Probability Of Events Research Articles

Addressing large scale patterns of no-flow events in rivers: An in-depth analysis with Achelous software

Establishment of hydrological criteria that could serve as guidelines for addressing intermittency is not an easy task. However, efforts in the last years are yielding promising advancements in this direction. Scientists have been working to unravel the complexities of intermittency dynamics. In this study, we aimed to investigate the characteristics of naturally intermittent water systems by exploring the occurrence of no-flow events. A hydrological model was employed to generate streamflow data. Our analysis encompassed a thorough examination of nineteen flow regime metrics, estimated across 2064 subbasins, with 190 of these meeting the criterion for intermittency. A custom Python library was deployed to automate the quantification of no-flow events, aligning with the concept of critical thresholds. Using the probabilistic t-distributed Stochastic Neighbor Embedding technique to capture complex patterns, three clusters were emerged. The first one was characterized by a low probability of no-flow events and a small number of no-flow events per year, the second cluster was abundant in no-flow events and demonstrated a tendency towards longer annual recession time scales. The third cluster stands out due to the significant variance in the duration of no-flow events. Concerning the time variability of the no-flow events, we concluded that they predominantly occurred during August. Both long- and short-term quantification of no-flow events should be under consideration so as to harmonize the naturally intermittent waterways with the water use requirements and the potential consequences of not meeting them. Future research should prioritize the investigation of hydroecology and ecohydrology in relation to streamflow dynamics and ecosystem interactions. By doing so, we can elevate our comprehension of how intermittent water systems function and their significance within the broader ecological context.

A wee exploration of techniques for risk assessments of extreme events

AbstractAssessing the behaviour of extreme events in univariate and multivariate settings entails many challenges, from the need to capture different sources of non-stationarity to adequately extrapolate into the tail of the distribution and compute probabilities of extreme events associated with high-dimensional vectors. Motivated by these common issues, we use a combination of methods from extreme-value theory, dimensionality reduction, non-parametric statistics, copula theory, and bootstrap model averaging to provide estimates of risk measures associated with environmental extremes. The work is tailored to the four data challenges presented in the EVA (2023) Conference Data Challenge competition, and the methods introduced here represent the approach taken by the Wee Extremes group.

A multi-layer constrained spectral k-embedded clustering methodology approach for intelligent partitioning of power grid to enhance resiliency in transmission networks

The intentional controlled islanding by intelligent partitioning of the power grid is considered as essential to protect the grid from cascading events, faults, and High Impact Low Probability (HILP) events. To enhance the resilience, stability, and security of the power grid, the proposed model in this paper intentionally divides the affected power network into islands. This paper presents an intelligent partitioning approach to create an islanding solution through multilayer graphs using spectral clustering. The controlled islanding algorithm uses a multi-criteria objective function that considers the correlation coefficients among the frequency of the buses and minimal disturbances in the real and reactive power. The proposed control technique is implemented in two phases. The first phase employs correlation coefficients between frequency of the buses and modularity clustering to identify clusters of coherent buses. During the second stage, all nodes are categorised into groups using Multi-level constrained Spectral Clustering (ML-CSC) to determine the solution for Intentional controlled Islanding that satisfies bus coherency with the minimum level of disruptions to real and reactive power flows across the boundaries. The proposed algorithm for resolving the generator coherency issue and an intelligent islanding solution is demonstrated by simulation experiments conducted on an IEEE 39-bus transmission test system developed in DIGSILENT Powerfactory version 2023. The MATLAB version R2023a is used to construct the ML-CSC control method. The results demonstrated that the proposed ML-CSC algorithm substantially impacts the functioning of the power system, enabling the formation of intelligent islanding during abnormal conditions. Also, the results clearly show that instead of using single-layer spectral clustering, the multi-layer spectral clustering yields a better intentional islanding solution with minimum power flow mismatch which enhances the transient stability of the islands.

