Multiple Scenario Analysis Research Articles

Overcoming strategic persistence: Effects of multiple scenario analysis on strategic reorientation

AbstractResearch SummaryTo thrive in an unpredictable world, managers must adapt their decision‐making to changing events. However, a major impediment to adaptation is strategic persistence, that is, the tendency to stick with previously successful strategies. We examined whether multiple scenario analysis can help to overcome the dysfunctional effects of strategic persistence. In a laboratory study using a multi‐round strategy simulation that required players to change strategies to succeed, we found that multiple scenario analysis alleviated the effects of strategic persistence by stimulating strategic reorientation, that is, adaptive shifts in patterns of strategic choice. Multiple scenario analysis influenced strategic reorientation indirectly, by fostering belief in a new strategy, and its effectiveness depended on prior performance. We discuss implications for research on the cognitive microfoundations of strategic adaptation.Managerial SummaryWhat can managers do when their previously successful strategy stops working? We found that using multiple scenario analysis, a common strategic planning technique can help decision makers switch to a new strategy. In our study, using a business simulation game that required players to change strategies to succeed, we found that those players who imagined different industry futures as part of a multiple scenario analysis exercise were more likely to believe in new strategies and as a result more likely to switch to the winning strategy. However, this intervention was less effective for players who had high levels of success with the old strategy. We discuss how scenario‐based intervention techniques can be used to assist managers with reorientating to a new strategy.

How much carbon storage will loss in a desertification area? Multiple policy scenario analysis from Gansu Province

Carbon storage plays a pivotal role in addressing climate change, maintaining ecological equilibrium, and fostering sustainable development. Gansu Province, situated in the arid to semi-arid region of Northwestern China, is confronted with substantial carbon storage losses as a result of ongoing ecological land desertification processes. However, studies on carbon storage loss under various scenarios in desertified regions are seldom reported. In this study, we delineated the ecological red line using quantified indicators encompassing multiple ecosystem service functions and ecological vulnerability sensitivity. Furthermore, we projected future land use and carbon storage transformations under multiple policy scenarios employing the patch-generating land use simulation (PLUS) model. Lastly, we unveiled spatial disparities in the driving factors behind alterations in carbon storage by geographically weighted regression model. Our findings suggest that: (1) The delineated ecological red line covers an area of 11.8 × 104 km2, approximately 27 % of the total land area of Gansu Province. (2) Quantitative findings reveal that the overall accuracy of the PLUS model reached an impressive 0.975, accompanied by a Kappa coefficient of 0.964, thus affirming the model's exceptional applicability. (3) Under the base line scenario, Gansu Province's carbon storage witnesses a consistent decline from 2000 to 2050, with a substantial total loss of 1.62 × 107 t over the ensuing three decades. The ecological red line scenario, by controlling 27 % of the land area in Gansu Province, achieves a 61.7 % effect of the global ecological scenario by 2050, thus reversing the declining trend in carbon storage. (4) Natural factors primarily influence carbon storage in the southeastern region, while human activity factors are distributed in the central region. This study offers scientifically robust policy recommendations to facilitate the attainment of carbon neutrality objective.

Quantitative evaluation of emergency shelters in mountainous areas among multiple scenarios: Evidence from Biancheng, China

Accurate evaluation of urban emergency shelters is key to improving disaster response capacity and government emergency management ability, especially in mountainous areas where disasters are common. Most existing studies focus on evaluating material aspects, such as the capacity and accessibility of emergency shelters, ignoring the impact of different scenarios in the same area such as topographic, demographic, and population spatial–temporal distribution differences. Considering the multiple scenarios factors influencing emergency shelter practice, this study constructed a validity, efficiency, and security evaluation framework which was tested in Biancheng, Hunan, China. A combination of qualitative multiple scenario analysis and quantitative Network Analysis method were used to portray existing emergency shelters during emergency evacuations. The important conclusions are as follows: (1) there are significant differences in the evaluation of emergency shelters in the different scenarios. Scenario I (considering terrain) and Scenario II (considering demographics) decreased the evaluation results, while Scenario III (considering time–space) increased the evaluation results. (2) All three scenarios could cause variation in the validity indicators (A) and efficiency indicators (B), but A1 were unchanged in Scenario II and B2 remained constant in Scenario III, while none of the security indicators (C) were affected except in Scenario I. (3) While different emergency shelters performed differently in different scenarios, overall ID2 performed best and ID5 performed worst. Consideration of all scenarios was clearly necessary for us to scientifically evaluate the real use of emergency shelters, which can effectively guide emergency shelter planning and thus improve the abilities of urban disaster relief.

