Relevant bibliographies by topics / Decision-Making Under Deep Uncertainty

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Decision-Making Under Deep Uncertainty'

Author: Grafiati
Published: 5 June 2025
Last updated: 15 July 2025

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

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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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Dissertations / Theses on the topic "Decision-Making Under Deep Uncertainty"

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Roach, Thomas Peter. "Decision making methods for water resources management under deep uncertainty." Thesis, University of Exeter, 2016. http://hdl.handle.net/10871/25756.

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Substantial anthropogenic change of the Earth’s climate is modifying patterns of rainfall, river flow, glacial melt and groundwater recharge rates across the planet, undermining many of the stationarity assumptions upon which water resources infrastructure has been historically managed. This hydrological uncertainty is creating a potentially vast range of possible futures that could threaten the dependability of vital regional water supplies. This, combined with increased urbanisation and rapidly growing regional populations, is putting pressures on finite water resources. One of the greatest
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McInerney, Robert E. "Decision making under uncertainty." Thesis, University of Oxford, 2014. http://ora.ox.ac.uk/objects/uuid:a34e87ad-8330-42df-8ba6-d55f10529331.

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Operating and interacting in an environment requires the ability to manage uncertainty and to choose definite courses of action. In this thesis we look to Bayesian probability theory as the means to achieve the former, and find that through rigorous application of the rules it prescribes we can, in theory, solve problems of decision making under uncertainty. Unfortunately such methodology is intractable in realworld problems, and thus approximation of one form or another is inevitable. Many techniques make use of heuristic procedures for managing uncertainty. We note that such methods suffer u
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Warren, Adam L. "Sequential decision-making under uncertainty /." *McMaster only, 2004.

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Hope, Susannah Jayne. "Decision making under spatial uncertainty /." Connect to thesis, 2005. http://repository.unimelb.edu.au/10187/1150.

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Errors are inherent to all spatial datasets and give rise to a level of uncertainty in the final product of a geographic information system (GIS). There is growing recognition that the uncertainty associated with spatial information should be represented to users in a comprehensive and unambiguous way. However, the effects on decision-making of such representations have not been thoroughly investigated. Studies from the psychological literature indicate decision-making biases when information is uncertain. This study explores the effects of representing spatial uncertainty, through an examinat
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Odame, Augustina Yaa Oye. "Water Decision-Making Under Uncertainty." DigitalCommons@USU, 2015. https://digitalcommons.usu.edu/etd/4576.

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This dissertation is made up of three separate studies under the unifying theme of “Water Decision-Making under Uncertainty.” The first study analyzed a farmer’s decision to invest in a more efficient irrigation system given uncertainty about future water supplies and his post-investment efficiency. It found the price at which farmers would no longer produce to be a bigger consideration in irrigation investment than previously thought. It also found support for a careful identification and consideration of all significant sources of uncertainty in order to create better policy incentives for i
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Wang, Di. "Essays on decision-making under uncertainty." Thesis, University of Nottingham, 2018. http://eprints.nottingham.ac.uk/49460/.

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This thesis consists of three closely related studies investigating individual decision-making under risk and uncertainty, with a focus on decision weighting. Chapter 1 provides an overview of the common themes and theoretical framework for this research. Chapter 2 reports the development of a simple method to measure the probability weighting function of Prospect Theory (Kahneman & Tversky, 1979) and rank-dependent utility theories. Our method, called the Neo-Lite method, is based on Abdellaoui et al. (2011)’s source method and the Neo-additive weighting function (Chateauneuf et al., 2007). I
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Péron, Martin Brice. "Optimal sequential decision-making under uncertainty." Thesis, Queensland University of Technology, 2018. https://eprints.qut.edu.au/120831/1/Martin%20Brice_Peron_Thesis.pdf.

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This thesis develops novel mathematical models to make optimal sequential decisions under uncertainty. One of the main objectives is to scale Markov decision processes, the framework of choice for selecting the best sequential decisions, to larger problems. The thesis is motivated by the management of the invasive tiger mosquito Aedes albopictus across the Torres Strait Islands, an archipelago of islands at the doorstep of the Australian mainland.
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Mirestean, Alin Tavi. "Decision making under uncertainty and bounded rationality." College Park, Md. : University of Maryland, 2005. http://hdl.handle.net/1903/2946.

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Thesis (Ph. D.) -- University of Maryland, College Park, 2005.<br>Thesis research directed by: Economics. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.
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Bayley, Timothy West, and Timothy West Bayley. "Decision Making Under Uncertainty in Water Resources." Diss., The University of Arizona, 2016. http://hdl.handle.net/10150/621871.

