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Comparing expert elicitation and model-based probabilistic technology cost forecasts for the energy transition.

ABSTRACT: We conduct a systematic comparison of technology cost forecasts produced by expert elicitation methods and model-based methods. Our focus is on energy technologies due to their importance for energy and climate policy. We assess the performance of several forecasting methods by generating probabilistic technology cost forecasts rooted at various years in the past and then comparing these with observed costs in 2019. We do this for six technologies for which both observed and elicited data are available. The model-based methods use either deployment (Wright's law) or time (Moore's law) to forecast costs. We show that, overall, model-based forecasting methods outperformed elicitation methods. Their 2019 cost forecast ranges contained the observed values much more often than elicitations, and their forecast medians were closer to observed costs. However, all methods underestimated technological progress in almost all technologies, likely as a result of structural change across the energy sector due to widespread policies and social and market forces. We also produce forecasts of 2030 costs using the two types of methods for 10 energy technologies. We find that elicitations generally yield narrower uncertainty ranges than model-based methods. Model-based 2030 forecasts are lower for more modular technologies and higher for less modular ones. Future research should focus on further method development and validation to better reflect structural changes in the market and correlations across technologies.

SUBMITTER: Meng J

PROVIDER: S-EPMC8271727 | biostudies-literature |

REPOSITORIES: biostudies-literature

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A strategy to improve expert technology forecasts.

Project description:Forecasts of the future cost and performance of technologies are often used to support decision-making. However, retrospective reviews find that many forecasts made by experts are not very accurate and are often seriously overconfident, with realized values too frequently falling outside of forecasted ranges. Here, we outline a hybrid approach to expert elicitation that we believe might improve forecasts of future technologies. The proposed approach iteratively combines the judgments of technical domain experts with those of experts who are knowledgeable about broader issues of technology adoption and public policy. We motivate the approach with results from a pilot study designed to help forecasters think systematically about factors beyond the technology itself that may shape its future, such as policy, economic, and social factors. Forecasters who received briefings on these topics provided wider forecast intervals than those receiving no assistance.

| S-EPMC8166153 | biostudies-literature

Probabilistic Forecasts Using Expert Judgment: The Road to Recovery From COVID-19

Project description:The COVID-19 pandemic has had a devastating effect on many industries around the

| S-EPMC9669511 | biostudies-literature

Recalibrating probabilistic forecasts of epidemics.

Project description:Distributional forecasts are important for a wide variety of applications, including forecasting epidemics. Often, forecasts are miscalibrated, or unreliable in assigning uncertainty to future events. We present a recalibration method that can be applied to a black-box forecaster given retrospective forecasts and observations, as well as an extension to make this method more effective in recalibrating epidemic forecasts. This method is guaranteed to improve calibration and log score performance when trained and measured in-sample. We also prove that the increase in expected log score of a recalibrated forecaster is equal to the entropy of the PIT distribution. We apply this recalibration method to the 27 influenza forecasters in the FluSight Network and show that recalibration reliably improves forecast accuracy and calibration. This method, available on Github, is effective, robust, and easy to use as a post-processing tool to improve epidemic forecasts.

| S-EPMC9799311 | biostudies-literature

Probabilistic forecasts of trachoma transmission at the district level: A statistical model comparison.

Project description:The World Health Organization and its partners are aiming to eliminate trachoma as a public health problem by 2020. In this study, we compare forecasts of TF prevalence in 2011 for 7 different statistical and mechanistic models across 9 de-identified trachoma endemic districts, representing 4 unique trachoma endemic countries. We forecast TF prevalence between 1-6 years ahead in time and compare the 7 different models to the observed 2011 data using a log-likelihood score. An SIS model, including a district-specific random effect for the district-specific transmission coefficient, had the highest log-likelihood score across all 9 districts and was therefore the best performing model. While overall the deterministic transmission model was the least well performing model, although it did comparably well to the other models for 8 of 9 districts. We perform a statistically rigorous comparison of the forecasting ability of a range of mathematical and statistical models across multiple endemic districts between 1 and 6 years ahead of the last collected TF prevalence data point in 2011, assessing results against surveillance data. This study is a step towards making statements about likelihood and time to elimination with regard to the WHO GET2020 goals.

| S-EPMC5340843 | biostudies-other

Probabilistic projections of granular energy technology diffusion at subnational level.

Project description:Projections of granular energy technology diffusion can support decision-making on climate mitigation policies and infrastructure investments. However, such projections often do not account for uncertainties and have low spatial resolution. S-curve models of technology diffusion are widely used to project future installations, but the results of the different models can vary significantly. We propose a method to create probabilistic projections of granular energy technology diffusion at subnational level based on historical time series data and testing how various projection models perform in terms of accuracy and uncertainty to inform the choice of models. As a case study, we investigate the growth of solar photovoltaics, heat pumps, and battery electric vehicles at municipality level throughout Switzerland in 2000-2021 (testing) and until 2050 (projections). Consistently for all S-curve models and technologies, we find that the medians of the probabilistic projections anticipate the diffusion of the technologies more accurately than the respective deterministic projections. While accuracy and probabilistic density intervals of the models vary across technologies, municipalities, and years, Bertalanffy and two versions of the generalized Richards model estimate the future diffusion with higher accuracy and sharpness than logistic, Gompertz, and Bass models. The results also highlight that all models come with trade-offs and eventually a combination of models with weights is needed. Based on these weighted probabilistic projections, we show that, given the current dynamics of diffusion in solar photovoltaics, heat pumps, and battery electric vehicles in Switzerland, the net-zero emissions target would be missed by 2050 with high certainty.

| S-EPMC10578461 | biostudies-literature

Comparing trained and untrained probabilistic ensemble forecasts of COVID-19 cases and deaths in the United States.