Stratigraphic analysis of XES02: Implications for the sequence stratigraphic paradigm

ABSTRACT Sequence stratigraphy has the potential to provide a consistent method for integrating data, correlating strata, defining stratigraphic evolution, and generating quantifiable predictions. However, the consistent application requires a precise definition of concepts, stratigraphic units, bounding surfaces, and workflow. Currently no single generally accepted approach to sequence stratigraphic analysis exists, nor are there any robust tests of models and methods. Applying conventional sequence stratigraphic analysis to strata from an analog laboratory experiment (eXperimental EarthScape02, XES02) with known boundary conditions and chronology provides some initial robust testing of the models and methods. Despite stratigraphic architectures apparently consistent with those expected within the sequence stratigraphic paradigm, blind-test applications yield: 1) deducted erroneous base-level curves, 2) systems-tract classification mismatches, 3) disconnected systems-tracts type and actual base level, 4) time-transgressive basin-floor fans, and 5) missing systems tracts. Stratigraphic forward models using base-level curves derived from Wheeler diagrams cannot match the timing, redeposited-sediment volume, and depositional environments observed in the XES02 experiment. These mismatches result from common Wheeler diagram construction practice, producing poorly resolved base-level minima timing and base-level fall durations, hence inaccurate fall rates. Consequently, reconstructions of controlling factors based on stratal architectures remain uncertain, making predictions similarly uncertain. A reasonable path forward is to properly acknowledge these uncertainties while performing stratigraphic analysis and to address them through multiple scenario analysis and modeling.

A skill‐specific dynamic labour supply and labour demand framework: A scenario analysis for the Western Balkan countries to 2030

AbstractThis paper pursues a multiple scenario analysis until 2030 to identify past and potential future labour supply and demand dynamics of four different skill groups in the six Western Balkan (WB6) countries. Simulation results of the baseline scenario show that skill shortages and surpluses would coexist: In all WB6 countries, the low educated would experience labour shortages within or shortly after the projection period while in some WB6 countries there is evidence of growing excess labour among Med‐VETs, Med‐GENs and the highly educated. Additional scenarios point to important country‐specific policy options that can help to mitigate these projected labour market imbalances.

Toward the economic-environmental sustainability of smallholder farming systems through judicious management strategies and optimized planting structures

Sustainable crop production practices have become a global issue. However, current smallholder farming systems face many challenges due to the unreasonable structure and management strategies associated with planting crops. Although optimization is crucial, implementation remains unclear. Accordingly, here we investigate three typical smallholder farming systems in Quzhou County, the North China Plain, China, including 100 smallholder family units growing either wheat-maize, cotton, or vegetables. Changes in economic and environmental indicators are comprehensively analyzed through optimized planting structure and management strategies. Results show that although the vegetable system is the most profitable (i.e., by magnitudes of 10.7 and 6.5 compared to wheat-maize and cotton systems, respectively), environmental pressures associated with this system are greater (by magnitudes of 2.2–2.6 and 4.1–5.6, respectively). The Emergy Sustainability Index value was lower in the vegetable system (0.1) compared to the wheat-maize (0.4) and cotton (3.9) systems. Findings highlight that the economic-environmental nexus trade-offs among these three cropping systems under the status quo are highly unfavorable to sustainable crop production. Moreover, suitable management strategies and optimized planting structures can mitigate the global warming potential by 30–57% with a 102–120% increase in profits, which hinges largely on management strategies and targeted requirements. Our multiple scenario analysis shows that achieving a synergy between the economic-environmental nexus among various cropping systems is viable at county level. Results from this study can be used as a reference for other developing regions worldwide dominated by smallholders using various cropping systems to achieve sustainable smallholder agriculture.

