arXiv subjects

Matjaz Perc

Publications and source records attributed to Matjaz Perc.

At least 19 recordsLinked to original sources

arXiv2026

Coordinated incentives in AI-generated misinformation governance

With the rapid diffusion of AI-generated content, AI-driven misinformation is becoming increasingly pervasive and difficult to govern, undermining information credibility and social trust. This study models the strategic interdependence among a government regulator, an AI enterprise, and users through a three-party evolutionary game that incorporates heterogeneous rewards and punishments. From the resulting replicator equations, we characterize the evolutionary stability of competing governance and production strategies. The analysis indicates that neither unilateral regulation nor market incentives alone can effectively curb misinformation. Instead, an evolutionarily stable regime of real-information production arises only when regulatory rewards and punishment intensity, enterprise reputation loss, and user adoption incentives collectively surpass critical thresholds. The findings highlight the need for coordinated and adaptive policy mixes that align regulatory instruments with enterprise behavior and user uptake while managing governance costs.

physics.soc-ph↗

arXiv2026

Diffusion-induced instabilities promote cooperation in eco-evolutionary networks

Understanding how cooperation persists despite the advantage of selfish behavior remains a central challenge in evolutionary dynamics. Classical models of public goods dilemmas predict dominance of defectors, yet natural and social systems often sustain cooperation. We study an eco-evolutionary public goods game on complex networks where cooperators and defectors diffuse at different rates. When the isolated system is in a defector-dominated coexistence regime, faster dispersal of defectors than cooperators leads to a symmetry-breaking transition that produces localized clusters of cooperators. In heterogeneous networks, nodes with higher connectivity become significantly more likely to exhibit cooperative dominance. A degree-based mean-field reduction supports this result by showing that network connectivity controls an effective coupling strength proportional to node degree, thereby producing a bifurcation that separates defector-dominated and cooperative states. We also address why not all hubs become cooperative by means of a multistability analysis. These results reveal how asymmetric mobility and heterogeneous connectivity jointly promote cooperation in structured populations.

physics.soc-ph↗

arXiv2026

Charting the life of Billboard hits through memory, turnover, and predictability

Rankings shape the visibility and success of cultural products, yet their temporal dynamics remain underexplored when comparing distinct ranked objects within the same domain. Here, we use nearly seven decades of Billboard Hot 100 songs and six decades of Billboard 200 albums to investigate how success emerges, persists, and differs between songs and albums. We find that albums exhibit a heavier-tailed permanence distribution and reenter the charts more often than songs, whereas songs typically have longer uninterrupted runs. Similarity between successive charts decays much faster for songs than for albums, suggesting that individual hits reflect shorter-lived collective attention, while albums retain longer cultural memory. Rank-turbulence divergence shows that consecutive charts are similar, but that top positions are dominated more by rank reshuffling than by turnover. Entropy-based analyses reveal high uncertainty in rank movements, with distinct historical patterns for songs and albums and a strong dependence on trajectory length. Clustering of trajectories shows that chart success is organized into a small number of typical pathways, including canonical rise-and-fall trajectories, high-end persistence, and monotonic decline. Together, these results show that musical charts are not merely records of popularity, but dynamic memory systems in which attention, turnover, and predictability interact differently for songs and albums.