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Hydrology is a field fraught with uncertainty. Uncertainty comes from both our inability to perfectly know the true nature of constant system components of hydrologic systems (e.g. hydraulic conductivity, geologic structure, etc.) and our inability to perfectly predict the behavior of variable system components (e.g. future precipitation, future streamflow, etc.). Hydrologic literature has increasingly recognized that within the bounds of uncertainty, many acceptable hydrologic models exist and differ in their predictions. Modeling applications that recognize this uncertainty have become mor
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Lu, Shaohua. "Essays on Strategic Decision Making under Uncertainty." The Ohio State University, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=osu1437747505.

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Books on the topic "Decision-Making Under Deep Uncertainty"

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Marchau, Vincent A. W. J., Warren E. Walker, Pieter J. T. M. Bloemen, and Steven W. Popper, eds. Decision Making under Deep Uncertainty. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-05252-2.

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Marchau, Vincent A. W. J. Decision Making under Deep Uncertainty: From Theory to Practice. Springer Nature, 2019.

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Biswas, Tapan. Decision-Making under Uncertainty. Macmillan Education UK, 1997. http://dx.doi.org/10.1007/978-1-349-25817-8.

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Greengard, Claude, and Andrzej Ruszczynski, eds. Decision Making Under Uncertainty. Springer New York, 2002. http://dx.doi.org/10.1007/978-1-4684-9256-9.

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Bell, David E. Decision making under uncertainty. Course Technology, 1995.

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Brown, Donald J. Affective Decision Making Under Uncertainty. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59512-8.

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Consigli, Giorgio, Daniel Kuhn, and Paolo Brandimarte, eds. Optimal Financial Decision Making under Uncertainty. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-41613-7.

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Geweke, John, ed. Decision Making Under Risk and Uncertainty. Springer Netherlands, 1992. http://dx.doi.org/10.1007/978-94-011-2838-4.

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Ceberio, Martine, and Vladik Kreinovich, eds. Decision Making Under Uncertainty and Constraints. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-16415-6.

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Raiffa, Howard. Decision analysis: Introductory lectures on choices under uncertainty. McGraw-Hill, 1997.

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Book chapters on the topic "Decision-Making Under Deep Uncertainty"

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Lempert, R. J. "Robust Decision Making (RDM)." In Decision Making under Deep Uncertainty. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-05252-2_2.

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Brown, Casey, Scott Steinschneider, Patrick Ray, Sungwook Wi, Leon Basdekas, and David Yates. "Decision Scaling (DS): Decision Support for Climate Change." In Decision Making under Deep Uncertainty. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-05252-2_12.

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Ben-Haim, Yakov. "Info-Gap Decision Theory (IG)." In Decision Making under Deep Uncertainty. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-05252-2_5.

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Marchau, Vincent A. W. J., Warren E. Walker, Pieter J. T. M. Bloemen, and Steven W. Popper. "Introduction." In Decision Making under Deep Uncertainty. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-05252-2_1.

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Hemez, François M., and Kendra L. Van Buren. "Info-Gap (IG): Robust Design of a Mechanical Latch." In Decision Making under Deep Uncertainty. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-05252-2_10.

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de Neufville, Richard, Kim Smet, Michel-Alexandre Cardin, and Mehdi Ranjbar-Bourani. "Engineering Options Analysis (EOA): Applications." In Decision Making under Deep Uncertainty. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-05252-2_11.

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Sowell, Jesse. "A Conceptual Model of Planned Adaptation (PA)." In Decision Making under Deep Uncertainty. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-05252-2_13.

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Bloemen, Pieter J. T. M., Floris Hammer, Maarten J. van der Vlist, Pieter Grinwis, and Jos van Alphen. "DMDU into Practice: Adaptive Delta Management in The Netherlands." In Decision Making under Deep Uncertainty. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-05252-2_14.

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Kwakkel, Jan H., and Marjolijn Haasnoot. "Supporting DMDU: A Taxonomy of Approaches and Tools." In Decision Making under Deep Uncertainty. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-05252-2_15.

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Popper, Steven W. "Reflections: DMDU and Public Policy for Uncertain Times." In Decision Making under Deep Uncertainty. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-05252-2_16.

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Conference papers on the topic "Decision-Making Under Deep Uncertainty"

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Ott, Joshua, Sung-Kyun Kim, Amanda Bouman, et al. "Risk-aware Meta-level Decision Making for Exploration Under Uncertainty." In 2024 10th International Conference on Control, Decision and Information Technologies (CoDIT). IEEE, 2024. http://dx.doi.org/10.1109/codit62066.2024.10708134.