Multiple Scenario Analysis of Battery Energy Storage System Investment: Measuring Economic and Circular Viability

Circular business models for batteries have been revealed in earlier research to achieve economic viability while reducing total resource consumption of raw materials. The objective of this study is to measure the economic performance of the preferred business model by creating different scenarios comparing second life (spent) and new battery investment for seven different European regions and four energy management strategies. Findings reveal levels of economic ability for a total of 34 scenarios simulated, including direct savings per kWh, a total change in energy costs per year, battery charge/discharge cycles, and comparative breakeven analyses. Regional effects are also measured based on day-ahead electricity prices and solar irradiation. The minimum payback time is 7 years before battery system investment costs are covered. The most viable energy management strategies also had the highest number of charge/discharge cycles, which decreases battery lifetime. Investment in a second life battery compared to a new battery reduced the payback time by 0.5 to 2 years due to lower investment costs. However, the estimated lifetime range (3 to 10 years) is lower compared to a new battery (5 to 15 years), which questions the circular business model viability for the scenarios studied. Energy management strategies should be combined and customized to increase economic benefits.

Facilitating Adoption of Evidence-Based Cervical Cancer Screening Strategies in the Peruvian Amazon Using a Novel Methodology: The Integrative Systems Praxis for Implementation Research (INSPIRE)

PURPOSE The aim to this work was to develop an operational research methodology for context-appropriate selection, adaptation, and evaluation of the implementation of novel cervical cancer screening and treatment methods. METHODS We developed the Integrative Systems Practice for Implementation Research (INSPIRE) methodology, a multifaceted strategy that blends existing theoretical implementation research frameworks and defines specific research methods for use at each phase. INSPIRE is a participatory, iterative process involving 4 phases: system understanding, finding leverage, acting, and learning/adapting. Mixed methods were used to meet the objectives of each INSPIRE phase. Qualitative methods were grounded in soft systems methodology and CFIR, and quantitative methods in RE-AIM. Pathway and scenario analyses supplemented standard research methods. RESULTS We engaged more than 90 multilevel stakeholders in the design of a new and improved screen-and-treat system. Elaboration of system process maps through triangulation of the mixed-methods data served to create a shared reference of the current system in participatory discussions. Significant leverage opportunities were identified, including reducing fragmentation, inefficiency, and a lack of standardization to increase women’s acceptability of screening and adherence to the continuum of care. A variety of interventions were evaluated using a multiple scenario analysis tool and, ultimately, stakeholders recommended adoption of human papillomavirus testing/self-sampling to increase coverage and ablative treatment of all human papillomavirus–positive women to reduce loss to follow up. Implementation was launched in July 2019, and iterative application of INSPIRE to early implementation challenges is being used to ensure real-time adaptation to emerging system behaviors and the acceptability and sustainability of the program. CONCLUSION Continued success in the engagement of stakeholders in shared decision making suggests that using the INSPIRE methodology in designing implementation strategies increases a sense of ownership in the process, which may lead to more sustainable screening programs in low- and middle-income countries compared with top-down approaches.

