Keywords

Deep uncertainty, exploratory modeling, robust decision making, scenario discovery

Location

Session D5: Advancing in Environmental Decision Making Under Deep Uncertainty: Emerging Tools and Challenges

Start Date

12-7-2016 2:50 PM

End Date

12-7-2016 3:10 PM

Description

There is a growing interest in model-based decision support under deep uncertainty, reflected in a variety of approaches and techniques being put forward in the literature. A key idea shared among these various approaches and techniques 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 the various ways in which the various uncertainties might be resolved. This paper presents an open source library for performing exploratory modeling. This exploratory modeling workbench is implemented in Python. It is designed to (i) support the generation and execution of series of computational experiments, including support for parallelization on a single machine or high performance cluster; 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 simulation models, identify the policy relevant combinations of uncertain factors, assess the efficacy of policy options to address these uncertain factors, and thus iteratively improve candidate strategies.

 
Jul 12th, 2:50 PM Jul 12th, 3:10 PM

The Exploratory Modeling Workbench An open source toolkit for exploratory modeling, scenario discovery, and (multi-objective) robust decision making

Session D5: Advancing in Environmental Decision Making Under Deep Uncertainty: Emerging Tools and Challenges

There is a growing interest in model-based decision support under deep uncertainty, reflected in a variety of approaches and techniques being put forward in the literature. A key idea shared among these various approaches and techniques 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 the various ways in which the various uncertainties might be resolved. This paper presents an open source library for performing exploratory modeling. This exploratory modeling workbench is implemented in Python. It is designed to (i) support the generation and execution of series of computational experiments, including support for parallelization on a single machine or high performance cluster; 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 simulation models, identify the policy relevant combinations of uncertain factors, assess the efficacy of policy options to address these uncertain factors, and thus iteratively improve candidate strategies.