Improving stochastic analysis for tradeoffs in multi‐criteria value models

Caddell, John; Dabkowski, Matthew F.; Driscoll, Patrick J.; DuBois, Patrick

Improving stochastic analysis for tradeoffs in multi‐criteria value models

Date

2020

Authors

Caddell, John

Dabkowski, Matthew F.

Driscoll, Patrick J.

DuBois, Patrick

Publisher

Journal of Multi-Criteria Decision Analysis

Abstract

In multi‐criteria value modelling (MCVM), uncertainty clouds decision making by complicating choices that inevitably must be made among an offering of competing alternatives. In stochastic MCVM settings involving value versus cost (risk) tradeoffs, exposing and communicating ramifications that choice instigates becomes increasingly difficult directly in response to the increased sophistication of mathematical methods needed to distinguish between alternatives, leaving the decision maker potentially unprepared for eventual opportunity or disaster. This article introduces an approach called realization analysis that leverages stochastic simulation, probability theory and tradespace geometry to estimate full spectrum outcome probabilities in an effort to enrich decision making and reduce the potential for surprise or regret, demonstrating its effectiveness when competing alternatives are tightly co‐mingled in their potential realizations causing issues for traditional stochastic dominance methods. The approach is surprisingly straightforward to implement, with results easily communicated to technical and non‐technical decision makers. A brief acquisition case study involving competing alternatives is presented to illustrate how this approach offers advantages for multi‐criteria decision analysis practice.

Keywords

Multi‐criteria value modelling (MCVM)

Citation

In multi-criteria value modelling (MCVM), uncertainty clouds decision making by complicating choices that inevitably must be made among an offering of competing alternatives. In stochastic MCVM settings involving value versus cost (risk) tradeoffs, exposing and communicating ramifications that choice instigates becomes increasingly difficult directly in response to the increased sophistication of mathematical methods needed to distinguish between alternatives, leaving the decision maker potentially unprepared for eventual opportunity or disaster. This article introduces an approach called realization analysis that leverages stochastic simulation, probability theory and tradespace geometry to estimate full spectrum outcome probabilities in an effort to enrich decision making and reduce the potential for surprise or regret, demonstrating its effectiveness when competing alternatives are tightly co-mingled in their potential realizations causing issues for traditional stochastic dominance methods. The approach is surprisingly straightforward to implement, with results easily communicated to technical and non-technical decision makers. A brief acquisition case study involving competing alternatives is presented to illustrate how this approach offers advantages for multi-criteria decision analysis practice.