Scenario generation using adaptive sampling: The case of resource scarcity. (May 2016)

• Record Type: Journal Article
• Authors: Islam, Tushith; Pruyt, Erik
• Is Part Of: Environmental modelling & software. Volume 79(2016:May)
• Page Start: 285
• Page End: 299
• Publication Date: 2016-05
• Subjects: Adaptive sampling -- Scenario discovery -- Uncertainty -- Exploratory modeling and analysis -- Behavioral spectrum -- System dynamics; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2015.09.014
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Static input-oriented sampling approaches are often used for generating model-based scenarios. However, for models of deeply uncertain and dynamically complex issues, there is no guarantee that such approaches reveal the total behavioral spectrum that could be generated by simulating them. In this paper, we present an adaptive output-oriented sampling approach for exploring the full behavioral spectrum that could be generated by computational models in view of generating interesting, even previously undiscovered, scenarios. In this paper, we use a resource scarcity model to illustrate the approach, show the difference between static sampling and adaptive sampling, and demonstrate the usefulness for scenario discovery of the latter combined with other methods. We show that this approach can be used for revealing the behavioral spectrum of models, identifying regions of the input space that generate particular behaviors, and selecting (sets of) scenarios that are representative in terms of output and input spaces. Highlights: We introduce adaptive sampling to explore the output spectrum of simulation models. We use roughness to characterize the dynamic complexity of transient scenarios. We show that adaptive sampling supports scenario discovery and exemplar selection. Using a resource scarcity model, we identify strengths, weaknesses/improvements.