The environmental impacts of municipal solid waste landfills in Europe: A life cycle assessment of proper reference cases to support decision making

In Europe, 23% of the generated municipal solid waste (MSW) was landfilled in 2017. Despite the landfill targets which define waste and landfill requirements, there is still high variability in the waste management performance between EU Member States. Aim of the study was to give an overview of the variability of environmental impacts of MSW sanitary landfills in Europe in relation to the different levels of implementation of the requirements. Life cycle assessment (LCA) was adopted as tool to define the impacts of the different landfill conditions over a 100-year period. Based on previous studies, consistent methodological choices were made to allow comparability of the results. Four reference cases were defined based on average bulk MSW compositions to represent the European conditions, with L0 values of 18, 61, 90 and 138 [m3 CH4/t waste]. Furthermore, multiple scenario analysis was used to increase the relevance of the assessment and address the variability of site-specific factors, such as waste composition, climatic conditions and landfill management, which influence the impacts of landfills. Results of the study showed the range of potential impacts in Europe in relation to the variation of influencing factors, with values for climate change ranging from 124 to 841 kg CO2 eq., and with environmental savings obtained for categories such as ecotoxicity and human toxicity for scenarios with landfill gas - to - energy (LFGTE) solutions. The results emphasized the dependence of landfill impacts on waste composition, but also on the LFG treatment and climatic conditions. The outcome of the study also highlight how low amounts of biodegradable fractions reduce the impacts of landfills, as well as their variability in relation to leachate production rates or LFG treatment solutions. Therefore the overall results support the current targets and requirements reported in the Waste Directive 2008/98/EC, Circular Economy package and Landfill Directive 1999/31/EC.

Exogenous Shocks，Real Estate Prices and Enterprise Investment

Since the outbreak of the Global Financial Crisis in 2008, the evolutions of real estate prices and enterprise investment have become the important features of China’s economy. According to the National Bureau of Statistics in China, during the period from 2009 to 2016, the growth rate of China’s fixed asset investment takes a downward trend. The growth rate of the investment in 2009 was as high as 30.1%, while in 2016, it was only 7.9%. However, during the same period, the growth rate of real estate prices showed a V”shape. From 2009 to 2013, the growth rate of commercial housing in China was stable at 7.5%, and during the period from 2014 to 2016, the growth rate of housing prices rose rapidly from 1.4% to 10.1%. According to the data released by the People’s Bank of China, 45.6% of the new loans in 2016 were related to real estate loans and the issue of transforming the economy from real to fictitious was widely discussed in various circles. Along with the government’s continued macroeconomic regulation measures of the real estate market, the current slowdown in enterprise investment growth and the sharp rise in real estate prices have eased, but the sources of this problem have not been completely eliminated, such as housing prices rising from the first and second tier cities to the third and fourth tier cities. Physical investment, especially private investment growth is still not optimistic. In view of this, a clear understanding of the intrinsic link between real estate prices and enterprise investment is of great significance for preventing and defusing China’s systemic financial risks and introducing more effective macroeconomic regulation policies. Based on the stylized facts of China’s economic running since 2008, this paper constructs a New Keynesian Dynamic Stochastic General Equilibrium model with enterprises’ Balance Sheet Recession” mechanism, focusing on exploring how the features of the housing rigid demand and enterprises’weak foreign demand influence the relationship between enterprise investment and real estate prices. The results show that: First, the positive shock of the housing preference which origins from the rigid housing demand crowds in the real investment by a small margin while increasing housing prices. Second, the negative shock of the enterprise balance sheet, stemming from the weak foreign demand, decreases enterprise investment through the mechanisms of Financing Premium”and Balance Sheet Recession”, which causes the surplus funds to shift to the real estate market and then pushes up housing prices. Further, through the multiple scenario analysis of different tax burdens in enterprises, the research finds that confronted with the negative shock of the enterprise balance sheet, the lower corporate taxes can help to stimulate enterprise investment, and alleviate the phenomenon that the funds transfer from the real economy to the virtual economy. The main contributions of this paper are reflected in three aspects. First, in theory, this paper pays attention to the transmission mechanism of the enterprise balance sheet, and introduces the theory of Balance Sheet Recession” into the New Keynesian Dynamic Stochastic General Equilibrium model. The current research in this area is very scarce. This study expands the theoretical research on the problem of the enterprise balance sheet. Second, from the perspective of research, this paper pays attention to the portrayal of the stylized facts of China’s economy, and focuses on the mechanism of the link between current real estate prices and enterprise investment, which plays an important role in the objective and comprehensive understanding of the relationship between the two. The third one is on the policy implication. In view of the different tax burdens of enterprises, this paper examines the effects of both shocks from housing preference and the enterprise balance sheet on the relationship between real estate prices and enterprise investment, which can provide a certain theoretical basis for the government decision-making departments to issue corresponding macro-control policies.

