Portfolio Decision Analysis Research Articles

Selection of biomass and bioenergy applications from rice production waste: an integrated method of a circular bioeconomy-based fuzzy inference system and portfolio decision analysis

Selection of biomass and bioenergy applications from rice production waste: an integrated method of a circular bioeconomy-based fuzzy inference system and portfolio decision analysis

Portfolio decision analysis for pandemic sentiment assessment based on finance and web queries

AbstractCOVID-19 has spread worldwide, affecting people’s health and the socio-economic environment. Such a pandemic is responsible for people’s deteriorated mood, pessimism, and lack of trust in the future. This paper presents a portfolio decision analysis framework for policymakers aiming at recovering the population from psychological distress. Specifically, we explore the relative relevance of a country to the overall “mood of the world” in light of pursuing predefined targets through optimization criteria. Toward this aim, we design a statistical indicator for measuring the mood by considering the financial markets’ outcomes and the people’s online searches about COVID-19. Then, we adapt existing portfolio selection models to evaluate the role of an extensive collection of countries and stock markets based on different criteria. More precisely, such criteria are established assuming “rational” goals of a policymaker, namely to aspire to a general and stable optimism and avoid waves of opposite moods or excess pessimism. Empirical experiments validate the theoretical proposal. The employed dataset contains 39 countries selected on the basis of data reliability and relevance in the context of COVID-19. Data on daily Google Trends searches of the term “coronavirus” (and its translations) and closing prices of relevant domestic stock indexes are considered for 2020 to develop the statistical mood indicator. Results offer different insights based on the selected optimization criteria. The practical implications of the proposed models have been illustrated through arguments based on a National Recovery and Resilience Plan-type normative framework.

Modeling criteria and project interactions in portfolio decision analysis with the Choquet integral

We present a general framework to deal with multicriteria portfolio decision analysis problems in which between-projects independence or within-project independence do not necessarily hold. The Choquet integral preference model is a non-additive integral widely used in multicriteria decision analysis to take into account the possible interactions between criteria. In this case, we apply the Choquet integral, on the one hand, to deal with the interactions between criteria used to evaluate the projects and, on the other hand, to take into account the interactions between projects in the portfolio global evaluation. To reduce the number of parameters necessary to define the considered preference model and keep the problem tractable, we use the 2-additive Choquet integral that assigns a value to each entity and to each pair of entities only. An example shows how to apply our proposal to a multicriteria portfolio decision analysis problem.

Modeling project interactions in multiattribute portfolio decision analysis: Axiomatic foundations and practical implications

A common approach to modeling project interactions in multiattribute project portfolio selection is to augment the additive portfolio utility function, in which portfolio utility is the sum of the included projects’ utilities, with additional terms representing the synergy/cannibalization effects triggered by selecting specific subsets of projects. In this paper we develop a set of sufficient and necessary assumptions for representing preferences among multiattribute project portfolios with a quasi-symmetric multilinear utility function and show how this function gives rise to interpreting interaction effects as additional terms in the additive portfolio utility function. To foster practical applicability of these theoretical contributions, we also develop techniques to elicit such portfolio utility functions as well as optimization models to identify the feasible portfolio that satisfies relevant resource and other constraints with the maximal expected utility. In recognition that incorporating project interactions necessitates increased involvement of decision makers in assessing the interaction effects and results in computationally more challenging portfolio optimization problems, we analyze the importance of modeling interactions through series of simulation studies based on randomly generated and real-world data sets. Specifically, we examine the impact that omitting project interactions has on the project-level decision recommendations and on the expected utility of the recommended portfolio.

