arXiv subjects

Jesús Gómez-Gardeñes

Publications and source records attributed to Jesús Gómez-Gardeñes.

At least 19 recordsLinked to original sources

arXiv2026

Interplay between evolutionary and epidemic time scales challenges the outcome of control policies

The classical SIR model, assuming constant viral traits, represents the cornerstone model for computing key indicators during epidemic outbreaks, such as the expected peak of infections or the impact of control policies. Viral evolution has been reported to challenge the physics of the SIR model, changing the nature of the epidemic transitions or the early-time dynamics of outbreaks. {Here we consider a minimal extension of the SIR model, allowing infectiousness to evolve,} to explore how the latter mechanism affects the two aforementioned indicators. We show that evolution induces a non-monotonic behavior of the epidemic peak with the basic reproduction number and undermines the impact of control policies, as lifting interventions too early can lead to worse epidemic scenarios than no action. We derive analytical expressions for the critical mutation rate and intervention time governing this behavior and identify a strong asymmetry between control strategies: while shortening the infectious period hinders transmission without suppressing the evolution of viral infectiousness, lowering transmission both reduces cases and slows down this evolution.

q-bio.PE↗

arXiv2026

When one protocol fits none: Self-organized network routing through evolutionary game dynamics

Packet routing on scale-free networks faces a fundamental trade-off: shortest-path routing is efficient at low demand but funnels traffic through hubs and jams early, whereas congestion-aware routing postpones jamming at the price of a sharper collapse. Since neither paradigm dominates across the full range of traffic load, here we ask whether the appropriate balance can emerge endogenously rather than being imposed by design. To answer this, we recast adaptive packet routing on networks as an evolutionary game letting a heterogeneous population of strategies compete for prevalence under selection pressure generated by their own performance. We study this competition under two formalisms (strategy anchored to the packet or to the generating node), global and local update rules, and two payoff metrics. Across every implementation the evolutionary dynamics yield the same outcome: the jamming transition is delayed relative to shortest-path routing while the violent collapse of fixed congestion-aware routing is avoided. This improvement emerges spontaneously, without centralized coordination or global information. Crucially, under local update rules, the node-level volatility of strategy choices peaks sharply at the transition, furnishing a purely local early-warning signal of imminent jamming that requires no global monitoring.

physics.soc-ph↗

arXiv2026

Higher-order interactions in ecology can be hidden in plain sight

Higher-order interactions are increasingly recognized as a key component of ecological dynamics. However, we show that higher-order Lotka-Volterra dynamics can, in some scenarios, be accurately reproduced by effective pairwise models fitted to the same abundance time series. Consequently, higher-order interactions cannot, in general, be inferred from time-series data alone. We further identify a fundamental problem of mechanistic identifiability, whereby distinct interaction mechanisms generate nearly indistinguishable dynamics, potentially leading to accurate yet misleading ecological interpretations. Our results highlight the need to complement time-series data with additional ecological information to infer interaction structure reliably.

q-bio.PE↗

arXiv2026

Group dynamics shape contagion onsets and multistable active phases under collective reinforcement

Group-based reinforcement can induce discontinuous transitions from inactive to active phases in higher-order contagion models. However, these results are typically obtained on static interaction structures or within mean-field approximations that neglect temporal changes in group composition. Here, we show that group dynamics is not a secondary effect but a central aspect that determines the macroscopic transition class of higher-order contagion processes. We develop an analytically tractable approximate master equation model that effectively interpolates between quenched and mean-field limits through a group composition turnover rate. Our results reveal the rich impact of time-varying structures: it can induce discontinuous phase transition, broaden the bistable region, and at the same time promote or suppress contagion near criticality. Moreover, when real-world turnover rates and group-size heterogeneity are taken into account, the system exhibits a qualitatively richer phase diagram with four distinct dynamical phases, combining continuous or discontinuous transitions with localized or delocalized activity. In localized regimes, we uncover multistable active phases with multiple coexisting active states, which are observed in neither the annealed nor the quenched limits, and extend classical absorbing-active bistability. Finally, we demonstrate that the emergence of discontinuous transitions in real-world systems requires stronger nonlinear reinforcement than previously thought, indicating that simulations in static structures can yield qualitatively misleading predictions.

