Risk and Decision Analysis

Sensor networks are not anticipatory, but ideally they should be. In order to detect intruders, the environment of the possible attack scene can be monitored, and the position of sensors modified in order to increase the probability of detection. When faced with a resourceful intruder, the problem of sensing context becomes more difficult, and must expand to include monitoring for information about the intruders' objectives and resources. We call the continuous contest between intruders and sensor network designers an adversarial design game, and discuss ways the sensor network designers may proceed, using human design processes and automated design techniques.

Our main objective is to propose a recently-developed representational formalism, the Evolving Transformation System (ETS), as a general structural tool for decision and risk analysis, as opposed to the conventional numeric tools. We believe ETS to be the first formalism developed specifically with the goal of (properly understood) structural representation in mind.

We outline the use of ETS in the representation of an “insider's view” of a hypothetical terrorist plot. The example should be treated as that of an internal view of a generic planning process. We emphasize that the same tools can be used for modeling the external view of a decision process (and its execution).

The “atomic” representational unit of ETS is a structured event, and sequences of such events form processes that represent real human (including business) activities consisting of a series of various human actions. Each such action can be viewed structurally as an event transforming several “incoming” information processes into some “outgoing” information processes. Once the ETS concept of a class of (similar) processes is introduced, any process from that class can then be viewed as composed, in a class-specific manner, out of some simpler processes belonging to the constituent classes. Thus, a purchase process is, on the one hand, an element of the class of structurally similar purchase processes, while on the other hand, is a building-block of a larger business process, structurally fitting into it in a modular manner. Moreover, the concept of class in the formalism is introduced in such a way as to allow one – having constructed a class description, or, more formally, class representation – to predict/anticipate the overall structure of any process from that class.

It should become clear that the proposed framework could easily be adapted to address a broad range of needs, including decision modeling and analysis, anticipation of possible outcomes, various kinds of monitoring including surveillance, etc.

In this paper, the authors show how the concepts of anticipation and risk management are applied in order to effect a paradigm shift in a specific sector of the steel industry. This transformation can be observed in at least three areas: the production, sales, and marketing of highly engineered components and services; the creation of a systematic analytical approach to customers' plants and equipment; the development of a “Techno-Commercial Analysis” as a communication tool designed to undergird and stimulate anticipation-based risk management strategies in steel mills. The steel industry is particularly good for validating the concepts of anticipation-based risk management. First, the steel industry is lagging in respect to modern information technologies and so-called rationalization. Second, the steel industry is in an expansion phase, even while it is facing extreme competition. These factors have motivated plant owners and managers to optimize the potential of their operations. Therefore, they are increasingly willing to invest in new strategies to achieve this goal.

Risk assessment is relevant only if it has predictive relevance. In this sense, the anticipatory perspective has yet to contribute to more adequate predictions. For purely physics-based phenomena, predictions are as good as the science describing such phenomena. For the dynamics of the living, the physics of the matter making up the living is only a partial description of their change over time. The space of possibilities is the missing component, complementary to physics and its associated predictions based on probabilistic methods. The inverse modeling problem, and moreover the reverse computation model guide anticipatory-based predictive methodologies. An experimental setting for the quantification of anticipation is advanced and structural measurement is suggested as a possible mathematics for anticipation-based risk assessment.