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Journal articles on the topic 'Decision-Making Under Deep Uncertainty'

Author: Grafiati

Published: 5 June 2025

Last updated: 15 July 2025

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1

Weisbach, David. "Introduction: Legal Decision Making under Deep Uncertainty." Journal of Legal Studies 44, S2 (2015): S319—S335. http://dx.doi.org/10.1086/686261.

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Grover, Aditya. "Generative Decision Making Under Uncertainty." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 13 (2023): 15440. http://dx.doi.org/10.1609/aaai.v37i13.26807.

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In the fields of natural language processing (NLP) and computer vision (CV), recent advances in generative modeling have led to powerful machine learning systems that can effectively learn from large labeled and unlabeled datasets. These systems, by and large, apply a uniform pretrain-finetune pipeline on sequential data streams and have achieved state-of-the-art-performance across many tasks and benchmarks. In this talk, we will present recent algorithms that extend this paradigm to sequential decision making, by casting it as an inverse problem that can be solved via deep generative models.

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Pei, Zhihao, Angela M. Rojas-Arevalo, Fjalar J. de Haan, Nir Lipovetzky, and Enayat A. Moallemi. "Reinforcement learning for decision-making under deep uncertainty." Journal of Environmental Management 359 (May 2024): 120968. http://dx.doi.org/10.1016/j.jenvman.2024.120968.

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Paredes-Vergara, Matías, Rodrigo Palma-Behnke, and Jannik Haas. "Characterizing decision making under deep uncertainty for model-based energy transitions." Renewable and Sustainable Energy Reviews 192 (March 2024): 114233. http://dx.doi.org/10.1016/j.rser.2023.114233.

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Volosova, Aleksandra, and Ekaterina Matiukhina. "Using artificial intelligence for effective decision-making in corporate governance under conditions of deep uncertainty." SHS Web of Conferences 89 (2020): 03008. http://dx.doi.org/10.1051/shsconf/20208903008.

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The article deals with research related to the use of artificial intelligence technologies for effective decision-making in corporate governance under conditions of deep uncertainty. To process uncertainty, it is proposed to use the cognitive capabilities of artificial intelligence. Cognitivism can be used to implement intuitive, psychological and other components of the internal mental activity of a person when making decisions. These capabilities allow one to make informed decisions and predict the consequences of these decisions. To study the properties of deep uncertainty, the authors sugg

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Babovic, Filip, Ana Mijic, and Kaveh Madani. "Decision making under deep uncertainty for adapting urban drainage systems to change." Urban Water Journal 15, no. 6 (2018): 552–60. http://dx.doi.org/10.1080/1573062x.2018.1529803.

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Su, Han, Feifei Dong, Yong Liu, Rui Zou, and Huaicheng Guo. "Robustness-Optimality Tradeoff for Watershed Load Reduction Decision Making under Deep Uncertainty." Water Resources Management 31, no. 11 (2017): 3627–40. http://dx.doi.org/10.1007/s11269-017-1689-3.

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Kreinovich, Vladik. "Ordered Weighted Averaging (OWA), Decision Making under Uncertainty, and Deep Learning: How Is This All Related?" Information 13, no. 2 (2022): 82. http://dx.doi.org/10.3390/info13020082.

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Among many research areas to which Ron Yager contributed are decision making under uncertainty (in particular, under interval and fuzzy uncertainty) and aggregation—where he proposed, analyzed, and utilized ordered weighted averaging (OWA). The OWA algorithm itself provides only a specific type of data aggregation. However, it turns out that if we allow several OWA stages, one after another, we obtain a scheme with a universal approximation property—moreover, a scheme which is perfectly equivalent to modern ReLU-based deep neural networks. In this sense, Ron Yager can be viewed as a (grand)fat

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Coals, Peter, Dawn Burnham, Paul J. Johnson, et al. "Deep Uncertainty, Public Reason, the Conservation of Biodiversity and the Regulation of Markets for Lion Skeletons." Sustainability 11, no. 18 (2019): 5085. http://dx.doi.org/10.3390/su11185085.