A portfolio decision analysis approach for selecting a subset of interdependent actions: The case of a regional climate roadmap in Finland

In this paper, we present a structured approach based on portfolio decision analysis to support the consideration of interdependencies between actions (i.e. interactions) in the selection of an efficient portfolio. One of the main challenges in modelling interactions is that the possible number of them between the pairs of actions increases exponentially with the number of actions. In environmental management, the problems can include tens of possible actions potentially leading to hundreds of pairwise interactions between them. For example, a strategy for mitigating climate change can consist of various actions in different sectors for improving technology, reducing emissions and the sequestration of carbon. Our approach aims to reduce the burden of assessing interactions by initially selecting a shortlist of actions based on specific heuristics and focusing on modelling interactions exclusively within this chosen set of actions. Another feature of the approach is the use of holistic evaluation of interactions to further reduce the cognitive load of stakeholders making the assessment. As a possible disadvantage, these features may increase the imprecision related to the results of the model. To analyse the impacts of this imprecision, we propose a way to carry out sensitivity analysis on the basis of how intensively the interactions would be taken into account in the modelling. The applicability of the approach was tested in a case related to the roadmap to a carbon neutral North Savo region in Finland by the year 2035. The approach helped to better understand synergies and trade-offs when putting the actions of the roadmap into practice, which is expected to lead to better results in terms of preparedness and adaptation to climate change.

A structured methodology for essential medicines lists and health emergency stockpiles: Experience with the Emergency Medicines Buffer Stock in the United Kingdom

IntroductionFormularies of essential medicines, such as Essential Medicines Lists (EMLs) and health emergency stockpiles, are intended to be always available, including in emergency situations, acting as important tools for access to medicines. The Emergency Medicines Buffer Stock (EMBS) in the United Kingdom (UK) was a stockpile of critical medicines managed by the UK Department of Health and Social Care (DHSC). We propose a new methodology for selecting and including medicines in EMLs and health emergency stockpiles and empirically apply it for selecting medicines in the case of the UK EMBS. MethodsWe used Multi-Attribute Value Theory and Portfolio Decision Analysis to develop a three-phase methodological framework for medicines selection, involving: (i) the decision context definition and selection of evaluation criteria, (ii) the therapeutic area prioritisation, and (iii) the medicines value-for-money evaluation and product selection. The EMBS application took place in 2018–2019 and focused on therapeutic area prioritisation, involving primary data collection through expert interviews (n = 4), a workshop with DHSC decision-makers (n = 13), and an online survey with National Clinical Directors and relevant experts (n = 24). A Monte Carlo simulation supported therapeutic area prioritisation using the British National Formulary (BNF) classification. FindingsTwo criteria sets were selected for i) therapeutic area prioritisation, reflecting the value concerns of population need and shortage severity, and ii) medicines evaluation, reflecting magnitude of clinical benefit and supply vulnerability, among others.Primary evidence was collected for “national need” and “shortage severity”, based on which a “population health loss” index was developed. A total of 51 therapeutic areas were ranked using their index value while assessing the robustness of the ranking. The top ranked therapeutic area was antisecretory drugs and mucosal protectants, closely followed by diabetes drugs. ConclusionsThe methodological application generated a ranking of therapeutic areas based on expected “population health loss” index, while addressing evidence uncertainty. The methodology can be adapted for other EMLs and emergency stockpile contexts to inform medicines selection.

A ranking approach for robust portfolio decision analysis based on multilinear portfolio utility functions and incomplete preference information

Uncertainties are often present in typical portfolio decision analysis (PDA), and it is prudent to conduct a robust PDA to identify project portfolios that perform reasonably well across the entire range of uncertain parameter values. Previous research on robust PDA and applications has almost entirely relied on additive portfolio preference functions, with little research on robustness under multilinear portfolio utility functions (PUFs). A ranking approach for robust PDA is proposed based on multilinear PUFs, incomplete preference information, and the algorithm for accelerating stochastic multicriteria acceptability analysis (SMAA). First, the initial set of portfolios is screened through various constraints to produce a smaller set of desirable portfolios. Second, several types of incomplete preference information are thought to be elicited with regard to multilinear PUFs, including preference information on coefficients, portfolios, and uncertainties on preference elicitation techniques. Third, the SMAA decomposition algorithm is implemented as a ranking approach to perform robust PDA on the basis of multilinear PUFs, incomplete preference information, and potentially numerous portfolios in the set of desirable portfolios. Finally, the approach is applied to a case to validate its effectiveness, and comparisons among additive, multiplicative, and multilinear PUFs in terms of decision recommendations are performed.

