Neurocognitive assessment in obsessive compulsive disorder patients: Adherence to behavioral decision models.

In economics, models of decision-making under risk are widely investigated. Since many empirical studies have shown patterns in choice behavior that classical models fail to predict, several descriptive theories have been developed. Due to an evident phenotypic heterogeneity, obsessive compulsive disorder (OCD) patients have shown a general deficit in decision making when compared to healthy control subjects (HCs). However, the direction for impairment in decision-making in OCD patients is still unclear. Hence, bridging decision-making models widely used in the economic literature with mental health research may improve the understanding of preference relations in severe patients, and may enhance intervention designs. We investigate the behavior of OCD patients with respect to HCs by means of decision making economic models within a typical neuropsychological setting, such as the Cambridge Gambling Task. In this task subjects have to decide the amount of their initial wealth to invest in each risky decision. To account for heterogenous preferences, we have analyzed the micro-level data for a more informative analysis of the choices made by the subjects. We consider two influential models in economics: the expected value (EV), which assumes risk neutrality, and a multiple reference points model, an alternative formulation of Disappointment theory. We find evidence that (medicated) OCD patients are more consistent with EV than HCs. The former appear to be more risk neutral, namely, less sensitive to risk than HCs. They also seem to base their decisions on disappointment avoidance less than HCs.

On the Relationship between Safety and Decision Significance.

Risk analysts are often concerned with identifying key safety drivers, that is, the systems, structures, and components (SSCs) that matter the most to safety. SSCs importance is assessed both in the design phase (i.e., before a system is built) and in the implementation phase (i.e., when the system has been built) using the same importance measures. However, in a design phase, it would be necessary to appreciate whether the failure/success of a given SSC can cause the overall decision to change from accept to reject (decision significance). This work addresses the search for the conditions under which SSCs that are safety significant are also decision significant. To address this issue, the work proposes the notion of a θ-importance measure. We study in detail the relationships among risk importance measures to determine which properties guarantee that the ranking of SSCs does not change before and after the decision is made. An application to a probabilistic safety assessment model developed at NASA illustrates the risk management implications of our work.

Deciding with Thresholds: Importance Measures and Value of Information.

Risk-informed decision making is often accompanied by the specification of an acceptable level of risk. Such target level is compared against the value of a risk metric, usually computed through a probabilistic safety assessment model, to decide about the acceptability of a given design, the launch of a space mission, etc. Importance measures complement the decision process with information about the risk/safety significance of events. However, importance measures do not tell us whether the occurrence of an event can change the overarching decision. By linking value of information and importance measures for probabilistic risk assessment models, this work obtains a value-of-information-based importance measure that brings together the risk metric, risk importance measures, and the risk threshold in one expression. The new importance measure does not impose additional computational burden because it can be calculated from our knowledge of the risk achievement and risk reduction worth, and complements the insights delivered by these importance measures. Several properties are discussed, including the joint decision worth of basic event groups. The application to the large loss of coolant accident sequence of the Advanced Test Reactor helps us in illustrating the risk analysis insights.