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Public reason is a formal concept in political theory. There is a need to better understand how public reason might be elicited in making public decisions that involve deep uncertainty, which arises from pernicious and gross ignorance about how a system works, the boundaries of a system, and the relative value (or disvalue) of various possible outcomes. This article is the third in a series to demonstrate how ethical argument analysis—a qualitative decision-making aid—may be used to elicit public reason in the presence of deep uncertainty. The first article demonstrated how argument analysis i

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Radonjic, Ognjen. "Animal spirits: Keynes' theory of rational investment decision-making under conditions of fundamental uncertainty." Theoria, Beograd 52, no. 1 (2009): 17–44. http://dx.doi.org/10.2298/theo0901017r.

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Keynes's term, animal spirits, has been mistakenly confused with irrational decision-making. However, if we accept Keynes' assumption that future is fundamentally uncertain and nonergodic, animal spirits become key factor that makes continual process of investment decision-making possible. On the other hand, if animal spirits blunt, investment activity dwindles and makes emergence of deep economic crisis likely.

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Reis, Julia, and Julie Shortridge. "Impact of Uncertainty Parameter Distribution on Robust Decision Making Outcomes for Climate Change Adaptation under Deep Uncertainty." Risk Analysis 40, no. 3 (2019): 494–511. http://dx.doi.org/10.1111/risa.13405.

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Cassidy, Rachel, and Charles F. Manski. "Tuberculosis diagnosis and treatment under uncertainty." Proceedings of the National Academy of Sciences 116, no. 46 (2019): 22990–97. http://dx.doi.org/10.1073/pnas.1912091116.

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In 2017, 1.6 million people worldwide died from tuberculosis (TB). A new TB diagnostic test—Xpert MTB/RIF from Cepheid—was endorsed by the World Health Organization in 2010. Trials demonstrated that Xpert is faster and has greater sensitivity and specificity than smear microscopy—the most common sputum-based diagnostic test. However, subsequent trials found no impact of introducing Xpert on morbidity and mortality. We present a decision-theoretic model of how a clinician might decide whether to order Xpert or other tests for TB, and whether to treat a patient, with or without test results. Our

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Șerban, Florentin, and Silvia Dedu. "Modeling Sustainable Economic Decisions Under Uncertainty: A Robust Optimization Framework via Nonlinear Scalarization." Sustainability 17, no. 13 (2025): 6157. https://doi.org/10.3390/su17136157.

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Sustainable economic decision making increasingly requires robust methodologies capable of withstanding deep uncertainty, particularly in volatile financial and resource-constrained environments. This paper introduces a unified optimization framework based on nonlinear scalarizing functionals, designed to support resilient planning under structural ambiguity. By integrating performance objectives with risk boundaries, the proposed model generalizes classical robustness paradigms—such as strict and reliable robustness—into a single tractable and economically interpretable formulation. A key inn

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Kwakkel, Jan H., Marjolijn Haasnoot, and Warren E. Walker. "Comparing Robust Decision-Making and Dynamic Adaptive Policy Pathways for model-based decision support under deep uncertainty." Environmental Modelling & Software 86 (December 2016): 168–83. http://dx.doi.org/10.1016/j.envsoft.2016.09.017.

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Wang, Shuo, Kailun Feng, and Yaowu Wang. "Modeling Performance and Uncertainty of Construction Planning under Deep Uncertainty: A Prediction Interval Approach." Buildings 13, no. 1 (2023): 254. http://dx.doi.org/10.3390/buildings13010254.

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In construction planning, decision making has a great impact on final project performance. Hence, it is essential for project managers to assess the construction planning and make informed decisions. However, disproportionately large uncertainties occur during the construction planning stage; in the worst case, reliable probability distributions of uncertainties are sometimes unavailable due to a lack of information before construction implementation. This situation constitutes a deep uncertainty problem, making it a challenge to perform a probability-based uncertainty assessment. The current

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Witt, Tobias, and Matthias Klumpp. "Multi-Period Multi-Criteria Decision Making under Uncertainty: A Renewable Energy Transition Case from Germany." Sustainability 13, no. 11 (2021): 6300. http://dx.doi.org/10.3390/su13116300.

