The Exploratory Modeling Workbench: An open source toolkit for exploratory modeling, scenario discovery, and (multi-objective) robust decision making. (October 2017)
- Record Type:
- Journal Article
- Title:
- The Exploratory Modeling Workbench: An open source toolkit for exploratory modeling, scenario discovery, and (multi-objective) robust decision making. (October 2017)
- Main Title:
- The Exploratory Modeling Workbench: An open source toolkit for exploratory modeling, scenario discovery, and (multi-objective) robust decision making
- Authors:
- Kwakkel, Jan H.
- Abstract:
- Abstract: There is a growing interest in model-based decision support under deep uncertainty, reflected in a variety of approaches being put forward in the literature. A key idea shared among these is the use of models for exploratory rather than predictive purposes. Exploratory modeling aims at exploring the implications for decision making of the various presently irresolvable uncertainties. This is achieved by conducting series of computational experiments that cover how the various uncertainties might resolve. This paper presents an open source library supporting this. The Exploratory Modeling Workbench is implemented in Python. It is designed to (i) support the generation and execution of series of computational experiments; and (ii) support the visualization and analysis of the results from the computational experiments. The Exploratory Modeling Workbench enables users to easily perform exploratory modeling with existing models, identify the policy-relevant uncertainties, assess the efficacy of policy options, and iteratively improve candidate strategies. Highlights: This paper introduces the open source python library ema_workbench. The design of the library is provided. The design is motivated by conceptual considerations and a deep uncertainty taxonomy. The workbench is illustrated using a stylized environmental planning problem.
- Is Part Of:
- Environmental modelling & software. Volume 96(2017)
- Journal:
- Environmental modelling & software
- Issue:
- Volume 96(2017)
- Issue Display:
- Volume 96, Issue 2017 (2017)
- Year:
- 2017
- Volume:
- 96
- Issue:
- 2017
- Issue Sort Value:
- 2017-0096-2017-0000
- Page Start:
- 239
- Page End:
- 250
- Publication Date:
- 2017-10
- Subjects:
- Deep uncertainty -- Exploratory modeling -- Scenario discovery -- Many-objective robust decision making
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 - Journal URLs:
- http://www.sciencedirect.com/science/journal/13648152 ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.envsoft.2017.06.054 ↗
- Languages:
- English
- ISSNs:
- 1364-8152
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 3791.522800
British Library DSC - BLDSS-3PM
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- 4644.xml