The `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 andthe

*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.

Implementation: `welfareDecisionAnalysis`

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).

Implementation: `eviSimulation`

, `individualEvpiSimulation`

Implementation: `plsr.mcSimulation`

, `VIP`

Implementation: `estimate`

Implementation: `random.estimate`

Implementation: `mcSimulation`

The function `decisionSupport`

integrates the most important features of this
package into a single function. It is wrapped arround the functions
`welfareDecisionAnalysis`

, `plsr.mcSimulation`

,
`VIP`

and `individualEvpiSimulation`

.

World Agroforestry Centre (ICRAF) 2015

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/.

Lutz GĂ¶hring lutz.goehring@gmx.de, Eike Luedeling (ICRAF) eike@eikeluedeling.com

Maintainer: Eike Luedeling eike@eikeluedeling.com

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.

`welfareDecisionAnalysis`

, `eviSimulation`

, `mcSimulation`

Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.

Please suggest features or report bugs with the GitHub issue tracker.

All documentation is copyright its authors; we didn't write any of that.