Physics

We present a microscopic driving algorithm that prescribes the acceleration using three parameters: the distance to the leading vehicle, to the next traffic light and to the nearest stopping point when the next traffic light is in the red phase. We apply this algorithm to construct decision trees that enable two driving behaviors: aggressive and careful. The focus of this study is to analyze the amount of aggressive drivers that are needed in order to generate a traffic gridlock in a portion of a city with signalized intersections. At rush hour, aggressive drivers will enter the intersection regardless if they have enough time or space to clear it. When their traffic light changes they block other drivers, thus providing the conditions for a gridlock to develop. We find that gridlocks emerge even with very few aggressive drivers present. These results support the idea of promoting good driving behavior to avoid heavy congestion during rush hours.

Often times, a candidate's attractiveness is directly associated with his clear ideologies and opinions on various policies and social issues. Using the ideas of stochastic differential equations and Ornstein-Uhlenbeck Process, we develop a phenomenological model to understand the effect of (un)clearly communicating a candidate's stance on policies to the voting public. We will show that, counter intuitively, there are quantifiable advantages to be vague on one's stance.

Due to its wide reaching implications for everything from identifying hotspots of income inequality to political redistricting, there is a rich body of literature across the sciences quantifying spatial patterns in socioeconomic data. In particular, the variability of indicators relevant to social and economic well-being between localized populations is of great interest, as it pertains to the spatial manifestations of inequality and segregation. However, heterogeneity in population density, sensitivity of statistical analyses to spatial aggregation, and the importance of pre-drawn political boundaries for policy intervention may decrease the efficacy and relevance of existing methods for analyzing spatial socioeconomic data. Additionally, these measures commonly lack either a framework for comparing results for qualitative and quantitative data on the same scale, or a mechanism for generalization to multi-region correlations. To mitigate these issues associated with traditional spatial measures, here we view local deviations in socioeconomic variables from a topological lens rather than a spatial one, and use a novel information theoretic network approach based on the Generalized Jensen Shannon Divergence to distinguish distributional quantities across adjacent regions. We apply our methodology in a series of experiments to study the network of neighboring census tracts in the continental US, quantifying the decay in two-point distributional correlations across the network, examining the county-level socioeconomic disparities induced from the aggregation of tracts, and constructing an algorithm for the division of a city into homogeneous clusters. These results provide a new framework for analyzing the variation of attributes across regional populations, and shed light on new, universal patterns in socioeconomic attributes.

While perceptual decision making in humans is often considered to be governed by evidence accumulator models (like drift-diffusion), mechanisms driving harder situations where prospection of future scenarios is necessary remain largely unknown. Here, experimental and computational evidence is given in favour of a mechanism in which prospection of possible future payoffs associated to each available choice could be used, through the internal estimation of the corresponding Shannon's entropy S . So, the decision would be triggered as soon as S reaches a threshold which ensures that a choice is reliable enough. We illustrate this idea using a task in which subjects have to navigate sequentially through a maze on the computer screen while avoiding trajectory overlaps, forcing them to use memory and prospection skills that we indirectly capture through eye-tracking. Comparison of the experimental data to that from virtual (ideal) subjects allows us to verify that the performances observed, as well as the distribution of decision-making times, of humans are compatible with the aforementioned mechanism.

In our daily lives, we rely on the proper functioning of supply networks, from power grids to water transmission systems. A single failure in these critical infrastructures can lead to a complete collapse through a cascading failure mechanism. Counteracting strategies are thus heavily sought after. In this article, we introduce a general framework to analyse the spreading of failures in complex networks and demonstrate that both weak and strong connections can be used to contain damages. We rigorously prove the existence of certain subgraphs, called network isolators, that can completely inhibit any failure spreading, and we show how to create such isolators in synthetic and real-world networks. The addition of selected links can thus prevent large scale outages as demonstrated for power transmission grids.

Empirical studies show that epidemiological models based on an epidemic's initial spread rate often fail to predict the true scale of that epidemic. Most epidemics with a rapid early rise die out before affecting a significant fraction of the population, whereas the early pace of some pandemics is rather modest. Recent models suggest that this could be due to the heterogeneity of the target population's susceptibility. We study a computer malware ecosystem exhibiting spread mechanisms resembling those of biological systems while offering details unavailable for human epidemics. Rather than comparing models, we directly estimate reach from a new and vastly more complete data from a parallel domain, that offers superior details and insight as concerns biological outbreaks. We find a highly heterogeneous distribution of computer susceptibilities, with nearly all outbreaks initially over-affecting the tail of the distribution, then collapsing quickly once this tail is depleted. This mechanism restricts the correlation between an epidemic's initial growth rate and its total reach, thus preventing the majority of epidemics, including initially fast-growing outbreaks, from reaching a macroscopic fraction of the population. The few pervasive malwares distinguish themselves early on via the following key trait: they avoid infecting the tail, while preferentially targeting computers unaffected by typical malware.