From the Editor: Belated Recognition for the 2021 Clemen–Kleinmuntz Decision Analysis Best Paper Award Winner and Finalist

Free AccessAboutSectionsView PDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareShare onFacebookTwitterLinked InEmail Go to SectionFree Access HomeDecision AnalysisAhead of Print From the Editor: Belated Recognition for the 2021 Clemen–Kleinmuntz Decision Analysis Best Paper Award Winner and FinalistVicki M. BierVicki M. BierPublished Online:9 May 2023https://doi.org/10.1287/deca.2023.0477The winning paper for 2021 was “Friction and Decision Rules in Portfolio Decision Analysis,” by Gary J. Summers (Summers 2021). To put this paper in context, the practice of decision analysis typically involves identifying performance objectives and managerial preferences, quantifying uncertainties, and then using them as inputs to assess alternatives. An exhaustive theory-driven literature in decision analysis provides foundations for this practice, assuming that objectives, preferences, and uncertainties are well understood and available to quantify. The award-winning paper by Summers highlights that this quantification may not be accurate in practice, leading to “friction” in decision analysis models. This friction can systematically lead to a loss in decision quality. The article provides examples from multiple domains, and new analysis to illustrate this friction. As such, the article underscores the importance of calibrating the inputs to decision analysis models. It promises to be a springboard for future research on understanding the causes of friction in decision analysis models, its implications, and mitigation methods.I congratulate Gary J. Summers on this excellent paper and am especially glad that Decision Analysis was able to help a full-time practitioner bring forth important and insightful ideas in a manner that is compelling to both academics and practitioners.The finalist for 2021 was “Preference-Approval Structures in Group Decision Making: Axiomatic Distance and Aggregation,” by Yucheng Dong, Yao Li, Ying He, and Xia Chen (Dong et al. 2021). Although decision analysis commonly focuses on decision making by an individual or a group acting as an individual, this paper focuses on group decision making. It combines two popular approaches for aggregating individual preferences, ranked voting and approval voting, which have compensating strengths and weaknesses. Ranked voting leverages the preference ranking central to the decision analysis approach, but is subject to strategic manipulation (where individuals misrepresent their preferences); approval voting is immune to strategic manipulation, but does not provide the complete ranking required by decision analysis. The article shows that combining them leads to superior performance. Future empirical research and practice is likely to significantly benefit from the rigorous foundational treatment provided by this article.Congratulations to Dong et al. for this excellent and pragmatic contribution to the challenging topic of group decision making.Thanks also to committee members Saurabh Bansal (Pennsylvania State University) and Robert Bordley (University of Michigan) for cochairing the award committee for 2021, and for providing extensive input to the paper descriptions given above. My sincere apologies for this belated recognition to all.ReferencesDong Y, Li Y, He Y, Chen X (2021) Preference–approval structures in group decision making: Axiomatic distance and aggregation. Decision Anal. 18(4):273–295. https://doi.org/10.1287/deca.2021.0430.Link, Google ScholarSummers GJ (2021) Friction and decision rules in portfolio decision analysis. Decision Anal. 18(2):101–120. https://doi.org/10.1287/deca.2020.0421.Link, Google Scholar Previous Back to Top Next FiguresReferencesRelatedInformation Articles In Advance Article Information Metrics Information Published Online:May 09, 2023 Copyright © 2023, INFORMSCite asVicki M. Bier (2023) From the Editor: Belated Recognition for the 2021 Clemen–Kleinmuntz Decision Analysis Best Paper Award Winner and Finalist. Decision Analysis 0(0). https://doi.org/10.1287/deca.2023.0477 PDF download

Parallel and comparative use of three multicriteria decision support methods in an environmental portfolio problem

