CyberSec Research

Cooperation and competition are fundamental forces shaping both natural and human systems, yet their interplay remains poorly understood. The Prisoner's Dilemma Game (PDG) has long served as a foundational framework in Game Theory for studying cooperation and defection, yet it overlooks explicit competitive interactions. Contest Theory, in turn, provides tools to model competitive dynamics, where success depends on the investment of resources. In this work, we bridge these perspectives by extending the PDG to include a third strategy, fighting, governed by the Tullock contest success function, where success depends on relative resource investments. This model, implemented on a square lattice, examines the dynamics of cooperation, defection, and competition under resource accumulation and depletion scenarios. Our results reveal a rich phase diagram in which cooperative and competitive strategies coexist under certain critical resource investments, expanding the parameter space for cooperation beyond classical limits. Fighters delay the dominance of defectors by mediating interactions, expanding the conditions under which cooperation persists. This work offers new insights into the evolution of social behaviors in structured populations, bridging cooperation and competition dynamics.

In social contexts where individuals consume varying amounts, such as shared meals or bar gatherings, splitting the total bill equally often yields surprisingly fair outcomes. In this work, we develop a statistical physics framework to explain this emergent fairness by modeling individual consumption as stochastic variables drawn from a realistic distribution, specifically the gamma distribution. Introducing a Boltzmann-like weighting factor, we derive exact analytical expressions for the partition function, average consumption, variance, and entropy under economic or social penalization constraints. Numerical simulations, performed using the Marsaglia-Tsang algorithm, confirm the analytical results with high precision. Drawing a direct parallel between individual consumption and ideal gas particle energy in the canonical ensemble, we show how the law of large numbers, mutual compensation, and the effective ordering induced by penalization combine to make equal cost-sharing statistically robust and predictable. These findings reveal that what appears to be an informal social convention is, in fact, grounded in the same fundamental principles that govern the collective behavior of particles in thermodynamic systems, highlighting the interdisciplinary power of statistical physics.

Human mobility forms the backbone of contact patterns through which infectious diseases propagate, fundamentally shaping the spatio-temporal dynamics of epidemics and pandemics. While traditional models are often based on the assumption that all individuals have the same probability of infecting every other individual in the population, a so-called random homogeneous mixing, they struggle to capture the complex and heterogeneous nature of real-world human interactions. Recent advancements in data-driven methodologies and computational capabilities have unlocked the potential of integrating high-resolution human mobility data into epidemic modeling, significantly improving the accuracy, timeliness, and applicability of epidemic risk assessment, contact tracing, and intervention strategies. This review provides a comprehensive synthesis of the current landscape in human mobility-informed epidemic modeling. We explore diverse sources and representations of human mobility data, and then examine the behavioral and structural roles of mobility and contact in shaping disease transmission dynamics. Furthermore, the review spans a wide range of epidemic modeling approaches, ranging from classical compartmental models to network-based, agent-based, and machine learning models. And we also discuss how mobility integration enhances risk management and response strategies during epidemics. By synthesizing these insights, the review can serve as a foundational resource for researchers and practitioners, bridging the gap between epidemiological theory and the dynamic complexities of human interaction while charting clear directions for future research.

Sustainable resource use in large societies requires social institutions that specify acceptable behavior and punish violators. Because mutual monitoring becomes prohibitively costly as populations grow, we examine whether sustainability can be maintained when only anonymized information is available. Using the evolutionary dynamical-systems game framework, we model the common-pool resource management game. In the model, each player's harvesting decisions shape the resource dynamics and depend on the resource's state, the player's wealth, and the group average wealth. Strategies are encoded as two-parameter decision-making functions that mutate across generations. Evolutionary simulations reveal that players self-organize into clusters that alternate harvesting turns: individuals within a cluster harvest synchronously, while the clusters themselves take turns. The emergent institutional rule is strikingly simple: "wait when rich, harvest when below average." While the majority cluster tends to exploit the minority, moderate diversity in decision parameters of strategies allows "turn-taking of turns" between the majority and minority roles, improving efficiency, equity, and resistance to selfish mutants. We quantify the difficulty of managing institutions as population size increases. When group size is fixed, the minimum number of groups required for cooperation grows exponentially with group size. If, however, groups enlarge gradually, the scaling transitions to a power law, indicating that institutions remain stable when they are first built in small populations and subsequently adapted to larger ones. Our findings provide a theoretical basis for the self-organization of institutions in large societies, illuminating how anonymized information can coordinate behavior and how institutional success depends on its developmental trajectory.

