Journal articles: 'Low probability events'

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Koehler, Jonathan J., and Laura Macchi. "Thinking About Low-Probability Events." Psychological Science 15, no. 8 (August 2004): 540–46. http://dx.doi.org/10.1111/j.0956-7976.2004.00716.x.

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Chaudhry, Shereen J., Michael Hand, and Howard Kunreuther. "Broad bracketing for low probability events." Journal of Risk and Uncertainty 61, no. 3 (December 2020): 211–44. http://dx.doi.org/10.1007/s11166-020-09343-4.

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AbstractIndividuals tend to underprepare for rare, catastrophic events because of biases in risk perception. A simple form of broad bracketing—presenting the cumulative probability of loss over a longer time horizon—has the potential to alleviate these barriers to accurate risk perception and increase protective actions such as purchasing flood insurance. However, it is an open question whether broad bracketing effects last over time: There is evidence that descriptive probability information is ignored when decisions are based on “experience” (repeatedly and in the face of feedback), which characterizes many protective decisions. Across six incentive-compatible experiments with high stakes, we find that the broad bracketing effect does not disappear or change size when decisions are made from experience. We also advance our understanding of the mechanisms underlying broad bracketing, finding that, while cumulative probability size is a strong driver of the effect, this is dampened for larger brackets leading people to be less sensitive to probability size.

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Munro, D. J., O. K. Ersoy, M. R. Bell, and J. S. Sadowsky. "Neural network learning of low-probability events." IEEE Transactions on Aerospace and Electronic Systems 32, no. 3 (July 1996): 898–910. http://dx.doi.org/10.1109/7.532251.

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Camerer, Colin F., and Howard Kunreuther. "Decision Processes for Low Probability Events: Policy Implications." Journal of Policy Analysis and Management 8, no. 4 (1989): 565. http://dx.doi.org/10.2307/3325045.

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Merz, B., F. Elmer, and A. H. Thieken. "Significance of "high probability/low damage" versus "low probability/high damage" flood events." Natural Hazards and Earth System Sciences 9, no. 3 (June 30, 2009): 1033–46. http://dx.doi.org/10.5194/nhess-9-1033-2009.

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Abstract. The need for an efficient use of limited resources fosters the application of risk-oriented design in flood mitigation. Flood defence measures reduce future damage. Traditionally, this benefit is quantified via the expected annual damage. We analyse the contribution of "high probability/low damage" floods versus the contribution of "low probability/high damage" events to the expected annual damage. For three case studies, i.e. actual flood situations in flood-prone communities in Germany, it is shown that the expected annual damage is dominated by "high probability/low damage" events. Extreme events play a minor role, even though they cause high damage. Using typical values for flood frequency behaviour, flood plain morphology, distribution of assets and vulnerability, it is shown that this also holds for the general case of river floods in Germany. This result is compared to the significance of extreme events in the public perception. "Low probability/high damage" events are more important in the societal view than it is expressed by the expected annual damage. We conclude that the expected annual damage should be used with care since it is not in agreement with societal priorities. Further, risk aversion functions that penalise events with disastrous consequences are introduced in the appraisal of risk mitigation options. It is shown that risk aversion may have substantial implications for decision-making. Different flood mitigation decisions are probable, when risk aversion is taken into account.

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Popov, Pavel P., Athanasios Sideris, and William A. Sirignano. "Low-Probability Events Leading to Rocket Engine Combustion Instability." AIAA Journal 55, no. 3 (March 2017): 919–29. http://dx.doi.org/10.2514/1.j055276.

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Williams, M. M. R., and M. C. Thorne. "The estimation of failure rates for low probability events." Progress in Nuclear Energy 31, no. 4 (January 1997): 373–476. http://dx.doi.org/10.1016/s0149-1970(96)00022-4.

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Bussière, Matthieu, and Marcel Fratzscher. "Low probability, high impact: Policy making and extreme events." Journal of Policy Modeling 30, no. 1 (January 2008): 111–21. http://dx.doi.org/10.1016/j.jpolmod.2007.03.007.

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Bonilla, Claudio A. "SOCIAL CHOICE AND TIME CONSISTENCY WITH LOW-PROBABILITY EVENTS." Macroeconomic Dynamics 21, no. 7 (June 6, 2016): 1706–11. http://dx.doi.org/10.1017/s1365100515001030.