Integrating ecosystem services flows into water security simulations in water scarce areas: Present and future

Water resources are indispensable resources for human survival. Researchers have used different methods to evaluate regional water security situations to ensure sustainable development. However, existing water security assessment models focus on the current status of water security in a static state and do not introduce flow characteristics of water resources into regional water security assessments. On the other hand, the quality of water resources should be reflected in not only current water security situations but also change trends of water security. Therefore, this paper attempts to integrate ecosystem service flows into a water security simulation using a simplified service path attribution networks (SPANs) model. Simultaneously, by simulating future scenarios and comparing future and current water security statuses, this paper systematically evaluates regional water security status and provides water security management and control suggestions that meet the requirements of regional development. The results show that water security in Henan Province and Shandong Province is lacking, and water resource management and control measures need to be strengthened to improve the water security in these areas. The water security assessment framework established in this paper not only reasonably assesses the status of water security but also provides a development model that meets the needs of the study area through a multiple scenario analysis. This study provides a scientific basis for government departments to formulate water management policies.

Interest rates forecasting and stress testing in India: a PCA-ARIMA approach

In the Basel III era, measuring, managing, mitigating and forecasting interest rate risk has become significant. In the first study of its kind, this paper develops a principal component analysis-based forecasting of interest rates of different maturities and stress testing approach in a univariate auto-regressive integrated moving average (ARIMA) framework in the context of India. Debating the existence of multiple representations of interest rates in the Indian market, the study broke down all the short-term, as well as long-term interest rates to derive the optimal principal component of unique interest rates. These unique interest rates are further exercised to forecast the future interest rates through the ARIMA model. The rolling average method was applied to the recently created Indian volatility index (VIX) to place the stress points. The model performance was examined over this stress point of building multiple scenario analysis. The study found that ARIMA (2-1-1) forecasting model of interest rates produced a better forecast result, both in the case of in-sample and out-of-sample performances as well as in stress periods. From the forecasting results, the study found that the proportionate gain in yield is higher as the maturity increases. By examining the stress period on the basis of eight quarters (2007: Q4 of 2009: Q3) rolling average on Indian volatility Indices, the survey concluded that the Indian economy will reach a stable situation signalled by less volatile interest rates post the second quarter of 2018.

An evaluation framework to build a cost-efficient crop monitoring system. Experiences from the extension of the European crop monitoring system

This paper presents an evaluation framework followed to identify cost-efficient alternatives to extend the MARS Crop Yield Forecasting System (MCYFS), run by the European Commission Joint Research Centre since 1992, to other main producing areas of the world: Eastern European Neighbourhood, Asia, Australia, South America and North America. These new systems would follow the principles and components of the MCYFS Europe: a meteorological data infrastructure, a remote sensing data infrastructure, a crop modelling platform, statistical tools, a team of analysts and a crop area estimation component. The framework designed evaluates the performance of the possible MCYFS-like system realizations against six defined objectives and their costs. Possible monitoring systems are based on a combination of different technical solutions for each of the MCYFS components, and are evaluated through an automatic algorithm that calculates the expected system performance –relying on a priori expert judgement–, the costs, and possible risks to construct some technical solutions, to finally identify the cost-efficient ones. A baseline system, achieving the minimum required performance, was identified as the most efficient starting point for the MCYFS extension in all the geographical areas. Such system would be built upon: (i) near real-time reanalysis meteorological products; (ii) remote sensing data from low-resolution (~1 km) platforms with a long-term product archive; (iii) crop models based on crop-specific model calibration from experimental data published in scientific literature; (iv) statistical methods based on trend and regression analysis applied to national level; (v) a team of analysts with specific technical profiles (on meteorology, remote sensing, and agronomy); and (vi) digital classification of very high resolution imagery supported by non-expensive ground surveys for area estimation. In countries where accessibility to local data and resources is high the baseline system can be upgraded enhancing some of the components: sub-national statistical analysis with additional statistical methods like multiple regression or scenario analysis; recruitment of experts on local agricultural conditions in the team of analysts; local calibration of crop models with experimental data; and exploiting high and low resolution biophysical products from remote sensing for crop monitoring.

