Abstract:
In this paper, we examine the role which decision analysis can play in a situation
requiring a mass evacuation. In particular, we focus on the influence diagram as a
tool for reasoning and supporting decision-makers under conditions of risk and
uncertainty. This powerful modelling tool can help to bridge multiple specialist
domains and provide a common framework for supporting decision-makers in
different agencies.
An influence diagram is also referred to as a decision network and can be
considered as an extension of a Bayesian network. Like a Bayesian network, it
contains chance nodes which represent random variables and deterministic nodes
which represent deterministic functions of input variables. However, in addition,
an influence diagram contains decision nodes which represent decisions under
local control and utility nodes which can represent a variety of costs and benefits.
These might be measured in several dimensions including casualties and monetary
units. Advantages of Bayesian networks and influence diagrams over more
traditional risk and safety modelling approaches such as event trees and fault trees
are discussed - in particular, the ease with which they represent dependencies
between many factors and the different types of reasoning supported at the same
time, e.g. predictive reasoning and diagnostic reasoning.
An illustrative, generic influence diagram is presented of a situation
corresponding to a CBRNE attack. We then consider how this generic model can
be applied to a more specific scenario such as an attack at a sporting event. A
variety of potential uses of the model are identified and discussed, along with
problems which are likely to be encountered in model development. We argue that
this modelling approach provides a useful framework to support cost-effectiveness
studies and high-level trade-offs between alternative possible security measures
and other resources impacting on response and recovery operations.