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Methods of multi-criteria decision making (MCDM) from operations research have been applied to provide information for making long-term decisions in the energy sector, and energy policy. For example, in sustainability evaluations, multiple conflicting criteria can be considered. While most MCDM approaches have been applied to evaluate energy systems in a single period, the multi-criteria evaluation of energy system evolution over time has received less attention. To evaluate such transition paths, multi-period MCDM approaches can be used. Because of long-term planning horizons, deep uncertaint

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Хачикян, П. П. "PSYCHOLOGICAL ASPECTS OF DECISION-MAKING FOR PREVENTION OF EMERGENCIES IN AVIATION UNDER UNCERTAINTY." ПРОБЛЕМЫ БЕЗОПАСНОСТИ ПОЛЕТОВ, no. 3 (June 28, 2024): 22–35. http://dx.doi.org/10.36535/0235-5000-2024-03-3.

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Чрезвычайные ситуации (ЧС) в авиации наносят значительный материальный ущерб и сопровождаются человеческими жертвами. Эффективное предупреждение и профилактика возникновения ЧС является деятельностью, которая позволяет снизить вероятность наступления катастрофических последствий в авиации в условиях глубокой неопределённости. В статье проводится анализ значимости риск-ориентированного подхода при построении многоэлементных систем обеспечения безопасности при ЧС в авиации. Обсуждаются существующие решения в области оценки риска. Представлены модели оценки риска возникновения ЧС в авиации в зави

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Moallemi, Enayat A., Sondoss Elsawah, and Michael J. Ryan. "Model-based multi-objective decision making under deep uncertainty from a multi-method design lens." Simulation Modelling Practice and Theory 84 (May 2018): 232–50. http://dx.doi.org/10.1016/j.simpat.2018.02.009.

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Kwakkel, Jan H., Warren E. Walker, and Marjolijn Haasnoot. "Coping with the Wickedness of Public Policy Problems: Approaches for Decision Making under Deep Uncertainty." Journal of Water Resources Planning and Management 142, no. 3 (2016): 01816001. http://dx.doi.org/10.1061/(asce)wr.1943-5452.0000626.

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SATO, Ichiro, Daiju NARITA, Daikichi OGAWADA, and Akiko MATSUMURA. "DECISION MAKING UNDER DEEP UNCERTAINTY APPLIED TO PLANNING AND EVALUATION OF CALIMATE CHANGE ADAPTATION MEASURES." Japanese Journal of JSCE 81, no. 2 (2025): n/a. https://doi.org/10.2208/jscejj.24-00243.

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Morayo Ogunsina, Christianah Pelumi Efunniyi, Olajide Soji Osundare, Samuel Olaoluwa Folorunsho, and Lucy Anthony Akwawa. "Neuro-Symbolic integration in autonomous robotics: A framework for enhanced decision-making." Engineering Science & Technology Journal 5, no. 9 (2024): 2709–23. http://dx.doi.org/10.51594/estj.v5i9.1546.

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This review paper explores the integration of neuro-symbolic reasoning and deep learning within autonomous robotics, proposing a novel framework to enhance decision-making processes in dynamic environments. The paper begins by examining the challenges AI models face, particularly in context-aware decision-making, and highlights the limitations of existing approaches. It then presents a conceptual model that synergizes the interpretability of symbolic reasoning with the perceptual power of deep learning. This integrated framework is designed to improve real-time contextual understanding, decisi

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Scholte, Mirre, Vincent Marchau, Jan Kwakkel, Maroeska Rovers, and Janneke Grutters. "OP07 Dealing With Uncertainty In Early Health Technology Assessment: An Exploration Of Methods For Decision-Making Under Deep Uncertainty." International Journal of Technology Assessment in Health Care 38, S1 (2022): S3—S4. http://dx.doi.org/10.1017/s0266462322000678.