physics.soc-ph↗

arXiv2026

Strategic commitments shape collective cybersecurity under AI inequality

The growing integration of AI into cybersecurity is reshaping the balance between attackers and defenders. When access to advanced AI-enabled defence tools is uneven, resource-limited defenders may be unable to adopt effective protection, creating persistent system vulnerabilities. We study the impact of differential AI access using an evolutionary game-theoretic model in a finite population. We first show that when high-capability defence is costly, the population is driven toward low-cost, weak-defence behaviour, sustaining attacks and weakening long-run security. To address this problem, we introduce differential access to AI defence tools by allowing defenders to choose between low- and high-capability protection based on their resources. We then examine the role of a small group of committed defenders who always adopt strong defence and influence others through social learning. Although commitment increases the prevalence of strong defence, it alone cannot stabilise secure outcomes due to high defence costs. We therefore incorporate a targeted subsidy to remove the cost disadvantage from committed defenders. Our analysis shows that subsidised commitment significantly increases strong defence adoption, suppresses successful attacks, and improves overall system resilience. Simulations across a broad parameter space confirm that subsidies consistently outperform commitment alone. In addition, social-welfare analysis shows improved defender outcomes while keeping attacker gains low. These findings suggest that targeted support for key defenders can be an effective mechanism for stabilising cybersecurity in AI-driven environments and provide a theoretical bridge between cybersecurity policy, AI governance, and strategic allocation of defensive AI capabilities.

cs.AI↗

arXiv2026

Gender shapes the relationship between productivity and journal prestige in science

Gender disparities in academia manifest and persist in various aspects of the scientific enterprise, yet their influence on the interplay between research productivity and journal prestige remains underexplored. Here we analyze the academic trajectories of over 6,000 elite Brazilian researchers by jointly tracking their annual productivity and the average prestige of the journals in which they publish. By projecting individual career years onto a standardized productivity-prestige plane and applying Bayesian hierarchical modeling, we find that male researchers are more likely to follow productivity-oriented trajectories and are markedly overrepresented in the hyperprolific region of this plane. Female peers, in contrast, more often occupy regions that prioritize journal prestige over publication quantity. Although male researchers publish more throughout their careers, their female counterparts achieve comparable or higher average journal prestige, particularly in later career stages and among outlier individuals. Male researchers also exhibit greater temporal persistence in their productivity and impact levels and are especially averse to simultaneously changing both metrics compared to their female peers. Among non-outliers, productivity and career age have a negative overall impact on the average journal prestige of researchers of both genders, with slightly stronger effects observed among female researchers; however, these patterns vary across disciplines, highlighting the complexity and heterogeneity of academic careers.

physics.soc-ph↗

arXiv2026

Interpretable liquid crystal phase classification via two-by-two ordinal patterns

Liquid crystal textures encode rich structural information, yet mapping these images to mesophase identity remains challenging because visually similar patterns can arise from distinct structures. Here we present a simple, interpretable representation that maps textures to a 75-dimensional frequency vector of two-by-two ordinal patterns, grouped into eleven symmetry-based types to characterize a large-scale dataset spanning seven mesophases. Combined with a simple machine learning classifier, this lightweight representation yields near-perfect phase recognition, including the difficult distinction between smectic A and smectic B mesophases. Our approach generalizes to unseen compounds and accurately distinguishes between phase identity and material origin. Unlike deep learning methods, each ordinal pattern is readily interpretable, and model explanations augmented with network visualizations of pattern interactions reveal the specific types and pairwise dependencies that drive each mesophase decision, providing compact, physically meaningful summaries of texture determinants. These results establish two-by-two ordinal patterns as an interpretable and scalable tool for liquid crystal image analysis, with potential applications to other complex patterned systems in materials science.

cond-mat.soft↗

arXiv2026

Effects of Stochastic Games on Evolutionary Dynamics in Structured Populations

Continuously changing environments have a paramount role in the evolution of cooperative behavior. Previous works have shown that the transitions among different games, as the feedback between behaviors and environments, can promote cooperative behavior significantly. Quantitative analysis, however, is limited to homogeneous populations, while realistic populations in nature are often more complex and highly heterogeneous. We hereby provide an analytical treatment of when the evolution of cooperation can be supported in stochastic games, applying to arbitrary spatial heterogeneity and payoff structure. We highlight that the rule and frequency of game changes can have surprisingly diverse effects on evolutionary outcomes, depending on the governing social dilemmas. While stochastic games favor the evolution of cooperation in donation games, this is not the case for public goods games and snowdrift games. Hence, our framework and model results offer a more subtle insight into the long-standing enigma.

