mau-package {mau}R Documentation

mau

Description

Provides functions for the creation, evaluation and test of decision models based in Multi Attribute Utility Theory (MAUT).

Details

MAUT models are defined employing a decision tree where similarity relations between different index utilities are defined, this helps to group utilities following a criteria of similarity. Each final node has an utility and weight associated, the utility of any internal node in the decision tree is computed by adding the weighted sum of eaf of its final nodes. In a model with n indexes, a criteria is composed by C \subset \{1,\ldots,n\}, the respective utility is given by:

\sum_{i \in C}^n w_i u_i( x_i )

Currently, each utility is defined like a piecewise risk aversion utility, those functions are of the following form:

a x + b

or

a e^{cx} + b

The current capabilities of mau are:

  1. Read a list of risk aversion utilities defined in a standardized format.

  2. Evaluate utilities of a table of indexes.

  3. Load decision trees defined in column standard format.

  4. Compute criteria utilities and weights for any internal node of the decision tree.

  5. Simulate weights employing Dirichlet distributions under addition constraints in weights.

Author(s)

Maintainer: Pedro Guarderas pedro.felipe.guarderas@gmail.com

Other contributors:

References

Bell D., Raiffa H. and Tversky A. (1988). Decision Making: Descriptive, normative and prescriptive interactions. Cambridge University Press.

Clement R. (1991). Marking Hard Decision: An introduction to decision analysis. PWS-Kent Publishing Co.

Ward E. (1992). Utility Theories: Measurements and Applications. Kluwer Academic Publishers.

Barron FH and Barrett BE (1996). “Decision Quality Using Ranked Attribute Weights.” Manage. Sci., 42(11), pp. 1515–1523. ISSN 0025-1909, doi: 10.1287/mnsc.42.11.1515.

Bodily SE (1992). “Introduction: The Practice of Decision and Risk Analysis.” Interfaces, 22(6), pp. 1-4. doi: 10.1287/inte.22.6.1.

See Also

Useful links:

Examples

library( mau )
vignette( topic = 'Running_MAUT', package = 'mau' )
[Package mau version 0.1.2 Index]