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A key result in macroeconomics is the “time inconsistency of short-run optimal plans.” It is argued that inflationary bias results if central bankers do not precommit to a monetary policy rule. The macro literature, however, does not address the way in which board members arrive at the “optimal choice of inflation rate.” That is a matter of a micro subfield called social choice. If we consider that on any board, members have different priors about the states of nature for the economy, but they all receive the same signal before deciding, then they will have different posterior probabilities for the states, even if they have many data, if one state has a low probability of occurring, such as an unlikely catastrophic-risk event. Thus, it is not clear what the optimal plan is. Therefore, discretion rather than rules may be the optimal plan in social choice settings.

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Demir, Sercan, and Murat Erkoc. "Contingency Inventory Reservation for Low-Probability High-Impact Events." International Journal of Knowledge-Based Organizations 9, no. 2 (April 2019): 1–20. http://dx.doi.org/10.4018/ijkbo.2019040101.

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This article investigates reservation contracts for contingency inventory management between two buyers and a single supplier under a game theoretic framework. Two channel structures are considered in this context. In the first setting, the buyers simultaneously move to offer reservation fees to the supplier, who in turn, decides on the inventory amount she wants to carry for each buyer. In the second setting, the supplier moves first and offers nonrefundable-deductible reservation fees for the buyers, who respond with their respective reservation quantities. By reserving through a shared supplier, the buyers enable a contingency inventory pool which alleviates overage risk for the supplier and enables availability of products after low-probability high-impact events. Conditions for successful implementation of contingency reservation contracts are investigated. The results obtained for both channel structures were contrasted. It is shown that in a market where the buyers have more negotiation power, reservation contracts are more likely to achieve inventory buildup under relatively lower event probabilities.

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Li, Shu, Jin-Zhen Li, Yi-Wen Chen, Xin-Wen Bai, Xiao-Peng Ren, Rui Zheng, Li-Lin Rao, Zuo-Jun Wang, and Huan Liu. "Can Overconfidence be Debiased by Low-Probability/High-Consequence Events?" Risk Analysis 30, no. 4 (April 2010): 699–707. http://dx.doi.org/10.1111/j.1539-6924.2010.01371.x.

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Heimann, D. I., and T. S. Glickman. "Computing risk profiles for composite low-probability high-consequence events." Annals of Operations Research 9, no. 1 (December 1987): 545–60. http://dx.doi.org/10.1007/bf02054754.

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OLTEANU, Florin-Catalin, and Catalin GHEORGHE. "ASPECTS REGARDING THE QUALITATIVE ANALYSIS OF RISKS DUE TO THE OCCURRENCE OF LOW PROBABILITY AND VERY HIGH IMPACT EVENTS." Review of the Air Force Academy 14, no. 1 (May 16, 2016): 133–40. http://dx.doi.org/10.19062/1842-9238.2016.14.1.19.

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McClelland, Gary H., William D. Schulze, and Don L. Coursey. "Insurance for low-probability hazards: A bimodal response to unlikely events." Journal of Risk and Uncertainty 7, no. 1 (August 1993): 95–116. http://dx.doi.org/10.1007/bf01065317.

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Woo, Ming-ko, and Robin Thorne. "Summer Low Flow Events in the Mackenzie River System." ARCTIC 69, no. 3 (September 2, 2016): 286. http://dx.doi.org/10.14430/arctic4581.

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Most northern rivers experience recurrent low flow conditions in the summer (June to September), and rivers of the Mackenzie Basin are no exception. Low flow affects water supply, poses problems for river traffic, and can adversely affect aquatic ecology. Factors that affect summer low flow, which encompasses flows below specified discharge thresholds of concern, include evapotranspiration that leads to water loss from flow-contributing areas, antecedent high flow in which peak discharge is followed by gradual recession to low flow, rainfall and local glacier melt events that interrupt low discharge, replenishments of flow from upstream drainage networks, and arbitrary termination of summer low flow at the end of September. The storage mechanism of large lakes and the regulation effect of reservoirs can produce low flow regimes that differ from those exhibited by rivers without such storage functions. For most rivers, low flow events of longer duration cause larger deficits, and events with large deficits are accompanied by lower minimum discharge. The deficit-to-demand ratio measures the extent to which river flow fails to satisfy water needs. Applying this index to rivers of the Mackenzie drainage shows the hazard of streamflow drought in the basin. Low flow attributes can be summarized by their probability distributions: Gumbel distribution for minimum discharge of events and generalized exponential distribution for event duration. By fitting theoretical distributions to recorded events, one can estimate the probability of occurrence of low flow events that did not occur in the historical past.

