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The Volatility of Data Space: Topology Oriented Sensitivity Analysis

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  • Jing Du
  • Arika Ligmann-Zielinska

Abstract

Despite the difference among specific methods, existing Sensitivity Analysis (SA) technologies are all value-based, that is, the uncertainties in the model input and output are quantified as changes of values. This paradigm provides only limited insight into the nature of models and the modeled systems. In addition to the value of data, a potentially richer information about the model lies in the topological difference between pre-model data space and post-model data space. This paper introduces an innovative SA method called Topology Oriented Sensitivity Analysis, which defines sensitivity as the volatility of data space. It extends SA into a deeper level that lies in the topology of data.

Suggested Citation

  • Jing Du & Arika Ligmann-Zielinska, 2015. "The Volatility of Data Space: Topology Oriented Sensitivity Analysis," PLOS ONE, Public Library of Science, vol. 10(9), pages 1-21, September.
    • Handle: RePEc:plo:pone00:0137591
    DOI: 10.1371/journal.pone.0137591
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    References listed on IDEAS

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    1. Arika Ligmann-Zielinska & Daniel B Kramer & Kendra Spence Cheruvelil & Patricia A Soranno, 2014. "Using Uncertainty and Sensitivity Analyses in Socioecological Agent-Based Models to Improve Their Analytical Performance and Policy Relevance," PLOS ONE, Public Library of Science, vol. 9(10), pages 1-13, October.
    2. Saltelli, Andrea & Ratto, Marco & Tarantola, Stefano & Campolongo, Francesca, 2006. "Sensitivity analysis practices: Strategies for model-based inference," Reliability Engineering and System Safety, Elsevier, vol. 91(10), pages 1109-1125.
    3. Thogmartin, Wayne E., 2010. "Sensitivity analysis of North American bird population estimates," Ecological Modelling, Elsevier, vol. 221(2), pages 173-177.
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