Project description:The U.S. COVID-19 Forecast Hub aggregates forecasts of the short-term burden of COVID-19 in the United States from many contributing teams. We study methods for building an ensemble that combines forecasts from these teams. These experiments have informed the ensemble methods used by the Hub. To be most useful to policymakers, ensemble forecasts must have stable performance in the presence of two key characteristics of the component forecasts: (1) occasional misalignment with the reported data, and (2) instability in the relative performance of component forecasters over time. Our results indicate that in the presence of these challenges, an untrained and robust approach to ensembling using an equally weighted median of all component forecasts is a good choice to support public health decision-makers. In settings where some contributing forecasters have a stable record of good performance, trained ensembles that give those forecasters higher weight can also be helpful.

| S-EPMC9247236 | biostudies-literature

Probabilistic forecasts of international bilateral migration flows.

Project description:We propose a method for forecasting global human migration flows. A Bayesian hierarchical model is used to make probabilistic projections of the 39,800 bilateral migration flows among the 200 most populous countries. We generate out-of-sample forecasts for all bilateral flows for the 2015 to 2020 period, using models fitted to bilateral migration flows for five 5-y periods from 1990 to 1995 through 2010 to 2015. We find that the model produces well-calibrated out-of-sample forecasts of bilateral flows, as well as total country-level inflows, outflows, and net flows. The mean absolute error decreased by 61% using our method, compared to a leading model of international migration. Out-of-sample analysis indicated that simple methods for forecasting migration flows offered accurate projections of bilateral migration flows in the near term. Our method matched or improved on the out-of-sample performance using these simple deterministic alternatives, while also accurately assessing uncertainty. We integrate the migration flow forecasting model into a fully probabilistic population projection model to generate bilateral migration flow forecasts by age and sex for all flows from 2020 to 2025 through 2040 to 2045.

| S-EPMC9436307 | biostudies-literature

Experiences of Structured Elicitation for Model-Based Cost-Effectiveness Analyses.

Project description:Empirical evidence supporting the cost-effectiveness estimates of particular health care technologies may be limited, or it may even be missing entirely. In these situations, additional information, often in the form of expert judgments, is needed to reach a decision. There are formal methods to quantify experts' beliefs, termed as structured expert elicitation (SEE), but only limited research is available in support of methodological choices. Perhaps as a consequence, the use of SEE in the context of cost-effectiveness modelling is limited.This article reviews applications of SEE in cost-effectiveness modelling with the aim of summarizing the basis for methodological choices made in each application and recording the difficulties and challenges reported by the authors in the design, conduct, and analyses.The methods used in each application were extracted along with the criteria used to support methodological and practical choices and any issues or challenges discussed in the text. Issues and challenges were extracted using an open field, and then categorised and grouped for reporting.The review demonstrates considerable heterogeneity in methods used, and authors acknowledge great methodological uncertainty in justifying their choices. Specificities of the context area emerging as potentially important in determining further methodological research in elicitation are between- expert variation and its interpretation, the fact that substantive experts in the area may not be trained in quantitative subjects, that judgments are often needed on various parameter types, the need for some form of assessment of validity, and the need for more integration with behavioural research to devise relevant debiasing strategies.This review of experiences of SEE highlights a number of specificities/constraints that can shape the development of guidance and target future research efforts in this area.

| S-EPMC6021555 | biostudies-literature

Psoriasis Prevalence and Severity by Expert Elicitation.

Project description:IntroductionAn estimated 2-4% of Western populations are thought to have psoriasis, with a regional incidence ranging from 0.09% to 11.43%. Variance in estimates is a result of differences in study populations, methodology, regional differences, and definitions of disease. Reliable prevalence estimates of plaque psoriasis are challenging to establish. Further, the distribution of psoriasis severity in the population is unknown. This study aims to establish the utility of expert elicitation (EE) as a method for estimating unknown parameters in dermatology by (1) estimating the prevalence of psoriasis in the adult population, and (2) estimating previously unknown disease severity distribution.MethodsAn expert panel of 11 Canadian dermatologists with demonstrated expertise in psoriasis was formed. A proof-of-concept EE exercise estimated psoriasis prevalence in the general population in Canada, followed by estimation of psoriasis disease severity distribution by body surface area (BSA). Expert estimates were consolidated using Bayesian methods to statistically model the data and represent uncertainty.ResultsThe median prevalence of psoriasis in the adult population using the Bayesian estimate was 3.0% (95% credibility interval, 2.7-3.3%), compared with the estimated mean prevalence of 3.4% (95% confidence interval, 2.2-4.9%). By EE, the estimated cumulative distribution of disease severity assessed by BSA suggests that approximately 50% of patients have a BSA of < 3% and 78% of patients have a BSA of < 10%, with only 2% having a BSA of > 50%.ConclusionThe EE approach resulted in prevalence estimates that had a narrow distribution and were consistent with published literature, supporting its value in dermatology as a complementary method to help guide decision-making in areas where evidence is scarce or uncertain.

| S-EPMC8163919 | biostudies-literature

Continuous-trait probabilistic model for comparing multi-species functional genomic data

Project description:We developed a probabilistic method to simultaneously infer genome-wide heterogeneous evolutionary states. We applied the proposed method to analyze a cross-species DNA replication timing (RT) dataset from the same cell type in five primate species. We demonstrated that the proposed model enables discovery of genomic regions with distinct evolutionary patterns of RT.

2018-06-20 | GSE111733 | GEO

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