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Zhao, Qihang, Biqi Liu, Xiangluan Dong, Ruiting Qu, Xinliu Wang, and Yan Zhao. "An Auxiliary Decision-Making Method for Distributed Resources Under Load Uncertainty." In 2024 Second International Conference on Cyber-Energy Systems and Intelligent Energy (ICCSIE). IEEE, 2024. http://dx.doi.org/10.1109/iccsie61360.2024.10698352.

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Jain, Dinesh, Srinivas Anumasa, and P. K. Srijith. "Decision Making under Uncertainty with Convolutional Deep Gaussian Processes." In CoDS COMAD 2020: 7th ACM IKDD CoDS and 25th COMAD. ACM, 2020. http://dx.doi.org/10.1145/3371158.3371383.

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Moallemi, Enayat A., Sondoss Elsawah, Hasan H. Turan, and Michael J. Ryan. "MULTI-OBJECTIVE DECISION MAKING IN MULTI-PERIOD ACQUISITION PLANNING UNDER DEEP UNCERTAINTY." In 2018 Winter Simulation Conference (WSC). IEEE, 2018. http://dx.doi.org/10.1109/wsc.2018.8632316.

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Haklidir, Mehmet, and Hakan Temeltas. "Autonomous Driving Systems for Decision-Making Under Uncertainty Using Deep Reinforcement Learning." In 2022 30th Signal Processing and Communications Applications Conference (SIU). IEEE, 2022. http://dx.doi.org/10.1109/siu55565.2022.9864806.

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JABINI, AMIN, and ERIK A. JOHNSON. "HETEROGENEOUS SENSOR PLACEMENT UNDER UNCERTAINTY." In Structural Health Monitoring 2023. Destech Publications, Inc., 2023. http://dx.doi.org/10.12783/shm2023/36834.

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This study presents a heterogeneous sensor placement optimization framework using deep reinforcement learning (DRL) that considers system parameter uncertainty. The sensor placement problem is a well-established combinatorial optimization problem characterized by inherent parameter uncertainties that affect system responses that sensors measure. These uncertainties render deterministic solutions insufficient and necessitate a computationally tractable approach to account for the uncertainties. The proposed method incorporates a Markov decision process (MDP) as a stochastic environment, and a s
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Prasad, D., T. Patankar, A. Pandey, et al. "AI Fast-Tracking Infill Well Identification Under Uncertainty for a Complex Deep Gas Field and Accelerating Field Development Decision." In APOGCE 2024. SPE, 2024. http://dx.doi.org/10.2118/221271-ms.

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Abstract Objective/Scope R’ field is a tight, deep gas-condensate volatile-oil reservoir, in a geologically complex volcanic depositional setting. Vertical and deviated wells penetrate several zones, multi-stage fractured making it commercially viable. Ramp-up and sustaining production at the planned plateau production rate through intensive development drilling campaigns was a challenge. The primarily transient production profiles and recoveries per well are highly variable and the identification of sweet spots and good quality reservoirs(pay) is fraught with uncertainty. There was an urgency
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Zhou, Zejian, and Hao Xu. "Switching Deep Reinforcement Learning based Intelligent Online Decision Making for Autonomous Systems under Uncertain Environment." In 2018 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, 2018. http://dx.doi.org/10.1109/ssci.2018.8628889.

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Sanaei, Alireza, Shuai He, Joshua Pope, Santosh Verma, Rick Mifflin, and Amr El-Bakry. "Apply Reduced-Physics Modeling to Accelerate Depletion Planning Optimization Under Subsurface Uncertainty." In SPE Annual Technical Conference and Exhibition. SPE, 2022. http://dx.doi.org/10.2118/210217-ms.

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Abstract Robust decision-making regarding reservoir management using model-based strategies requires a large number of evaluations which can be enormously time-consuming if incorporating the full-field simulation. This challenge becomes more acute when dealing with subsurface uncertainty represented by multiple geologic scenarios. Various reduced-physics and reduced-order models such as streamlines and upscaling are commonly applied to accelerate the optimization process by reducing the computational burden of each evaluation. In this paper we propose an innovative integrated workflow that app
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Kostyuchenko, Yuriy V., Yulia Sztoyka, Ivan Kopachevsky, Igor Artemenko, and Maxim Yuschenko. "Multisensor satellite data for water quality analysis and water pollution risk assessment: decision making under deep uncertainty with fuzzy algorithm in framework of multimodel approach." In Remote Sensing for Agriculture, Ecosystems, and Hydrology, edited by Christopher M. Neale and Antonino Maltese. SPIE, 2017. http://dx.doi.org/10.1117/12.2276151.