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IntroductionIn early stages, the consequences of innovations are often unknown or deeply uncertain. This complicates health economic modelling. The field of decision-making under deep uncertainty (DMDU) uses exploratory modelling (EM) to help decision-makers make sound decisions under conditions of deep uncertainty (i.e., when stakeholders do not know, or cannot agree on, the system model, the probability distributions to place over the inputs to these models, which consequences to consider, and their relative importance). The aim of this research was to evaluate the potential of EM for the ea

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Doroshenko, Lyubov, Loretta Mastroeni, and Alessandro Mazzoccoli. "Wavelet and Deep Learning Framework for Predicting Commodity Prices Under Economic and Financial Uncertainty." Mathematics 13, no. 8 (2025): 1346. https://doi.org/10.3390/math13081346.

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The analysis of commodity markets—particularly in the energy and metals sectors—is essential for understanding economic dynamics and guiding decision-making. Financial and economic uncertainty indices provide valuable insights that help reduce price uncertainty. This study employs wavelet analyses and wavelet energy-based measures to investigate the relationship between these indices and commodity prices across multiple time scales. The wavelet approach captures complex, time-varying dependencies, offering a more nuanced understanding of how uncertainty indices influence commodity price fluctu

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Singh, Prerna, Baabak Ashuri, and Adjo Amekudzi-Kennedy. "Application of Dynamic Adaptive Planning and Risk-Adjusted Decision Trees to Capture the Value of Flexibility in Resilience and Transportation Planning." Transportation Research Record: Journal of the Transportation Research Board 2674, no. 9 (2020): 298–310. http://dx.doi.org/10.1177/0361198120929012.

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Transportation infrastructure around the world is under pressure to perform with ever-changing climate scenarios, unpredictable disasters, and stress on resources stemming from rapid urbanization and population growth. Current approaches to developing resilience applied to the transportation system focus primarily on engineering resilience and do not explicitly deal with deep uncertainties arising from climate change. This paper reviews adaptation, a critical aspect of a resilient system in an uncertain and changing environment, as applied in the transportation resilience literature. It compar

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Forni, Laura G., S. E. Galaitsi, Vishal K. Mehta, et al. "Exploring scientific information for policy making under deep uncertainty." Environmental Modelling & Software 86 (December 2016): 232–47. http://dx.doi.org/10.1016/j.envsoft.2016.09.021.

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Kee, Tris, and Frankie Fu. "Applying Information Gap Decision Theory for Uncertainty Management in Building Lifecycle Assessment." Buildings 14, no. 12 (2024): 3729. http://dx.doi.org/10.3390/buildings14123729.

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This study applies Info-Gap Decision Theory (IGDT) to manage uncertainties in early-stage lifecycle assessment (LCA) in the building sector, focusing on carbon emissions and cost optimization. The building industry significantly contributes to global carbon emissions, making robust LCA models crucial for achieving environmental improvements. Traditional LCA methods often overlook deep uncertainties, leading to unreliable outcomes. To address this, this research integrates IGDT, providing a non-probabilistic approach that enhances decision-making under uncertainty. The study develops an optimiz

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Constantino, Sara M., and Elke U. Weber. "Decision-making under the deep uncertainty of climate change: The psychological and political agency of narratives." Current Opinion in Psychology 42 (December 2021): 151–59. http://dx.doi.org/10.1016/j.copsyc.2021.11.001.

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Hachikyan, P. P. "Ornithological flight safety as a task of decision making in conditions of uncertainty." Civil Aviation High Technologies 27, no. 2 (2024): 25–42. http://dx.doi.org/10.26467/2079-0619-2024-27-2-25-42.

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The problem of aircraft collision with birds (bird strike) is becoming more relevant with the growing trends of air transportation. According to the International Civil Aviation Organization (ICAO), over the seven-year period, 97751 aircraft collisions with animals in 105 countries around the world were recorded. In approximately half of the cases (56093 incidents), damage to the aircraft of various types is reported. According to some estimates, the annual damage from aircraft bird strikes is about 610 million US dollars. The article analyzes the effect of the aircraft bird strike threat (orn

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Ashraf Vaghefi, Saeid, Veruska Muccione, Kees C. H. van Ginkel, and Marjolijn Haasnoot. "Using Decision Making under Deep Uncertainty (DMDU) approaches to support climate change adaptation of Swiss Ski Resorts." Environmental Science & Policy 126 (December 2021): 65–78. http://dx.doi.org/10.1016/j.envsci.2021.09.005.

