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Why humans fight has no easy answer. However, understanding better how humans fight could inform future interventions, hidden shifts and casualty risk. Fusion-fission describes the well-known grouping behavior of fish etc. fighting for survival in the face of strong opponents: they form clusters ('fusion') which provide collective benefits and a cluster scatters when it senses danger ('fission'). Here we show how similar clustering (fusion-fission) of human fighters provides a unified quantitative explanation for complex casualty patterns across decades of Israel-Palestine region violence, as well as the October 7 surprise attack -- and uncovers a hidden post-October 7 shift. State-of-the-art data shows this fighter fusion-fission in action. It also predicts future 'super-shock' attacks that will be more lethal than October 7 and will arrive earlier. It offers a multi-adversary solution. Our results -- which include testable formulae and a plug-and-play simulation -- enable concrete risk assessments of future casualties and policy-making grounded by fighter behavior., Comment: Comments welcome. Working paper

What large-scale cohesive behaviors -- desirable or dangerous -- can suddenly emerge from systems with interacting humans, machinery and software including AI? When will they emerge? How will they evolve and be controlled? Here we offer some answers to these urgent questions by introducing an aggregation model that accounts for entities' inter- and intra-species diversities. It yields a novel multi-dimensional generalization of existing aggregation physics. We derive exact analytic solutions for the time-to-cohesion and growth-of-cohesion for two species, and some generalizations for an arbitrary number of species. These solutions reproduce -- and offer a microscopic explanation for -- an anomalous nonlinear growth feature observed in related real-world systems, e.g. Hamas-Hezbollah online support, human-machine team interactions, AI-determined topic coherence. A key takeaway is that good and bad 'surprises' will appear increasingly quickly as humans-machinery-AI etc. mix more -- but the theory offers a rigorous approach for understanding and controlling this.

Self-assembly is a key process in living systems - from the microscopic biological level (e.g. assembly of proteins into fibrils within biomolecular condensates in a human cell) through to the macroscopic societal level (e.g. assembly of humans into common-interest communities across online social media platforms). The components in such systems (e.g. macromolecules, humans) are highly diverse, and so are the self-assembled structures that they form. However, there is no simple theory of how such structures assemble from a multi-species pool of components. Here we provide a very simple model which trades myriad chemical and human details for a transparent analysis, and yields results in good agreement with recent empirical data. It reveals a new inhibitory role for biomolecular condensates in the formation of dangerous amyloid fibrils, as well as a kinetic explanation of why so many diverse distrust movements are now emerging across social media. The nonlinear dependencies that we uncover suggest new real-world control strategies for such multi-species assembly., Comment: 20 pages, 4 Figures

Online communities featuring 'anti-X' hate and extremism, somehow thrive online despite moderator pressure. We present a first-principles theory of their dynamics, which accounts for the fact that the online population comprises diverse individuals and evolves in time. The resulting equation represents a novel generalization of nonlinear fluid physics and explains the observed behavior across scales. Its shockwave-like solutions explain how, why and when such activity rises from 'out-of-nowhere', and show how it can be delayed, re-shaped and even prevented by adjusting the online collective chemistry. This theory and findings should also be applicable to anti-X activity in next-generation ecosystems featuring blockchain platforms and Metaverses., Comment: Feedback welcome to neiljohnson@gwu.edu

A better understanding of how support evolves online for undesirable behaviors such as extremism and hate, could help mitigate future harms. Here we show how the highly irregular growth curves of groups supporting two high-profile extremism movements, can be accurately described if we generalize existing gelation models to account for the facts that the number of potential recruits is time-dependent and humans are heterogeneous. This leads to a novel generalized Burgers equation that describes these groups' temporal evolution, and predicts a critical influx rate for potential recruits beyond which such groups will not form. Our findings offer a new approach to managing undesirable groups online -- and more broadly, managing the sudden appearance and growth of large macroscopic aggregates in a complex system -- by manipulating their onset and engineering their growth curves.