Time-Dependent Aftershock Probabilistic Forecasting and Ensemble Model Implementation: Insights from the 2021 Ms 7.4 Maduo Earthquake

Abstract On 22 May 2021, an Ms 7.4 earthquake with a focal depth of 17 km struck the Maduo region of Qinghai province, breaking a 3.8-year quiescence of strong earthquakes (magnitude >7.0) in mainland China. This event has increased stress on the Maqin–Maqu segment of the Kunlunshan fault, heightening the potential for a large earthquake in the region. In this study, we employed the epidemic-type aftershock sequence (ETAS) model and Reasenberg–Jones (R–J) model to fit the aftershock sequence following the mainshock, analyzing the temporal response and its ability to influence future generations. Concurrently, ensemble models were explored to leverage the strengths of both the ETAS and R–J models. Short-term forecasts of the probability and occurrence rate of aftershock events with varying magnitudes were conducted for the next three days. Then statistical methods, that is, receiver-operating characteristic diagrams and information gain, were used to evaluate the unconditional and relative performance. Our findings include that the ETAS model indicates a high decay rate with many aftershocks triggered by previous events, whereas the R–J model shows a normal decay rate with a higher proportion of strong aftershocks. The ETAS and R–J models perform better than random guesses for different aftershock magnitudes, but the ETAS model is somewhat affected by the problem of missing small earthquakes for a short period of time after the mainshock. The ensemble model that chooses the minimum strategy shows promise, especially in the early period, suggesting a reasonable and cautious decision approach should be chosen during the unstable stage. This study highlights the importance of short-term aftershock probability estimation for seismic research and decision-making. In the absence of more accurate models, current analytical approaches within the operational earthquake forecasting framework remain valuable. Continuous testing, feedback, and refinement of forecasting models, along with the development of ensemble models, are essential for enhancing seismic risk assessment and mitigation strategies.

УПРАВЛЕНИЕ НАДЕЖНОСТЬЮ И РИСКОМ В ХОЗЯЙСТВЕ АВТОМАТИКИ И ТЕЛЕМЕХАНИКИ

the article is devoted to the research of dependability and risk management systems at JSCo “Russian Railways” taking into account the specifics in signaling and interlocking division. On this topic the author reviews the current regulatory framework (domestic and foreign), addressing to the problems of its harmonization and proper adaptation within the company. The state of affairs in automation of dependability and risk management processes is considered, which is implemented in several corporate automated systems: “AS URRAN”, “AS ANSh”, “EKP URRAN-Sh”, “AS ANPSh”, “EK ASUI FA”, “AS FA”. A comparative analysis of these systems made it possible to determine the main directions for their improvement in order to maintain the authenticity, relevance and importance of the information contained in them. Despite many ways to model events that may lead to a violation of the transportation process safety, the article discusses one of them: the creation of the tree structure of probable events that have a sequential connection with each other. The author believes that the application of this method in practice will allow to establish typical trends in the occurrence of such cases. It will have a positive impact on the quality of predictive analytics in the company and will improve the existing risk-based approach. In addition, a rethought way to determine and visually present the level of train delays risk caused by failures in signaling and interlocking division is proposed. The central place here is occupied by heat maps of risk and its components.

Insight process safety of a hydrogen turbine supply system: A comprehensive dynamic risk assessment using a fuzzy Bayesian network

This study conducts a comprehensive risk assessment for a selected hydrogen supply system within a pilot hydrogen power plant. The assessment begins with hazard identification through Hazard and Operability (HAZOP) analysis, followed by quantification using bow-tie analysis and consequence modeling to evaluate the impacts of credible scenarios resulting from random failures. Furthermore, a fuzzy Bayesian network is employed to address uncertainties inherent in the quantitative approaches and to account for event combinations and dependencies. Sensitivity analysis is subsequently conducted to identify critical events. Utilizing three quantitative approaches, fault tree analysis, the Bayesian network, without and with evidence updating, the probability of the top event is determined to be 4.52 × 10−2/year, 4.50 × 10−2/year, and 4.70 × 10−2/year, respectively. Additionally, the estimated firezone resulting from the explosion's 300 mbar overpressure was 12 m. The findings highlight the significant role of pressure regulator valve and pressure transmitter failures, with posterior probabilities of 0.17/year and 1.90 × 10−3/year, in ensuring the system's safety.

Unsupervised Machine Learning-Based Singularity Models: A Case Study of the Taiwan Strait Basin

