Utility Decision Analysis Research Articles

Focusing on Important Problems

Research in Multiple Criteria Decision Making (MCDM) and its subfields (multi-attribute utility theory or Decision Analysis, AHP, Goal Programming, MCDA, multi-objective mathematical programming, EMO) is very active. However, our general feeling is that bright, young people do not focus their energies sufficiently on solving important problems despite many very important problems that deserve attention. Some of the problems are critical to the well-being of the world. The purpose of our essay is to identify these very important problems (or mega trends, as they are sometimes called) and briefly discuss how the research community could help solve or at least alleviate such problems. The reader could interpret our essay as suggestions for a research agenda or program. It does not matter whether we are AHP scholars, multi-objective optimizers, or decision analysts, everyone’s contribution is needed.

An introduction to decision science for conservation.

Biodiversity conservation decisions are difficult, especially when they involve differing values, complex multidimensional objectives, scarce resources, urgency, and considerable uncertainty. Decision science embodies a theory about how to make difficult decisions and an extensive array of frameworks and tools that make that theory practical. We sought to improve conceptual clarity and practical application of decision science to help decision makers apply decision science to conservation problems. We addressed barriers to the uptake of decision science, including a lack of training and awareness of decision science; confusion over common terminology and which tools and frameworks to apply; and the mistaken impression that applying decision science must be time consuming, expensive, and complex. To aid in navigating the extensive and disparate decision science literature, we clarify meaning of common terms: decision science, decision theory, decision analysis, structured decision‐making, and decision‐support tools. Applying decision science does not have to be complex or time consuming; rather, it begins with knowing how to think through the components of a decision utilizing decision analysis (i.e., define the problem, elicit objectives, develop alternatives, estimate consequences, and perform trade‐offs). This is best achieved by applying a rapid‐prototyping approach. At each step, decision‐support tools can provide additional insight and clarity, whereas decision‐support frameworks (e.g., priority threat management and systematic conservation planning) can aid navigation of multiple steps of a decision analysis for particular contexts. We summarize key decision‐support frameworks and tools and describe to which step of a decision analysis, and to which contexts, each is most useful to apply. Our introduction to decision science will aid in contextualizing current approaches and new developments, and help decision makers begin to apply decision science to conservation problems.

Information value-based optimization of structural and environmental monitoring for offshore wind turbines support structures

The use of load and structural performance measurement information is vital for efficient structural integrity management and for the cost of energy production with Offshore Wind Turbines (OWTs). OWTs are dynamically sensitive structures subject to an interaction with a control unit exposed to repeated cyclic wind and wave loads causing deterioration and fatigue. This study focuses on the quantification of the value of structural and environmental information on the integrity management of OWT structures, with the focus on fatigue of welded joints. By utilizing decision analysis, structural reliability methods, measurement data, as well as the cost-benefit models, a Value of Information (VoI) analysis can be performed to quantify the most beneficial measurement strategy. The VoI assessment is demonstrated for the integrity management of a butt welded joint of a monopile support structure for a 3 MW OWT with a hub height of approximately 71m. The conditional value of three-year measured oceanographic information and one-year strain monitoring information is quantified posteriori in conjunction with an inspection and repair planning. This paper provides insights on how much benefits can be achieved through structural and environmental information, with practical relevance on reliability-based maintenance of OWT structures.

Reinforcement Learning Aided Robot-Assisted Navigation: A Utility and RRT Two-Stage Approach

This work proposes a robot-assisted navigation approach based on user intent adjustment, in the context of robotic walkers. Walkers are prescribed to users with gait disorders so that they can support their body weight on the upper limbs, however, the manipulation of such devices can be cumbersome for some users. Common problems for the users are lack of dexterous upper limb control and visual impairments. These problems can render walkers’ users helpless, making them unable to operate these devices effectively and efficiently. We present a new approach to robot-assisted navigation using a utility decision and safety analysis procedure with user intent adjustments learned by reinforcement learning (RL) and supported on a rapidly-exploring random tree inspired algorithm. The proposed approach offers full control of the assistive platform to the user until obstacles are detected. In dangerous scenarios, corrections are computed in order that the assistive platform avoids collisions and follows social norms, effectively guiding the user through the environment while enforcing safer routes. The experimental validation was carried out in a virtual environment and in a real world scenario using a robotic walker built in our lab (ISR-AIWALKER). Experimental results have shown that the proposed approach provides a reliable solution to the robot-assisted navigation of a robotic walker, in particular the use of utility theory to evaluate candidate motions together with a RL model increases the safety of the user’s navigation.