Assessing cumulative impacts of the proposed Lower Mekong Basin hydropower cascade on the Mekong River floodplains and Delta – Overview of integrated modeling methods and results

Approach, methods, and results from a comprehensive integrated water resources modeling framework for the Lower Mekong Basin (LMB) are presented in this paper. The modeling suite was specifically tailored to the known conditions and data availability at key locations within the LMB and more particularly within the floodplains of Cambodia and the Mekong River Delta of Vietnam. Model simulation output was used to project cumulative changes to hydraulic, sediment, and water quality parameters likely to occur in this relatively unique system under multiple scenarios of hydropower development in the LMB. While substantial changes in river flow regimes are predicted as a result of hydropeaking operations especially when combined with dry-season drawdowns to maximize power production, the multiple-scenario analysis indicated that overall, the largest changes are expected to be in loss of sediment and nutrient transport and reduction of water quality due to increased salinity intrusion into areas of the Mekong Delta.

Fuel-price reform to achieve climate and energy policy goals in Saudi Arabia: A multiple-scenario analysis

Saudi Arabia experiences annual growth of 6% in its power demand. Generation expansion has been driven by low domestic retail fuel prices leading to a power generation mix based on fossil fuels only. In light of current climate change discussions, this research assesses future generation expansion under different potential fuel-price reforms by an enhanced OSeMOSYS model. Results demonstrate that domestic retail fuel price levels >20% [>60%] of expected international wholesale fuel prices1 are necessary to minimize emissions when considering emissions penalties [without pricing for emissions]. By 2030 renewables can reach 70% penetration by capacity and 30% by energy.

Adapting the Basel II advanced internalratings- based models for International Financial Reporting Standard 9

Banks around the globe are implementing International Financial Reporting Standard 9 (IFRS 9), which is a considerable effort. A key element of IFRS 9 is a forward-looking “expected loss” impairment model, which is a significant shift from the incurred-loss model. We examine how we may use advanced internal-ratings-based (A-IRB) models in the estimation of expected credit losses for IFRS 9 purposes. We highlight the necessary model adaptations required to satisfy the new accounting standard. By leveraging on the A-IRB models, banks can lessen their modeling efforts in fulfilling IFRS 9 and capture the synergy between different modeling endeavors within institutions. In outlining the proposed probability of default, loss given default and exposure at default models, we provide detailed examples of how they may be implemented on secured lending. Moreover, in discussing the issues related to the estimation of the expected credit loss for IFRS 9, we highlight the challenges involved and propose practical solutions to deal with them. For instance, we propose the use of a convexity adjustment approach to circumvent the need for assigning probabilities in multiple-scenario analysis.

Effective Design and Planning Specification of Low Impact Development Practices Using Water Management Analysis Module (WMAM): Case of Malaysia

