, 35, 39SD. See Standard deviationSDG. See Strategic decisions groupSDG grid, 37Semivariance (SV), 95, 96Sequential additivity, 87–88, 101Sequential consistency, 86SEUT. See Subjective expected utility theorySharing knowledge and information towards

economic success (SKITES), 168,171–183

Small and medium-sized enterprise (SME),170, 183

Sociotechnical, 56, 72, 264Staffing problem, 213–215, 232–233Stage-gate process, 292–294, 329Stakeholder involvement, 22Stakeholders, 263, 264, 271–273, 276, 278Standard deviation (SD), 136, 137, 139–144Standardization, 241–256State tree, 90, 91, 96, 97Statistical analysis, 141–144Statistical decision theory, 8Status quo bias, 152, 155Strategic buckets, 34Strategic choice, 264Strategic decisions group (SDG), 10, 12Strategic fit, 8Strategic partnership, 336Student classification, 214Student selection, 213–239Subjective expected utility theory (SEUT), 8Subjective probability, 8Suboptimisation, 152–153, 160Substitute technologies, 243, 246Sump, 56, 59, 62, 65, 72SV. See SemivarianceSwing weighting, 344, 351–353Synergy, 45–48Synergy effects, 243System complexity, 336

TTA. See Therapeutic areaTAM. See Technology acceptance modelTarget product profile (TPP), 291, 292Task complexity, 193Technical success (probability of), 132, 133,

135–137Technology acceptance model (TAM), 194,

197Technology management, 247

Therapeutic area (TA), 133, 135–142, 144,146, 287, 302–303, 308–310, 314, 318,326, 327

Threshold, 41, 42Timing, 34, 38Tornado diagram, 9TPP. See Target product profileTriage, 42TRIZ, 173Type 45, 53–75

UUncertainty, 132, 134, 135, 145, 146Unmet medical need (UMN), 301, 315Unrelated future costs, 376–377User acceptance, 190UTA, 117Utility function, 8, 11

VValue added by analysis, 41, 42Value/cost ratio, 268, 276Value-focused thinking, 35, 173, 337, 338,

340, 342, 348, 354, 366Value for money triangle, 372, 373Value function, 57, 71Value of information, 29, 30, 48Value scales, 370V -efficient portfolios, 110V -efficient supported portfolios, 110, 124Veto evaluation, 224–226Visual complexity, 192, 193V -nondominated points, 110V -nondominated supported points, 110Von Neumann-Morgenstern utility function,

151Voting, 168–170, 173, 177, 180, 182, 183

WWACC. See Weighted average cost of capitalWealth level, 82, 90, 93, 95, 100Weapon systems, 334Web 2.0, 168, 169, 182Weighted average cost of capital (WACC), 295Widely adopted technology, 247, 248, 250Within criteria weight, 365, 371, 372Within criterion weights, 59, 65Work breakdown structure, 214World Health Organization, 361, 374

ZZionts-Wallenius procedure, 114