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Mumpower, Jeryl L. "Lottery Games and Risky Technologies: Communications About Low-Probability/High-Consequence Events." Risk Analysis 8, no. 2 (June 1988): 231–35. http://dx.doi.org/10.1111/j.1539-6924.1988.tb01176.x.

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Chang, Chiung-Ting. "Applying a problem-solving scenario in risk communication of low probability events." Environmental Hazards 18, no. 4 (February 4, 2019): 326–34. http://dx.doi.org/10.1080/17477891.2019.1575790.

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Patt, Anthony G. "Extreme outcomes: the strategic treatment of low probability events in scientific assessments." Risk Decision and Policy 4, no. 1 (April 1, 1999): 1–15. http://dx.doi.org/10.1080/135753099348067.

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Nikitin, N. V., and M. S. Fanchenko. "Frequency of low-probability events for power-control system of nuclear reactors." Soviet Atomic Energy 67, no. 2 (August 1989): 587–93. http://dx.doi.org/10.1007/bf01125253.

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Sánchez-Cartas, Juan Manuel, Alberto Tejero, and Gonzalo León. "Algorithmic Pricing and Price Gouging. Consequences of High-Impact, Low Probability Events." Sustainability 13, no. 5 (February 26, 2021): 2542. http://dx.doi.org/10.3390/su13052542.

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Algorithmic pricing may lead to more efficient and contestable markets, but high-impact, low-probability events such as terror attacks or heavy storms may lead to price gouging, which may trigger injunctions or get sellers banned from platforms such as Amazon or eBay. This work addresses how such events may impact prices when set by an algorithm and how different markets may be affected. We analyze how to mitigate these high-impact events by paying attention to external (market conditions) and internal (algorithm design) features surrounding the algorithms. We find that both forces may help in partially mitigating price gouging, but it remains unknown which forces or features may lead to complete mitigation.

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Teigen, Karl Halvor. "When are low-probability events judged to be ‘probable’? Effects of outcome-set characteristics on verbal probability estimates." Acta Psychologica 67, no. 2 (May 1988): 157–74. http://dx.doi.org/10.1016/0001-6918(88)90011-x.

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Pradlwarter, H. J., and G. I. Schuëller. "Assessment of low probability events of dynamical systems by controlled Monte Carlo simulation." Probabilistic Engineering Mechanics 14, no. 3 (July 1999): 213–27. http://dx.doi.org/10.1016/s0266-8920(98)00009-5.

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O'Brien, Christopher. "Insurance Regulation and the Global Financial Crisis: A Problem of Low Probability Events." Geneva Papers on Risk and Insurance - Issues and Practice 35, no. 1 (January 2010): 35–52. http://dx.doi.org/10.1057/gpp.2009.36.

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Khaloie, Hooman, Amir Abdollahi, Masoud Rashidinejad, and Pierluigi Siano. "Risk-based probabilistic-possibilistic self-scheduling considering high-impact low-probability events uncertainty." International Journal of Electrical Power & Energy Systems 110 (September 2019): 598–612. http://dx.doi.org/10.1016/j.ijepes.2019.03.021.

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Li, Chao, Yimin Shi, Ping Gao, Yang Shen, Chenchen Ma, and Dawei Shi. "Diagnostic model of low visibility events based on C4.5 algorithm." Open Physics 18, no. 1 (March 25, 2020): 33–39. http://dx.doi.org/10.1515/phys-2020-0007.

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AbstractIn this study the low visibility in Nanjing city is classified and predicted using observed data during 2014 to 2016 with machine-learning based decision tree algorithm (4.5). For this purpose, the model was trained with 3/4th of the data samples until the self-learning accuracy of the model reached 88.32%. The remaining 1/4th of the data samples were used to verify the model’s prediction ability, with the test accuracy reaching 88.34% indicating a good classification diagnosis effect of the model. The results produced with model, generated through learning from the training sample, it is found that the relative humidity, PM10 and PM2.5 are important factors in diagnosing “whether low visibility events will occur in Nanjing”: When relative humidity is favorable (i.e. <90%) and PM2.5 concentration is not high enough (i.e. <146), the probability of low visibility events may reduce; when relative humidity is relatively favorable (i.e. ≥ 90%) with a PM10 concentration ≥ 59, low visibility events are more likely to occur; when relative humidity is extremely favorable (i.e. ≥ 96%) with a low PM10 concentration (i.e. <59), there is also a high probability that low visibility events will occur.