Cost Benefit and Utility Decision Analysis of Turbinoplasty with Adenotonsillectomy for Pediatric Sleep-Disordered Breathing.

Use decision analysis techniques to assess the potential utility gains/losses and costs of adding bilateral inferior turbinoplasty to tonsillectomy/adenoidectomy (T/A) for the treatment of obstructive sleep-disordered breathing (oSDB) in children. Use sensitivity analysis to explore the key variables in the scenario. Cost-utility decision analysis model. Hypothetical cohort. Computer software (TreeAge Software, Williamstown, Massachusetts) was used to construct a decision analysis model. The model included the possibility of postoperative complications and persistent oSDB after surgery. Baseline clinical and quality-adjusted life year (QALY) parameters were estimated using published data. Cost data were estimated from Centers for Medicare and Medicaid 2018 databases ( www.cms.gov ). Sensitivity analyses were completed to assess for key model parameters. The utility analysis of the baseline model favored the addition of turbinoplasty (0.8890 vs 0.8875 overall utility) assuming turbinate hypertrophy was present. Sensitivity analysis indicated the treatment success increase (%) provided by concurrent turbinoplasty was the key parameter in the model. A treatment success increase of 3% of turbinoplasty was the threshold where concurrent turbinoplasty was favored over T/A alone. The incremental cost-effectiveness ratio (ICER) of $27,333/QALY for the baseline model was favorable to the willingness-to-pay threshold of $50,000 to $100,000/QALY for industrialized nations. The addition of turbinoplasty for children with turbinate hypertrophy to T/A for the treatment of pediatric oSDB is beneficial from both a utility and cost-benefit analysis standpoint even if the benefits of turbinoplasty are relatively modest.

Economic analysis of implementing virtual reality therapy for pain among hospitalized patients

Virtual reality (VR) has emerged as a novel and effective non-pharmacologic therapy for pain, and there is growing interest to use VR in the acute hospital setting. We sought to explore the cost and effectiveness thresholds VR therapy must meet to be cost-saving as an inpatient pain management program. The result is a framework for hospital administrators to evaluate the return on investment of implementing inpatient VR programs of varying effectiveness and cost. Utilizing decision analysis software, we compared adjuvant VR therapy for pain management vs. usual care among hospitalized patients. In the VR strategy, we analyzed potential cost-savings from reductions in opioid utilization and hospital length of stay (LOS), as well as increased reimbursements from higher patient satisfaction as measured by the Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) survey. The average overall hospitalization cost-savings per patient for the VR program vs. usual care was $5.39 (95% confidence interval –$11.00 to $156.17). In a probabilistic sensitivity analysis across 1000 hypothetical hospitals of varying size and staffing, VR remained cost-saving in 89.2% of trials. The VR program was cost-saving so long as it reduced LOS by ≥14.6%; the model was not sensitive to differences in opioid use or HCAHPS. We conclude that inpatient VR therapy may be cost-saving for a hospital system primarily if it reduces LOS. In isolation, cost-savings from reductions in opioid utilization and increased HCAHPS-related reimbursements are not sufficient to overcome the costs of VR.

Decision Evaluation of Response Strategies in Emergency Management Using Imprecise Assessments

This paper focuses on the decision evaluation of different response strategies in emergency management utilizing decision analysis with imprecise information. A method for the selection of response strategies in emergency management, as well as a model for the representation of catastrophic consequences, are proposed. In emergency management decision problems, the available estimates of probabilities, utilities, costs, and priority weights are often subject to large degrees of uncertainty and imprecision. When uncertainty prevails in the input data and large societal values are at stake, coping with this lack of precision becomes very important in decision making processes. The method employs representation of imprecision in probabilities, utilities, and weights on attributes in the form of interval statements and comparisons together with a formal, comprehensive, and comprehensible description of a catastrophic consequence facilitating the use of preferential statements between catastrophic consequences. The method proposed can be viewed as a more frugal decision analysis method, decreasing the efforts needed in elicitation of input statements which often is a cumbersome threshold for the use of decision analysis techniques. It is suggested as a complement to cost/benefit approaches and other approaches relying on inaccessible probabilistic data either when probability assessments regarding catastrophic events are too uncertain or when pure monetary scales are deemed inadequate.