physics.soc-ph↗

arXiv2026

Bipartite structure and dynamics of political corruption networks

Political corruption is inherently an affiliation process linking agents to corruption cases; yet it is often studied via one-mode projections that connect co-offenders within the same scandal, implying a loss of information that potentially confounds properties of agents and cases. Here, we adopt a bipartite representation to analyze datasets of corruption scandals in Brazil and Spain spanning nearly three decades. By tracking the temporal growth of these networks, we quantify density and redundancy measures to capture partner reuse and co-occurrence across cases. Networks in both countries become progressively sparser over time, and agent redundancy is systematically higher than case redundancy, indicating a small cadre of recidivists who recombine largely with novice partners rather than forming durable co-offending ties. These networks exhibit near-exponential degree distributions, reflecting low recidivism and likely high coordination costs and secrecy constraints of large-scale scandals. Our bipartite view further reveals a moderate cross-mode disassortative degree mixing between agents and cases, with high-degree agents distributing their activity across small cases and large scandals mainly comprising low-degree participants. Finally, identifying atypical individuals within the bipartite structure reveals criminal trajectories marked by a gradual rise in network embeddedness that can appear ordinary in agent-projected networks.

physics.soc-ph↗

arXiv2025

Complete strategy spaces reveal hidden pathways to cooperation

Understanding how cooperation emerges and persists is a central challenge in evolutionary game theory. Existing models often rely on restricted, hand-picked strategy sets, which can overlook critical behavioural pathways. A recent four-strategy framework showed that cheap talk can promote cooperation through local interactions, yet it remained unclear whether modelled strategies might alter these conclusions. Here, we extend this framework to the complete set of eight strategies that naturally arise from communication and decision-making rules. We show that incorporating the full strategy space dramatically changes the evolutionary landscape. Cooperation becomes both more robust and more versatile, driven by novel pathways absent in the restricted model. In particular, we uncover a previously overlooked mechanism in which suspicious cooperation catalyses a cyclic dynamic that sustains cooperation. Conversely, the assumed role of strategic defection in the biased model is fragile, acting mainly as a spoiler rather than a genuine evolutionary attractor. The complete model further reveals a rich spectrum of long-term behaviours, including stable coexistence among up to seven strategies and time-varying patterns of partial coexistence. These results demonstrate that the full strategy space unlocks hidden routes to cooperative behaviour and highlight the importance of comprehensive modelling when explaining the emergence of cooperation.

q-bio.PE↗

arXiv2025

Robustness and resilience of complex networks

Complex networks are ubiquitous: a cell, the human brain, a group of people and the Internet are all examples of interconnected many-body systems characterized by macroscopic properties that cannot be trivially deduced from those of their microscopic constituents. Such systems are exposed to both internal, localized, failures and external disturbances or perturbations. Owing to their interconnected structure, complex systems might be severely degraded, to the point of disintegration or systemic dysfunction. Examples include cascading failures, triggered by an initially localized overload in power systems, and the critical slowing downs of ecosystems which can be driven towards extinction. In recent years, this general phenomenon has been investigated by framing localized and systemic failures in terms of perturbations that can alter the function of a system. We capitalize on this mathematical framework to review theoretical and computational approaches to characterize robustness and resilience of complex networks. We discuss recent approaches to mitigate the impact of perturbations in terms of designing robustness, identifying early-warning signals and adapting responses. In terms of applications, we compare the performance of the state-of-the-art dismantling techniques, highlighting their optimal range of applicability for practical problems, and provide a repository with ready-to-use scripts, a much-needed tool set.

physics.soc-ph↗

arXiv2025

Trilobite tridents: A model of lift, drag and stability for queue formation

The trident like cephalic projections of Walliserops trifurcatus have been interpreted as sexually selected weapons for intraspecific combat. We evaluate one possible, alternative hypothesis grounded in biomechanics and collective behaviour: that tridents contributed to hydrodynamic lift and queue stability, thereby conferring energetic and mechanical advantages during group locomotion. Under this hypothesis, lift could offset gravitational forces, enabling greater locomotor efficiency, while mechanically linked formations, where tridents rested on the pygidia of leading individuals, enhanced pitch and roll stability and minimized costly accelerations and collisions. These formations may also have facilitated hydrodynamic drafting, which preceded trident evolution, allowing weaker individuals to conserve energy and remain integrated within the group.