The identification of geochemical anomalies in oil and gas indicators is a fundamental task in oil and gas exploration, as the process of oil and gas accumulation is a low probability event. Machine learning algorithms for anomaly detection are applicable to the identification of oil and gas geochemical anomalies related to oil and gas accumulation. However, when using oil and gas indicators for anomaly detection, the diversity of these indicators often leads to the influence of the indicator redundancy on the identification of such features. Therefore, it is particularly important to select appropriate oil and gas indicators for anomaly detection. In this study, a hybrid model combining unsupervised machine learning methods and singularity analysis methods was used to evaluate oil and gas indicator anomalies using geochemical data from the Taiwan Strait Basin. The models used in this study include the singularity index model (LSP), the principal component model combined with the singularity index model (PCA and LSP), and the cluster analysis combined with the principal component model and the singularity index model (CLA-PCA-LSP). PCA models can reduce the dimensions of the data and retain as much information as possible. CLA divides data samples into different groups, so that samples within the same group are more similar and samples between different groups are less similar. LSP is mainly used for measuring the setting and singular degree of local anomalies in multi-scale geochemistry, geophysics, and other types of local anomalies, and it has a unique advantage in extracting low and weak anomalies and nonlinear characteristics. The results of the study show that the results obtained using the CLA-PCA-LSP hybrid model are very similar to those obtained by performing PCA on the entire index and then calculating the singularity index. This also verifies that, for the study areas of the Jiulongjiang Depression and Jinjiang Depression, we can select oil and gas indicators that are favorable for exploration analysis, without including all indicators in the analysis scope, thereby improving the computational efficiency. The application of a singularity analysis method and generalized self-similarity principle in extracting the geochemical information of oil and gas indicators in the Taiwan Strait Basin highlights key technologies such as the identification of weak anomalies, decomposition of composite anomalies, and integration of spatial information. The combination anomalies delineated by the singularity analysis method and S-A method not only reflect the spatial relationship with known oil and gas reservoir distribution, but also show the multiple combination anomalies in unknown areas, providing favorable guidance for the next exploration direction in the Taiwan Strait Basin.

Secular and modulator-specific drifts in the predictive performance of a rapid lung function decline algorithm: a cystic fibrosis patient registry study

PurposePredicting the onset of pulmonary exacerbation for people with cystic fibrosis (CF) is critical to identify those at high risk in advance and allows timely clinical intervention. The FEV1-indicated exacerbation signal (FIES) has been proposed by the CF Learning Network to identify pulmonary exacerbation events, but temporal predictive performance of this marker is unknown.MethodsTo assess the epidemiologic impact of secular trends in CF care and treatments on algorithms aimed at prediction of pulmonary exacerbation, we evaluated prediction model “drift” using a longitudinal retrospective cohort of 29,476 individuals from the U.S. CF Foundation Patient Registry (2011–2018). The FIES marker was defined for each clinical encounter using relative drops in lung function, measured as forced expiratory volume in 1 s of % predicted (FEV1pp). We formed predictive probabilities for each FIES event via a target function for a predictive algorithm of FEV1pp. Models were trained using 2 year data windows with prediction performance tested on subsequent years.ResultsYear-over-year FIES event discrimination remained consistent (area under the receiver-operator characteristic curve, AUC > 80%). Annual drift was < 1% with actual drop in AUC ~ 3% in 6 years. The model performed best in individuals with moderate-to-low baseline FEV1pp (AUC range: 84–85%). For relatively higher FEV1pp levels, discrimination was slightly lower with higher AUC variability.ConclusionWhile model fit improved by accounting for modulator therapy, prediction performance was similar. Most impactful model drifts occurred after 2012 coinciding with increased ivacaftor modulator use. Additional evaluation will be necessary given recent, broad uptake of highly effective modulators.

Assessing the cost-effectiveness of replacing antimetabolites with mTOR inhibitors in heart transplant immunosuppression in China: a network meta-analysis-based economic evaluation.

Although several pharmacoeconomic studies have assessed the cost-effectiveness of maintenance immunosuppressive regimens for heart transplant recipients, economic comparisons between various combination drug therapies remain sparse. This study used an economic evaluation based on network meta-analysis to assess the cost-effectiveness of four immunosuppressive regimens for adult heart transplant recipients in China. We conducted a systematic search for clinical trials in PubMed, Embase, Cochrane Library, Web of Science, China National Knowledge Infrastructure (CNKI), Wanfang Data, and VIP database. A validated Markov model was adapted to reflect the Chinese medical landscape. Four maintenance immunosuppression regimens were considered: tacrolimus/mycophenolate mofetil (TAC/MMF), cyclosporine/mycophenolate mofetil (CSA/MMF), everolimus/cyclosporine (EVL/CSA), and sirolimus/tacrolimus (SRL/TAC). The probabilities of health events were derived from a comprehensive literature review. Direct medical costs, adjusted for 2022 values, were from public documents and websites, while utilities for quality-adjusted life-years (QALYs) were taken from previous studies. Primary outcomes were mean lifetime cost, QALYs, and cost-effectiveness, with a willingness-to-pay (WTP) threshold set at three times China's GDP per capita in 2022. Sensitivity analyses were conducted to test the robustness of the results. The base case analysis identified TAC/MMF as the most cost-effective regimen, producing a mean of 6.31 QALYs per patient at a cost of Chinese Yuan (CNY) 534,182.89. Sensitivity analyses consistently reinforced TAC/MMF as the most cost-effective and robust choice. TAC/MMF is the most cost-effective maintenance immunosuppressive regimen for heart transplant recipients within the Chinese health system. The study findings are reinforced by sensitivity analyses, affirming their robustness amid various uncertainties.