Much of the research on networks using patent data focuses on citations and the collaboration networks of inventors, hence regarding patents as a positive sign of invention. However, patenting is, most importantly, a strategic action used by companies to compete with each other. This study sheds light on inter-organisational adversarial relationships in patenting for the first time. We constructed and analysed the network of companies connected via patent opposition relationships that occurred between 1980 and 2018. A majority of the companies are directly or indirectly connected to each other and hence form the largest connected component. We found that in the network, many companies disapprove patents in various industrial sectors as well as those owned by foreign companies. The network exhibits heavy-tailed, power-law-like degree distribution and assortative mixing, making it an unusual type of topology. We further investigated the dynamics of the formation of this network by conducting a temporal network motif analysis, with patent co-ownership among the companies considered. By regarding opposition as a negative relationship and patent co-ownership as a positive relationship, we analysed where collaboration may occur in the opposition network and how such positive relationships would interact with negative relationships. The results identified the structurally imbalanced triadic motifs and the temporal patterns of the occurrence of triads formed by a mixture of positive and negative relationships. Our findings suggest that the mechanisms of the emergence of the inter-organisational adversarial relationships may differ from those of other types of negative relationships hence necessitating further research.

Regional quarantine policies, in which a portion of a population surrounding infections are locked down, are an important tool to contain disease. However, jurisdictional governments -- such as cities, counties, states, and countries -- act with minimal coordination across borders. We show that a regional quarantine policy's effectiveness depends upon whether (i) the network of interactions satisfies a balanced-growth condition, (ii) infections have a short delay in detection, and (iii) the government has control over and knowledge of the necessary parts of the network (no leakage of behaviors). As these conditions generally fail to be satisfied, especially when interactions cross borders, we show that substantial improvements are possible if governments are outward-looking and proactive: triggering quarantines in reaction to neighbors' infection rates, in some cases even before infections are detected internally. We also show that even a few lax governments -- those that wait for nontrivial internal infection rates before quarantining -- impose substantial costs on the whole system. Our results illustrate the importance of understanding contagion across policy borders and offer a starting point in designing proactive policies for decentralized jurisdictions.

The functionality of nodes in a network is often described by the structural feature of belonging to the giant component. However, when dealing with problems like transport, a more appropriate functionality criterion is for a node to belong to the network's backbone, where the flow of information and of other physical quantities (such as current) occurs. Here we study percolation in a model of interdependent resistor networks and show the effect of spatiality on their coupled functioning. We do this on a realistic model of spatial networks, featuring a Poisson distribution of link-lengths. We find that interdependent resistor networks are significantly more vulnerable than their percolation-based counterparts, featuring first-order phase transitions at link-lengths where the mutual giant component still emerges continuously. We explain this apparent contradiction by tracing the origin of the increased vulnerability of interdependent transport to the crucial role played by the dandling ends. Moreover, we interpret these differences by considering an heterogeneous k -core percolation process which enables to define a one-parameter family of functionality criteria whose constraints become more and more stringent. Our results highlight the importance that different definitions of nodes functionality have on the collective properties of coupled processes, and provide better understanding of the problem of interdependent transport in many real-world networks.

Online users are typically active on multiple social media networks (SMNs), which constitute a multiplex social network. It is becoming increasingly challenging to determine whether given accounts on different SMNs belong to the same user; this can be expressed as an interlayer link prediction problem in a multiplex network. To address the challenge of predicting interlayer links , feature or structure information is leveraged. Existing methods that use network embedding techniques to address this problem focus on learning a mapping function to unify all nodes into a common latent representation space for prediction; positional relationships between unmatched nodes and their common matched neighbors (CMNs) are not utilized. Furthermore, the layers are often modeled as unweighted graphs, ignoring the strengths of the relationships between nodes. To address these limitations, we propose a framework based on multiple types of consistency between embedding vectors (MulCEV). In MulCEV, the traditional embedding-based method is applied to obtain the degree of consistency between the vectors representing the unmatched nodes, and a proposed distance consistency index based on the positions of nodes in each latent space provides additional clues for prediction. By associating these two types of consistency, the effective information in the latent spaces is fully utilized. Additionally, MulCEV models the layers as weighted graphs to obtain better representation. In this way, the higher the strength of the relationship between nodes, the more similar their embedding vectors in the latent representation space will be. The results of our experiments on several real-world datasets demonstrate that the proposed MulCEV framework markedly outperforms current embedding-based methods, especially when the number of training iterations is small.