Probabilistic Scenario Analyses Research Articles

Cost-effectiveness of prophylactic ramosetron in the prevention of postoperative nausea and vomiting.

This study was conducted to assess the cost-effectiveness of prophylactic use of ramosetron compared to no antiemetic medications for the prevention of postoperative nausea and vomiting (PONV) from the healthcare payer and societal perspectives in South Korea. A decision analytic model was constructed to assess the cost-effectiveness of prophylactic ramosetron use versus no antiemetic therapy at 24-hour and 48-hour periods post-surgery over a 5-day duration. The model was populated using costs and utility parameters from published studies as well as from surveys of an expert panel of physicians using structured questionnaires. The cost parameters included the costs of drugs, treatment, patient time, productivity loss, and transportation. Effectiveness was measured using quality adjusted life years (QALYs). The study outcome was the incremental cost-effectiveness ratio (ICER). The parameter uncertainties were addressed using deterministic and probabilistic scenario analyses. The base-case analysis showed that, on average, patients treated with prophylactic ramosetron had lower costs from both the healthcare payer (US$16.88 vs US$17.33) and societal (US$16.89 vs US$18.72) perspectives and higher QALYs (0.0121 vs 0.0114) over the 5-day study duration compared to patients without any antiemetic medications. Deterministic and probabilistic sensitivity analyses were conducted to examine the robustness of results for the parameters included in the model. The acceptability curve probability showed that treating patients with ramosetron compared to no antiemetic medications was more than 99% cost-effective at a willingness-to pay threshold of US$5,000/QALY from both payer and societal perspectives. The results demonstrated that prophylactic use of ramosetron compared to no antiemetic therapy is highly cost-effective to prevent PONV for patients undergoing surgery from both healthcare payer and societal perspectives. The cost effectiveness is the result of the decrease in the incidence of PONV and the direct treatment costs of severe PONV with improved patient quality of life.

The circular bioeconomy of the olive oil industry: Deterministic and probabilistic profitability of olive mill by-product gasification

In the novel paradigm of the circular economy the value of products, materials, and resources is retained in the economy for as long as possible, thereby minimizing waste generation. In this context, the olive oil producing countries are presented with a new opportunity that lies in the large amount of by-products generated by the olive oil industry, in particular olive pomace. Among the existing options for valorizing it, gasification is a technically proven and economically viable process, especially for moderately high energy prices. This research aims to analyze the profitability of olive pomace gasification, identifying the main uncertainty factors that determine said profitability. To that end, sensitivity analyses and probabilistic scenario analyses using the Monte Carlo method are conducted for the first time in this research context. The results show a positive profit margin of €19.27 per ton of milled olives, a Net Present Value (NPV) of €453,067, and an Internal Rate of Return (IRR) of 9.7 %. The sensitivity analysis indicates that the electricity price is the main determinant of profitability, while other factors, such as the biomass price or the sale of by-products (biochar), are less important. The Monte Carlo analysis reveals the uncertainty involved in these types of projects, showing that the probability of the investment being profitable (NPV>0) is only 25.1 %. The assessment evidences that this kind of projects presents high capital needs and strongly uncertain profitability, recommending the implementation of public and private green finance instruments to overcome high-capital barriers and de-risk these investments.

Advanced financial market forecasting: integrating Monte Carlo simulations with ensemble Machine Learning models

<abstract><p>This paper presents a novel integration of Machine Learning (ML) models with Monte Carlo simulations to enhance financial forecasting and risk assessments in dynamic market environments. Traditional financial forecasting methods, which primarily rely on linear statistical and econometric models, face limitations in addressing the complexities of modern financial datasets. To overcome these challenges, we explore the evolution of financial forecasting, transitioning from time-series analyses to sophisticated ML techniques such as Random Forest, Support Vector Machines, and Long Short-Term Memory (LSTM) networks. Our methodology combines an ensemble of these ML models, each providing unique insights into market dynamics, with the probabilistic scenario analysis of Monte Carlo simulations. This integration aims to improve the predictive accuracy and risk evaluation in financial markets. We apply this integrated approach to a quantitative analysis of the SPY Exchange-Traded Fund (ETF) and selected major stocks, focusing on various risk-reward ratios including Sharpe, Sortino, and Treynor. The results demonstrate the potential of our approach in providing a comprehensive view of risks and rewards, highlighting the advantages of combining traditional risk assessment methods with advanced predictive models. This research contributes to the field of applied mathematical finance by offering a more nuanced, adaptive tool for financial market analyses and decision-making.</p></abstract>