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arXiv2025

Ideological polarization in static networks: A multidimensional approach for opinion alignment

Polarization, defined as the emergence of sharply divided groups with opposing and often extreme views, is an increasingly prominent feature of modern societies. While many studies analyze this phenomenon in the context of single issues, such as public opinion on abortion or immigration, this approach overlooks that political and social attitudes rarely develop in isolation. Instead, many issues are interconnected, shaped by overarching ideological frameworks that guide interpretations and position-taking across multiple topics. These frameworks produce coherent yet polarized worldviews that reinforce group boundaries. In this work, we propose and study a multi-topic opinion dynamics model that captures these interdependencies. Each issue is represented as a separate dimension in a shared opinion space, allowing us to model not only attitudes toward individual topics but also the structure of ideological alignment across them. A central feature is topic correlation, which enables us to explore how polarization emerges when opinions on one issue influence attitudes on others. The model also incorporates homophily, a mechanism where individuals are more likely to interact with those similar to themselves. We analyze the asymptotic behavior of the model by identifying its most relevant fixed points, supported by theoretical analysis and numerical simulations. We then examine how the multidimensional opinion space shapes the emergence and stability of polarization, and apply the model to empirical data from the American National Election Studies, interpreting observed opinion patterns within our framework.

physics.soc-ph↗

arXiv2025

Explosive adoption of corrupt behaviors in social systems with higher-order interactions

Human behaviors in social systems are often shaped by group pressure and collective norms, especially since the rise of social media platforms. However, in the context of adopting misbehaviors, most existing contagion models rely on pairwise interactions and thus fail to capture group-level dynamics. To fill this gap, we introduce a higher-order extension of the Honesty-Corruption-Ostracism (HCO) model to study the emergence of systemic corruption in populations where individuals interact through group structures. The model incorporates contagion-like transitions mediated by hyperedges of arbitrary order, capturing the influence of peer pressure in group settings. Analytical and numerical results show that higher-order interactions induce discontinuous (explosive) transitions between fully honest and fully corrupt regimes, separated by a bistable phase. This abrupt behavior disappears in the pairwise limit, highlighting the destabilizing effect of group interactions. Furthermore, we establish a general correspondence between our model and broader classes of social contagion dynamics with symmetry breaking, recovering previous results as limiting cases. These findings underscore the critical role of higher-order structure in shaping behavioral adoption processes and the stability of social systems.

physics.soc-ph↗

arXiv2025

Social polarization promoted by sparse higher-order interactions

Many social interactions are group-based, yet their role in social polarization remains largely unexplored. To bridge this gap here we introduce a higher-order framework that takes into account both group interactions and homophily. We find that group interactions can strongly enhance polarization in sparse systems by limiting agents' exposure to dissenting views. Conversely, they can suppress polarization in fully connected societies, an effect that intensifies as the group size increases. Our results highlight that polarization depends not only on the homophily strength but also on the structure and microscopic arrangement of group interactions.

physics.soc-ph↗

arXiv2025

Genotype networks drive oscillating endemicity and epidemic trajectories in viral evolution

Rapidly evolving viruses use antigenic drift as a key mechanism to evade host immunity and persist in real populations. While traditional models of antigenic drift and epidemic spread rely on low-dimensional antigenic spaces, genomic surveillance data reveal that viral evolution produces complex antigenic genotype networks with hierarchical modular structures. In this study, we present an eco-evolutionary framework in which viral evolution and population immunity dynamics are shaped by the structure of antigenic genotype networks. Using synthetic networks, we demonstrate that network topology alone can drive transitions between stable endemic states and recurrent seasonal epidemics. Furthermore, our results show how the integration of the genotype network of the H3N2 influenza in our model allows for estimating the emergence times of various haplotypes resulting from its evolution. Our findings underscore the critical role of the topology of genotype networks in shaping epidemic behavior and, besides, provide a robust framework for integrating real-world genomic data into predictive epidemic models.

q-bio.PE↗

arXiv2025

In itinere infections covertly undermine localized epidemic control in metapopulations

Metapopulation models have traditionally assessed epidemic dynamics by emphasizing local in situ interactions within defined subpopulations, often neglecting transmission occurring during mobility phases in itinere. Here, we extend the Movement-Interaction-Return (MIR) metapopulation framework to explicitly include contagions acquired during transit, considering agents traveling along shared transportation networks. We reveal that incorporating in itinere contagion entails a notable reduction of the epidemic threshold and a pronounced delocalization of the epidemic trajectory, particularly significant in early-stage outbreaks.

