Skip to content

Today, I'm presenting a guest post from Behailu Bekera, a first-year EMSE PhD student working in the SEED Group.  He is studying the relationship between risk-based and resilience-based approaches to systems analysis.

Resilience is defined as the capability of a system with specific characteristics before, during and after a disruption to absorb the disruption, recover to an acceptable level of performance, and sustain that level for an acceptable period of time. Resilience is an emerging approach towards safety. Conventional risk assessment methods are typically used to determine the negative consequences of potential undesired events, understand the nature of and to reduce the level of risk involved. In contrast, the resilience approach emphasizes on anticipation of potential disruptions, giving appropriate attention to perceived danger and establishing response behaviors aimed at either building the capacity to withstand the disruption or recover as quickly as possible after an impact. Anticipation refers to the ability of a system to know what to expect and prepare itself accordingly in order to effectively withstand disruptions. The ability to detect the signals of an imminent disruption is captured by the attentive property of resilience. Once the impact takes place, the system must know how to efficiently respond with the aim of quick rebound.

Safety, as we know it traditionally, is usually considered as something a system or an organization possesses as evidenced by the measurements of failure probability, risk and so on. Concerning the new approach, Hollnagel and Woods argue that safety is something an organization or a system does. Seen from a resilience point of view, safety is a characteristic of how a system performs in the face of disruptions, how it can absorb or dampen the impacts or how it can quickly reinstate itself after suffering perturbation.

Resilience may allow for a more proactive approach for handling risk. It puts the system on a path of continuous performance evaluation to ensure safety at all times. Resilient systems will be flexible enough to accommodate different safety issues in multiple dimensions that may arise and also robust enough to maintain acceptable performance.

We have just published a paper, "Characterizing the Performance of the Conway-Maxwell Poisson Generalized Linear Model" in the journal Risk Analysis

Here is the abstract:

Count data are pervasive in many areas of risk analysis; deaths, adverse health outcomes, infrastructure system failures, and traffic accidents are all recorded as count events, for example. Risk analysts often wish to estimate the probability distribution for the number of discrete events as part of doing a risk assessment. Traditional count data regression models of the type often used in risk assessment for this problem suffer from limitations due to the assumed variance structure. A more flexible model based on the Conway-Maxwell Poisson (COM-Poisson) distribution was recently proposed, a model that has the potential to overcome the limitations of the traditional model. However, the statistical performance of this new model has not yet been fully characterized. This article assesses the performance of a maximum likelihood estimation method for fitting the COM-Poisson generalized linear model (GLM). The objectives of this article are to (1) characterize the parameter estimation accuracy of the MLE implementation of the COM-Poisson GLM, and (2) estimate the prediction accuracy of the COM-Poisson GLM using simulated data sets. The results of the study indicate that the COM-Poisson GLM is flexible enough to model under-, equi-, and overdispersed data sets with different sample mean values. The results also show that the COM-Poisson GLM yields accurate parameter estimates. The COM-Poisson GLM provides a promising and flexible approach for performing count data regression.

Enjoy!