Probabilistic scenario analysis of integrated road-power infrastructures with hybrid fleets of EVs and ICVs

Electric Vehicles (EVs) are key contributors to the reduction of CO2 emissions. However, reliance on EVs must come with the guarantee that the integrated road-power infrastructure is capable of providing adequate mobility serviceability, even in case of disruption due to accidents or disturbances due to traffic jams. In this paper, we propose a probabilistic scenario analysis framework to quantify service losses in terms of delays that vehicles (both EVs and Internal Combustion Vehicles (ICVs)) may incur due to different car accident scenarios. The framework is based on modelling the System of Systems (SoS) comprised by road network, electric power system and vehicles, with graph theory and Finite State Machines (FSMs), respectively, and then embedding the model within a probabilistic scenario analysis, wherein meaningful disruption scenarios are sampled, service losses are measured (specifically as the ratio between the increase in travel time spent along the origin-destination routes on the road network following a disruption, and the corresponding travel time in nominal traffic conditions), and the economic losses and transport reliability of the infrastructure are assessed. To exemplify the application of the framework, we consider a benchmark road-power infrastructure in New York state travelled by a mixed fleet of EVs and ICVs, with different EVs penetration levels and under car accidental scenarios of different magnitudes. By using the insightful graphical representation of the results in terms of traffic volume across different road sections, the framework allows comparing alternative road-power infrastructure designs (e.g., critical roads, optimal gas and charging station locations, power network structure and topology, …) with respect to travel times, economic service losses and transport reliability considering different nominal and disruption scenarios under different EVs penetration levels service.

Cost-effectiveness and budget impact of pembrolizumab+axitinib versus sunitinib in patients with advanced clear-cell renal cell carcinoma in the Netherlands.

The phase 3 clinical trial KEYNOTE-426 suggested a higher efficacy regarding overall survival (OS) and progression-free survival (PFS) of pembrolizumab+axitinib compared to sunitinib as a first-line treatment for patients with advanced renal cell carcinoma. In this analysis, the potential cost-effectiveness of this combination treatment versus sunitinib for patients with advanced clear-cell renal cell carcinoma (accRCC) was examined from the societal perspective in the Netherlands. For this analysis, a partitioned survival model was constructed. Clinical data were obtained from the published KEYNOTE-426 trial reports; data on costs and (dis-)utilities were derived from published literature. Costs outside of the healthcare sector included treatment-related travel, informal care and productivity loss. Next to a probabilistic scenario analysis, various scenario analyses were performed that aimed at survival extrapolation, different utility values, treatment duration and drug pricing, as well as restricting the cohort to patients with an intermediate or poor prognosis. Further, a budget impact analysis over three years was conducted, in which a sensitivity analysis concerning ranges in costs and the number of patients was applied. Moreover, a scenario concerning increasing market penetration of pembrolizumab+axitinib up to a market share of 80% in the third year was analyzed. The incremental cost-effectiveness ratio (ICER) of pembrolizumab+axitinib was estimated at €368,396/quality-adjusted life year (QALY) gained, with an incremental QALY gain of 0.55 over sunitinib. The probability of cost-effectiveness at a willingness-to-pay threshold of €80,000/QALY was estimated at 0%, a 50% probability was estimated at €340,000/QALY. Cost-effectiveness was not achieved in any of the applied scenarios. The budget impact over three years amounted to €417.3 million upon instantaneous and full replacement of sunitinib, and to €214.9 million with increasing market penetration. Pembrolizumab+axitinib was not estimated to be cost-effective compared to sunitinib as a first-line treatment for patients with accRCC in the Netherlands from a societal perspective. In none of the analyzed scenarios, cost-effectiveness was achieved. However, price reductions and shorter treatment durations might lead to a more favorable ICER.

