61 research outputs found
Dynamics of organizational culture: Individual beliefs vs. social conformity
The complex nature of organizational culture challenges our ability to infers
its underlying dynamics from observational studies. Recent computational
studies have adopted a distinct different view, where plausible mechanisms are
proposed to describe a wide range of social phenomena, including the onset and
evolution of organizational culture. In this spirit, this work introduces an
empirically-grounded, agent-based model which relaxes a set of assumptions that
describes past work - (a) omittance of an individual's strive for achieving
cognitive coherence, (b) limited integration of important contextual factors -
by utilizing networks of beliefs and incorporating social rank into the
dynamics. As a result, we illustrate that: (i) an organization may appear to be
increasingly coherent in terms of organizational culture, yet be composed of
individuals with reduced levels of coherence, (ii) the components of social
conformity - peer-pressure and social rank - are influential at different
aggregation levels.Comment: 20 pages, 8 figure
Uncovering the fragility of large-scale engineering project networks
Engineering projects are notoriously hard to complete on-time, with project
delays often theorised to propagate across interdependent activities. Here, we
use a novel dataset consisting of activity networks from 14 diverse,
large-scale engineering projects to uncover network properties that impact
timely project completion. We provide the first empirical evidence of the
infectious nature of activity deviations, where perturbations in the delivery
of a single activity can impact up to 4 activities downstream, leading to large
perturbation cascades. We further show that perturbation clustering
significantly affects project overall delays. Finally, we find that poorly
performing projects have their highest perturbations in high reach nodes, which
can lead to largest cascades, while well performing projects have perturbations
in low reach nodes, resulting in localised cascades. Altogether, these findings
pave the way for a network-science framework that can materially enhance the
delivery of large-scale engineering projects.Comment: 13 pages, 3 figures, 7 supplementary figure
Modelling indirect interactions during failure spreading in a project activity network
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.Comment: l5 pages, 7 Figures, Submitte
Evaluating the role of risk networks on risk identification, classification and emergence
Modern society heavily relies on strongly connected, socio-technical systems.
As a result, distinct risks threatening the operation of individual systems can
no longer be treated in isolation. Consequently, risk experts are actively
seeking for ways to relax the risk independence assumption that undermines
typical risk management models. Prominent work has advocated the use of risk
networks as a way forward. Yet, the inevitable biases introduced during the
generation of these survey-based risk networks limit our ability to examine
their topology, and in turn challenge the utility of the very notion of a risk
network. To alleviate these concerns, we proposed an alternative methodology
for generating weighted risk networks. We subsequently applied this methodology
to an empirical dataset of financial data. This paper reports our findings on
the study of the topology of the resulting risk network. We observed a modular
topology, and reasoned on its use as a robust risk classification framework.
Using these modules, we highlight a tendency of specialization during the risk
identification process, with some firms being solely focused on a subset of the
available risk classes. Finally, we considered the independent and systemic
impact of some risks and attributed possible mismatches to their emerging
nature.Comment: 21 pages, 7 Figures, 4 tables, To appear in Journal of Network Theory
in Financ
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