Complex Systems Summer School 2015

The Ebola virus in West Africa has infected almost 30,000 and killed over 11,000 people. Recent models of Ebola Virus Disease (EVD) have often made assumptions about how the disease spreads, such as uniform transmissibility and homogeneous mixing within a population. In this paper, we test whether these assumptions are necessarily correct, and offer simple solutions that may improve disease model accuracy. First, we use data and models of West African migration to show that EVD does not homogeneously mix, but spreads in a much more predictable manner. Next, we estimate the initial growth rate of EVD within country administrative divisions and find that it significantly decreases with population density. Finally, we test whether EVD strains have uniform transmissibility through a novel statistical test, and find that certain strains appear more often than expected by chance.LINK

Viewing Input-Output (IO) tables as weighted complex networks, we investigate how certain characteristics of an economy are associated with the internal structural “shape” defined by its IO flows, as considered separately from its absolute magnitude. In this initial exploration, we examine domestic Input-Output table data from the Organisation for Economic Co-operation and Development (OECD) for 62 national economies from 7 different years spanning 1995 to 2011. By normalizing link weights so that information about absolute magnitudes is discarded, we consider only a network’s “shape” as described by the relative magnitudes of flows between sectors. These normalized networks are then compared using three different similarity measures: one topological in the sense that it captures certain structural properties abstracted from their particular locations within the network, and others that are “geometric” in that they involve direct comparisons of corresponding intersectoral links at the same positions within their respective economies. Clustering analyses then indicate which aspects of an economy might be associated with the structural features captured by each of these perspectives. The topological perspective, provided here by zero-dimensional persistent homology barcodes of Input-Output networks, seems to distinguish economies of different magnitudes (as measured though the natural log of GDP and population) despite its consideration of network “shapes” as independent of their absolute sizes. This discrimination of size also coincides with differences in import and export percentages of GDP, demonstrating that the internal topological connectivity of an economy can provide indirect insight into its absolute size and the nature of its external flows. Meanwhile, “geometric” similarity measures distinguish economies in terms of a distinct set of indicators, confirming the presence of purely topological hallmarks of certain economic properties to which “geometric” perspectives are indiscriminate. Along with these preliminary observations, we discuss the potential for applying higher-dimensional persistent homology to study IO networks.LINK

Complex social-ecological systems can be difficult to study and manage. Simulation models can facilitate exploration of system behavior under novel conditions, and participatory modeling can involve stakeholders in developing appropriate management processes. Participatory modeling already typically involves qualitative structural validation of models with stakeholders, but with increased data and more sophisticated models, quantitative behavioral validation may be possible as well. In this study, we created a novel agent-based-model applied to a specific context: Zimbabwean non-governmental organization the Muonde Trust has been collecting data on their agro-pastoral system for the last 35 years and had concerns about land-use planning and the effectiveness of management interventions in the face of climate change. We collaboratively created an agent-based model of their system using their data archive, qualitatively calibrating it to the observed behavior of the real system without tuning any parameters to match specific quantitative outputs. We then behaviorally validated the model using quantitative community-based data and conducted a sensitivity analysis to determine the relative impact of underlying parameter assumptions, Indigenous management interventions, and different rainfall variation scenarios. We found that our process resulted in a model which was successfully structurally validated and sufficiently realistic to be useful for Muonde researchers as a discussion tool. The model was inconsistently behaviorally validated, however, with some model variables matching field data better than others. We observed increased model system instability due to increasing variability in underlying drivers (rainfall), and also due to management interventions that broke feedbacks between the components of the system. Interventions that smoothed year-to-year variation rather than exaggerating it tended to improve sustainability. The Muonde trust has used the model to successfully advocate to local leaders for changes in land-use planning policy that will increase the sustainability of their system. Note: Jon Solera, Seven Points Consulting (US), K. B. Wilson, The Muonde Trust (ZW), Aaron C. Fisher, Lawrence Livermore National Laboratory, (US), Abraham Mawere Ndlovu, The Muonde Trust (ZW), and Emmanuel Mhike Hove, The Muonde Trust (ZW) contributed substantially to this project.LINK

Social resilience to challenges is an important component of sustainability. We explore the diversity and flexibility of the social networks of a rural Zimbabwean community in order to understand their resilience in the face of the AIDS epidemic, hyper-inflation in the 2000s, and increasing variability of rainfall due to climate change. We combine social network analysis with ethnographic accounts to find that broad concepts of relatedness help families adopt AIDS orphans, while household structures are flexible over time, with small groups forming in lands newly available for re-settlement. Change in household membership was attributable more to immigration/emigration than to birth/death.LINK

This study is a first, exploratory attempt to use quantitative semantics techniques and topological analysis to analyze systemic patterns arising in a complex political system. In particular, we use a rich data set covering all speeches and debates in the UK House of Commons between 1975 and 2014. By the use of dynamic topic modeling (DTM) and topological data analysis (TDA) we show that both members and parties feature specific roles within the system, consistent over time, and extract global patterns indicating levels of political cohesion. Our results provide a wide array of novel hypotheses about the complex dynamics of political systems, with valuable policy applicationsLINK

