decisionSupport: Monte Carlo Simulations for Binary Decision Making Under Uncertainty

Monte Carlo simulations support quantitative analysis of binary welfare-based decision making. The package calculates optimal decisions by maximizing expected welfare and performs Value of Information Analysis to guide resource allocation for uncertainty reduction. Developed by Eike Luedeling at the University of Bonn, it is published under an Open Access license.

Use Cases

• Choosing between two alternatives under uncertainty based on maximizing expected welfare.
• Allocating resources to reduce uncertainty in a decision problem using Value of Information Analysis.
• Calculating the Expected Value of Information for prospective estimates via Monte Carlo simulation.
• Computing Individual Expected Value of Perfect Information for sensitivity analysis.
• Using the standalone Monte Carlo functionality for general probabilistic modeling.

Strengths

• Implements a dual-level decision support framework for both actual and meta decisions.
• Provides a concrete method for Value of Information Analysis, a key technique in decision theory.
• Monte Carlo simulation functionality is described as usable independently.

Limitations

• Description metadata is limited; actual data quality requires manual inspection after download.
• Column-level documentation is absent; field semantics must be inferred after download.
• Row count and dataset scale are unknown, which may limit suitability assessment.

Provenance

Source

University of Bonn

Collection Method

Likely generated via computational methods and Monte Carlo simulations.