Quantitative Support of Decision Making under Uncertainty.
decisionSupport package supports the quantitative analysis of
welfare based decision making processes using Monte Carlo simulations. This
is an important part of the Applied Information Economics (AIE) approach
developed in Hubbard (2014). These decision making processes can be
categorized into two levels of decision making:
The actual problem of interest of a policy maker which we call the underlying welfare based decision on how to influence an ecological-economic system based on a particular information on the system available to the decision maker and
the meta decision on how to allocate resources to reduce the uncertainty in the underlying decision problem, i.e to increase the current information to improve the underlying decision making process.
The first problem, i.e. the underlying problem, is the problem of choosing the decision which maximizes expected welfare. The welfare function can be interpreted as a von Neumann-Morgenstern utility function. Whereas, the second problem, i.e. the meta decision problem, is dealt with using the Value of Information Analysis (VIA). Value of Information Analysis seeks to assign a value to a certain reduction in uncertainty or, equivalently, increase in information. Uncertainty is dealt with in a probabilistic manner. Probabilities are transformed via Monte Carlo simulations.
The functionality of this package is subdivided into three main parts: (i) the welfare based analysis of the underlying decision, (ii) the meta decision of reducing uncertainty and (iii) the Monte Carlo simulation for the transformation of probabilities and calculation of expectation values. Furthermore, there is a wrapper function around these three parts which aims at providing an easy-to-use interface.
Welfare based Analysis of the Underlying Decision Problem
The Meta Decision of Reducing Uncertainty
The meta decision of how to allocate resources for uncertainty reduction can be analyzed with this package in two different ways: via (i) Expected Value of Information Analysis or (ii) via Partial Least Squares (PLS) analysis and Variable Importance in Projection (VIP).
Expected Value of Information (EVI)
Partial Least Squares (PLS) analysis and Variable Importance in Projection (VIP)
Solving the Practical Problem of Calculating Expectation Values by Monte Carlo Simulation
Multivariate Random Number Generation
Monte Carlo Simulation
Integrated Welfare Decision and Value of Information Analysis: A wrapper function
decisionSupport integrates the most important features of this
package into a single function. It is wrapped arround the functions
The R-package decisionSupport is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version: GNU GENERAL PUBLIC LICENSE, Version 3 (GPL-3)
The R-package decisionSupport is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with the R-package decisionSupport. If not, see http://www.gnu.org/licenses/.
Maintainer: Eike Luedeling firstname.lastname@example.org
Hubbard, Douglas W., How to Measure Anything? - Finding the Value of "Intangibles" in Business, John Wiley & Sons, Hoboken, New Jersey, 2014, 3rd Ed, http://www.howtomeasureanything.com/.
Hugh Gravelle and Ray Rees, Microeconomics, Pearson Education Limited, 3rd edition, 2004.
Want to suggest features or report bugs for rdrr.io? Use the GitHub issue tracker.