There is a significant amount of online human activity which is either clandestine or illicit in nature, and hence where individuals operate under fear of exposure or capture. Yet there is little theoretical understanding of what models best describe the resulting dynamics. Here we address this gap, by analyzing the evolutionary dynamics of the supporters behind the 95 pro-ISIS online communities (i.e. self-organized social media groups) that appeared recently on a global social media site. We show that although they do not follow a conventional (i.e. size-based) preferential attachment (PA) model, their dynamical evolution can be explained by a new variant that we introduce here, which we refer to as active attraction model (AA). This AA model takes into account the locality and group heterogeneity which undoubtedly feature in humans' online behavior under pressure, but which are not contained in conventional PA models. The AA model captures both group-specific and macroscopic observations over all size ranges -- as opposed to just the tail for large groups or groups' initial growth -- suggesting that heterogeneity and locality play a crucial role in the dynamics of online extremist support. We derive approximate expressions for the group size distributions in two simple systems that involve simultaneously the mechanisms of group joining (governed by either PA or AA), group leaving, and account banning, and show how these processes influence the group size distributions. We believe this work will serve in helping understand a broad spectrum of online human activities which are either clandestine or illicit in nature, and hence where individuals operate under fear of exposure or capture., Comment: 8 pages, 6 figures

Individual heterogeneity is a key characteristic of many real-world systems, from organisms to humans. However its role in determining the system's collective dynamics is typically not well understood. Here we study how individual heterogeneity impacts the system network dynamics by comparing linking mechanisms that favor similar or dissimilar individuals. We find that this heterogeneity-based evolution can drive explosive network behavior and dictates how a polarized population moves toward consensus. Our model shows good agreement with data from both biological and social science domains. We conclude that individual heterogeneity likely plays a key role in the collective development of real-world networks and communities, and cannot be ignored., Comment: 6 pages, 4 figures

Though many aggregation theories exist for physical, chemical and biological systems, they do not account for the significant heterogeneity found, for example, in populations of living objects. This is unfortunate since understanding how heterogeneous individuals come together in support of an extremist cause, for example, represents an urgent societal problem. Here we develop such a theory and show that the intrinsic population heterogeneity can significantly delay the gel transition point and change the gel's growth rate. We apply our theory to examine how humans aggregate online in support of a particular extremist cause. We show that the theory provides an accurate description of the online extremist support for ISIS (so-called Islamic State) which started in late 2014., Comment: Figures available separately online

We analyze the paper of Nathan D. Grubaugh et al. (Nature 546, 401-405, 2017) and find that it does not offer a convincing quantitative explanation for what generated the temporal distribution of human Zika virus (ZIKV) cases shown in their paper (Fig. 1d). We criticize this aspect because it is this understanding of how human cases develop from day-today and week-to-week within an area such as these Ground Zeros, that policymakers need in order to mitigate future outbreaks. We present results that strongly suggest that the missing piece is everyday human visit-revisit behavior. These results reproduce the human outbreak data in the key areas of Miami in 2016 very well, and give policymakers specific predictions for how changes in human flow through these areas will affect, and hence can be used to mitigate, future ZIka outbreaks in Miami and beyond., Comment: 5 pages, 2 figure

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 though online spaces such as chatrooms 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 which focuses on the time-dependent interplay between the {\em mobility through} and the {\em 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., Comment: 14 pages, 16 figures

The common real-world feature of individuals migrating through a network -- either in real space or online -- significantly complicates understanding of network processes. Here we show that even though a network may appear static on average, underlying nodal mobility can dramatically distort outbreak profiles. Highly nonlinear dynamical regimes emerge in which increasing mobility either amplifies or suppresses outbreak severity. Predicted profiles mimic recent outbreaks of real-space contagion (social unrest) and online contagion (pro-ISIS support). We show that this nodal mobility can be renormalized in a precise way for a particular class of dynamical networks.

We show that accounting for internal character among interacting, heterogeneous entities generates rich phase 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 population's diversity decreases. Our analytical theory helps explain why a particular class of universality is so common in the real world, despite fundamental differences in the underlying entities. Furthermore, it correctly predicts the non-monotonic temporal variation in connectivity observed recently in one such system.

Even photosynthesis -- the most basic natural phenomenon underlying Life on Earth -- involves the non-trivial 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 non-equilibrium 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). 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., Comment: 7 pages, 3 figures

Organic Zener diodes with a precisely adjustable reverse breakdown from -3 V to -15 V without any influence on the forward current-voltage curve are realized. This is accomplished by controlling the width of the charge depletion zone in a pin-diode with an accuracy of one nanometer independently of the doping concentration and the thickness of the intrinsic layer. The breakdown effect with its exponential current voltage behavior and a weak temperature dependence is explained by a tunneling mechanism across the HOMO-LUMO gap of neigh- boring molecules. The experimental data are confirmed by a minimal Hamiltonian model approach, including coherent tunneling and incoherent hopping processes as possible charge transport pathways through the effective device region., Comment: to appear in Nano Letters Article ASAP (2010)