Modelling indirect interactions during failure spreading in a project activity network

Abstract

Spreading broadly refers to the notion of an entity propagating throughout a networked system via its interacting components. Evidence of its ubiquity and severity can be seen in a range of phenomena, from disease epidemics to financial systemic risk. In order to understand the dynamics of these critical phenomena, computational models map the probability of propagation as a function of direct exposure, typically in the form of pairwise interactions between components. By doing so, the important role of indirect interactions remains unexplored. In response, we develop a simple model that accounts for the effect of both direct and subsequent exposure, which we deploy in the novel context of failure propagation within a real-world engineering project. We show that subsequent exposure has a significant effect in key aspects, including the: (a) final spreading event size, (b) propagation rate, and (c) spreading event structure. In addition, we demonstrate the existence of ‘hidden influentials’ in large-scale spreading events, and evaluate the role of direct and subsequent exposure in their emergence. Given the evidence of the importance of subsequent exposure, our findings offer new insight on particular aspects that need to be included when modelling network dynamics in general, and spreading processes specifically.

Uncovering the fragility of large-scale engineering projects

“…In the context of projects, and though theoretically plausible [13,29,30], there has been little empirical evidence to support the hypothesis of such cascades taking place within activity networks, beyond anecdotal observations within real-world projects [7,[31][32][33]. As a result, there has been limited adoption of network science tools and techniques to better understand project complexity in general, and propagation effects [16] within activity networks specifically.…”

Risk Propagation Model and Simulation of Schedule Change in Construction Projects: A Complex Network Approach

“…Ellinas et al [8] examined the likelihood of a large-scale catastrophe triggered by a single task failure in an AON network, and the propagation process was measured by the topological, temporal, and quality aspects of the AON network. Furthermore, the Ellinas model was extended to include the role of indirect exposure [21] and the impact of contractor activity [36]. However, for a node in the Ellinas model, its failure risk at a given time step is perceived as the maximum risk, rather than the combined risk, due to its predecessors.…”

“…Previous studies mainly focused on identification and evaluation of schedule risks [2-4, 9, 14], simulation of schedule risks in construction projects [10,13,15], and analysis of schedule risks with activity sensitivity information [16][17][18], but less attention has been paid to the topologies of AON networks and predicting the behavior of risk propagation from an overall perspective [8]. Managing risk propagation is a challenging task across numerous fields, ranging from finance [19], infrastructure management [20], and project management [8,[21][22][23]. Despite the contextual differences of these domains, complex networks have been successfully applied to modeling the process of sequential risk propagation throughout the network and analyzing the impact of the parameters and characteristics on the merits of risk propagation.…”

The Domino Effect: An Empirical Exposition of Systemic Risk Across Project Networks

“…The average impact of tasks that benefit from local mitigation ðCS o l ; 8i such that R a l ð0; 100Þ; normalized over NÞ decreases as local mitigation increasessee Figure 10. In detail, and in the spirit of Baños et al (2013) and Ellinas (2018), we consider the average impact of the tasks that benefit from local mitigation in order to identify whether these tasks are "extraordinary" or "average," in terms of the cascade size that is triggered from their failure. As such, we note that the average impact of tasks that benefits from low levels of mitigation is initially higha desirable outcome since these are the tasks which are capable of triggering large-scale failures.…”