We study properties of opinion formation on Wikipedia Ising Networks. Each Wikipedia article is represented as a node and links are formed by citations of one article to another generating a directed network of a given language edition with millions of nodes. Ising spins are placed at each node and their orientation up or down is determined by a majority vote of connected neighbors. At the initial stage there are only a few nodes from two groups with fixed competing opinions up and down while other nodes are assumed to have no initial opinion with no effect on the vote. The competition of two opinions is modeled by an asynchronous Monte Carlo process converging to a spin polarized steady-state phase. This phase remains stable with respect to small fluctuations induced by an effective temperature of the Monte Carlo process. The opinion polarization at the steady-state provides opinion (spin) preferences for each node. In the framework of this Ising Network Opinion Formation model we analyze the influence and competition between political leaders, world countries and social concepts. This approach is also generalized to the competition between three groups of different opinions described by three colors, for example Donald Trump, Vladimir Putin, Xi Jinping or USA, Russia, China within English, Russian and Chinese editions of Wikipedia of March 2025. We argue that this approach provides a generic description of opinion formation in various complex networks.

Media reputation plays an important role in attracting venture capital investment. However, prior research has focused too narrowly on general media exposure, limiting our understanding of how media truly influences funding decisions. As informed decision-makers, venture capitalists respond to more nuanced aspects of media content. We introduce the concept of media memorability - the media's ability to imprint a startup's name in the memory of relevant investors. Using data from 197 UK startups in the micro and nanotechnology sector (funded between 1995 and 2004), we show that media memorability significantly influences investment outcomes. Our findings suggest that venture capitalists rely on detailed cues such as a startup's distinctiveness and connectivity within news semantic networks. This contributes to research on entrepreneurial finance and media legitimation. In practice, startups should go beyond frequent media mentions to strengthen brand memorability through more targeted, meaningful coverage highlighting their uniqueness and relevance within the broader industry conversation.

Especially in regions with high solar irradiation, photocatalysis presents a promising low-cost "green" hydrogen production option. Thus, this paper analyzes impacts of increasing photocatalysis shares on the European energy system using an open-source energy system optimization model covering the electricity, industry, and heating sectors with high spatial and temporal resolution. Photocatalysis deployment is investigated at various market shares by exogenously altering photocatalysis costs. The results show that integrating photocatalysis necessitates systematic adjustments since it lacks the flexible load attributes of water electrolysis. Therefore, a significant geographic shift in hydrogen production and demand from the Northwest to South Europe is expected in the case of large-scale photocatalysis adoption. Despite these challenges, installed photocatalysis shows costs within the photocatalysis cost projections. Thus, photocatalysis could contribute to a critical diversification of hydrogen production, easing material demands for other renewable technologies. Nevertheless, it requires strategic planning to avoid lock-ins and to maximize its potential.

Counting the number of isomers of a chemical molecule is one of the formative problems of graph theory. However, recent progress has been slow, and the problem has largely been ignored in modern network science. Here we provide an introduction to the mathematics of counting network structures and then use it to derive results for two new classes of molecules. In contrast to previously studied examples, these classes take additional chemical complexity into account and thus require the use of multi-variate generating functions. The results illustrate the elegance of counting theory, highlighting it as an important tool that should receive more attention in network science.

Decentralized renewable energy (DRE) systems have become a cornerstone of electrification efforts in remote and underserved areas. Yet, while global attention has focused on expanding access through solar mini-grids and off-grid solutions, far less emphasis has been placed on ensuring the long-term operation of these systems. In many fragile contexts, weak Operation and Maintenance (O&M) frameworks undermine the reliability and resilience of energy access, leading to premature system failure. This Perspective examines how structural and contextual barriers such as limited technical expertise, inadequate maintenance models, and insufficient local integration of energy systems contribute to this hidden challenge. We argue that O&M should be reframed as a strategic function and embedded from the design phase onward. Building on insights from energy-scarce regions, we outline a pathway that combines user-friendly maintenance protocols, offline educational platforms, and community-based toolkits to support energy availability. This approach aims to empower local actors, support system functionality, and advance a sustainable energy transition.

