Supporting Scenario-Based Decision-Making With Multi-Objective Optimization

Scenarios are commonly used to support decision-making by evaluating how each decision alternative performs in each scenario. These evaluations are then used to identify the preferred alternative in view of all scenarios. Many suggested approaches interpret scenarios as mutually exclusive states, which enables the use of subjective expected utility (SEU) to aggregate the scenario-specific performance but requires estimates on the scenario probabilities. Other approaches treat scenarios as attributes and utilize multi-attribute value theory (MAVT) to capture alternatives' overall performance, in which case both the likelihood and importance of scenarios are captured by attribute weights. In this paper, we establish a series of theoretical results demonstrating that Pareto optimality serves as a noncontroversial solution concept for both approaches and thus propose using the alternatives' performances in each scenario as the objective functions of a multi-objective optimization model. This allows the use of existing multi-objective optimization approaches, such as Pareto Race and robust portfolio modeling, to support the decision-maker in identifying the preferred alternative. The use of multi-objective optimization avoids the difficult task of eliciting scenario probabilities. We illustrate our ideas with a small-scale example and a scenario-based foresight application, with data from a real-world application.