Probabilistic Scenario Analysis of Integrated Road-Power Infrastructures with Hybrid Fleets of Evs and Icvs

Probabilistic Scenario Analysis of Integrated Road-Power Infrastructures with Hybrid Fleets of Evs and Icvs

Conditional Probabilistic Population Forecasting

Since policy-makers often prefer to think in terms of alternative scenarios,the question has arisen as to whether it is possible to make conditionalpopulation forecasts in a probabilistic context. This paper shows that it isboth possible and useful to make these forecasts. We do this with two differentkinds of examples. The first is the probabilistic analog of deterministicscenario analysis. Conditional probabilistic scenario analysis is essentialfor policy-makers because it allows them to answer “what if” typequestions properly when outcomes are uncertain. The second is a newcategory that we call “future jump-off date forecasts”. Future jump-offdate forecasts are valuable because they show policy-makers the likelihoodthat crucial features of today’s forecasts will also be present in forecastsmade in the future.

The cost-effectiveness of glasdegib in combination with low-dose cytarabine, for the treatment of newly diagnosed acute myeloid leukemia in adult patients who are not eligible to receive intensive induction chemotherapy in Canada

Aim The clinical efficacy and safety of DAURISMO (glasdegib) combined with low-dose cytarabine (LDAC) were demonstrated in the BRIGHT AML 1003 study among newly diagnosed acute myeloid leukemia patients who are not eligible to receive intensive chemotherapy. This study aims to evaluate its cost-effectiveness versus LDAC alone and azacitidine from a Canadian payer perspective. Materials and Methods A partitioned-survival model was developed with three health states: progression-free survival (PFS), relapse/progression and death. Clinical inputs were obtained from the BRIGHT AML 1003 study for glasdegib and LDAC, and from the two trial publications and indirect treatment comparison for azacitidine. Drug acquisition/administration, disease management, adverse event and end-of-life costs were considered. All costs were measured in Canadian dollars. Cost-effectiveness of glasdegib + LDAC was assessed against LDAC alone in main population, and against azacitidine by bone marrow blasts (BMB). A weighted average ICER was calculated to represent the current treatment use of Canadian clinical practice. The reference-case analysis was conducted probabilistically, and numerous probabilistic scenario analyses were conducted. Results The incremental cost-effectiveness ratios (ICERs) compared to LDAC alone was CAD $177,065 (a mean gain of 0.41 QALYs and an incremental cost of CAD $72,695), to azacitidine in 20–30% and >30% BMB group were CAD $178,201 (a mean gain of 0.34 QALYs and an incremental cost of CAD $59,889) and dominant (a mean gain of 0.28 QALYs while reducing costs by CAD $7,856) respectively, resulting in a weighted average ICER of CAD $81,310 per QALY. Limitations and conclusions Though uncertainties remain with the generated PFS curve, the derived azacitidine curves, administration and vial wastage, the model has been built under the best available evidence and relied on clinical opinions where there were data gaps. The weighted average ICER suggests that glasdegib + LDAC is cost-effective at a CAD $100,000 willingness-to-pay threshold.

Economic analysis of new workplace technology including productivity and injury: The case of needle-less injection in swine.

Increasing intensification in swine production has led to new and specialized technologies, but the occupational health and safety impacts are rarely quantified in the business plans for adoption. Needle-less injection has potential to increase productivity and eliminate needle stick injury in workers, but it is not clear whether these benefits offset high capital investment and potential increases in musculoskeletal loads. This economic evaluation employed probabilistic scenario analysis using injury, cost, and production data gathered from interviews with swine producers in Manitoba and Saskatchewan. After adoption of needle-less injection, rates of needle-stick injury went down with no measureable effect on upper limb musculoskeletal disorders, resulting in lower health and safety costs for needle-less injectors. Needle-less injection duration was 40% faster once workers acclimatized, but large start-up costs mean economic benefits are realized only after the first year. The incremental benefit cost ratio promoted adoption of needle-less injectors over conventional needles for the base case of a 1200 sow barn; the conventional method is beneficial for barns with 600 sows or less. Findings indicate that well-designed technologies have the potential to achieve the dual ergonomics goals of enhancing human wellbeing and system performance. We anticipate that the economic and decision models developed in this study can be applied to other new technologies in agriculture and animal production.