Developers are increasingly looking for the best management practices to reduce the risk of floods in rapidly growing urban areas. Low impact development (LID) is regarded as one of the most suitable solutions for urban stormwater management and thus the U.S. EPA’s (Environmental Protection Agency) SWMM5.1 (Storm Water Management Model) added the hydrological simulation function for LID structures in 2009. However, SWMM5.1 cannot consider the optimal or best physical specifications of LID design and planning fitted to a study area, nor can it instantly derive the best combination for multiple LID designs and plans. Therefore, in this study, a web-based decision support system (DSS) for the EPA’s SWMM 5.1, referred to as the Water Management Analysis Module (WMAM) is used to decide the most effective specifications of design and planning parameters for LID structure. This study was carried out over an urban catchment of University Technology Malaysia campus located in Johor, Malaysia. The hydrologic cycles with and without LID were simulated using EPA SWMM5.1. The sensitivity analysis and multiple scenario analysis were performed using WMAM. As a result, the effective specification of LID design parameters indicates that peak flow is reduced to 20.95% and 17.5% for two sub-catchments, S1 (highest by area) and S6 (lowest by area) by installing an LID structure. Thus, this study provides a tool for the best solution for what values for physical parameters will be the best for a specified LID type and what capacities can achieve the particular objectives using WMAM.

Adapting Basel's A-IRB Models for IFRS 9 Purposes

Banks around the globe are implementing IFRS 9 which is a considerable effort. A key element of IFRS 9 is a forward-looking “expected loss” impairment model, which is a significant shift from the current incurred loss model. In this paper, we examine how we may use A-IRB models in the estimation of expected credit losses for IFRS 9 purposes. We highlight the necessary model adaptations required to satisfy the new accounting standard. By leveraging on the A-IRB models, banks can lessen their modeling efforts in fulfilling IFRS 9 and capture the synergy among different modeling endeavors within the institutions. In outlining the proposed PD, LGD, and EAD models, we provide detailed examples of how they may be implemented on secured lending. Moreover, in discussing the issues related to the estimation of the expected credit loss for IFRS 9, we highlight the challenges involved and propose practical solutions to deal with them. For instance, we propose the use of a convexity adjustment approach to circumvent the need of assigning probabilities in multiple-scenario analysis.

Simulation as an ethical imperative and epistemic responsibility for the implementation of medical guidelines in health care.

Guidelines orient best practices in medicine, yet, in health care, many real world constraints limit their optimal realization. Since guideline implementation problems are not systematically anticipated, they will be discovered only post facto, in a learning curve period, while the already implemented guideline is tweaked, debugged and adapted. This learning process comes with costs to human health and quality of life. Despite such predictable hazard, the study and modeling of medical guideline implementation is still seldom pursued. In this article we argue that to systematically identify, predict and prevent medical guideline implementation errors is both an epistemic responsibility and an ethical imperative in health care, in order to properly provide beneficence, minimize or avoid harm, show respect for persons, and administer justice. Furthermore, we suggest that implementation knowledge is best achieved technically by providing simulation modeling studies to anticipate the realization of medical guidelines, in multiple contexts, with system and scenario analysis, in its alignment with the emerging field of implementation science and in recognition of learning health systems. It follows from both claims that it is an ethical imperative and an epistemic responsibility to simulate medical guidelines in context to minimize (avoidable) harm in health care, before guideline implementation.

Model risk – daring to open up the black box

AbstractWith the increasing use of complex quantitative models in applications throughout the financial world, model risk has become a major concern. Such risk is generated by the potential inaccuracy and inappropriate use of models in business applications, which can lead to substantial financial losses and reputational damage. In this paper, we deal with the management and measurement of model risk. First, a model risk framework is developed, adapting concepts such as risk appetite, monitoring, and mitigation to the particular case of model risk. The usefulness of such a framework for preventing losses associated with model risk is demonstrated through case studies. Second, we investigate the ways in which different ways of using and perceiving models within an organisation both lead to different model risks. We identify four distinct model cultures and argue that in conditions of deep model uncertainty, each of those cultures makes a valuable contribution to model risk governance. Thus, the space of legitimate challenges to models is expanded, such that, in addition to a technical critique, operational and commercial concerns are also addressed. Third, we discuss through the examples of proxy modelling, longevity risk, and investment advice, common methods and challenges for quantifying model risk. Difficulties arise in mapping model errors to actual financial impact. In the case of irreducible model uncertainty, it is necessary to employ a variety of measurement approaches, based on statistical inference, fitting multiple models, and stress and scenario analysis.