Pedro D. Manrique

New online ecology of adversarial aggregates: ISIS and beyond

Tackling the advance of online threats Online support for adversarial groups such as Islamic State (ISIS) can turn local into global threats and attract new recruits and funding. Johnson et al. analyzed data collected on ISIS-related websites involving 108,086 individual followers between 1 January 1 and 31 August 2015. They developed a statistical model aimed at identifying behavioral patterns among online supporters of ISIS and used this information to predict the onset of major violent events. Sudden escalation in the number of ISIS-supporting ad hoc web groups (“aggregates”) preceded the onset of violence in a way that would not have been detected by looking at social media references to ISIS alone. The model suggests how the development and evolution of such aggregates can be blocked. Science, this issue p. 1459 Online activity of ad hoc extremist groups can be modeled, providing predictive power and potential countermeasures. Support for an extremist entity such as Islamic State (ISIS) somehow manages to survive globally online despite considerable external pressure and may ultimately inspire acts by individuals having no history of extremism, membership in a terrorist faction, or direct links to leadership. Examining longitudinal records of online activity, we uncovered an ecology evolving on a daily time scale that drives online support, and we provide a mathematical theory that describes it. The ecology features self-organized aggregates (ad hoc groups formed via linkage to a Facebook page or analog) that proliferate preceding the onset of recent real-world campaigns and adopt novel adaptive mechanisms to enhance their survival. One of the predictions is that development of large, potentially potent pro-ISIS aggregates can be thwarted by targeting smaller ones.

Context matters: Improving the uses of big data for forecasting civil unrest: Emerging phenomena and big data

Open Source Indicators (OSI) such as Google Trends (GT) promise to uncover the social dynamics associated with behavior that precede episodes of civil unrest. There are myriad reasons why societies may become unstable: Our analysis does not require or inquire the underlying reasons for discontent but instead takes into account differences associated with variegated social contexts. This paper examines instances of this volatile behavior and suggests a simple model for predicting civil unrest events using GT as an open source indicator (OSI). It grounds the possibilities for prediction on the fact that social processes occur within a particular social context. As such, paying attention to the particular signals associated from each country is an important moderator for any model keen on predicting cases of extreme social behavior such as civil unrest.

Women’s connectivity in extreme networks

Women show superior connectivity to men in extreme networks, even though they are typically outnumbered. A popular stereotype is that women will play more minor roles than men as environments become more dangerous and aggressive. Our analysis of new longitudinal data sets from offline and online operational networks [for example, ISIS (Islamic State)] shows that although men dominate numerically, women emerge with superior network connectivity that can benefit the underlying system’s robustness and survival. Our observations suggest new female-centric approaches that could be used to affect such networks. They also raise questions about how individual contributions in high-pressure systems are evaluated.

Resource Letter MPCVW-1: Modeling Political Conflict, Violence, and Wars: A Survey

This Resource Letter provides a guide into the literature on modeling and explaining political conflict, violence, and wars. Although this literature is dominated by social scientists, multidisciplinary work is currently being developed in the wake of myriad methodological approaches that have sought to analyze and predict political violence. The works covered herein present an overview of this abundance of methodological approaches. Since there is a variety of possible data sets and theoretical approaches, the level of detail and scope of models can vary quite considerably. The review does not provide a summary of the available data sets, but instead highlights recent works on quantitative or multi-method approaches to modeling different forms of political violence. Journal articles and books are organized in the following topics: social movements, diffusion of social movements, political violence, insurgencies and terrorism, and civil wars.

Atypical viral dynamics from transport through popular places

The flux of visitors through popular places undoubtedly influences viral spreading-from H1N1 and Zika viruses spreading through physical spaces such as airports, to rumors and ideas spreading through online spaces such as chat rooms and social media. However, there is a lack of understanding of the types of viral dynamics that can result. Here we present a minimal dynamical model that focuses on the time-dependent interplay between the mobility through and the occupancy of such spaces. Our generic model permits analytic analysis while producing a rich diversity of infection profiles in terms of their shapes, durations, and intensities. The general features of these theoretical profiles compare well to real-world data of recent social contagion phenomena.

