A Process Using Risk Analysis to Select Construction Methods for Pipeline Crossings of Major Rivers

1998 ◽  
Author(s):  
Terri J. Covlin ◽  
Gregg D. O’Neil ◽  
Moness Rizkalla ◽  
Russell Morrison
Keyword(s):  
Risk Analysis ◽  
Decision Makers ◽  
Approval Process ◽  
Decision Tool ◽  
Regulatory Relationship ◽  
Internal Process ◽  
Significant Challenge ◽  
Construction Methods ◽  
Technical Risks ◽  
The Cost

Pipeline river crossings pose a significant challenge for pipeline owners and environmental regulators to balance the associated costs and risks, particularly where environmental impacts may occur. For years, NOVA Gas Transmission Ltd. (NGTL) has managed the regulatory approval process using a well-established process. However, regulatory expectations are increasingly neutral to the cost of crossing installations and are demanding a more thorough assessment of a wider variety of river crossing options, particularly trenchless technologies. As a result, NGTL has recently enhanced the internal process by incorporating a structured decision tool. This tool is able to quantitatively assess technical risks as well as ‘soft’ influences such as “Regulatory Relationship” and “Delay in Approval”. The result is a clear decision with the necessary buy-in, where all risks were identified quantitatively and well understood by decision makers.

scholarly journals Healthcare Expenditures Associated with Heart Failure in Saudi Arabia: A Cost of Illness Study

Healthcare ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 988
Author(s):  
Ahmed Alghamdi ◽  
Eman Algarni ◽  
Bander Balkhi ◽  
Abdulaziz Altowaijri ◽  
Abdulaziz Alhossan
Keyword(s):  
Heart Failure ◽  
Saudi Arabia ◽  
Cost Of Illness ◽  
Economic Burden ◽  
Indirect Costs ◽  
Health Care Expenditures ◽  
Decision Makers ◽  
Hospitalization Costs ◽  
Illness Study ◽  
The Cost

Heart failure (HF) is considered to be a global health problem that generates a significant economic burden. Despite the growing prevalence in Saudi Arabia, the economic burden of HF is not well studied. The aim of this study was to estimate the health care expenditures associated with HF in Saudi Arabia from a social perspective. We conducted a multicenter cost of illness (COI) study in two large governmental centers in Riyadh, Saudi Arabia using 369 HF patients. A COI model was developed in order to estimate the direct medical costs associated with HF. The indirect costs of HF were estimated based on a human capital approach. Descriptive and inferential statistics were analyzed. The direct medical cost per HF patient was $9563. Hospitalization costs were the major driver in total spending, followed by medication and diagnostics costs. The cost significantly increased in line with the disease progression, ranging from $3671 in class I to $16,447 in class IV. The indirect costs per working HF patient were $4628 due to absenteeism, and $6388 due to presenteeism. The economic burden of HF is significantly high in Saudi Arabia. Decision makers need to focus on allocating resources towards strategies that prevent frequent hospitalizations and improve HF management and patient outcomes in order to lower the growing economic burden.

Analysis of Overweight Truck Permit Policy in New Jersey

2021 ◽  
pp. 036119812110266
Author(s):  
Sami Demiroluk ◽  
Hani Nassif ◽  
Kaan Ozbay ◽  
Chaekuk Na
Keyword(s):  
New Jersey ◽  
Environmental Conditions ◽  
Decision Makers ◽  
Actual Cost ◽  
Department Of Transportation ◽  
Heavy Weight ◽  
Vehicle Route ◽  
Heavy Trucks ◽  
Maintenance And Rehabilitation ◽  
The Cost

The roadway infrastructure constantly deteriorates because of environmental conditions, but other factors such as exposure to heavy trucks exacerbates the rate of deterioration. Therefore, decision-makers are constantly searching for ways to optimize allocation of the limited funds for repair, maintenance, and rehabilitation of New Jersey’s infrastructure. New Jersey legislation requires operators of overweight (OW) trucks to obtain a permit to use the infrastructure. The New Jersey Department of Transportation (NJDOT) issues a variety of permits based on the types of goods carried. These permits allow OW trucks to use the infrastructure either for a single trip or for multiple trips. Therefore, one major concern is whether the permit revenue of the agency can recoup the actual cost of damage to the infrastructure caused by these OW trucks. This study investigates whether NJDOT’s current permit fee program can collect enough revenue to meet the actual cost of damage to the infrastructure caused by these heavy-weight permit trucks. The infrastructure damage is estimated by using pavement and bridge deterioration models and New Jersey permit data from 2013 to 2018 containing vehicle configuration and vehicle route. The analysis indicates that although the cost of infrastructure damage can be recovered for certain permit types, there is room for improvement in the permit program. Moreover, based on permit rules in other states, the overall rank of the New Jersey permit program is evaluated and possible revisions are recommended for future permit policies.