Safety, Efficiency, and Mental Workload in Simulated Teledriving of a Vehicle as Functions of Camera Viewpoint.

Teleoperation services are expected to operate on-road and often in urban areas. In current teleoperation applications, teleoperators gain a higher viewpoint of the environment from a camera on the vehicle's roof. However, it is unclear how this viewpoint compares to a conventional viewpoint in terms of safety, efficiency, and mental workload. In the current study, teleoperators (n = 148) performed driving tasks in a simulated urban environment with a conventional viewpoint (i.e., the simulated camera was positioned inside the vehicle at the height of a driver's eyes) and a higher viewpoint (the simulated camera was positioned on the vehicle roof). The tasks required negotiating road geometry and other road users. At the end of the session, participants completed the NASA-TLX questionnaire. Results showed that participants completed most tasks faster with the higher viewpoint and reported lower frustration and mental demand. The camera position did not affect collision rates nor the probability of hard braking and steering events. We conclude that a viewpoint from the vehicle roof may improve teleoperation efficiency without compromising driving safety, while also lowering the teleoperators' mental workload.

Osteomielitis en Brasil: Un análisis epidemiológico de 2019-2023

Introduction Osteomyelitis is a bone infection characterized by the progressive destruction of cortical bone and medullary cavity, and is a relevant disease due to its probable deleterious consequences for patients. Objective: The study aims to investigate the epidemiology of osteomyelitis in Brazil, focusing on hospitalizations by the SUS (Unified Health System) and deaths between 2019 and 2023, to understand regional disparities. Methodology: A cross-sectional descriptive study, with a quantitative approach to the data, through secondary data collected in the Department of Computer Science of the SUS (DATASUS). The data refer to hospital morbidity due to osteomyelitis during the years from 2019 to 2023, with the period evaluated being from January 2019 to December 2023. Results: There were 71,817 hospitalizations for osteomyelitis in Brazil. The Southeast region had the highest prevalence of cases (40.59%) and reached a higher mortality rate than the other regions. It was noted that the ages most affected by the disease are between 40 and 49 years old. Finally, it was verified that in all regions the male sex is more affected than the female sex. Conclusion: Some risk groups were identified in the study; among them, the male sex, age between 40-49 years and the Southeast region due to the high mortality rate from the disease. The high prevalence of the disease and its high morbidity and mortality reinforce the need for early diagnosis and appropriate treatment.

Autocatalytic Sets and Assembly Theory: A Toy Model Perspective.

Assembly Theory provides a promising framework to explain the complexity of systems such as molecular structures and the origins of life, with broad applicability across various disciplines. In this study, we explore and consolidate different aspects of Assembly Theory by introducing a simplified Toy Model to simulate the autocatalytic formation of complex structures. This model abstracts the molecular formation process, focusing on the probabilistic control of catalysis rather than the intricate interactions found in organic chemistry. We establish a connection between probabilistic catalysis events and key principles of Assembly Theory, particularly the probability of a possible construction path in the formation of a complex object, and examine how the assembly of complex objects is impacted by the presence of autocatalysis. Our findings suggest that this presence of autocatalysis tends to favor longer consecutive construction sequences in environments with a low probability of catalysis, while this bias diminishes in environments with higher catalysis probabilities, highlighting the significant influence of environmental factors on the assembly of complex structures.

Using Rejection Sampling Probability of Acceptance as a Measure of Independence

This article proposes a new association statistic for determining whether random variables are statistically independent. The proposed association statistic can also be used to examine the strength of both linear and nonlinear dependency between variables. This statistic is derived by examining how the conditional probabilities of events differ from their corresponding marginal probabilities. The new statistic is defined in terms of the probability of acceptance commonly associated with the Accept–Reject algorithm and has a very simple formula. The simulated density of the probability of acceptance can be used to measure the degree of uncertainty of the estimated values of the new association statistic. The results from simulations as well as examples that employ real data indicate that this new association statistic is very powerful in detecting linear and nonlinear associations between two random variables. Supplementary materials for this article are available online.