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Хачикян, П. П. "ДИНАМИЧЕСКОЕ АДАПТИВНОЕ ПЛАНИРОВАНИЕ КАК ИНСТРУМЕНТ ПРИНЯТИЯ РЕШЕНИЙ ДЛЯ СНИЖЕНИЯ НЕГАТИВНЫХ ПОСЛЕДСТВИЙ ЧРЕЗВЫЧАЙНЫХ СИТУАЦИЙ ПРИРОДНОГО ХАРАКТЕРА". Проблемы безопасности и чрезвычайных ситуаций, № 4 (1 серпня 2024): 9–27. https://doi.org/10.36535/0869-4176-2024-04-2.

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Анализируется проблема принятия управленческих решений для снижения негативных последствий чрезвычайных ситуаций (ЧС) природного характера. ЧС природного характера рассматриваются как события в поле глубокой неопределенности, прогноз наступления которых, как и масштаб их последствий, не всегда может быть рассчитан с применением вероятностных моделей, что предопределяет необходимость использования иных инструментов принятия решений. Рассматривается применение динамического адаптивного планирования (ДАП) в качестве сценарного инструмента принятия решений в условиях глубокой неопределённости для

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Tomin, Nikita, Alexey Zhukov, and Alexander Domyshev. "Deep Reinforcement Learning for Energy Microgrids Management Considering Flexible Energy Sources." EPJ Web of Conferences 217 (2019): 01016. http://dx.doi.org/10.1051/epjconf/201921701016.

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The problem of optimally activating the flexible energy sources (short- and long-term storage capacities) of electricity microgrid is formulated as a sequential decision making problem under uncertainty where, at every time-step, the uncertainty comes from the lack of knowledge about future electricity consumption and weather dependent PV production. This paper proposes to address this problem using deep reinforcement learning. To this purpose, a specific deep learning architecture has been used in order to extract knowledge from past consumption and production time series as well as any avail

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Nezhadettehad, Alireza, Arkady Zaslavsky, Abdur Rakib, and Seng W. Loke. "Uncertainty-Aware Parking Prediction Using Bayesian Neural Networks." Sensors 25, no. 11 (2025): 3463. https://doi.org/10.3390/s25113463.

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Parking availability prediction is a critical component of intelligent transportation systems, aiming to reduce congestion and improve urban mobility. While traditional deep learning models such as Long Short-Term Memory (LSTM) networks have been widely applied, they lack mechanisms to quantify uncertainty, limiting their robustness in real-world deployments. This paper proposes a Bayesian Neural Network (BNN)-based framework for parking occupancy prediction that explicitly models both epistemic and aleatoric uncertainty. Although BNNs have shown promise in other domains, they remain underutil

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Felsen, Gidon, and Zachary F. Mainen. "Midbrain contributions to sensorimotor decision making." Journal of Neurophysiology 108, no. 1 (2012): 135–47. http://dx.doi.org/10.1152/jn.01181.2011.

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Making decisions about future actions is a fundamental function of the nervous system. Classical theories hold that separate sets of brain regions are responsible for selecting and implementing an action. Traditionally, action selection has been considered the domain of high-level regions, such as the prefrontal cortex, whereas action generation is thought to be carried out by dedicated cortical and subcortical motor regions. However, increasing evidence suggests that the activity of individual neurons in cortical motor structures reflects abstract properties of “decision variables” rather tha

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Babovic, Filip, and Ana Mijic. "Economic Evaluation of Adaptation Pathways for an Urban Drainage System Experiencing Deep Uncertainty." Water 11, no. 3 (2019): 531. http://dx.doi.org/10.3390/w11030531.

