Abstract: We are investigating decision aids that present potential courses of action available to emergency responders. To determine whether these aids improve decision quality, however, we first developed test scenarios that were challenging in well-understood ways to ensure testing under the full breadth of representative decision-making situations. We devised a three-step method of developing scenarios: define the decision space, determine the cost components of each decision's potential consequences based on the principles of Robust Decision Making, then choose conflicting pairs of cost components (e.g., a small fire, implying low property damage, in a densely inhabited area, which implies high personal injury). In a validation of this approach, experiment participants made decisions faster in non-ambiguous cases versus cases that included this principled introduction of ambiguity. Our Principled Ambiguity Method of scenario design is also appropriate for other domains as long as they can be analyzed in terms of costs of decision alternatives.

Abstract: In this contribution, we introduce a prototype of a decision support tool for cost-optimal response in security management. The threat situation of a closed infrastructure, exposed to multiple threats, and the corresponding response actions are modeled by a continuous-time Markov decision process (CMDP). Since the CMDP cannot be solved exactly for large infrastructures, the response actions are determined from a heuristic, based on an index rule. The decision support tool's user interface displays the infrastructure's current threat state and proposes the heuristic response actions to the decision maker. In this way, global situation awareness can be enhanced and the decision maker is able to initiate an almost cost-optimal response action in short time.