Within the continuous endeavour of improving the efficiency and resilience of air transport, the trend of using concepts and metrics from statistical physics has recently gained momentum. This scientific discipline, which integrates elements from physics and statistics, aims at extracting knowledge about the microscale rules governing a (potentially complex) system when only its macroscale is observable. Translated to air transport, this entails extracting information about how individual operations are managed, by only studying coarse-grained information, e.g. average delays. We here review some fundamental concepts of statistical physics, and explore how these have been applied to the analysis of time series representing different aspects of the air transport system. In order to overcome the abstractness and complexity of some of these concepts, intuitive definitions and explanations are provided whenever possible. We further conclude by discussing the main obstacles towards a more widespread adoption of statistical physics in air transport, and sketch topics that we believe may be relevant in the future.

During an epidemic outbreak, individuals often modify their behavior in response to global prevalence cues, using spatially mediated adaptations such as reduced mobility or transmission range. In this work, we investigate the impact of distance-based adaptive behaviors on epidemic dynamics, where a fraction of the population adjusts its transmission range and susceptibility to infection based on global prevalence. We consider three adaptation scenarios: a constant adaptive fraction, a power-law dependence and a sigmoidal dependence of adaptive fraction on global prevalence. In the spatially well-mixed regime, we analytically obtain critical adaptation thresholds necessary for epidemic mitigation and in the spatially static regime, we establish bounds for the thresholds using continuum percolation results. Our results indicate that a linear adaptive response to prevalence provides no additional advantage over a constant adaptive fraction in controlling outbreaks, and a highly super-linear response is required to suppress epidemic spread. For a sigmoidal adaptation, we identify conditions under which oscillations in prevalence can emerge, with peak prevalence exhibiting a non-monotonic dependence on the width of the sigmoidal function, suggesting an optimal parameter range that minimizes epidemic severity. We obtain prevalence, final epidemic size, and peak prevalence as functions of adaptation parameters in all adaptation scenarios considered, providing a comprehensive characterization of the effects of spatial adaptation based on global prevalence information in shaping adaptive epidemic dynamics.

Accurately measuring street dimensions is essential to evaluating how their design influences both travel behavior and safety. However, gathering street-level information at city scale with precision is difficult given the quantity and complexity of urban intersections. To address this challenge in the context of pedestrian crossings - a crucial component of walkability - we introduce a scalable and accurate method for automatically measuring crossing distance at both marked and unmarked crosswalks, applied to America's 100 largest cities. First, OpenStreetMap coordinates were used to retrieve satellite imagery of intersections throughout each city, totaling roughly three million images. Next, Meta's Segment Anything Model was trained on a manually-labelled subset of these images to differentiate drivable from non-drivable surfaces (i.e., roads vs. sidewalks). Third, all available crossing edges from OpenStreetMap were extracted. Finally, crossing edges were overlaid on the segmented intersection images, and a grow-cut algorithm was applied to connect each edge to its adjacent non-drivable surface (e.g., sidewalk, private property, etc.), thus enabling the calculation of crossing distance. This achieved 93 percent accuracy in measuring crossing distance, with a median absolute error of 2 feet 3 inches (0.69 meters), when compared to manually-verified data for an entire city. Across the 100 largest US cities, median crossing distance ranges from 32 feet to 78 feet (9.8 to 23.8m), with detectable regional patterns. Median crossing distance also displays a positive relationship with cities' year of incorporation, illustrating in a novel way how American cities increasingly emphasize wider (and more car-centric) streets.

In this work we show that as little as 40 kg of 60%-enriched uranium can be used to build a crude nuclear weapon with a kiloton yield. While too large to fit on a missile, such a weapon could be delivered by shipping container. This analysis is motivated by the June 2025 Israeli and US attacks on Iran, especially the bombings of the nuclear facilities at Natanz, Fordow, and Isfahan. The Iranian stockpile of approximately 408 kg of 60%-enriched uranium is, at the time of writing, inaccessible to IAEA inspectors and stored in secret. The rapid clandestine relocation of this material in June 2025 creates an opportunity for aspiring nuclear terrorists to divert an amount that could be used in the construction of an improvised gun-type nuclear weapon in the style of Little Boy.