Choosing how new lines should be installed in a power grid, or Transmission Expansion Planning (TEP) is a problem of considerable complexity. Any power grid has a large number (hundreds to tens of thousands) of components, meaning that any upgrades must take into account the current infrastructure. Additionally, there are many possible additional lines that one could add to an existing power grid. Furthermore, any design must be weighted by investment and operation cost. This requires analyzing the optimal use of generation and transmission assets using optimal power flow models that simulate the physical laws governing power flows (Kirchhoff’s Laws) as well as infrastructure limits. Given the importance of this problem, many approaches have been tested. However, we found that there were still some tools, related to the Complex Systems environment, which have not yet been applied in this field. We therefore undertake the project of exploring the possibilities of some of these tools. The research we have completed while at CSSS has allowed us to write three working papers, each detailing a different application of Complex Systems tools to TEP: Part I: Generating Random Networks that are Consistent with Power Transmission. Part II: An Agent-Based Model for Transmission Expansion Planning. Part III: Using Topological Information to Build More Robust Networks These working papers represent the first steps we have taken, highlighting what new techniques can do when applied to well-known problems. We have enjoyed this research and intend to expand and publish our results in the near future.LINK

Modeling migratory paths of birds is an emerging area of complexity science in which attracts interdisciplinary collaborations from ornithologists, computer scientists, epidemiologist, and policy makers. However, due to the unpredictability of climate and habitat changes, our understanding of bird migratory paths is still limited. Not accounting for environmental changes when modeling bird migration will mislead our conservation efforts. Hence, in this paper, we are using validated training data to predict future stopover sites of migratory bird species. We used white-fronted geese migratory paths as training data and modeled stopover sites with Markov Chains to predict future changes on the stopover sites.This prediction will allow researchers to realize how to better conserve the habitat locations at and around stopover sites in the light of current climate changes. Since birds often stop and interact with the nearby environment, our work will also allow researchers to predict potential dangers emerged from long-range migrations, such as new avian viruses along these routes.LINK

Hierarchical organizations typically rely on a chain of command in which supervisors instruct subordinates to perform certain actions in order to advance the interests and objectives of the organization. The trust between supervisors and subordinates is essential for the functioning of such hierarchical organizations, as it ensures the eventual execution of instructions. Despite this, blind trust and obedience may lead to the execution of instructions with negative consequences, which may have been avoided had a subordinate been more critical of a supervisor’s instructions. We thus aim to model the development of trust between subordinates, their supervisors and the structures of the organization itself, subject to the understanding of the consequences of instructions by subordinates and the eventual outcomes of executed instructions. This is achieved through the construction of an agent-based model that is capable of replicating a number of intuitive behaviors and the use of this model to provide an indication of how trust develops over time.LINK

This research aims to explore the complexity inherent in the physical mass of cities—specifically in the materials that are stored within buildings and infrastructure. There is an important link between stocked materials and urban sustainability, particularly related to better understanding the resource requirements for the maintenance and growth of cities. This research explores materials stocked in Japanese cities, using a database with detailed GIS data of materials stored in buildings and roads at a resolution of one square kilometer. The analysis focuses on a single year, 2009 for buildings and 2010 for roads, and it includes two primary investigations: 1) urban scaling analyses to explore relationships between mass stocked, urban population, and other variables; and 2) non-parametric kernel estimations to better understand influential factors contributing to per-capita mass values. Results show a linear scaling relationship between mass stocked in buildings and urban area population, as well as a sublinear scaling relationships between mass stocked in roads, urban area population, and land area. The non-parametric kernel estimations revealed population density as a potentially influential factor.LINK

Brain Sciences have revealed that we/it lives "on the edge of chaos" exhibiting "a self-organized criticality" that is tentatively balanced between normalcy and madness. Over the course of history, humans have used various agents and activities to shape, influence and control this living-chaos, ranging from substances such as caffeine, sugar, drugs etc., to activities such as the arts (including music), social-discourse/therapy, meditation etc. Of these, music has a pervasive role in shaping our moods and helping us transition between different mental states, as well as maintain it for extended periods of time. While we have been using music and other techniques to help us control and shape the internal chaos, it is only in the last century or so that the quantitative instrumentation of this massively complex system that comprises of close to a 100-billion neurons networked into a 1000-trillion synaptic edifice has been attained. And of late, affordable, wearable neurorecording devices (i.e., EEG’s) are available on the market, thus facilitating the quantitative study of the influence of music in brain dynamics feasible on a large-scale/crowd-sourcing sense. To help come to terms with the complexity of our 1000-trillion synaptic edifice, we need to gather data on a vast scale and analyze it creatively. This paper shows that the data from a wearable EEG may be used for tracking subtle differences in the brain-state as the subject listens to variations on a musical piece. We demonstrate proof-of-concept that wearable-EEG recordings are able to differentiate the electrophysiological response to a mechanically-generated piano performance as compared to an expressive human-performed version of the same piece. As no conscious effort is required of the listener, this approach has the potential to remove the stated/revealed preference effect in the fields of biomusicology and beyond.