Prototype Geographic Information System for Agricultural Water Quality Management Using CropSyst

A prototype raster geographic information system (GIS) coupled with a decision support submodel for agricultural nitrogen nonpoint source pollution analysis is presented herein. This analysis is an extension of the one-dimensional approach used to simulate and analyze farm production systems and their impact on the environment. The farm system was rasterized into an aggregation of spatial units with homogeneous physical and management characteristics. A crop model to simulate the farm and the environmental response to management alternatives was integrated with this prototype GIS. The system coordinates the running of the crop model on each homogeneous unit and results are passed to a maximum expected utility decision analysis submodel. Only the crop yield and chemical leaching are considered in the decision model. Based on the utility of these two parameters and the probability of realization, the management alternative that potentially leads to the highest yield and lowest nitrogen leaching will be reco...

TH‐C‐224C‐06: Using Quality of Life Information to Rationally Incorporate Normal Tissue Effects Into Treatment Plan Evaluation and Scoring

Purpose: Modern radiotherapy treatment planning systems provide an increasing number of competing plans. The decision to select a particular plan for treatment is generally made by a radiation oncologist based on training and clinical experience. The criteria applied are often poorly‐defined, qualitative, and largely based on tradition and familiarity. A “realistic” approach utilizing decision analysis tools to evaluate/rank treatment plans based on quality adjusted life years (QALY) expectancy was developed. Method and Materials: The decision analysis methods were applied to the concept of uncomplicated tumor control probability (UTCP). The expected outcome for an anticipated course of radiation was described as a series of probabilities: alive, free of disease without complication; alive with disease; alive with complication, etc. For each of these states of health, a utility can be assigned based on published work or empirical estimates. The total QALY' for a particular treatment plan represent the product of duration‐weighted states of health. The formalism for UTCP was generalized to incorporate the total QALY (UTCPQALY) for a particular treatment. Results: This approach was applied to compare the clinical treatment plans of 200 patients who received high‐dose external‐beam for unresectable non‐small cell lung cancer. Thirty nine out of the 200 patients developed radiation‐induced pneumonitis with the grade distribution of 8.7%, 69.6%, and 21.7% for grades 1, 2, and 3, respectively. The plan ranking based on the traditional UTCP and pneumonitis QALY‐weighted UTCP (UTCPQALY) values were different. The UTCP varied significantly, reaching values of 0.5. Using UTCPQALY scoring, the mild complications importance is downplayed compared to the severe complications, with all UTCPQALY values above 0.85. The QALY‐weighted UTCP's appear to provide better clinical realistic differentiator between plans. Conclusion: The construct presented represents a potential improvement in the current methods used to compare plans. Formulas presented can be readily incorporated into planning systems.

Incremental cost-effectiveness of initial cataract surgery

PurposeThe purpose of this study was to perform a reference case, cost-utility analysis of initial cataract surgery using the current literature on cataract outcomes and complications. DesignComputer-based econometric modeling. MethodsVisual acuity data of patients treated and observed over a 4-month postoperative period was obtained from the US National Cataract Patient Outcomes Research Team (PORT). The results from this prospective study were combined with other studies that investigated the complication rates of cataract surgery to complete the cohort of patients and outcomes. These synthesized data were incorporated with time-tradeoff utility values, decision analysis, and econometric modeling to account for the time value of money. Main outcome measuresThe number of quality-adjusted life-years (QALYs) gained was calculated for the study group undergoing cataract extraction in the first eye when the vision was the same in both eyes. This was divided into the cost of the procedure to find the year 2000 nominal US dollars spent per quality-adjusted life-year ($/QALY) gained. ResultsInitial cataract surgery, compared with observation, resulted in a mean gain of 1.776 QALYs per patient treated. A 3% annual discount rate was used to account for the benefit over time, yielding 1.25 QALYs gained. The mean cost of treatment (also discounted at a 3% annual rate) of each patient totaled $2525. The cost divided by the discounted benefit resulted in $2020/QALY gained for this procedure. ConclusionsInitial cataract surgery seems to be highly cost-effective compared with procedures across multiple medical specialties. This information, incorporating patient preferences into evidenced-based medicine, will play an increasingly important role in the evaluation of health care in the future.