While paradoxical linkages famously violate the Chebyshev-Grubler-Kutzbach criterion by exhibiting unexpected mobility, we identify an opposing phenomenon: a class of linkages that appear mobile according to the same criterion, yet are in fact rigid. We refer to these as hypo-paradoxical linkages, and proceed to analyze and illustrate their behavior. We use the same tools to further explain the unexpected positive mobility of Bennet mechanism.

1.1 Background Parks and the greening of schoolyards are examples of urban green spaces that have been praised for their environmental, social, and economic benefits in cities all over the world. More studies show that living near green spaces is good for property values. However, there is still disagreement about how strong and consistent these effects are in different cities (Browning et al., 2023; Grunewald et al., 2024; Teo et al., 2023). 1.2 Purpose This systematic review is the first to bring together a lot of geographical and statistical information that links greening schoolyards to higher property prices, as opposed to just green space in general. By focusing on schoolyard-specific interventions, we find complex spatial, economic, and social effects that are often missed in larger studies of green space. 1.3 Methods This review followed the PRISMA guidelines and did a systematic search and review of papers that were published in well-known journals for urban studies, the environment, and real estate. The criteria for inclusion stressed the use of hedonic pricing or spatial econometric models to look at the relationship between urban green space and home values in a quantitative way. Fifteen studies from North America, Europe, and Asia met the requirements for inclusion (Anthamatten et al., 2022; Wen et al., 2019; Li et al., 2019; Mansur & Yusuf, 2022).

Understanding the intrinsic mechanisms of social platforms is an urgent demand to maintain social stability. The rise of large language models provides significant potential for social network simulations to capture attitude dynamics and reproduce collective behaviors. However, existing studies mainly focus on scaling up agent populations, neglecting the dynamic evolution of social relationships. To address this gap, we introduce DynamiX, a novel large-scale social network simulator dedicated to dynamic social network modeling. DynamiX uses a dynamic hierarchy module for selecting core agents with key characteristics at each timestep, enabling accurate alignment of real-world adaptive switching of user roles. Furthermore, we design distinct dynamic social relationship modeling strategies for different user types. For opinion leaders, we propose an information-stream-based link prediction method recommending potential users with similar stances, simulating homogeneous connections, and autonomous behavior decisions. For ordinary users, we construct an inequality-oriented behavior decision-making module, effectively addressing unequal social interactions and capturing the patterns of relationship adjustments driven by multi-dimensional factors. Experimental results demonstrate that DynamiX exhibits marked improvements in attitude evolution simulation and collective behavior analysis compared to static networks. Besides, DynamiX opens a new theoretical perspective on follower growth prediction, providing empirical evidence for opinion leaders cultivation.

In this paper, we study Axelrod's model of social dynamics, introducing the concept of Cultural Diversity ($D$), defined as the variety of sizes of clusters or cultural domains formed, which measures the complexity of the system. We find that the maximum of $D$ agrees with the critical point where the monocultural/multicultural phase transition occurs, tending to a minimum value when the system's degrees of freedom, cultural traits $q$, are far from the critical point $q_c$. We show that at $q_c$ the entropy also reaches its maximum value, that is, the phase transition for this model is of the order-order type. Thus, multiculturalism is not synonymous with cultural diversity, as is commonly assumed in the literature.

Urban morphology has long been recognized as a factor shaping human mobility, yet comparative and formal classifications of urban form across metropolitan areas remain limited. Building on theoretical principles of urban structure and advances in unsupervised learning, we systematically classified the built environment of nine U.S. metropolitan areas using structural indicators such as density, connectivity, and spatial configuration. The resulting morphological types were linked to mobility patterns through descriptive statistics, marginal effects estimation, and post hoc statistical testing. Here we show that distinct urban forms are systematically associated with different mobility behaviors, such as reticular morphologies being linked to significantly higher public transport use (marginal effect = 0.49) and reduced car dependence (-0.41), while organic forms are associated with increased car usage (0.44), and substantial declines in public transport (-0.47) and active mobility (-0.30). These effects are statistically robust (p < 1e-19), highlighting that the spatial configuration of urban areas plays a fundamental role in shaping transportation choices. Our findings extend previous work by offering a reproducible framework for classifying urban form and demonstrate the added value of morphological analysis in comparative urban research. These results suggest that urban form should be treated as a key variable in mobility planning and provide empirical support for incorporating spatial typologies into sustainable urban policy design.