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As Decision Making under Deep Uncertainty methodologies are becoming more widely utilised, there has been a growth in the use and generation of Adaptation Pathways. These are meant to convey to policy makers how short-term adaptations can act as elements of longer-term adaptation strategies. However, sets of Adaptation Pathways do not convey the individual pathway’s relative costs and benefits. To address this problem in relation to urban pluvial flooding, an economic analysis of a set of Adaptation Pathways was conducted. Initially, a methodology to conduct an economic assessment for determin

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Alohali, Manal Abdullah, Hamed Alqahtani, Abdulbasit Darem, Monir Abdullah, Yunyoung Nam, and Mohamed Abouhawwash. "Integrating cyber-physical systems with embedding technology for controlling autonomous vehicle driving." PeerJ Computer Science 11 (June 10, 2025): e2823. https://doi.org/10.7717/peerj-cs.2823.

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Cyber-physical systems (CPSs) in autonomous vehicles must handle highly dynamic and uncertain settings, where unanticipated impediments, shifting traffic conditions, and environmental changes all provide substantial decision-making issues. Deep reinforcement learning (DRL) has emerged as a strong tool for dealing with such uncertainty, yet current DRL models struggle to ensure safety and optimal behaviour in indeterminate settings due to the difficulties of understanding dynamic reward systems. To address these constraints, this study incorporates double deep Q networks (DDQN) to improve the a

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Malekpour, Shirin, Warren E. Walker, Fjalar J. de Haan, Niki Frantzeskaki, and Vincent A. W. J. Marchau. "Bridging Decision Making under Deep Uncertainty (DMDU) and Transition Management (TM) to improve strategic planning for sustainable development." Environmental Science & Policy 107 (May 2020): 158–67. http://dx.doi.org/10.1016/j.envsci.2020.03.002.

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Wu, Zenan, Liqin Tian, Yan Wang, Jianfei Xie, Yuquan Du, and Yi Zhang. "Network Security Defense Decision-Making Method Based on Stochastic Game and Deep Reinforcement Learning." Security and Communication Networks 2021 (December 16, 2021): 1–13. http://dx.doi.org/10.1155/2021/2283786.

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Aiming at the existing network attack and defense stochastic game models, most of them are based on the assumption of complete information, which causes the problem of poor applicability of the model. Based on the actual modeling requirements of the network attack and defense process, a network defense decision-making model combining incomplete information stochastic game and deep reinforcement learning is proposed. This model regards the incomplete information of the attacker and the defender as the defender’s uncertainty about the attacker’s type and uses the Double Deep Q-Network algorithm

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Housh, Mashor, and Tomer Aharon. "Info-Gap Models for Optimal Multi-Year Management of Regional Water Resources Systems under Uncertainty." Sustainability 13, no. 6 (2021): 3152. http://dx.doi.org/10.3390/su13063152.

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The common practices for the planning and management of Water Resources Systems (WSSs) have been challenged in the last few decades by global climate change processes, which are observed around the world in increasing frequencies. Climate change is manifested by climate variability, temperature increase, and extreme events such as droughts and floods, which have a decisive effect on natural resource availability and in turn on water quality. Historical records may not be sufficient to reliably account for uncertain future predictions under climate change conditions. While such highly uncertain

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Yang, Yixian. "Path Planning under High-dimensional Input States Based on Deep Q-Network." Highlights in Science, Engineering and Technology 120 (December 25, 2024): 576–85. https://doi.org/10.54097/r6fs0580.

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The field of autonomous navigation continues to face challenges in path planning, particularly when addressing the complex, high-dimensional input states that conventional algorithms struggle to process efficiently. This study presents a novel path-planning approach that utilizes Deep Q-Networks (DQN) to manage intricate and multidimensional environmental data. By integrating a DQN with path planning, this study aims to develop an adaptive system capable of making real-time decisions in dynamic environments. The methodology involves training a neural network to approximate the Q-function, enab

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Henry H. James, Razu Pawel, and Gawin Saduf. "Autonomous Vehicles and Robust Decision-Making in Dynamic Environments." Fusion of Multidisciplinary Research, An International Journal 1, no. 2 (2020): 110–21. https://doi.org/10.63995/nstn6884.