Environmental management problems are often portfolio problems where the task is to find a set of actions that meets different objectives (e.g., the reduction of greenhouse gas emissions) and constraints (e.g., costs). We report experiences from deploying multiple operations research (OR) methods in a real decision-making setting and discuss the insights gained from this process. The applied methods were Multi-Attribute Value Theory (MAVT), the project portfolio selection tool Your Own Decision Aid (YODA) and Robust Portfolio Modelling (RPM). The methods were applied in a portfolio case evaluating three peatland rewetting options (“No action”, “Restoration”, “Damming”) for 79 drained peatland stands in an important recreational and nature conservation area in southern Finland. The pros and cons of the methods were evaluated, as well as their key methodological challenges, in a participatory environmental portfolio case. The applied methods yielded similar results in terms of the superiority of rewetting options. The strength of MAVT was its ability to highlight the strengths and weaknesses of all three rewetting options for a single peatland stand. YODA's strength was its simplicity and the possibility to apply it independently via the Internet. RPM made it possible to determine the priority of peatland stands within constraints, even without precise preference information. To our knowledge, this is the first systematic evaluation of three methods representing different ‘method categories’ (MAVT, multi-criteria elimination, portfolio decision analysis) applied to a real environmental problem.

Incomplete risk-preference information in portfolio decision analysis

Portfolio decision analysis models support decisions on the allocation of resources among assets with uncertain outcomes (e.g., investments, projects or stocks). These models require information on the decision maker’s risk-preferences which can be difficult to obtain in practice. Stochastic dominance criteria show promise in this regard as they can compare portfolios without exact specification of risk-preferences, but the current literature lacks practical approaches for generating the efficient frontier, i.e., the set of those portfolios that are not stochastically dominated by any other portfolio. We address this gap by developing models to identify sets of portfolios that are efficient in the sense of second- or third-order stochastic dominance (SSD, TSD). These models provide novel insights into the composition of portfolios belonging to the efficient frontier by, e.g., identifying those assets that are included in all efficient portfolios. Moreover, the identification of the efficient frontier makes it possible to utilize additional information on the decision maker’s risk-preferences to further reduce the set of admissible portfolio alternatives, and to analyze the implications this information has on the amount of capital that should be allocated to each individual asset. We illustrate the usefulness of these models with applications in project portfolio selection and financial portfolio diversification.

Multicriteria decision support for project portfolio selection with the FITradeoff method

The current global scenario of a greater scarcity of resources, uncertainty in several dimensions and problems that seek to reconcile multiple criteria to achieve the best possible results are issues commonly related to problems of selecting a portfolio of projects. This paper presents a new proposal for using the flexible and interactive method, FITradeoff, for portfolio decision analysis in the presence of possible partial information about decision-makers’ preferences. The proposed approach, specifically, uses the concept of c-optimal portfolios and refinement strategies of feasibility and efficiency during the process of generating a portfolio while endeavoring to keep both computational and cognitive efforts within reasonable limits. This approach is built into a Decision Support System. After applying several tests, what can be concluded from the computational results in randomly generated instances is that this method exhibits good performance both in terms of minimizing the computational effort and reducing the cognitive effort demanded from the decision-maker.

Portfolio decision analysis with a generalized balance approach

We consider a resource allocation problem in which the decision maker is responsible for selecting a group of projects to generate a portfolio, with the aim of maximizing the total benefit, and distributing the budget in a balanced way across different categories. We assume that the decision maker defines an interval for the desired share of each category. We propose augmented ε-constraint method to generate the nondominated solutions considering two objective functions: total benefit and balance among categories. The solution time increases with the problem size, and augmented ε-constraint method fails to find all nondominated solutions. We therefore develop a variable neighborhood search algorithm for the purpose of generating the nondominated frontier. Our computational experiments show that the nondominated frontier can be accurately estimated with respect to different performance measures.

Multi-period portfolio decision analysis: A case study in the infrastructure management sector

This paper presents an approach to select and plan the optimal execution of potential investment activities. The model is composed by a computational part, in the form of a combinatorial optimization problem, coupled with a preference elicitation module used to capture subjective judgments. In particular, the structure of the elicitation module draws from portfolio decision analysis and Multi-Attribute Value Theory and shows how their use can be integrated with a multi-period optimization problem with activities durations and constraints on their overlaps. The problem formulation was inspired by a real-world infrastructure management case in the energy distribution sector and tested on a dataset of more than three hundred activities of improvement of infrastructure conditions. Finally, the approach proposed in this paper is validated by analyzing its results and its robustness concerning the input data of the real-world case study.

