A monthly selection of publications by our science communication officer.
Crisis in complex social systems: A social theory view illustrated with the chilean case.
A. Mascareño, E. Goles, G. A. Ruz, Complexity, March 2016
“The article argues that crises are a distinctive feature of complex social systems. A quest for connectivity of communication leads to increase systems’ own robustness by constantly producing further connections. When some of these connections have been successful in recent operations, the system tends to reproduce the emergent pattern, thereby engaging in a non-reflexive, repetitive escalation of more of the same communication.”
Big Data in Asset Management: Knowledge Discovery in Asset Data by the Means of Data Mining,
D. Galar, M. Kans, B. Schmidt, WCEAM 2015, March 2016
“Assets are complex mixes of complex systems, built from components which, over time, may fail. The ability to quickly and efficiently determine the cause of failures and propose optimum maintenance decisions, while minimizing the need for human intervention is necessary[…]. This paper proposes a knowledge discovery process based on CRISP-DM for failure diagnosis using big data sets. The process is exemplified by applying it on railway infrastructure assets.”
A Complex Systems Approach to Causal Discovery in Psychiatry
G. Saxe, A. Statnikov, D. Fenyo et al, March 2016
“Conventional research methodologies and data analytic approaches in psychiatric research are unable to reliably infer causal relations without experimental designs […]. This article describes a series of studies to validate a novel hybrid computational approach–the Complex Systems-Causal Network (CS-CN) method–designed to integrate causal discovery within a complex systems framework for psychiatric research.”
E. Mitleton-Kelly, A. Paraskevas, and C. Day (eds.), Handbook of Research Methods in Complexity Science: Theory & Application, 2016.
Zhesi Shen, Shinan Cao, Wen-Xu Wang, Zengru Di, and H. Eugene Stanley, Physical Review E 2016. Read
explain recent findings that widespread social network media use leads to reduced happiness. However the relation between popularity and happiness is poorly understood. A Friendship paradox does not necessarily imply a Happiness paradox where most individuals are less happy than their friends. Here we report the first direct observation of a significant Happiness Paradox in a large-scale online social network of 39, 110 Twitter users. Our results reveal that popular individuals are indeed happier and that a majority of individuals experience a significant Happiness paradox. The magnitude of the latter effect is shaped by complex interactions between individual popularity, happiness, and the fact that users cluster assortatively by level of happiness. Our results indicate that the topology of online social networks and the distribution of happiness in some populations can cause widespread psycho-social effects that affect the well-being of billions of individuals.”
Swami Iyer, Timothy Killingback, PLOS Feb. 2016.
Multi-scale Modeling and Simulation of Complex Systems – Opportunities and Challenges
A. Ravitz, T. A. Mazzuchi, S. Sarkani, IIE Transactions on Healthcare Systems Engineering Feb. 2016.
“Healthcare, like other industries, large corporations, and institutions, is a complex system composed of many diverse interacting components. Frequently, to improve performance of a system, healthcare decision-makers face opportunities or mandates to implement innovations (new technology, processes, and services). These dynamics are frequently difficult for decision-makers to observe and understand. This research defines a modeling and simulation framework that provides decision-makers with prospective insight into the likely performance to expect once an innovation is implemented in a complex system”.
Informal leadership, interaction, cliques and productive capacity in organizations: A collectivist analysis
R. Marion, J. Christiansen, H. W. Klar, C. Shreiber, M. A. Erdener, The Leadership Quarterly, 2016.
“This study proposes that dynamically changing organizations can achieve stable productive capacity (or environmentally stable states) by adaptively processing internal and external volatility. It tests this proposal with agent network measures rather than with more traditional variables. We examine three such network dynamics that, according to the collective perspectives of complexity theory, influence a network’s capacity to perform: informal leadership, interaction among agents, and clique engagement”.
A web-based tool for identifying strategic intervention points in complex systems
S. Moschoyiannis, N. Elia, A. Penn, D. Lloyd, C. Knight, 2016.
“Steering a complex system towards a desired outcome is a challenging task. The lack of clarity on the system’s exact architecture and the often scarce scientific data upon which to base the operationalisation of the dynamic rules that underpin the interactions between participant entities are two contributing factors. We describe an analytical approach that builds on Fuzzy Cognitive Mapping (FCM) to address the latter and represent the system as a complex network.”
Understanding Complex Urban Systems: Integrating Multidisciplinary Data in Urban Models
C. Walloth, E. Gebetsroither-Geringer, F. Atun, L. C. Werner, Springer, 2016
This second volume of the book series “Understanding Complex Urban Systems” aims to point out how the modeling of complex urban systems can be improved by overcoming data-related challenges.