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Autonomous vehicles (AVs) are at the forefront of transforming transportation, requiring robust decision-making capabilities to navigate dynamic environments effectively. These vehicles rely on advanced sensors, machine learning algorithms, and real-time data processing to make split-second decisions that ensure safety and efficiency. Key challenges include accurately perceiving the environment, predicting the behavior of other road users, and responding to unpredictable conditions such as changing weather, road obstacles, and traffic patterns. Robust decision-making in AVs integrates various

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Mahmoud, Hisham Ahmed, and Ibrahim M. Ibrahim. "Adaptive Hybrid Algorithms for Real-Time Decision-Making in Autonomous Systems." Asian Journal of Research in Computer Science 18, no. 5 (2025): 55–64. https://doi.org/10.9734/ajrcos/2025/v18i5638.

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Recent breakthroughs in computational intelligence have enabled remarkable advances in decision-making systems operating within dynamic, complex environments. The work presented in this paper looks into the incorporation of three major techniques: Reinforcement Learning, Deep Neural Networks, and Fuzzy Logic in developing hybrid models in order to be able to tackle some major challenges of adaptability, handling uncertainty, and high-dimensionality data processing. These hybrid frameworks have applications in domains such as autonomous vehicle navigation, health care, robotics, and supply chai

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Ling, Jiajing, Kushagra Chandak, and Akshat Kumar. "Integrating Knowledge Compilation with Reinforcement Learning for Routes." Proceedings of the International Conference on Automated Planning and Scheduling 31 (May 17, 2021): 542–50. http://dx.doi.org/10.1609/icaps.v31i1.16002.

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Sequential multiagent decision-making under partial observability and uncertainty poses several challenges. Although multiagent reinforcement learning (MARL) approaches have increased the scalability, addressing combinatorial domains is still challenging as random exploration by agents is unlikely to generate useful reward signals. We address cooperative multiagent pathfinding under uncertainty and partial observability where agents move from their respective sources to destinations while also satisfying constraints (e.g., visiting landmarks). Our main contributions include: (1) compiling doma

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Biswas, Dr Kuheli, Mr Amal Das, Mr Ashim Kumar Choudhury, and Sanjib Kumar Datta. "On Uncertainty and Vagueness: A Comparative Study." International Journal of Research and Innovation in Social Science IX, no. V (2025): 1044–52. https://doi.org/10.47772/ijriss.2025.90500089.

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This paper delves into a multidimensional exploration of uncertainty through various theoretical lenses, including Probability Theory, Possibility Theory, Plausibility Theory, Belief Theory, Fuzzy Logic, Evidence Theory, and Vague Theory. Each framework offers a distinct perspective: Probability Theory deals with randomness, Possibility and Plausibility address feasibility and belief, Belief Theory integrates multiple sources of information, and Fuzzy Logic captures imprecision through degrees of truth. Vague Theory extends the discourse further by modeling information that is not only impreci

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Carlsson Kanyama, Annika, Jorge Luis Zapico, Chatarina Holmberg, and Per Wikman-Svahn. "“The Greatest Benefit Is to Think Differently”: Experiences of Developing and Using a Web-Based Tool for Decision-Making under Deep Uncertainty for Adaptation to Sea Level Rise in Municipalities." Sustainability 16, no. 5 (2024): 2044. http://dx.doi.org/10.3390/su16052044.

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The need for handling the deep uncertainty surrounding the future climate has led to various novel and robust approaches for decision-making under deep uncertainty (DMDU) when adapting to climate change. Here, an online and self-explanatory web-based tool was developed and tested with civil servants from five municipalities in Sweden challenged by rising sea levels. The municipalities used the tool by themselves and were then interviewed about the usability of the tool, the perceived urgency of climate change adaptation, and the possibilities for municipalities for handling the flexible soluti

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Zaruba, Viktor, and Taras Chmeruk. "ADVANTAGES AND DISADVANTAGES ANALYSIS OF PRODUCTION PLANNING APPROACHES UNDER UNCERTAINTY CONDITIONS." Bulletin of the National Technical University "Kharkiv Polytechnic Institute" (economic sciences), no. 2 (April 2, 2024): 3–6. https://doi.org/10.20998/2519-4461.2024.2.3.