Shared benefit by Material Flow Cost Accounting in the food supply chain – The case of berry pomace as upcycled by-product of a black currant juice production

Abstract Berry fruits such as black currants are known to have health-promoting effects as they contain high amounts of phytochemicals (polyphenols, anthocyanins) and dietary fibers concentrated in the peel and seeds. So far, the berry pomace by-product obtained from the production of berry juice is mainly subject to incineration and less recognized by companies as an opportunity to create additional value. Material Flow Cost Accounting is a powerful tool when it comes to the identification of resource inefficiencies in production systems and can provide useful information as an ad-hoc analysis supporting investment decisions, such as the extension of the product portfolio. This study investigates the material efficiency potential of a black currant juice production in Germany while considering potentials for cost reduction, revenue generation, and carbon dioxide mitigation resulting from the avoidance and upcycling of food waste. We used Material Flow Cost Accounting in combination with carbon footprinting and probabilistic scenario analysis to examine the subject. The analysis showed that, in terms of the global warming potential, the environmental performance of the main product, black currant juice, benefits from the upcycling of the pomace to a marketable product. From the economic side, we could not demonstrate a reasonable amortization of the additional investment costs. However, the scenario and uncertainty analysis revealed promising optimization strategies which would lead to an increase in profit of about 13%: first, additional use of apple pomace and second, the production of high-quality, organic pomace to achieve a selling price of at least €2.00/kg as a functional food supplement or in pharmaceutical and cosmetic applications.

Dynamic analysis of carbon dioxide emissions in China's petroleum refining and coking industry

As one of the energy-intensive industries, the CO2 emissions of China's petroleum refining and coking industry (PRCI) put significant pressure on China's emission reduction target. Considering this, the main purpose of this paper is to clarify the determinants of emission growth in China's PRCI and predict the emissions change trend in future. Based on an extended Kaya identity, this paper dynamically analyzed the impacts of investment scale, investment efficiency, energy intensity, energy structure and carbon dioxide emission factor on CO2 emissions from China's PRCI. Then, the possible CO2 emissions of China's PRCI in 2030 were dynamically predicted based on the application of probabilistic scenario analysis. The main findings include: (1) during 2000–2016, China's PRCI suffered 130.82 million tons CO2 emissions growth, investment scale expansion was the principal contributor stimulating CO2 emissions. Fortunately, the promoting effects exerted from this factor showed a downtrend along with the decreasing fixed-asset investment growth rate since China's economy has entered a new normal. (2) During the entire study period, energy structure adjustment brought about a mild increase in CO2 emissions rather than taking an active role in emission abatement. (3) Investment efficiency and energy intensity were two crucial inhibitors, followed by the carbon dioxide emission factor. However, the inhibiting effects resulting from these factors cannot completely counteract the promoting effects in most cases. (4). Scenario analysis indicates that the mitigation pathway of CO2 emissions in China's PRCI include properly curbing the fixed-asset investment, increasing the green investment for technology and equipment, paying more attention to the R&D of energy saving technology in PRCI and its downstream industry, and facilitating the use of clean electricity.

A novel multi-objective robust optimization model for unit commitment considering peak load regulation ability and temporal correlation of wind powers

With the increasing applications of wind energy & power technologies, the stochastic fluctuation and reverse peak load regulation characteristic of wind power brings great challenges to unit commitment of power systems. It is difficult to meet the demand of the peak load regulation with the wind power integration for the existing robust unit commitment models when only considering the cost. In order to solve this problem, an additional objective function of the ability of peak load regulation is constructed for unit commitment solutions. As a result, a novel multi-objective robust unit commitment model considering both the cost and peak load regulation is proposed. Moreover, probabilistic scenario analysis is applied to constraint the model of the fluctuant wind power, and temporal correlation of wind powers is also considered to further improve the economy in the long run. In order to deal with multi-objective problem for the robust optimization solutions, the above-mentioned problem is decomposed into two stages, i.e., a main problem and a subproblem. The main problem is to determine unit commitment and the subproblem is a "min–max optimization" one to obtain the unit outputs. Finally, the simulation analysis based on IEEE118 node system verified the robustness and the validity of the proposed model.