q-bio.PE↗

arXiv2025

Nearest neighbor permutation entropy detects phase transitions in complex high-pressure systems

Understanding the high-pressure phase behavior of carbon dioxide-hydrocarbon mixtures is of considerable interest owing to their wide range of applications. Under certain conditions, these systems are not amenable to direct visual monitoring, and experimentalists often rely on spectrophotometric data to infer phase behavior. Consequently, developing computationally efficient and robust methods to leverage such data is crucial. Here, we combine nearest neighbor permutation entropy, computed directly from in situ near-infrared absorbance spectra acquired during depressurization trials of mixtures of carbon dioxide and a distilled petroleum fraction, with an anomaly detection approach to identify phase transitions. We show that changes in nearest neighbor entropy effectively signal transitions from initially homogeneous mixtures to two-phase equilibria, thereby enabling accurate out-of-sample online predictions of transition pressures. Our approach requires minimum data preprocessing, no specialized detection techniques or visual inspection of the spectra, and is sufficiently general to be adapted for studying phase behavior in other high-pressure systems monitored via spectrophotometry.

physics.chem-ph↗

arXiv2025

Modeling temporal hypergraphs

Networks representing social, biological, technological or other systems are often characterized by higher-order interaction involving any number of nodes. Temporal hypergraphs are given by ordered sequences of hyperedges representing sets of nodes interacting at given points in time. In this paper we discuss how a recently proposed model family for time-stamped hyperedges - relational hyperevent models (RHEM) - can be employed to define tailored null distributions for temporal hypergraphs and to test and control for complex dependencies in hypergraph dynamics. RHEM can be specified with a given vector of temporal hyperedge statistics - functions that quantify the structural position of hyperedges in the history of previous hyperedges - and equate expected values of these statistics with their empirically observed values. This allows, for instance, to analyze the overrepresentation or underrepresentation of temporal hyperedge configurations in a model that reproduces the observed distributions of possibly complex sub-configurations, including but going beyond node degrees. Concrete examples include, but are not limited to, preferential attachment, repetition of subsets of any given size, triadic closure, homophily, and degree assortativity for subsets of any order.

physics.soc-ph↗

arXiv2025

Structural roles and gender disparities in corruption networks

Criminal activities are predominantly due to males, with females exhibiting a significantly lower involvement, especially in serious offenses. This pattern extends to organized crime, where females are often perceived as less tolerant to illegal practices. However, the roles of males and females within corruption networks are less understood. Here, we analyze data from political scandals in Brazil and Spain to shed light on gender differences in corruption networks. Our findings reveal that females constitute 10% and 20% of all agents in the Brazilian and Spanish corruption networks, respectively, with these proportions remaining stable over time and across different scandal sizes. Despite this disparity in representation, centrality measures are comparable between genders, except among highly central individuals, for which males are further overrepresented. Additionally, gender has no significant impact on network resilience, whether through random dismantling or targeted attacks on the largest component. Males are more likely to be involved in multiple scandals than females, and scandals predominantly involving females are rare, though these differences are explained by a null network model in which gender is randomly assigned while maintaining gender proportions. Our results further reveal that the underrepresentation of females partially explains gender homophily in network associations, although in the Spanish network, male-to-male connections exceed expectations derived from a null model.