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Modern manufacturing enterprises face difficulties in operational production planning due to the uncertainty that constantly affects their activities. However, there is a problem that company leaders do not always know the advantages and disadvantages of certain CRM solutions and their level of decision support, which creates challenges for effective planning. The article examines the existing approaches to operational planning of production, depending on the degree of considering the uncertainties of demand. A classification of approaches has been proposed, which includes four main categories

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Zisad, Sharif Noor, Etu Chowdhury, Mohammad Shahadat Hossain, Raihan Ul Islam, and Karl Andersson. "An Integrated Deep Learning and Belief Rule-Based Expert System for Visual Sentiment Analysis under Uncertainty." Algorithms 14, no. 7 (2021): 213. http://dx.doi.org/10.3390/a14070213.

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Visual sentiment analysis has become more popular than textual ones in various domains for decision-making purposes. On account of this, we develop a visual sentiment analysis system, which can classify image expression. The system classifies images by taking into account six different expressions such as anger, joy, love, surprise, fear, and sadness. In our study, we propose an expert system by integrating a Deep Learning method with a Belief Rule Base (known as the BRB-DL approach) to assess an image’s overall sentiment under uncertainty. This BRB-DL approach includes both the data-driven an

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Fong, Simon James, Gloria Li, Nilanjan Dey, Rubén González Crespo, and Enrique Herrera-Viedma. "Composite Monte Carlo decision making under high uncertainty of novel coronavirus epidemic using hybridized deep learning and fuzzy rule induction." Applied Soft Computing 93 (August 2020): 106282. http://dx.doi.org/10.1016/j.asoc.2020.106282.

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Coenen, Jannie, Rob van der Heijden, and Allard C. R. van Riel. "Making a Transition toward more Mature Closed-Loop Supply Chain Management under Deep Uncertainty and Dynamic Complexity: A Methodology." Sustainability 11, no. 8 (2019): 2318. http://dx.doi.org/10.3390/su11082318.

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This article develops a methodology to empirically study and cope with deep uncertainty and dynamic complexity when the actors in a traditional supply chain make a transition toward more mature closed-loop supply chain (CLSC) management. The methodology addressed calls for innovative research and decision-making approaches in this field. Mature, in this context, refers to moving operationally and mentally away from a stochastic, one-dimensional and static approach to CLSC management, towards an exploratory, multi-dimensional and dynamic approach. To empirically study and cope with deep uncerta

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Hekler, Achim, Titus J. Brinker, and Florian Buettner. "Test Time Augmentation Meets Post-hoc Calibration: Uncertainty Quantification under Real-World Conditions." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 12 (2023): 14856–64. http://dx.doi.org/10.1609/aaai.v37i12.26735.

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Communicating the predictive uncertainty of deep neural networks transparently and reliably is important in many safety-critical applications such as medicine. However, modern neural networks tend to be poorly calibrated, resulting in wrong predictions made with a high confidence. While existing post-hoc calibration methods like temperature scaling or isotonic regression yield strongly calibrated predictions in artificial experimental settings, their efficiency can significantly reduce in real-world applications, where scarcity of labeled data or domain drifts are commonly present. In this pap

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S. Sathyapriya. "Cubic Spherical Neutrosophic Geometric Weighted Bonferroni Mean Operator for Selecting a Machine using MCDM Techniques." Advances in Nonlinear Variational Inequalities 28, no. 2s (2024): 118–36. https://doi.org/10.52783/anvi.v28.2523.

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Neutrosophic cubic fuzzy sets (NCFSs) involve interval-valued and single-valued neutrosophic sets and are used to describe uncertainty or fuzziness more efficiently. The aggregation of neutrosopic cubic fuzzy information is crucial and necessary in a decision-making theory. To get a better solution to decision-making problems under a neutrosophic cubic fuzzy environment. The main objective of this study propose the Cubic Spherical Neutrosophic Geometric Bonferroni Mean Operator (CSNGBM) and the Cubic Spherical Neutrosophic Geometric Weighted Bonferroni Mean Operator (CSNGWBM) to find the best

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