No abstract

Today's typical multi-criteria decision analysis is based on classical expected utility theory that assumes a mythical "Rational Individual" immune to psychological influences such as anticipated regret. It is therefore in conflict with rational individuals who trade-off some benefits and forgo the alternative with the highest total classical utility for a more balanced alternative in order to reduce their levels of anticipated regret. This paper focuses on decision making under certainty. It presents a reference-dependent regret model (RDRM) in which the level of regret that an individual experiences depends on the absolute values rather than the differences of the utilities of the chosen and forgone alternatives. The RDRM best choice may differ from the conventional linear additive utility model, the analytic hierarchy process, and the regret theory of Bell and Loomes and Sugden. Examples are presented that indicate that RDRM is the better predictive descriptor for decision making under certainty. RDRM satisfies transitivity of the alternatives under pairwise comparisons and models rank reversal consistent with observed reasonable choices under dynamic or distinct situations. Like regret theory, the RDRM utilities of all the alternatives under consideration are interrelated. For complex trade-off studies regret is incorporated as an element of a cost-utility-regret analysis that characterizes each alternative in terms of its monetary cost, an aggregate performance utility, and a regret value. This provides decision makers adequate information to compare the alternatives and depending on their values they may trade-off some performance and/or cost to avoid high levels of regret. The result is a well-balanced alternative often preferred by reasonable decision makers to the optimal choice of classical multi-attribute utility analysis. The model can readily be extended to incorporate rejoicing to suit decision makers who seek it. The approach is illustrated using a hypothetical but realistic aircraft selection problem.

The primary goal of good project risk management should be to successfully deliver projects for the lowest cost at an acceptable level of risk. This requires the systematic development and implementation of a set of Risk Response Actions (RRA) that achieves the lowest total project cost for a given probability of success while meeting technical performance and schedule. We refer to this set as the "efficient RRA set". This work presents a practical and mathematically sound approach for determining the efficient RRA set. It builds on some of Markowitz' s portfolio selection principles and introduces several conceptual and modeling differences to properly treat project technical risks. The set of RRAs is treated as whole and not just individual risks. The efficient RRA set is determined based on "Outcome Cost Vs Probability of Success". The risks and RRAs are characterized using scenarios, decision trees, and cumulative probability distributions. The analysis provides information that enables decision-makers to select the efficient RRA set that explicitly takes their attitude toward project risk into account. Decision-makers should find it both useful and practical for sound decision-making under uncertainty/risk and efficiently optimizing project success. The computations are readily performed using commercially available Monte Carlo simulation tools. The approach is detailed using a realistic but simplified case of a project with two technical risks.

Today's typical probabilistic cost analysis assumes an "ideal" project that is devoid of the human and organizational considerations that heavily influence the success and cost of real-world projects. In the real world "Money Allocated Is Money Spent" (MAIMS principle); cost underruns are rarely available to protect against cost overruns while task overruns are passed on to the total project cost. Realistic cost estimates therefore require a modified probabilistic cost analysis that simultaneously models the cost management strategy including budget allocation. Psychological influences such as overconfidence in assessing uncertainties and dependencies among cost elements and risks are other important considerations that are generally not addressed. It should then be no surprise that actual project costs often exceed the initial estimates and are delivered late and/or with a reduced scope. This paper presents a practical probabilistic cost analysis model that incorporates recent findings in human behavior and judgment under uncertainty, dependencies among cost elements, the MAIMS principle, and project management practices. Uncertain cost elements are elicited from experts using the direct fractile assessment method and fitted with three-parameter Weibull distributions. The full correlation matrix is specified in terms of two parameters that characterize correlations among cost elements in the same and in different subsystems. The analysis is readily implemented using standard Monte Carlo simulation tools such as @Risk and Crystal Ball. The analysis of a representative design and engineering project substantiates that today's typical probabilistic cost analysis is likely to severely underestimate project cost for probability of success values of importance to contractors and procuring activities. The proposed approach provides a framework for developing a viable cost management strategy for allocating baseline budgets and contingencies. Given the scope and magnitude of the cost-overrun problem, the benefits are likely to be significant.