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Colecchia, Federico, and Akram Khan. "On the Frequency Distribution of Neutral Particles from Low-Energy Strong Interactions." Advances in High Energy Physics 2017 (2017): 1–8. http://dx.doi.org/10.1155/2017/8018470.

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The rejection of the contamination, or background, from low-energy strong interactions at hadron collider experiments is a topic that has received significant attention in the field of particle physics. This article builds on a particle-level view of collision events, in line with recently proposed subtraction methods. While conventional techniques in the field usually concentrate on probability distributions, our study is, to our knowledge, the first attempt at estimating the frequency distribution of background particles across the kinematic space inside individual collision events. In fact, while the probability distribution can generally be estimated given a model of low-energy strong interactions, the corresponding frequency distribution inside a single event typically deviates from the average and cannot be predicted a priori. We present preliminary results in this direction and establish a connection between our technique and the particle weighting methods that have been the subject of recent investigation at the Large Hadron Collider.

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Pienaar, Elsje. "Multifidelity Analysis for Predicting Rare Events in Stochastic Computational Models of Complex Biological Systems." Biomedical Engineering and Computational Biology 9 (January 2018): 117959721879025. http://dx.doi.org/10.1177/1179597218790253.

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Rare events such as genetic mutations or cell-cell interactions are important contributors to dynamics in complex biological systems, eg, in drug-resistant infections. Computational approaches can help analyze rare events that are difficult to study experimentally. However, analyzing the frequency and dynamics of rare events in computational models can also be challenging due to high computational resource demands, especially for high-fidelity stochastic computational models. To facilitate analysis of rare events in complex biological systems, we present a multifidelity analysis approach that uses medium-fidelity analysis (Monte Carlo simulations) and/or low-fidelity analysis (Markov chain models) to analyze high-fidelity stochastic model results. Medium-fidelity analysis can produce large numbers of possible rare event trajectories for a single high-fidelity model simulation. This allows prediction of both rare event dynamics and probability distributions at much lower frequencies than high-fidelity models. Low-fidelity analysis can calculate probability distributions for rare events over time for any frequency by updating the probabilities of the rare event state space after each discrete event of the high-fidelity model. To validate the approach, we apply multifidelity analysis to a high-fidelity model of tuberculosis disease. We validate the method against high-fidelity model results and illustrate the application of multifidelity analysis in predicting rare event trajectories, performing sensitivity analyses and extrapolating predictions to very low frequencies in complex systems. We believe that our approach will complement ongoing efforts to enable accurate prediction of rare event dynamics in high-fidelity computational models.

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Rega, Paul P., and Brian N. Fink. "Extending the Boundaries of Emergency Medical Simulation Education into High-Risk, Low Probability Events." CJEM 20, S2 (October 2, 2017): S84. http://dx.doi.org/10.1017/cem.2017.410.

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Cha, Eun Jeong, and Bruce R. Ellingwood. "Risk-averse decision-making for civil infrastructure exposed to low-probability, high-consequence events." Reliability Engineering & System Safety 104 (August 2012): 27–35. http://dx.doi.org/10.1016/j.ress.2012.04.002.

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Nelson, Teresa, and Suzyn Ornstein. "Preparing for the Unexpected: Managing Low Probability, Disruptive Events in Student International Travel Courses." Journal of Management Education 26, no. 3 (June 2002): 259–73. http://dx.doi.org/10.1177/10562902026003003.

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Sperstad, Iver Bakken, Gerd Kjølle, and Eivind Ødegaard Norum. "Accounting for uncertainties due to high-impact low-probability events in power system development." Electric Power Systems Research 193 (April 2021): 107015. http://dx.doi.org/10.1016/j.epsr.2020.107015.

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Papadakis, PhD, Giorgos, Zaid Chalabi, PhD, Swarna Khare, MSc, Angie Bone, MBChB, MSc, Shakoor Hajat, PhD, and Sari Kovats, PhD. "Health protection planning for extreme weather events and natural disasters." American Journal of Disaster Medicine 13, no. 4 (October 1, 2018): 227–36. http://dx.doi.org/10.5055/ajdm.2018.0303.