Introduction of a guaranteed basic income as a probable consequence of intellctual automation of labor

Objective: to form a holistic view of the unconditional basic income as a tool of social protection, taking into account the changes in the labor sphere and allowing to solve a number of growing problems associated with the consequences of the economy digitalization, as well as to assess the prospects of creating legal regulation on this issue. Methods: the study was conducted using both general and specific scientific methods, including analysis and synthesis, formal-legal method, systemic and comparative approaches, methods of legal forecasting and legal modeling. Results: unconditional basic income can become one of the elements of a new model of social security based on the unstable nature of labor relations, which begins to prevail over the model of sustainable employment traditional for industrial society. If established, this institution will be cross-sectoral within the labor law and social security law, providing social protection simultaneously to all members of society, most of whom in the near future will face an increased risk of being absorbed by technological unemployment. Unconditional basic income as an element of the social protection system will have to comply with the principle of the universal nature of protection based on social solidarity, on the one hand, and to take into account the need to balance the interests of the parties, on the other hand. As experimental models of basic income, which can be put into practice in the coming years, we can name the model of basic income as a measure of social support for persons involved in atypical forms of employment. This model will affect a number of existing provisions of labor legislation and will allow over time, in case of technological unemployment, to expand the circle of unconditional basic income recipients to all people. Scientific novelty: it consists in the fact that the unconditional basic income is analyzed from the viewpoint of its compliance with the fundamental principles of labor, employment, and social security. Practical significance: it consists in revealing the significance of basic income for the implementation of social policy and in forming recommendations for modeling experiments to introduce the unconditional basic income in the Russian Federation.

Dynamic Fault Tree Generation and Quantitative Analysis of System Reliability for Embedded Systems Based on SysML Models.

As embedded systems become increasingly complex, traditional reliability analysis methods based on text alone are no longer adequate for meeting the requirements of rapid and accurate quantitative analysis of system reliability. This article proposes a method for automatically generating and quantitatively analyzing dynamic fault trees based on an improved system model with consideration for temporal characteristics and redundancy. Firstly, an "anti-semantic" approach is employed to automatically explore the generation of fault modes and effects analysis (FMEA) from SysML models. The evaluation results are used to promptly modify the system design to meet requirements. Secondly, the Profile extension mechanism is used to expand the SysML block definition diagram, enabling it to describe fault semantics. This is combined with SysML activity diagrams to generate dynamic fault trees using traversal algorithms. Subsequently, parametric diagrams are employed to represent the operational rules of logic gates in the fault tree. The quantitative analysis of dynamic fault trees based on probabilistic models is conducted within the internal block diagram of SysML. Finally, through the design and simulation of the power battery management system, the failure probability of the top event was obtained to be 0.11981. This verifies that the design of the battery management system meets safety requirements and demonstrates the feasibility of the method.

Evaluation of Gastroesophageal Reflux in Symptomatic Young Infants Using Multichannel Intraluminal pH-Impedance Testing: A large Cohort Study from a Single Center.

This study aimed to assess the use of combined multichannel intraluminal impedance and pH studies (MII-pH) in a large group of symptomatic young infants, to characterize the occurrence of gastroesophageal reflux disease (GERD), and to establish temporal association of the reflux behaviors with gastroesophageal reflux using symptom indices. This is a retrospective cohort study on 181 infants who underwent MII-pH studies for clinical behaviors that were suggestive of GERD. Symptom index (SI) and symptom association probability (SAP) were used to establish symptom association with reflux. More than 100 GER episodes in 24 hours or acid reflux index > 10% was considered pathological reflux. A total of 181 infants (median age: 60 days, interquartile range [IQR]: 34-108) underwent MII-pH studies with median study duration of 22.41 hours (IQR: 21.5-23.32). A total of 4,070 hours of data were analyzed, with 8,480 reflux events (2,996 [35%] acidic, 5,484 [65%] nonacidic). A total of 2,541 symptoms were noted, 894 (35%) were temporally related to reflux events. A total of 113 infants (62.4%) had positive symptom association with SI > 50% and/or SAP > 95% for at least one symptom. There was modest symptom association for choking and gagging, but apnea, bradycardia, and desaturations had poor symptom association. Only 29 infants (16%) had pathological reflux, and only 18 infants (10%) had both pathological reflux and positive symptom association. MII-pH can be used to characterize GERD in young infants, along with establishing temporal association with symptoms. Pathological reflux in symptomatic young infants is not common, but symptom association may occur without frequent or acidic reflux. · Gastroesophageal reflux (GER) disease can be studied in young infants using MII-pH, to characterize the frequency and nature of GER events.. · The probability of GER events being associated temporally with GER symptoms can also be determined using MII-pH in this population.. · Using frequency of GER events, reflux indices, and symptom association indices with MII-pH, infants having true GER disease can be identified, thereby reducing unnecessary therapy.. · Symptom association may occur even without frequent or severe acidic reflux..