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Reports on the topic "Decision-Making Under Deep Uncertainty"

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Lempert, Robert J., Michelle Miro, and Diogo Prosdocimi. A DMDU Guidebook for Transportation Planning Under a Changing Climate. Edited by Benoit Lefevre and Ernesto Monter Flores. Inter-American Development Bank, 2021. http://dx.doi.org/10.18235/0003042.

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The effects of climate-related natural hazards pose a significant threat to sustainable development in Latin America and the Caribbean (LAC) region and in particular its transportation sector. Risk Management provides an appropriate framework for assessing and mitigating the impacts of climate change and other climate-related natural hazards on transportation systems and choosing actions to enhance their resilience. However, analysts and policymakers involved in transportation planning, policy, and investment face significant challenges in managing the risks triggered by the effects of climate
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Pasupuleti, Murali Krishna. Stochastic Computation for AI: Bayesian Inference, Uncertainty, and Optimization. National Education Services, 2025. https://doi.org/10.62311/nesx/rriv325.

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Abstract: Stochastic computation is a fundamental approach in artificial intelligence (AI) that enables probabilistic reasoning, uncertainty quantification, and robust decision-making in complex environments. This research explores the theoretical foundations, computational techniques, and real-world applications of stochastic methods, focusing on Bayesian inference, Monte Carlo methods, stochastic optimization, and uncertainty-aware AI models. Key topics include probabilistic graphical models, Markov Chain Monte Carlo (MCMC), variational inference, stochastic gradient descent (SGD), and Bayes
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Pasupuleti, Murali Krishna. Decision Theory and Model-Based AI: Probabilistic Learning, Inference, and Explainability. National Education Services, 2025. https://doi.org/10.62311/nesx/rriv525.

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Abstract Decision theory and model-based AI provide the foundation for probabilistic learning, optimal inference, and explainable decision-making, enabling AI systems to reason under uncertainty, optimize long-term outcomes, and provide interpretable predictions. This research explores Bayesian inference, probabilistic graphical models, reinforcement learning (RL), and causal inference, analyzing their role in AI-driven decision systems across various domains, including healthcare, finance, robotics, and autonomous systems. The study contrasts model-based and model-free approaches in decision-
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Schultz, Martin T., Keneth N. Mitchell, Brian K. Harper, and Todd S. Bridges. Decision Making Under Uncertainty. Defense Technical Information Center, 2010. http://dx.doi.org/10.21236/ada534878.

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Ballesteros, Luis, and Howard Kunreuther. Organizational Decision Making Under Uncertainty Shocks. National Bureau of Economic Research, 2018. http://dx.doi.org/10.3386/w24924.

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Marold, Juliane, Ruth Wagner, Markus Schöbel, and Dietrich Manzey. Decision-making in groups under uncertainty. Fondation pour une culture de sécurité industrielle, 2012. http://dx.doi.org/10.57071/361udm.

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The authors have studied daily decision-making processes in groups under uncertainty, with an exploratory field study in the medical domain. The work follows the tradition of naturalistic decision-making (NDM) research. It aims to understand how groups in this high reliability context conceptualize and internalize uncertainties, and how they handle them in order to achieve effective decision-making in their everyday activities. Analysis of the survey data shows that uncertainty is thought of in terms of issues and sources (as identified by previous research), but also (possibly a domain-specif
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Ranger, Nicola. Topic Guide. Adaptation: Decision making under uncertainty. Evidence on Demand, 2013. http://dx.doi.org/10.12774/eod_tg02.june2013.ranger.

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Wellman, Michael P. Distributed Decision Making and Plan Recognition Under Uncertainty. Defense Technical Information Center, 2000. http://dx.doi.org/10.21236/ada405436.

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Cassady, C. R., Heather L. Nachtmann, Edward A. Pohl, Alejandro Mendoza, Letitia Pohl, and Nick Rew. Maintenance Decision-Making Under Prognostic and Diagnostic Uncertainty. Defense Technical Information Center, 2005. http://dx.doi.org/10.21236/ada452058.

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Zhou, Enlu. Dynamic Decision Making under Uncertainty and Partial Information. Defense Technical Information Center, 2013. http://dx.doi.org/10.21236/ada591355.

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