Stochastic dynamic economic emission dispatch with unit commitment problem considering wind power integration

Summary This paper establishes a probabilistic scenario–based framework for the stochastic dynamic economic emission dispatch with unit commitment (SDEED-UC) problem, taking into account wind power uncertainty. To solve the stochastic UC problem, this paper presents a probabilistic scenario analysis approach to find the unit scheduling solution considering all the original scenarios under a predetermined probability level. And a reduced scenario set can be obtained by the simultaneous backward method. Consequently, the SDEED problem is decomposed into a number of deterministic multiobjective dispatch problems. For each scenario, an enhanced multiobjective particle swarm optimization (EMOPSO) algorithm integrated with the handling strategy for system operation constraints is proposed to produce the Pareto optimal solutions. Furthermore, the available reserve capacity by the UC solution is verified. The simulation results demonstrate that the proposed probabilistic scenario–based and EMOPSO approach is practicable for solving SDEED-UC from the perspectives of system operation economy, emission, and reliability simultaneously.

Short-Term Multiple Forecasting of Electric Energy Loads for Sustainable Demand Planning in Smart Grids for Smart Homes

Energy consumption in the form of fuel or electricity is ubiquitous globally. Among energy types, electricity is crucial to human life in terms of cooking, warming and cooling of shelters, powering of electronic devices as well as commercial and industrial operations. Users of electronic devices sometimes consume fluctuating amounts of electricity generated from smart-grid infrastructure owned by the government or private investors. However, frequent imbalance is noticed between the demand and supply of electricity, hence effective planning is required to facilitate its distribution among consumers. Such effective planning is stimulated by the need to predict future consumption within a short period. Although several interesting classical techniques have been used for such predictions, they still require improvement for the purpose of reducing significant predictive errors when used for short-term load forecasting. This research develops a near-zero cooperative probabilistic scenario analysis and decision tree (PSA-DT) model to address the lacuna of enormous predictive error faced by the state-of-the-art models. The PSA-DT is based on a probabilistic technique in view of the uncertain nature of electricity consumption, complemented by a DT to reinforce the collaboration of the two techniques. Based on detailed experimental analytics on residential, commercial and industrial data loads, the PSA-DT model outperforms the state-of-the-art models in terms of accuracy to a near-zero error rate. This implies that its deployment for electricity demand planning will be of great benefit to various smart-grid operators and homes.

Multiattribute Decision Making Using Multiperiod Probabilistic Weighted Fuzzy Axiomatic Design

Axiomatic Design is a methodology that provides a framework to systematically analyze the transformation of customer needs into functional requirements and design parameters. Decision-makers use Weighted Fuzzy Axiomatic Design (wFAD) in cases where there is imperfect information, and decision criteria have different priorities. In this paper, the wFAD approach is extended to situations which include uncertainty in future events and decision payoffs. In particular, we consider multiple future scenarios with different probabilities and time-dependent decision payoffs for each system range (SR). For such decision problems, we propose the Multiperiod Probabilistic Weighted Fuzzy Axiomatic Design (MPP-wFAD) methodology. The methodology utilizes probabilistic scenario analysis and SR forecast data to assist decision makers in attaining better decisions with improved expected payoffs. As a case study, we apply MPP-wFAD to tackle a multiattribute industry selection problem in Turkey and show the impact and usability of the method.

Lifecycle Based User Value Analysis of Rail - Road Level Crossings: Probabilistic Approach Using Monte Carlo Simulation

Railway operators are in continuous pressure to minimize maintenance and rehabilitation costs of infrastructures; at the same time they are expected to provide a reliable service by optimum allocation of natural and economical resources. Railway tracks and level crossings are long-lived assets where their service life stretch 30 to 100 years. This paper aims to show an approach that serve as a decision support whereby expert's knowledge can directly be integrated by using a delphiround. A proposed methodology is illustrated by application examples using level crossings commonly used in Austria by Wiener Lokalbahnen. Lifecycle based user – product performance – expectations and economic imperatives could be incorporated through the development of key performance metrics. Assessment criteria are set by allocation percentages for value criteria under these criteria are the sub value – contingencies where Monte Carlo simulation is used for probabilistic scenario analysis. The approach facilitates the first step for a detailed lifecycle cost analysis of infrastructures by supporting experts and non-expert decision makers to get a quicker overview on system benefits as well as serve as a bridge to the practical application of more robust data-driven lifecycle cost analysis.

