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      Defining the analytical complexity of decision problems under uncertainty based on their pivotal properties

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          Abstract

          Background

          Uncertainty poses a pervasive challenge in decision analysis and risk management. When the problem is poorly understood, probabilistic estimation exhibits high variability and bias. Analysts then utilize various strategies to find satisficing solutions, and these strategies can sometimes adequately address even highly complex problems. Previous literature proposed a hierarchy of uncertainty, but did not develop a quantitative score of analytical complexity.

          Methods

          In order to develop such a score, this study reviewed over 90 strategies to cope with uncertainty, including methods utilized by expert decision-makers such as engineers, military planners and others.

          Results

          It found that many decision problems have pivotal properties that enable their solution despite uncertainty, including small action space, reversibility and others. The analytical complexity score of a problem could then be defined based on the availability of these properties.

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          Most cited references78

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          Standards for reporting exist for many types of quantitative research, but currently none exist for the broad spectrum of qualitative research. The purpose of the present study was to formulate and define standards for reporting qualitative research while preserving the requisite flexibility to accommodate various paradigms, approaches, and methods.
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            A Behavioral Model of Rational Choice

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              A Difficulty in the Concept of Social Welfare

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                Author and article information

                Contributors
                Journal
                PeerJ Comput Sci
                PeerJ Comput Sci
                peerj-cs
                PeerJ Computer Science
                PeerJ Inc. (San Diego, USA )
                2376-5992
                8 July 2024
                2024
                : 10
                : e2195
                Affiliations
                [1 ]Epidemiology and Biostatistics, University of Illinois at Chicago , Chicago, Illinois, United States of America
                [2 ]Department of Medicine, Loyola University of Chicago , Maywood, Illinois, United States
                [3 ]Amazon Web Services , Herndon, Virginia, United States
                Author information
                http://orcid.org/0000-0002-3324-2220
                Article
                cs-2195
                10.7717/peerj-cs.2195
                11323092
                39145227
                aceb79dc-496a-4f65-b41a-ade68e66048a
                © 2024 Gutfraind

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited.

                History
                : 19 September 2023
                : 23 June 2024
                Funding
                Funded by: NIH grant R01-AI158666
                The project was sponsored by NIH grant R01-AI158666. There was no additional external funding received for this study. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Adaptive and Self-Organizing Systems
                Agents and Multi-Agent Systems
                Social Computing
                Theory and Formal Methods

                risk management,decision theory,radical uncertainty,complexity,probabilistic risk assessment,heuristics,satisficing,optimization

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