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Objective: There is a need to develop cost-effective methods to support public health policy makers plan ahead and make robust decisions on protective measures to safeguard against severe impacts of extreme weather events and natural disasters in the future, given competing demands on the social and healthcare resources, large uncertainty associated with extreme events and their impacts, and the opportunity costs associated with making ineffective decisions.Design: The authors combine a physics-based method known as nonextensive statistical mechanics for modeling the probability distribution of systems or processes exhibiting extreme behavior, with a decision-analytical method known as partitioned multiobjective risk method to determine the optimal decision option when planning for potential extreme events.Results: The method is illustrated using a simple hypothetical example. It is shown that partitioning the exceedance probability distribution of health impact into three ranges (low severity/high exceedance probability, moderate severity/medium exceedance probability, and high severity/low exceedance probability) leads to the correct estimation of the conditional expected impact in each range. Multiobjective optimization is used to determine the optimal decision option based on the perspective of the policy maker.Conclusion: This method constitutes a robust generic framework for the quantification of impacts and supporting decision-making under scenarios of extreme and catastrophic health risks.

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Castillo, M. Dolores, and Manuel G. Calvo. "Anxiety Gives Priority to Anticipation of Threatening Events." European Psychologist 5, no. 3 (September 2000): 234–44. http://dx.doi.org/10.1027//1016-9040.5.3.234.

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Reading times were collected to assess the probability and time course of inferences predictive of danger as a function of anxiety. Participants high or low in trait anxiety read (a) context sentences suggesting threat-related or nonthreat outcomes of events, followed by (b) disambiguating sentences in which a target word confirmed or disconfirmed the predicted outcome. The results revealed that low anxiety facilitated processing of nonthreat outcomes, whereas high anxiety facilitated processing of threatening outcomes. Furthermore, these selective effects occurred on a posttarget region of the disambiguating sentence; in contrast, all participants—regardless of anxiety level and threat content of stimuli—showed facilitation when reading the final region of that sentence. These findings indicate that anxiety leads one to prioritize (i. e., increased probability and reduced time) the anticipation of threat-related events.

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Thoen, Bart, Stijn Wielandt, and Lieven De Strycker. "Improving AoA Localization Accuracy in Wireless Acoustic Sensor Networks with Angular Probability Density Functions." Sensors 19, no. 4 (February 21, 2019): 900. http://dx.doi.org/10.3390/s19040900.

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Advances in energy efficient electronic components create new opportunities for wireless acoustic sensor networks. Such sensors can be deployed to localize unwanted and unexpected sound events in surveillance applications, home assisted living, etc. This research focused on a wireless acoustic sensor network with low-profile low-power linear MEMS microphone arrays, enabling the retrieval of angular information of sound events. The angular information was wirelessly transmitted to a central server, which estimated the location of the sound event. Common angle-of-arrival localization approaches use triangulation, however this article presents a way of using angular probability density functions combined with a matching algorithm to localize sound events. First, two computationally efficient delay-based angle-of-arrival calculation methods were investigated. The matching algorithm is described and compared to a common triangulation approach. The two localization algorithms were experimentally evaluated in a 4.25 m by 9.20 m room, localizing white noise and vocal sounds. The results demonstrate the superior accuracy of the proposed matching algorithm over a common triangulation approach. When localizing a white noise source, an accuracy improvement of up to 114% was achieved.

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Vaughan, Matthew T., Brian H. Tang, and Lance F. Bosart. "Climatology and Analysis of High-Impact, Low Predictive Skill Severe Weather Events in the Northeast United States." Weather and Forecasting 32, no. 5 (October 1, 2017): 1903–19. http://dx.doi.org/10.1175/waf-d-17-0044.1.

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Abstract This study identifies high-impact severe weather events with poor predictive skill over the northeast United States using Storm Prediction Center (SPC) convective outlooks. The objectives are to build a climatology of high-impact, low predictive skill events between 1980 and 2013 and investigate the differences in the synoptic-scale environment and severe weather parameters between severe weather events with low predictive skill and high predictive skill. Event-centered composite analyses, performed using the National Centers for Environmental Prediction Climate Forecast System Reanalysis and the North American Regional Reanalysis, suggest low predictive skill events occur significantly more often in low-shear environments. Additionally, a plurality of low probability of detection (POD), high-impact events occurred in low-shear, high-CAPE environments. Statistical analysis of low-shear, high-CAPE environments suggests high downdraft CAPE (DCAPE) and relatively dry lower levels of the atmosphere are associated with widespread severe weather events. DCAPE and dry boundary layer air may contribute to severe wind gusts through strong negative buoyancy and enhanced evaporative cooling of descending saturated parcels.