Analyzing uncertainty in a comparative life cycle assessment of hand drying systems

The goal of this study is to evaluate and compare the environmental impact (with a focus on global warming potential) of five hand drying systems: hands-under (HU) dryers, high-speed hands-under (HSHU) dryers, high-speed hands-in (HSHI) dryers, cotton roll towels, and paper towels. Another objective is to incorporate uncertainty into this comparative life cycle assessment (LCA) as a means of understanding the statistical robustness of the difference between the environmental impacts of the hand drying systems. We conducted a life cycle assessment in accordance with the ISO 14040/14044 standards using data primarily from publicly available reports. As part of the study, we performed a parameter uncertainty analysis for multiple scenarios to evaluate the impact of uncertainty in input data on the relative performance of products. In addition, we conducted a probabilistic scenario analysis of key drying system parameters in order to understand the implications of changing assumptions on the outcomes of the analyses. The scope of the analyses enabled us to draw robust conclusions about the relative environmental performance of the products. We can say with a high degree of confidence that the high-speed dryers have a lower impact than paper towels and cotton roll towels. Differentiating the performance of the hand dryers requires being more specific about framing assumptions. Under certain conditions, the HSHI dryer is expected to have a lower impact than the HU and HSHU dryers. However, under other conditions, one cannot say that the HSHI dryer is clearly better than the other dryers. We cannot differentiate the performance between the HU dryer, cotton roll towels, and paper towels. This work demonstrates the importance of going beyond traditional uncertainty analyses for comparative LCAs that are used for assertions of relative product environmental impact. Indeed, we found instances where the conclusions changed as a result of using the probabilistic scenario analysis. We outline important elements that should be included in future guidance on uncertainty analyses in comparative LCAs, including conducting parameter and scenario uncertainty analyses together and then using the outcomes to guide selection of parameters and/or choices to analyze further.

Conditional probabilistic estimates of 21st century greenhouse gas emissions based on the storylines of the IPCC-SRES scenarios

The conditional probabilistic scenario analysis combines statistical methods of uncertainty analysis at parameter level with storylines which recognize the deep uncertainty that exists for several underlying trends. The model calculations indicate that cumulative 21st century emissions could range from 800 to 2500 GtC in the absence of climate policy. This range originates partly from the underlying storylines, and partly from the probabilistic analysis. Among the most important parameters contributing to the uncertainty range are uncertainty in income growth, population growth, parameters determining energy demand, oil resources and fuel preferences. The contribution of these factors is also scenario-dependent.

Multivariate estimation for operational risk with judicious use of extreme value theory

The Basel II Accord requires participating banks to quantify operational risk according to a matrix of business lines and event types. Proper modeling of univariate loss distributions and dependence structures across those categories of operational losses is critical for proper assessment of overall annual operational loss distributions. We illustrate our proposed methodology using Loss Data Collection Exercise 2004 (LDCE 2004) data on operational losses across five loss event types. We estimate a multivariate likelihood-based statistical model, which illustrates the benefits and risks of using extreme value theory (EVT) in modeling univariate tails of event type loss distributions. We find that abandoning EVT leads to unacceptably low estimates of risk capital requirements, while indiscriminate use of EVT to all data leads to unacceptably high ones. The judicious middle approach is to use EVT where dictated by data, and after separating clear outliers that need to be modeled via probabilistic scenario analysis. We illustrate all computational steps in estimation of marginal distributions and copula with an application to one bank’s data (disguising magnitudes to ensure that bank’s anonymity). The methods we use to overcome heretofore unexplored technical problems in estimation of codependence across risk types scales easily to larger models, encompassing not only operational, but also other types of risks.

Conditional Probabilistic Population Forecasting

Summary Since policy‐makers often prefer to think in terms of alternative scenarios, the question has arisen as to whether it is possible to make conditional population forecasts in a probabilistic context. This paper shows that it is both possible and useful to make these forecasts. We do this with two different kinds of examples. The first is the probabilistic analog of deterministic scenario analysis. Conditional probabilistic scenario analysis is essential for policy‐makers because it allows them to answer “what if” type questions properly when outcomes are uncertain. The second is a new category that we call “future jump‐off date forecasts”. Future jump‐off date forecasts are valuable because they show policy‐makers the likelihood that crucial features of today's forecasts will also be present in forecasts made in the future.