Multicriteria decisions and portfolio analysis: land acquisition for biological and social objectives.

Resource allocation for land acquisition is a common multiobjective problem that involves complex trade-offs. The National Wildlife Refuge System (NWRS) of the U.S. Fish and Wildlife Service currently uses the Targeted Resource Acquisition Comparison Tool (TRACT) to allocate funds from the Migratory Bird Conservation Fund (MBCF; established through the Migratory Bird Hunting and Conservation Act of 1934) for land acquisition based on cost-benefit analysis, regional priority rankings of candidate land parcels available for acquisition, and the overall biological contribution to duck population objectives. However, current policy encourages decision makers to consider societal and economic benefits of lands acquired, in addition to their biological benefits to waterfowl. These decisions about portfolio elements (i.e., individual land parcels) require an analysis of the difficult trade-offs among multiple objectives. In the last decade the application of multicriteria decision analysis (MCDA) methods has been instrumental in aiding decision makers with complex multiobjective decisions. In this study, we present an alternative approach to developing land-acquisition portfolios using MCDA and modern portfolio theory (MPT). We describe the development of a portfolio decision analysis tool using constrained optimization for land-acquisition decisions by the NWRS. We outline the decision framework, describe development of the prototype tool in Microsoft Excel, and test the results of the tool using land parcels submitted as candidates for MBCF funding in 2019. Our results indicate that the constrained optimization outperformed the traditional TRACT method and ad hoc portfolios developed using current NWRS criteria.

Friction and Decision Rules in Portfolio Decision Analysis

In portfolio decision analysis, features comprise the objectives, alternatives, physics, and information that define a decision context. By modeling features, decision analysts forecast the expected utilities of the alternatives. A model is complete if it contains all the features. A model is well-calibrated if it correctly predicts the probability distributions of each alternative’s utility, whereas ill-calibrated models, like those that suffer the optimizer’s curse, do not. Friction identifies qualities of a situation that prevent decision analysts from creating complete, well-calibrated models. When friction is significant, can maximizing expected utility be a suboptimal decision rule? Is satisfying decision theory’s axioms a necessary or sufficient condition for good decision making? Can rules that violate the axioms outperform rules that satisfy them? A simulation study of how unbiased, imprecise forecasts of payoffs affect project selection finds that, for the example tested, the answers are yes, no, and yes, which suggests that further studies of friction may be worthwhile. Discussions of friction bookend the study, starting the paper by defining friction and concluding by presenting three frameworks, each one from a different field of study, that provide mathematical tools for studying friction.

Prioritizing investments in rapid response vaccine technologies for emerging infections: A portfolio decision analysis

This study reports on the application of a Portfolio Decision Analysis (PDA) to support investment decisions of a non-profit funder of vaccine technology platform development for rapid response to emerging infections. A value framework was constructed via document reviews and stakeholder consultations. Probability of Success (PoS) data was obtained for 16 platform projects through expert assessments and stakeholder portfolio preferences via a Discrete Choice Experiment (DCE). The structure of preferences and the uncertainties in project PoS suggested a non-linear, stochastic value maximization problem. A simulation-optimization algorithm was employed, identifying optimal portfolios under different budget constraints. Stochastic dominance of the optimization solution was tested via mean-variance and mean-Gini statistics, and its robustness via rank probability analysis in a Monte Carlo simulation. Project PoS estimates were low and substantially overlapping. The DCE identified decreasing rates of return to investing in single platform types. Optimal portfolio solutions reflected this non-linearity of platform preferences along an efficiency frontier and diverged from a model simply ranking projects by PoS-to-Cost, despite significant revisions to project PoS estimates during the review process in relation to the conduct of the DCE. Large confidence intervals associated with optimization solutions suggested significant uncertainty in portfolio valuations. Mean-variance and Mean-Gini tests suggested optimal portfolios with higher expected values were also accompanied by higher risks of not achieving those values despite stochastic dominance of the optimal portfolio solution under the decision maker’s budget constraint. This portfolio was also the highest ranked portfolio in the simulation; though having only a 54% probability of being preferred to the second-ranked portfolio. The analysis illustrates how optimization modelling can help health R&D decision makers identify optimal portfolios in the face of significant decision uncertainty involving portfolio trade-offs. However, in light of such extreme uncertainty, further due diligence and ongoing updating of performance is needed on highly risky projects as well as data on decision makers’ portfolio risk attitude before PDA can conclude about optimal and robust solutions.