physics.soc-ph↗

arXiv2025

Two-by-two ordinal patterns in art paintings

Quantitative analysis of visual arts has recently expanded to encompass a more extensive array of artworks due to the availability of large-scale digitized art collections. Consistent with formal analyses by art historians, many of these studies highlight the significance of encoding spatial structures within artworks to enhance our understanding of visual arts. However, defining universally applicable, interpretable, and sufficiently simple units that capture the essence of paintings and their artistic styles remains challenging. Here we examine ordering patterns in pixel intensities within two-by-two partitions of images from nearly 140,000 paintings created over the past thousand years. These patterns, categorized into eleven types based on arguments of continuity and symmetry, are both universally applicable and detailed enough to correlate with low-level visual features of paintings. We uncover a universal distribution of these patterns, with consistent prevalence within groups, yet modulated across groups by a nontrivial interplay between pattern smoothness and the likelihood of identical pixel intensities. This finding provides a standardized metric for comparing paintings and styles, further establishing a scale to measure deviations from the average prevalence. Our research also shows that these simple patterns carry valuable information for identifying painting styles, though styles generally exhibit considerable variability in the prevalence of ordinal patterns. Moreover, shifts in the prevalence of these patterns reveal a trend in which artworks increasingly diverge from the average incidence over time; however, this evolution is neither smooth nor uniform, with substantial variability in pattern prevalence, particularly after the 1930s.

physics.soc-ph↗

arXiv2025

Impact of inter-city interactions on disease scaling

Inter-city interactions are critical for the transmission of infectious diseases, yet their effects on the scaling of disease cases remain largely underexplored. Here, we use the commuting network as a proxy for inter-city interactions, integrating it with a general scaling framework to describe the incidence of seven infectious diseases across Brazilian cities as a function of population size and the number of commuters. Our models significantly outperform traditional urban scaling approaches, revealing that the relationship between disease cases and a combination of population and commuters varies across diseases and is influenced by both factors. Although most cities exhibit a less-than-proportional increase in disease cases with changes in population and commuters, more-than-proportional responses are also observed across all diseases. Notably, in some small and isolated cities, proportional rises in population and commuters correlate with a reduction in disease cases. These findings suggest that such towns may experience improved health outcomes and socioeconomic conditions as they grow and become more connected. However, as growth and connectivity continue, these gains diminish, eventually giving way to challenges typical of larger urban areas - such as socioeconomic inequality and overcrowding - that facilitate the spread of infectious diseases. Our study underscores the interconnected roles of population size and commuter dynamics in disease incidence while highlighting that changes in population size exert a greater influence on disease cases than variations in the number of commuters.

physics.soc-ph↗

arXiv2024

Evolutionary mechanisms that promote cooperation may not promote social welfare

Understanding the emergence of prosocial behaviours among self-interested individuals is an important problem in many scientific disciplines. Various mechanisms have been proposed to explain the evolution of such behaviours, primarily seeking the conditions under which a given mechanism can induce highest levels of cooperation. As these mechanisms usually involve costs that alter individual payoffs, it is however possible that aiming for highest levels of cooperation might be detrimental for social welfare -- the later broadly defined as the total population payoff, taking into account all costs involved for inducing increased prosocial behaviours. Herein, by comparatively analysing the social welfare and cooperation levels obtained from stochastic evolutionary models of two well-established mechanisms of prosocial behaviour, namely, peer and institutional incentives, we demonstrate exactly that. We show that the objectives of maximising cooperation levels and the objectives of maximising social welfare are often misaligned. We argue for the need of adopting social welfare as the main optimisation objective when designing and implementing evolutionary mechanisms for social and collective goods.

math.DS↗

arXiv2024

Global synchronization in generalized multilayer higher-order networks

Networks incorporating higher-order interactions are increasingly recognized for their ability to introduce novel dynamics into various processes, including synchronization. Previous studies on synchronization within multilayer networks have often been limited to specific models, such as the Kuramoto model, or have focused solely on higher-order interactions within individual layers. Here, we present a comprehensive framework for investigating synchronization, particularly global synchronization, in multilayer networks with higher-order interactions. Our framework considers interactions beyond pairwise connections, both within and across layers. We demonstrate the existence of a stable global synchronous state, with a condition resembling the master stability function, contingent on the choice of coupling functions. Our theoretical findings are supported by simulations using Hindmarsh-Rose neuronal and R\"{o}ssler oscillators. These simulations illustrate how synchronization is facilitated by higher-order interactions, both within and across layers, highlighting the advantages over scenarios involving interactions within single layers.

nlin.AO↗