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:
Read a list of risk aversion utilities defined in a standardized format.
Evaluate utilities of a table of indexes.
Load decision trees defined in column standard format.
Compute criteria utilities and weights for any internal node of the decision tree.
Simulate weights employing Dirichlet distributions under addition constraints in weights.