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Choi, Han Joo, Hyung Jun Moon, Won Jung Jeong, Gi Woon Kim, Jae Hyug Woo, Kyoung Mi Lee, Hyuk Joong Choi, Yong Jin Park, and Choung Ah Lee. "Effect of the Floor Level on the Probability of a Neurologically Favorable Discharge after Cardiac Arrest according to the Event Location." Emergency Medicine International 2019 (October 16, 2019): 1–6. http://dx.doi.org/10.1155/2019/9761072.

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As the number of people living in high-rise buildings increases, so does the incidence of cardiac arrest in these locations. Changes in cardiac arrest location affect the recognition of patients and emergency medical service (EMS) activation and response. This study aimed to compare the EMS response times and probability of a neurologically favorable discharge among patients who suffered an out-of-hospital cardiac arrest (OHCA) event while on a high or low floor at home or in a public place. This retrospective analysis was based on Smart Advanced Life Support registry data from January 2016 to December 2017. We included patients older than 18 years who suffered an OHCA due to medical causes. A high floor was defined as ≥3rd floor above ground. We compared the probability of a neurologically favorable discharge according to floor level and location (home vs. public place) of the OHCA event. Of the 6,335 included OHCA cases, 4,154 (65.6%) events occurred in homes. Rapid call-to-scene times were reported for high-floor events in both homes and public places. A longer call-to-patient time was observed for home events. The probability of a neurologically favorable discharge after a high-floor OHCA was significantly lower than that after a low-floor OHCA if the event occurred in a public place (adjusted odds ratio (aOR), 0.58; 95% confidence intervals (CI), 0.37–0.89) but was higher if the event occurred at home (aOR, 1.40; 95% CI, 0.96–2.03). Both the EMS response times to OHCA events in high-rise buildings and the probability of a neurologically favorable discharge differed between homes and public places. The results suggest that the prognosis of an OHCA patient is more likely to be affected by the building structure and use rather than the floor height.

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Vassilopoulos, Stephanos Ph. "Interpretation and Judgmental Biases in Socially Anxious and Nonanxious Individuals." Behavioural and Cognitive Psychotherapy 34, no. 2 (January 18, 2006): 243–54. http://dx.doi.org/10.1017/s1352465805002687.

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Interpretation and judgmental biases for threat-relevant stimuli are thought to play a role in anxiety disorders. To investigate whether social anxiety is associated with interpretation and judgmental biases for unambiguous external social events, individuals high and low in social anxiety (N = 36 per group) were presented with unambiguous scenarios depicting positive and mildly negative social events. Interpretations were assessed by participants' answers to open-ended questions and by their ratings for experimenter-provided, alternative explanations. In addition, for each event, participants indicated the probability that the event would happen to them and estimated their own emotional reaction to it. Compared to low socially anxious group, individuals high in social anxiety were more likely to interpret positive social events in a negative way and to catastrophize in response to unambiguous, mildly negative social events. Also, they estimated the emotional cost of negative social events as higher and the probabilities of positive social events as lower.

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Dai, Yi Lan, and Lei Chen. "Risk Assessment and Countermeasures of Occupational Fraud Conduct in Corporate Construction Projects." Applied Mechanics and Materials 599-601 (August 2014): 2130–33. http://dx.doi.org/10.4028/www.scientific.net/amm.599-601.2130.

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Risk management has been a part of enterprise engineering construction projects. Since previous studies mostly analyzed in a qualitative way. This paper utilized in a more quantitative way, which merges the use of questionnaires and the use of risk assessment formula for data processing. The results show that the probability and consequences of corporate construction projects in 18 career malpractice risk events fall into four categories: high-risk and high-probability, high-risk and low-probability, low risk and high probability, low risk and low probability. And accordingly it puts forward the corresponding countermeasures.

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Rheinberger, C. M. "Comment on "Significance of "high probability/low damage" versus "low probability/high damage" flood events" by Merz et al. (2009)." Natural Hazards and Earth System Sciences 10, no. 1 (January 5, 2010): 1–2. http://dx.doi.org/10.5194/nhess-10-1-2010.

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Abstract. This comment is meant to shed some light on the use of so-called "risk aversion functions" in the management of flood risks and other natural hazards as recently proposed in this journal (Merz et al., 2009). In particular, I resume the discussion as to whether the relative damage is a suitable indicator of risk aversion and lay out why the use of this indicator may lead to inefficient decisions upon flood mitigation measures.