Developing resilience pathways for interdependent infrastructure networks: A simulation-based approach with consideration to risk preferences of decision-makers

In this study, we propose a methodological framework to identify and evaluate cost-effective pathways for enhancing resilience in large-scale interdependent infrastructure systems, considering decision-makers’ risk preferences. We focus on understanding how decision-makers with varying risk preferences perceive the benefits from infrastructure resilience investments and compare them with upfront costs in the context of high-impact low-probability (HILP) events. First, we compute the costs of interventions as the sum of their capital costs and maintenance costs. The benefits of the interventions include the reduction in physical damage costs and business disruption losses resulting from the improved resilience of the network. In the final stage, we develop statistical models to predict the perceived net benefits of different network resilience configurations in power, water, and transport networks. These models are employed in an optimization framework to identify optimal resilience investment pathways. By incorporating Cumulative Prospect Theory (CPT) in the optimization framework, we show that decision-makers who assign higher weights to low probability events tend to allocate more resources towards post-disaster recovery strategies leading to increased resilience against HILP events, like earthquakes. We illustrate the methodology using a case study of the interdependent infrastructure network in Shelby County, Tennessee.

A multi-point leakage prediction statistical method using Bayesian inference in water distribution networks

ABSTRACT Leakage in water distribution networks (WDNs) not only leads to serious water loss and pipe contamination but also affects residents’ daily water. Accurate localization of leaks in WDN is significant to conserve water resources and reduce economic losses. However, in traditional optimization and verification methods for leak detection, factors such as modeling and pressure monitoring point errors are not taken into account, resulting in the deviation from the actual location of leaks. To address the mentioned issues, this article presents a WDN leakage probability prediction method based on Bayesian inference. The method converts the prior probability of leakage events into posterior probability. Then, by utilizing the posterior probability density function, the uncertainty of modeling and measurement errors in the hydraulic simulation model are quantified. The probability distribution of leakage pipes and leakage quantity within the leakage area is calculated, allowing for the location prediction and corresponding magnitude of leakage. The experimental results indicate that the prediction model can detect unknown quantities of leakage events. By collecting multiple sets of leakage data, it is possible to accurately predict the location and quantity of leaks, enhancing the efficiency of leakage detection in large-scale water supply networks and providing decision-making assistance for water utilities.

Modern extreme value statistics for Utopian extremes. EVA (2023) Conference Data Challenge: Team Yalla

AbstractCapturing the extremal behaviour of data often requires bespoke marginal and dependence models which are grounded in rigorous asymptotic theory, and hence provide reliable extrapolation into the upper tails of the data-generating distribution. We present a modern toolbox of four methodological frameworks, motivated by classical extreme value theory, that can be used to accurately estimate extreme exceedance probabilities or the corresponding level in either a univariate or multivariate setting. Our frameworks were used to facilitate the winning contribution of Team Yalla to the EVA (2023) Conference Data Challenge, which was organised for the 13$$^\text {th}$$ th International Conference on Extreme Value Analysis. This competition comprised seven teams competing across four separate sub-challenges, with each requiring the modelling of data simulated from known, yet highly complex, statistical distributions, and extrapolation far beyond the range of the available samples in order to predict probabilities of extreme events. Data were constructed to be representative of real environmental data, sampled from the fantasy country of “Utopia”.

Understanding uncertainty shocks and the role of black swans

Uncertainty shocks can trigger business cycles, bank runs and asset price fluctuations. But how do those shocks translate into changes in beliefs and actions? What makes people suddenly become uncertain? This paper provides an explanation for subjective uncertainty shocks, estimated on real-time GDP data, that fits puzzling features of professional forecasts. The key ingredients are that agents do not know the true model governing the distribution of macroeconomic outcomes, but they believe that distribution to be potentially skewed. When agents estimate higher probabilities of extreme tail events, their uncertainty rises. Our estimates reveal that learning about probabilities of tail events – black swans – explains much of the fluctuations in subjective uncertainty.