2
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Developing political-ecological theory: The need for many-task computing

      research-article
      *
      PLoS ONE
      Public Library of Science

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Models of political-ecological systems can inform policies for managing ecosystems that contain endangered species. To increase the credibility of these models, massive computation is needed to statistically estimate the model’s parameters, compute confidence intervals for these parameters, determine the model’s prediction error rate, and assess its sensitivity to parameter misspecification. To meet this statistical and computational challenge, this article delivers statistical algorithms and a method for constructing ecosystem management plans that are coded as distributed computing applications. These applications can run on cluster computers, the cloud, or a collection of in-house workstations. This downloadable code is used to address the challenge of conserving the East African cheetah ( Acinonyx jubatus). This demonstration means that the new standard of credibility that any political-ecological model needs to meet is the one given herein.

          Related collections

          Most cited references49

          • Record: found
          • Abstract: found
          • Article: found
          Is Open Access

          Accelerated modern human–induced species losses: Entering the sixth mass extinction

          Humans are causing a massive animal extinction without precedent in 65 million years.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Choosing Prediction Over Explanation in Psychology: Lessons From Machine Learning.

            Psychology has historically been concerned, first and foremost, with explaining the causal mechanisms that give rise to behavior. Randomized, tightly controlled experiments are enshrined as the gold standard of psychological research, and there are endless investigations of the various mediating and moderating variables that govern various behaviors. We argue that psychology's near-total focus on explaining the causes of behavior has led much of the field to be populated by research programs that provide intricate theories of psychological mechanism but that have little (or unknown) ability to predict future behaviors with any appreciable accuracy. We propose that principles and techniques from the field of machine learning can help psychology become a more predictive science. We review some of the fundamental concepts and tools of machine learning and point out examples where these concepts have been used to conduct interesting and important psychological research that focuses on predictive research questions. We suggest that an increased focus on prediction, rather than explanation, can ultimately lead us to greater understanding of behavior.
              Bookmark
              • Record: found
              • Abstract: not found
              • Article: not found

              From Factors to Factors: Computational Sociology and Agent-Based Modeling

                Bookmark

                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: MethodologyRole: SoftwareRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS One
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                2020
                24 November 2020
                : 15
                : 11
                : e0226861
                Affiliations
                [001] Lubar School of Business, University of Wisconsin-Milwaukee, Milwaukee, WI, United States of America
                Griffith University, AUSTRALIA
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Author information
                https://orcid.org/0000-0003-2408-2744
                Article
                PONE-D-19-33903
                10.1371/journal.pone.0226861
                7685461
                33232315
                27c3b853-aa97-4bc3-b36f-6fc98a6ab64c
                © 2020 Timothy Haas

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 7 December 2019
                : 29 October 2020
                Page count
                Figures: 3, Tables: 6, Pages: 26
                Funding
                The author(s) received no specific funding for this work.
                Categories
                Research Article
                Biology and Life Sciences
                Ecology
                Ecosystems
                Ecology and Environmental Sciences
                Ecology
                Ecosystems
                Biology and Life Sciences
                Organisms
                Eukaryota
                Animals
                Vertebrates
                Amniotes
                Mammals
                Cats
                Cheetahs
                Biology and Life Sciences
                Zoology
                Animals
                Vertebrates
                Amniotes
                Mammals
                Cats
                Cheetahs
                Ecology and Environmental Sciences
                Conservation Science
                Social Sciences
                Political Science
                Political Theory
                Biology and Life Sciences
                Computational Biology
                Ecosystem Modeling
                Biology and Life Sciences
                Ecology
                Ecosystems
                Ecosystem Modeling
                Ecology and Environmental Sciences
                Ecology
                Ecosystems
                Ecosystem Modeling
                Physical Sciences
                Mathematics
                Applied Mathematics
                Algorithms
                Research and Analysis Methods
                Simulation and Modeling
                Algorithms
                Biology and Life Sciences
                Ecology
                Ecosystems
                Ecosystem Functioning
                Ecology and Environmental Sciences
                Ecology
                Ecosystems
                Ecosystem Functioning
                Physical Sciences
                Mathematics
                Optimization
                Custom metadata
                All relevant data are within the manuscript and its Supporting information files.

                Uncategorized
                Uncategorized

                Comments

                Comment on this article