Nonadditive Multiattribute Utility Functions for Portfolio Decision Analysis

Axiomatic Foundations for Nonadditive Multiattribute Portfolio Utility Functions

Portfolio Decision Analysis for Evaluating Stakeholder Conflicts in Land Use Planning

Urban planning typically involves multiple actors and stakeholders with conflicting opinions and diverging preferences. The proposed development plans and actions greatly affect the quality of life of the local community at different spatial scales and time horizons. Consequently, it is important for decision-makers to understand and analyse the conflicting needs and priorities of the local community. This paper presents a decision analytic framework for evaluating stakeholder conflicts in urban planning. First, the stakeholders state their preferences regarding the actions in terms of a set of criteria and estimate the weight of each criterion. Then, a conflict index and overall value for each action is calculated. Next, a set of Pareto efficient portfolios of actions are generated by solving an optimization problem with different levels of conflict as a resource constraint. Finally, a sensitivity analysis of the actions is performed. The framework is demonstrated using real-world survey data collected in the municipality of Upplands Väsby, Sweden.

Robust portfolio decision analysis: An application to the energy research and development portfolio problem

Inspired by challenges in designing energy technology policy in the face of climate change, we address the problem of decision making under “deep uncertainty.” We introduce an approach we call Robust Portfolio Decision Analysis, building on Belief Dominance as a prescriptive operationalization of a concept that has appeared in the literature under a number of names. The Belief Dominance concept synthesizes multiple conflicting sources of information to uncover alternatives that are intelligent responses in the presence of many beliefs. We use this concept to determine the set of non-dominated portfolios and to identify corresponding robust individual alternatives, thereby uncovering viable alternatives that may not be revealed otherwise. Our approach is particularly appropriate with multiple stakeholders, as it helps identify common ground while leaving flexibility for negotiation. We develop a proof-of-concept application aimed at informing decisions over investments into clean energy technology R&D portfolios in the context of climate change and illustrate how Robust Portfolio Decision Analysis helps identify robust individual investments.

Portfolio decision analysis for risk-based maintenance of gas networks

Maintenance is fundamental for the safe and profitable operations of many critical assets, including gas networks. The inherent complexity of these assets and budgetary constraints pose significant challenges to the decision-making related to maintenance management, which requires trading-off among conflicting objectives while respecting technical and normative constraints. The difficulty of such decision-making stems from the incomplete knowledge of the technical parameters, operating conditions and degradation states of the components. Here, we address this challenge by proposing a risk-based maintenance framework for supporting decision makers in selecting maintenance plans that are optimal with respect to the objectives and constraints considered. We apply Robust Portfolio Modeling (RPM) to identify those maintenance decisions that are most effective in reducing the severity and likelihood of failures in the gas network. RPM allows us to handle partial knowledge on the objective values and on the preferences of decision-makers. We lay down the complete steps of the framework, including the quantification of the likelihood of failures and their consequences for the population and for the gas network operation. The framework is demonstrated on the high pressure natural gas pipeline network of Great Britain. The results reveal that if there are no spatial constraints on the budget allocation, maintenance actions are focused on some critical zones, e.g. Scotland and the southernmost part of England. Instead, if predefined maintenance budgets are divided among the different areas of the network, the selected maintenance projects are sub-optimal and the risk reduction achieved over the maintenance horizon is smaller.