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.
The preferential attachment (PA) process is a popular theory for explaining network power-law degree distributions. In PA, the probability that a new vertex adds an edge to an existing vertex depends on the connectivity of the target vertex. In real-
world networks, however, each vertex may have asymmetric accessibility to information. Here we address this issue using a new network-generation mechanism that incorporates asymmetric accessibility to upstream and downstream information. We show that this asymmetric information accessibility directly affects the power-law exponent, producing a broad range of values that are consistent with observations. Our findings shed new light on the possible mechanisms in three important real-world networks: a citation network, a hyperlink network, and an online social network.
Individual heterogeneity is a key characteristic of many real-world systems, from organisms to humans. However its role in determining the systems collective dynamics is typically not well understood. Here we study how individual heterogeneity impact
s 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.
What happens when you slow down part of an ultrafast network that is operating quicker than the blink of an eye, e.g. electronic exchange network, navigational systems in driverless vehicles, or even neuronal processes in the brain? This question jus
t adopted immediate commercial, legal and political importance following U.S. financial regulators decision to allow a new network node to intentionally introduce delays of microseconds. Though similar requests are set to follow, there is still no scientific understanding available to policymakers of the likely system-wide impact of such delays. Giving academic researchers access to (so far prohibitively expensive) microsecond exchange data would help rectify this situation. As a by-product, the lessons learned would deepen understanding of instabilities across myriad other networks, e.g. impact of millisecond delays on brain function and safety of driverless vehicle navigation systems beyond human response times.
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 (Fi
g. 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.
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 p
oints arising for different character-dependent grouping mechanisms. These critical points move in opposite directions as the populations 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.
The occurrence of high-T_c superconductivity in systems including the cuprates and the iron-based superconductors, is known to coincide with the existence of anomalous normal-state properties which have been associated with quantum criticality. We ar
gue here that this observation results from the fact that quantum criticality can allow the occurrence of very-strong-coupling superconductivity by preventing its suppression due to competing symmetry-breaking instabilities. Treating the electrons through a large-U ansatz yields their separation into boson quasiparticles which are directly involved in the formation of these instabilities, represented as their Bose condensates, and charge-carrying fermion quasiparticles which are affected by them indirectly. Within the critical regime, condensates corresponding to the different broken-symmetry states are combined; consequently their negative effect on the pairing of the fermions is strongly diminished, enabling high-T_c superconductivity to occur. The observed phase diagram of the hole-doped cuprates then derives from a hidden T=0 quantum phase transition between a Fermi-liquid and a non-Fermi-liquid broken-symmetry striped state. The pseudogap range within this diagram is found to include two distinct regimes, with partial pairing occurring in one of them.
A grand challenge in many-body quantum physics is to explain the apparent connection between quantum criticality and high-temperature superconductivity in the cuprates and similar systems, such as the iron pnictides and chalcogenides. Here we argue t
hat the quantum-critical regime plays an essential role in activating a strong-pairing mechanism: although pairing bosons create a symmetry-breaking instability which suppresses pairing, the combination of these broken-symmetry states within the critical regime can restore this symmetry for the paired quasiparticles. This condition is shown to be met within a large-U ansatz. A hidden quantum phase transition then arises between a Fermi-liquid and a non-Fermi-liquid broken-symmetry striped state, and a critical regime in which the broken-symmetry states are combined.
Proponents of Complexity Science believe that the huge variety of emergent phenomena observed throughout nature, are generated by relatively few microscopic mechanisms. Skeptics however point to the lack of concrete examples in which a single mechani
stic model manages to capture relevant macroscopic and microscopic properties for two or more distinct systems operating across radically different length and time scales. Here we show how a single complexity model built around cluster coalescence and fragmentation, can cross the fundamental divide between many-body quantum physics and social science. It simultaneously (i) explains a mysterious recent finding of Fratini et al. concerning quantum many-body effects in cuprate superconductors (i.e. scale of 10^{-9} - 10^{-4} meters and 10^{-12} - 10^{-6} seconds), (ii) explains the apparent universality of the casualty distributions in distinct human insurgencies and terrorism (i.e. scale of 10^3 - 10^6 meters and 10^4 - 10^8 seconds), (iii) shows consistency with various established empirical facts for financial markets, neurons and human gangs and (iv) makes microscopic sense for each application. Our findings also suggest that a potentially productive shift can be made in Complexity research toward the identification of equivalent many-body dynamics in both classical and quantum regimes.
Quantifying human group dynamics represents a unique challenge. Unlike animals and other biological systems, humans form groups in both real (offline) and virtual (online) spaces -- from potentially dangerous street gangs populated mostly by disaffec
ted male youths, through to the massive global guilds in online role-playing games for which membership currently exceeds tens of millions of people from all possible backgrounds, age-groups and genders. We have compiled and analyzed data for these two seemingly unrelated offline and online human activities, and have uncovered an unexpected quantitative link between them. Although their overall dynamics differ visibly, we find that a common team-based model can accurately reproduce the quantitative features of each simply by adjusting the average tolerance level and attribute range for each population. By contrast, we find no evidence to support a version of the model based on like-seeking-like (i.e. kinship or `homophily).