Design for Variety: A Methodology for Understanding the Costs of Product Proliferation

1996 ◽  
Author(s):  
Mark V. Martin ◽  
Kosuke Ishii
Keyword(s):  
Indirect Costs ◽  
Product Line ◽  
Decision Makers ◽  
Profit Margins ◽  
Design For Variety ◽  
Set Up ◽  
Market Coverage ◽  
Product Proliferation ◽  
The Cost

Abstract This paper further develops the previously introduced concept of Design for Variety (DFV). Our study seeks a tool that enables product managers to estimate the cost of introducing variety into their product line. This will help them to maximize market coverage while maintaining required profit margins. Variety incurs many indirect costs that are not always well understood or are difficult to capture. These costs are often not considered by people making the decision about introducing variety. Our DFV model attempts to capture these indirect costs through the measurement of three indices: commonality, differentiation point, and set-up cost. These indices will allow the decision makers to estimate some of the generally unmeasurable costs of providing variety. We conclude this paper by discussing our validation plans for testing the model in industry.

Heuristics as a new way of adjusting the end market value

2021 ◽  
Vol 27 (7) ◽  
pp. 1581-1599
Author(s):  
Semen Yu. BOGATYREV
Keyword(s):  
Behavioral Finance ◽  
Market Value ◽  
Measurement Methods ◽  
Decision Makers ◽  
Emotional Impact ◽  
Psychological Measurement ◽  
Survey Results ◽  
Financial Decision ◽  
The Cost

Subject. The study deals with heuristics as measures of the emotional impact of people who judge about the value and the final result of the valuation. I review ranges of the value variance when influenced by irrational factors. From psychological perspectives, some phenomena are explained with a set of heuristics that exist as part of behavioral finance. Objectives. Referring to the completed studies, I implement elements of behavioral finance, such as heuristics into the method for assessing how financial decision-makers and their emotions influence the value. Methods. The article is based on methods of induction and deduction to process survey results. Results. The article reveals the content of key methods for measuring emotions of financial decision-maker, which conclude on the value, being influenced by heuristics. I demonstrate tools for implementing psychological measurement methods as part of valuation. Conclusions and Relevance. Considering heuristics of value decision-makers, the appraiser and the cost analyst approximate the valuation result to the real conditions, when market actors are irrational. Doing so, they contribute to the quality of the result of appraisal. The findings are applicable to the practice of appraisers, cost analysts, fundamental analysts. Heuristics enrich and expands the classical apparatus of valuation and increases its quality.

scholarly journals Studying the prominence effect amid the COVID-19 crisis: implications for public health policy decision-making.

F1000Research ◽  
2021 ◽  
Vol 9 ◽  
pp. 1356
Author(s):  
Yossi Maaravi ◽  
Ben Heller
Keyword(s):  
Decision Making ◽  
Policy Decision ◽  
Matching Condition ◽  
Decision Makers ◽  
The Other ◽  
Choice Condition ◽  
Health Concerns ◽  
Prominence Effect ◽  
Novel Coronavirus ◽  
The Cost

The novel coronavirus disease 2019 (COVID-19) has brought with it crucial policy- and decision-making situations, especially when making judgments between financial and health concerns. One particularly relevant decision-making phenomenon is the prominence effect, where decision-makers base their decisions on the most prominent attribute of the object at hand (e.g., health concerns) rather than weigh all the attributes together. This bias diminishes when the decision-making mode inhibits heuristic processes. In this study, we tested the prominence of health vs. financial concerns across two decision-making modes - choice (prone to heuristics) and matching (mitigates heuristics) - during the peak of the COVID-19 in the UK using Tversky et al.’s classic experimental paradigm. We added to the classic experimental design a priming condition. Participants were presented with two casualty-minimization programs, differing in lives saved and costs: program X would save 100 lives at the cost of 55-million-pound sterling, whereas program Y would save 30 lives at the cost of 12-million-pound sterling. Half of the participants were required to choose between the programs (choice condition). The other half were not given the cost of program X and were asked to determine what the cost should be to make it as equally attractive as the program Y. Participants in both groups were primed for either: a) financial concerns; b) health concerns; or c) control (no priming). Results showed that in the choice condition, unless primed for financial concerns, health concerns are more prominent. In the matching condition, on the other hand, the prominence of health concerns did not affect decision-makers, as they all “preferred” the cheaper option. These results add further support to the practical relevance of using the proper decision-making modes in times of consequential crises where multiple concerns, interests, and parties are involved.