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Ellingwood, Bruce R., and Yi-Kwei Wen. "Risk-benefit-based design decisions for low-probability/high consequence earthquake events in Mid-America." Progress in Structural Engineering and Materials 7, no. 2 (2005): 56–70. http://dx.doi.org/10.1002/pse.191.

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Abadie, Luis Maria, Ibon Galarraga, and Elisa Sainz de Murieta. "Understanding risks in the light of uncertainty: low-probability, high-impact coastal events in cities." Environmental Research Letters 12, no. 1 (January 17, 2017): 014017. http://dx.doi.org/10.1088/1748-9326/aa5254.

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Critchley, Octavius Hunt. "A new treatment of low probability events with particular application to nuclear power plant incidents." Progress in Nuclear Energy 18, no. 3 (January 1986): 301–57. http://dx.doi.org/10.1016/0149-1970(86)90037-5.

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Mignan, Arnaud, Stefan Wiemer, and Domenico Giardini. "The quantification of low-probability–high-consequences events: part I. A generic multi-risk approach." Natural Hazards 73, no. 3 (April 18, 2014): 1999–2022. http://dx.doi.org/10.1007/s11069-014-1178-4.

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Huggel, Christian, Wilfried Haeberli, Andreas Kääb, Daniel Bieri, and Shaun Richardson. "An assessment procedure for glacial hazards in the Swiss Alps." Canadian Geotechnical Journal 41, no. 6 (December 1, 2004): 1068–83. http://dx.doi.org/10.1139/t04-053.

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Glacial hazards such as ice avalanches, glacial lake outburst floods, and debris flows have caused severe damage in populated mountain regions such as the Swiss Alps. Assessment of such hazards must consider basic glaciological, geomorphological, and hydraulic principles together with experience gained from previous events. An approach is presented here to assess the maximum event magnitude and probability of occurrence of glacial hazards. Analysis of magnitude is based on empirical relationships derived from published case histories from the Swiss Alps and other mountain regions. Probability of occurrence is difficult to estimate because of rapid changes in the nature of glacial systems, the low frequency of events, and the high complexity of the involved processes. Here, the probability is specified in qualitative and systematic terms based on indicators such as dam type, geometry, and freeboard height (for glacial lakes) and tendency of avalanche repetition, precursor events, and increased water supply to the glacier bed (for ice avalanche events). The assessment procedures are applied to a recent lake outburst with subsequent debris flow and to an ice avalanche in the Swiss Alps. The results yield reasonable event maxima that were not exceeded by actual events. The methods provide first-order assessments and may be applied in dynamic mountain environments where population and infrastructure growth require continuous evaluation of hazards.Key words: glacial hazards, lake outburst, debris flow, ice avalanche, hazard assessment procedure, probability of occurrence.

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Cardoso Junior, Moacyr Machado. "Identification and characterization of "Black Swans" in technological events in Brazil." Independent Journal of Management & Production 10, no. 2 (April 1, 2019): 411. http://dx.doi.org/10.14807/ijmp.v10i2.866.

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“Black swan” events represent a critical issue in risk analysis. Events with extremely low probability of occurrence are in general discarded from the risk analysis process. This paper aims to identify and characterize four accidents that occurred in Brazil into the following classes: “not a black swan”, “black swan: unknown-unknown”, “black swan: unknown-known” and “black Swan: not believed to occur”, by obtaining from experts the distribution of belief for the real probability of each class. Results showed that, throughout all cases analyzed, the class “black swan: unknown-unknown” was never reported, which means that none of the cases studied were a complete surprise to anyone. The method used was able to assign all accident events to the remaining classes. Probability distribution elicited from experts showed large disagreement among them, and the expected value was considered low. Nevertheless, the elicited distributions can be utilized in future risk analysis as a priori distribution in a Bayesian approach.

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Sheng, Jiawei, Qian Li, Yiming Hei, Shu Guo, Bowen Yu, Lihong Wang, Min He, Tingwen Liu, and Hongbo Xu. "A Joint Learning Framework for the CCKS-2020 Financial Event Extraction Task." Data Intelligence 3, no. 3 (2021): 444–59. http://dx.doi.org/10.1162/dint_a_00098.

