Publication | Open Access
Rhodium: Python Library for Many-Objective Robust Decision Making and Exploratory Modeling
29
Citations
26
References
2020
Year
Mathematical ProgrammingArtificial IntelligenceExploratory ModelingEngineeringSimulation ModellingRobustness (Computer Science)Complex SystemsSimulationAutonomous SystemsMultiple-criteria Decision AnalysisDecision AnalyticsAdaptive SystemsMordm FrameworksData ScienceUncertainty QuantificationManagementStatistical ComputingSystems EngineeringSensitivity AnalysisModeling And SimulationAutonomous Decision-makingDecision TheoryRobust OptimizationRisk AnalyticsRobust Decision MakingPredictive AnalyticsComputer ScienceRobust StrategiesPython LibraryInteractive Decision MakingRobust ModelingModel FrameworkIntelligent Decision MakingDecision ScienceModel AnalysisData Modeling
Rhodium is an open source Python library for robust decision making (RDM), many-objective robust decision making (MORDM), and exploratory modeling. These decision-support frameworks enable the identification of robust strategies for the management of complex environmental systems, by evaluating the tradeoffs among candidate strategies, and characterizing their vulnerabilities. Robust strategies refer to management options that perform sufficiently well or acceptably under a range of potential system conditions, rather than optimally in a single, nominal state of the world. Exploratory modeling allows for the simulation of the system under an ensemble of states of the world, so as to discover the ones with consequential effects on the system [1]. Rhodium facilitates rapid application of the RDM and MORDM frameworks by providing a suite of optimization, visualization, scenario discovery, and sensitivity analysis functions. Rhodium is written in Python and can interface with models written in Python, C and C++, Fortran, R, and Excel. The source code is freely available at https://github.com/Project-Platypus/Rhodium. <strong>Funding statement:</strong> This work was partially supported by the National Science Foundation under Grant No. (1639268). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
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