scholarly journals Neural Representation and Learning of Hierarchical 2-additive Choquet Integrals

2020 ◽  
Author(s):  
Roman Bresson ◽  
Johanne Cohen ◽  
Eyke Hüllermeier ◽  
Christophe Labreuche ◽  
Michèle Sebag
Keyword(s):  
Real World ◽  
Choquet Integral ◽  
Marginal Utility ◽  
Decision Makers ◽  
Neural Representation ◽  
Automatic Identification ◽  
Neural Architecture ◽  
Real World Applications ◽  
Choquet Integrals ◽  
The Cost

Multi-Criteria Decision Making (MCDM) aims at modelling expert preferences and assisting decision makers in identifying options best accommodating expert criteria. An instance of MCDM model, the Choquet integral is widely used in real-world applications, due to its ability to capture interactions between criteria while retaining interpretability. Aimed at a better scalability and modularity, hierarchical Choquet integrals involve intermediate aggregations of the interacting criteria, at the cost of a more complex elicitation. The paper presents a machine learning-based approach for the automatic identification of hierarchical MCDM models, composed of 2-additive Choquet integral aggregators and of marginal utility functions on the raw features from data reflecting expert preferences. The proposed NEUR-HCI framework relies on a specific neural architecture, enforcing by design the Choquet model constraints and supporting its end-to-end training. The empirical validation of NEUR-HCI on real-world and artificial benchmarks demonstrates the merits of the approach compared to state-of-art baselines.

scholarly journals Discrete confidence levels revealed by sequential decisions

2017 ◽  
Author(s):  
Matteo Lisi ◽  
Gianluigi Mongillo ◽  
Andrei Gorea
Keyword(s):  
Stimulus Level ◽  
Decision Makers ◽  
Optimal Decision ◽  
Confidence Levels ◽  
Opt Out ◽  
Under Sampling ◽  
Subjective Confidence ◽  
Optimal Decision Making ◽  
The Cost ◽  
Bayesian Strategy

AbstractWhether humans are optimal decision makers is still a debated issue in the realm of perceptual decisions. Taking advantage of the direct link between an optimal decision-making and the confidence in that decision, we offer a new dual-decisions method of inferring such confidence without asking for its explicit valuation. Our method circumvents the well-known miscalibration issue with explicit confidence reports as well as the specification of the cost-function required by ‘opt-out’ or post-decision wagering methods. We show that observers’ inferred confidence in their first decision and its use in a subsequent decision (conditioned upon the correctness of the first) fall short of both the ideal Bayesian strategy, as well as of an under-sampling approximation or a modified Bayesian strategy augmented with an additional bias term to accommodate global miscalibration of confidence. The observed data are instead significantly better fitted by a model positing that observers use only few confidence levels or states, at odds with the continuous confidence function of stimulus level prescribed by a normative behavior. These findings question the validity of normative-Bayesian accounts of subjective confidence and metaperceptual judgments.

scholarly journals Multi-Objective Pharmaceutical Portfolio Optimization under Uncertainty of Cost and Return

Mathematics ◽  
2021 ◽  
Vol 9 (18) ◽  
pp. 2339
Author(s):  
Mahboubeh Farid ◽  
Hampus Hallman ◽  
Mikael Palmblad ◽  
Johannes Vänngård
Keyword(s):  
Portfolio Optimization ◽  
Decision Makers ◽  
Optimization Under Uncertainty ◽  
Product Development Process ◽  
Multi Objective ◽  
Probability Of Success ◽  
Low Probability ◽  
Key Aspects ◽  
The Cost ◽  
Selection Of

This paper presents the study of multi-objective optimization of a pharmaceutical portfolio when both cost and return values are uncertain. Decision makers in the pharmaceutical industry encounter several challenges in deciding the optimal selection of drug projects for their portfolio since they have to consider several key aspects such as a long product-development process split into multiple phases, high cost and low probability of success. Additionally, the optimization often involves more than a single objective (goal) with a non-deterministic nature. The aim of the study is to develop a stochastic multi-objective approach in the frame of chance-constrained goal programming. The application of the results of this study allows pharmaceutical decision makers to handle two goals simultaneously, where one objective is to achieve a target return and another is to keep the cost within a finite annual budget. Finally, the numerical results for portfolio optimization are presented and discussed.

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