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

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  • Timothy Haas

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.

Suggested Citation

  • Timothy Haas, 2020. "Developing political-ecological theory: The need for many-task computing," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-26, November.
    • Handle: RePEc:plo:pone00:0226861
    DOI: 10.1371/journal.pone.0226861
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    References listed on IDEAS

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    1. Mishra, Sudhanshu, 2006. "Some new test functions for global optimization and performance of repulsive particle swarm method," MPRA Paper 2718, University Library of Munich, Germany.
    2. Timothy C Haas & Sam M Ferreira, 2016. "Combating Rhino Horn Trafficking: The Need to Disrupt Criminal Networks," PLOS ONE, Public Library of Science, vol. 11(11), pages 1-26, November.
    3. Tashkova, Katerina & Šilc, Jurij & Atanasova, Nataša & Džeroski, Sašo, 2012. "Parameter estimation in a nonlinear dynamic model of an aquatic ecosystem with meta-heuristic optimization," Ecological Modelling, Elsevier, vol. 226(C), pages 36-61.
    4. Grazzini, Jakob & Richiardi, Matteo, 2015. "Estimation of ergodic agent-based models by simulated minimum distance," Journal of Economic Dynamics and Control, Elsevier, vol. 51(C), pages 148-165.
    5. Sungho Shin & Ophelia S Venturelli & Victor M Zavala, 2019. "Scalable nonlinear programming framework for parameter estimation in dynamic biological system models," PLOS Computational Biology, Public Library of Science, vol. 15(3), pages 1-29, March.
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    1. Haas, Timothy C., 2024. "Models vetted against prediction error and parameter sensitivity standards can credibly evaluate ecosystem management options," Ecological Modelling, Elsevier, vol. 498(C).

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