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This paper presents a winning solution for the CCKS-2020 financial event extraction task, where the goal is to identify event types, triggers and arguments in sentences across multiple event types. In this task, we focus on resolving two challenging problems (i.e., low resources and element overlapping) by proposing a joint learning framework, named SaltyFishes. We first formulate the event extraction task as a joint probability model. By sharing parameters in the model across different types, we can learn to adapt to low-resource events based on high-resource events. We further address the element overlapping problems by a mechanism of Conditional Layer Normalization, achieving even better extraction accuracy. The overall approach achieves an F1-score of 87.8% which ranks the first place in the competition.

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Artale, M. Celeste, Yann Bouffanais, Michela Mapelli, Nicola Giacobbo, Nadeen B. Sabha, Filippo Santoliquido, Mario Pasquato, and Mario Spera. "An astrophysically motivated ranking criterion for low-latency electromagnetic follow-up of gravitational wave events." Monthly Notices of the Royal Astronomical Society 495, no. 2 (May 7, 2020): 1841–52. http://dx.doi.org/10.1093/mnras/staa1252.

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ABSTRACT We investigate the properties of the host galaxies of compact binary mergers across cosmic time. To this end, we combine population synthesis simulations together with galaxy catalogues from the hydrodynamical cosmological simulation eagle to derive the properties of the host galaxies of binary neutron star (BNS), black hole-neutron star (BHNS), and binary black hole (BBH) mergers. Within this framework, we derive the host galaxy probability, i.e. the probability that a galaxy hosts a compact binary coalescence as a function of its stellar mass, star formation rate, Ks magnitude, and B magnitude. This quantity is particularly important for low-latency searches of gravitational wave (GW) sources as it provides a way to rank galaxies lying inside the credible region in the sky of a given GW detection, hence reducing the number of viable host candidates. Furthermore, even if no electromagnetic counterpart is detected, the proposed ranking criterion can still be used to classify the galaxies contained in the error box. Our results show that massive galaxies (or equivalently galaxies with a high luminosity in Ks band) have a higher probability of hosting BNS, BHNS, and BBH mergers. We provide the probabilities in a suitable format to be implemented in future low-latency searches.

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Merz, B., F. Elmer, and A. H. Thieken. "Reply to Comment on "Significance of "high probability/low damage" versus "low probability/high damage" flood events" by C. M. Rheinberger (2009)." Natural Hazards and Earth System Sciences 10, no. 1 (January 5, 2010): 3–5. http://dx.doi.org/10.5194/nhess-10-3-2010.

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Abstract. In a comment to our recently published paper on the "Significance of "high probability/low damage" versus "low probability/high damage" flood events" (Merz et al., 2009), C. M. Rheinberger questions the use of relative damage as a suitable indicator for risk aversion and the use of the resulting risk aversion functions in judging flood mitigation measures. While the points of criticism are important and should be accounted for, most of these points are considered in our original paper. More importantly, we do not agree with the conclusion that the use of relative damage as indicator for risk aversion is generally not appropriate in decision making about flood mitigation measures.

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Fudenberg, Drew, Kevin He, and Lorens A. Imhof. "Bayesian posteriors for arbitrarily rare events." Proceedings of the National Academy of Sciences 114, no. 19 (April 25, 2017): 4925–29. http://dx.doi.org/10.1073/pnas.1618780114.

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We study how much data a Bayesian observer needs to correctly infer the relative likelihoods of two events when both events are arbitrarily rare. Each period, either a blue die or a red die is tossed. The two dice land on side 1 with unknown probabilities p1 and q1, which can be arbitrarily low. Given a data-generating process where p1≥cq1, we are interested in how much data are required to guarantee that with high probability the observer’s Bayesian posterior mean for p1 exceeds (1−δ)c times that for q1. If the prior densities for the two dice are positive on the interior of the parameter space and behave like power functions at the boundary, then for every ϵ> 0, there exists a finite N so that the observer obtains such an inference after n periods with probability at least 1−ϵ whenever np1≥N. The condition on n and p1 is the best possible. The result can fail if one of the prior densities converges to zero exponentially fast at the boundary.

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Prime, Thomas, Jennifer M. Brown, and Andrew J. Plater. "Physical and Economic Impacts of Sea-Level Rise and Low Probability Flooding Events on Coastal Communities." PLOS ONE 10, no. 2 (February 24, 2015): e0117030. http://dx.doi.org/10.1371/journal.pone.0117030.

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Journal articles on the topic 'Low probability events'

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