SALT   Software for Adversarial Life Testing

 

 

SALT was developed at the Institute for Reliability and Risk Analysis, and forms part of a suite of programs designed to work in the Microsoft Windows environment.


OVERVIEW

The program is based around the theory outlined in a paper by Nozer Singpurwalla and Dennis Lindley, entitled Adversarial Life Testing. If a consumer is unwilling to purchase a product because of poor prior opinion, then it may be beneficial to the manufacturer to offer a number of items to the consumer for testing, since the testing will change the consumer’s opinion. The paper develops statistical theory for dealing with this problem, and SALT implements this theory on the computer. The main aspects of the software are highlighted below:


DATA INPUT

In order to perform an analysis, the manufacturer and consumer must enter their prior beliefs and their utilities. In case there is some difficulty in quantifying beliefs and utilities, the software contains a module for eliciting these values.


ANALYSIS

Once the necessary data has been entered, we are in a position to perform an analysis in order to calculate the number of items that the manufacturer should offer. This can be time-consuming since the algorithm employed has exponential complexity.


RESULTS

A plot of expected utility

 

Once the analysis has been performed, we can observe the results in two different forms. One form is just a table of utilities for each possible number of units. The second form is a graph of expected utility. (A typical graph is displayed in the figure opposite). The entire analysis can be printed out.




OTHER FEATURES

The program has all the usual features of a Windows-based program, including full file management facilities and the facility to print the results either in tabular or graphical form.


REFERENCES

Lindley, D. V., and Singpurwalla, N. D. (1993). 'Adversarial Life Testing.' Journal of the Royal Statistical Society, Series B, 55,  4: 837-847.

 



 

Last Updated November 20, 2008

Institute for Reliability and Risk Analysis
Department of
Statistics
George Washington University

2140
Pennsylvania Ave. N.W.

Washington DC 20052