Alocação de recursos financeiros em projetos de risco na exploração de petróleo

As recentes mudanças no setor petrolífero implicam um novo ambiente de exploração de intensa competição no Brasil. As tendências de internacionalização da exploração obrigam as empresas a adotarem técnicas padronizadas de avaliação e comparação de prospectos visando à redução do risco. Dentro dessa perspectiva, os indivíduos necessitam de um processo decisório que contemple os objetivos da empresa, as metas desejadas e as restrições orçamentárias. Este artigo descreve um sistema teórico e prático de suporte à tomada de decisão baseada na teoria da utilidade e da análise da decisão, com o objetivo de estimar a melhor alocação de capital em uma bacia sedimentar. Além disso, esse sistema possibilita estimar o melhor nível de participação financeira de uma firma em um projeto de exploração de petróleo realizado em parceria.

Valuing environmental resources: A constructive approach

The use of contingent valuation (CV) methods for estimating the economic value of environmental improvements and damages has increased significantly. However, doubts exist regarding the validity of the usual willingness to pay CV methods. In this article, we examine the CV approach in light of recent findings from behavioral decision research regarding the constructive nature of human preferences. We argue that a principal source of problems with conventional CV methods is that they impose unrealistic cognitive demands upon respondents. We propose a new CV approach, based on the value-structuring capabilities of multiattribute utility theory and decision analysis, and discuss its advantages and disadvantages.

Mean-risk decision analysis

Some decision theorists criticize expected utility decision analysis and propose mean-risk decision analysis as a replacement. They claim that expected utility decision analysis neglects attitudes toward risk whereas mean-risk decision analysis accords these attitudes their proper status. However mean-risk decision analysis and expected utility decision analysis are not incompatible, and it is advantageous for decision theory to develop each in a way that complements the other. Here I present a mean-risk rule that governs preferences among options and options given states. This mean-risk rule complements an expected utility rule that takes the utility of an option-state pair as the utility of the option given the state. I argue for the mean-risk rule using principles concerning basic intrinsic desires. The rule is comparative, but the last section offers some suggestions for its quantitative development.

A descriptive model of choice for siting facilities

The siting of facilities for large-scale, novel technologies presents a formidable challenge to politcal risk management. This paper develops a model for describing the decision process for this type of problem at the level of societal systems. It explicitly considers the role of the relevant interested parties, each of whom brings to the siting debate its own set of objetives and attributes. We have labeled the approach a multiattribute multiparty model (MAMP to distinguish it from prescriptive techniques such as multiattribute utility analysis or decision analysis). The MAMP model is a natural extension of the burgeoning literature on the key role that limited time, attention, and information processing capabilities play in political decision making when there are uncertain outcomes and likely conflicts among interested parties. The model also highlights the importance of decentralized and sequential decision making and indicates the role that formal risk assessments have played at each stage of the process. We illustrate its application in the context of the decision proess associated with a proposed liquiefied natural gas terminal in California. The concluding portion of this paper suggests future rsearch needs for improving the credibility of analysis and facilitating collective action with respect to facility siting problems.

Comprehensive Evaluation of Long-Range Research and Development Strategies

Long-range research and development (R&D) planning must evaluate and compare programs with diverse objectives and uncertain outcomes, yielding multidimensional benefits and costs over many years. The relevance of several approaches in system theory to these considerations is discussed. Among those approaches are multiattribute utility analysis, decision analysis, time and state dependent preference analysis, portfolio selection, Paretian analysis, and interpretive structural modeling. A tentative procedure is outlined that integrates most of these approaches into one comprehensive methodology for R&D planning. National energy R&D strategic planning is taken as an example. In this methodology, a stochastic model of each R&D program is used to project its consequences in terms of improved future technology, as evaluated on several dimensions. A key feature of the approach is the evaluation of each new technology within the economic and technological context of the years of its deployment and use. Some aspects of implementation of the methodology are considered.