Keyword search (4,163 papers available)

"Uncertainty" Keyword-tagged Publications:

Title Authors PubMed ID
1 Adaptive sliding mode fault-tolerant control of an over-actuated hybrid VTOL fixed-wing UAV under transition flight Wang B; Zhao H; Hu X; Shen Y; Li N; 41475926
ENCS
2 Intolerance of uncertainty, psychological symptoms, and pain in long-term childhood cancer survivors: a report from the Childhood Cancer Survivor Study Alberts NM; Stratton KL; Leisenring WM; Pizzo A; Lamoureux É; Alschuler K; Flynn J; Krull KR; Jibb LA; Nathan PC; Olgin JE; Stinson JN; Armstrong GT; 40699439
PSYCHOLOGY
3 Near-optimal learning of Banach-valued, high-dimensional functions via deep neural networks Adcock B; Brugiapaglia S; Dexter N; Moraga S; 39454372
MATHSTATS
4 Exploring the effects of anthropogenic disturbance on predator inspection activity in Trinidadian guppies Brusseau AJP; Feyten LEA; Crane AL; Brown GE; 38476138
BIOLOGY
5 Development and performance assessment of a new opensource Bayesian inference R platform for building energy model calibration Hou D; Zhan D; Wang L; Hassan IG; Sezer N; 37936825
ENCS
6 How uncertainty affects information search among consumers: a curvilinear perspective He S; Rucker DD; 36471868
JMSB
7 UncertaintyFuseNet: Robust uncertainty-aware hierarchical feature fusion model with Ensemble Monte Carlo Dropout for COVID-19 detection Abdar M; Salari S; Qahremani S; Lam HK; Karray F; Hussain S; Khosravi A; Acharya UR; Makarenkov V; Nahavandi S; 36217534
ENCS
8 Development of a DREAM-based inverse model for multi-point source identification in river pollution incidents: Model testing and uncertainty analysis Zhu Y; Chen Z; 36191500
ENCS
9 Viral Anxiety Mediates the Influence of Intolerance of Uncertainty on Adherence to Physical Distancing Among Healthcare Workers in COVID-19 Pandemic Chung S; Lee T; Hong Y; Ahmed O; Silva WAD; Gouin JP; 35733798
PSYCHOLOGY
10 Decision-first modeling should guide decision making for emerging risks Morgan K; Collier ZA; Gilmore E; Schmitt K; 35104915
ENCS
11 Towards a better understanding of deep convolutional neural network processes for recognizing organic chemicals of environmental concern Sun X; Zhang X; Wang L; Li Y; Muir DCG; Zeng EY; 34388923
CHEMBIOCHEM
12 Assessing the regional biogenic methanol emission from spring wheat during the growing season: A Canadian case study Cai M; An C; Guy C; Lu C; Mafakheri F; 34182392
ENCS
13 A robust optimization model for tactical capacity planning in an outpatient setting Aslani N; Kuzgunkaya O; Vidyarthi N; Terekhov D; 33215335
ENCS
14 Qualitative threshold method validation and uncertainty evaluation: A theoretical framework and application to a 40 analytes liquid chromatography-tandem mass spectrometry method Camirand Lemyre F; Desharnais B; Laquerre J; Morel MA; Côté C; Mireault P; Skinner CD; 32476284
CHEMBIOCHEM
15 Quantifying construction waste reduction through the application of prefabrication: a case study in Anhui, China. Hao J, Chen Z, Zhang Z, Loehlein G 32358748
ENCS
16 An ecological framework of neophobia: from cells to organisms to populations. Crane AL, Brown GE, Chivers DP, Ferrari MCO 31599483
BIOLOGY
17 Worldwide contamination of food-crops with mycotoxins: Validity of the widely cited 'FAO estimate' of 25. Eskola M, Kos G, Elliott CT, Hajšlová J, Mayar S, Krska R 31478403
CHEMBIOCHEM
18 Influence of Head Tissue Conductivity Uncertainties on EEG Dipole Reconstruction. Vorwerk J, Aydin Ü, Wolters CH, Butson CR 31231178
PERFORM

 

Title:Decision-first modeling should guide decision making for emerging risks
Authors:Morgan KCollier ZAGilmore ESchmitt K
Link:https://pubmed.ncbi.nlm.nih.gov/35104915/
DOI:10.1111/risa.13888
Publication:Risk analysis : an official publication of the Society for Risk Analysis
Keywords:decision analysisdecision makingemerging risksrisk analysisuncertainty
PMID:35104915 Category: Date Added:2022-02-02
Dept Affiliation: ENCS
1 Center for Foodborne Illness Research and Prevention, Department of Food Science and Technology, Ohio State University, Columbus, Ohio, USA.
2 Department of Management, Radford University, Radford, Virginia, USA.
3 Department of Civil and Environmental Engineering, Carleton University, Ottawa, Canada.
4 Centre for Engineering in Society, Gina Cody School of Engineering, Concordia University, Montreal, Canada.

Description:

An emerging risk is characterized by scant published data, rapidly changing information, and an absence of existing models that can be directly used for prediction. Analysis may be further complicated by quickly evolving decision-maker priorities and the potential need to make decisions quickly as new information comes available. To provide a forum to discuss these challenges, a virtual conference, "Decision Making for Emerging Risks," was held on June 22-23, 2021, sponsored jointly by the Decision Analysis Society of the Institute for Operations Research and the Management Sciences and the Decision Analysis and Risk specialty group in the Society for Risk Analysis. Speakers reflected on the work to support decision-makers related to the COVID-19 pandemic as well as experiences in emerging risks across domains from cybersecurity, infrastructure, transportation, energy, food safety, national security, and climate change. Here, we distill the key findings to propose a set of best practice principles for a "decision-first" approach for emerging risks. These discussions underscore the importance of scoping the decision context and the shared responsibility for the development and implementation of the analysis between the analyst and the decision-maker when the context can evolve rapidly. Emerging risks may also favor simpler analytical approaches that increase transparency, ease of explanation, and ability to conduct new analyses quickly. Continued dialogue by the decision and risk analysis communities on the use and development of models for emerging risks will enhance the credibility and usefulness of these approaches.





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