Internal character dictates transition dynamics between isolation and cohesive grouping

We show that accounting for internal character among interacting heterogeneous entities generates rich transition behavior between isolation and cohesive dynamical grouping. Our analytical and numerical calculations reveal different critical points arising for different character-dependent grouping mechanisms. These critical points move in opposite directions as the populations diversity decreases. Our analytical theory may help explain why a particular class of universality is so common in the real world, despite the fundamental differences in the underlying entities. It also correctly predicts the nonmonotonic temporal variation in connectivity observed recently in one such system.

Nonequilibrium quantum systems: Divergence between global and local descriptions

Even photosynthesis—the most basic natural phenomenon underlying life on Earth—involves the nontrivial processing of excitations at the pico- and femtosecond scales during light-harvesting. The desire to understand such natural phenomena, as well as interpret the output from ultrafast experimental probes, creates an urgent need for accurate quantitative theories of open quantum systems. However it is unclear how best to generalize the well-established assumptions of an isolated system, particularly under nonequilibrium conditions. Here we compare two popular approaches: a description in terms of a direct product of the states of each individual system (i.e., a local approach) versus the use of new states resulting from diagonalizing the whole Hamiltonian (i.e., a global approach). The main difference lies in finding suitable operators to derive the Lindbladian and hence the master equation. We show that their equivalence fails when the system is open, in particular under the experimentally ubiquitous condition of a temperature gradient. By solving for the steady state populations and calculating the heat flux as a test observable, we uncover stark differences between the formulations. This divergence highlights the need to establish rigorous ranges of applicability for such methods in modeling nanoscale transfer phenomena—including during the light-harvesting process in photosynthesis.

Extreme alien light allows survival of terrestrial bacteria

Photosynthetic organisms provide a crucial coupling between the Suns energy and metabolic processes supporting life on Earth. Searches for extraterrestrial life focus on seeking planets with similar incident light intensities and environments. However the impact of abnormal photon arrival times has not been considered. Here we present the counterintuitive result that broad classes of extreme alien light could support terrestrial bacterial life whereas sources more similar to our Sun might not. Our detailed microscopic model uses state-of-the-art empirical inputs including Atomic Force Microscopy (AFM) images. It predicts a highly nonlinear survivability for the basic lifeform Rsp. Photometricum whereby toxic photon feeds get converted into a benign metabolic energy supply by an interplay between the membranes spatial structure and temporal excitation processes. More generally, our work suggests a new handle for manipulating terrestrial photosynthesis using currently-available extreme value statistics photon sources.

Survivability of Photosynthetic Bacteria In Non-Terrestrial Light

We explore whether a basic process of Life on Earth. Bacterial photosynthesis can in principle survive under alien light conditions defined by abnormal temporal correlations in the incident photon absorption times. Though unlike radiation from our own Sun, such extreme photon statistics have already been demonstrated under laboratory conditions and hence are allowed by the laws of physics. Our analysis exploits a detailed membrane model of the bacterial photosynthetic system Rs. Photometricum using state-of-the-art empirical inputs. Our results show that a broad range of extreme light conditions including those far beyond terrestrial sunlight do indeed support the metabolic needs of the terrestrial bacteria.

Multiscale dynamical network mechanisms underlying aging of an online organism from birth to death

We present the continuous-time evolution of an online organism network from birth to death which crosses all organizational and temporal scales, from individual components through to the mesoscopic and entire system scale. These continuous-time data reveal a lifespan driven by punctuated, real-time co-evolution of the structural and functional networks. Aging sees these structural and functional networks gradually diverge in terms of their small-worldness and eventually their connectivity. Dying emerges as an extended process associated with the formation of large but disjoint functional sub-networks together with an increasingly detached core. Our mathematical model quantifies the very different impacts that interventions will have on the overall lifetime, period of initial growth, peak of potency, and duration of old age, depending on when and how they are administered. In addition to their direct relevance to online extremism, our findings may offer insight into aging in other network systems of comparable complexity for which extensive in vivo data is not yet available.