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arXiv2025

Hyperedge Overlap drives Synchronizability of Systems with Higher-Order interactions

The microscopic organization of dynamical systems coupled via higher-order interactions plays a pivotal role in understanding their collective behavior. In this paper, we introduce a framework for systematically investigating the impact of the interaction structure on dynamical processes. Specifically, we develop an hyperedge overlap matrix whose elements characterize the two main aspects of the microscopic organization of higher-order interactions: the inter-order hyperedge overlap (non-diagonal matrix elements) and the intra-order hyperedge overlap (encapsulated in the diagonal elements). This way, the first set of terms quantifies the extent of superposition of nodes among hyperedges of different orders, while the second focuses on the number of nodes in common between hyperedges of the same order. Our findings indicate that large values of both types of hyperedge overlap hinder synchronization stability, and that the larger is the order of interactions involved, the more important is their role. Our findings also indicate that the two types of overlap have qualitatively distinct effects on the dynamics of coupled chaotic oscillators. In particular, large values of intra-order hyperedge overlap hamper synchronization by favoring the presence of disconnected sets of hyperedges, while large values of inter-order hyperedge overlap hinder synchronization by increasing the number of shared nodes between groups converging on different trajectories, without necessarily causing disconnected sets of hyperedges.

physics.soc-ph↗

arXiv2024

Balancing Mobility Behaviors to avoid Global epidemics from Local Outbreaks

Human interactions and mobility shape epidemic dynamics by facilitating disease outbreaks and their spatial spread across regions. Traditional models often isolate commuting and random mobility as separate behaviors, focusing either on short, recurrent trips or on random, exploratory movements. Here, we propose a unified formalism that allows a smooth transition between commuting and exploratory behavior based on travel and return probabilities. We derive an analytical expression for the epidemic threshold, revealing a non-monotonic dependence on recurrence rates: while recurrence tends to lower the threshold by increasing agent concentration in high-contact hubs, it counterintuitively raises the invasion threshold in low-mobility scenarios, suggesting that allowing recurrence may foster local outbreaks while suppressing global epidemics. These results provide a comprehensive understanding of the interplay between human mobility patterns and epidemic spread, with implications for containment strategies in structured populations.

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arXiv2024

Quantum-like approaches unveil the intrinsic limits of predictability in compartmental models

Obtaining accurate forecasts for the evolution of epidemic outbreaks from deterministic compartmental models represents a major theoretical challenge. Recently, it has been shown that these models typically exhibit trajectories' degeneracy, as different sets of epidemiological parameters yield comparable predictions at early stages of the outbreak but disparate future epidemic scenarios. Here we use the Doi-Peliti approach and extend the classical deterministic SIS and SIR models to a quantum-like formalism to explore whether the uncertainty of epidemic forecasts is also shaped by the stochastic nature of epidemic processes. This approach allows getting a probabilistic ensemble of trajectories, revealing that epidemic uncertainty is not uniform across time, being maximal around the epidemic peak and vanishing at both early and very late stages of the outbreak. Our results therefore show that, independently of the models' complexity, the stochasticity of contagion and recover processes poses a natural constraint for the uncertainty of epidemic forecasts.

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arXiv2024

Evolutionary game selection creates cooperative environments

The emergence of collective cooperation in competitive environments is a well-known phenomenon in biology, economics, and social systems. While most evolutionary game models focus on the evolution of strategies for a fixed game, how strategic decisions coevolve with the environment has so far mostly been overlooked. Here, we consider a game selection model where not only the strategies but also the game can change over time following evolutionary principles. Our results show that coevolutionary dynamics of games and strategies can induce novel collective phenomena, fostering the emergence of cooperative environments. When the model is taken on structured populations the architecture of the interaction network can significantly amplify pro-social behavior, with a critical role played by network heterogeneity and the presence of clustered groups of similar players, distinctive features observed in real-world populations. By unveiling the link between the evolution of strategies and games for different structured populations, our model sheds new light on the origin of social dilemmas ubiquitously observed in real-world social systems.

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arXiv2024

Human behavior-driven epidemic surveillance in urban landscapes

We introduce a surveillance strategy specifically designed for urban areas to enhance preparedness and response to disease outbreaks by leveraging the unique characteristics of human behavior within urban contexts. By integrating data on individual residences and travel patterns, we construct a Mixing matrix that facilitates the identification of critical pathways that ease pathogen transmission across urban landscapes enabling targeted testing strategies. Our approach not only enhances public health systems' ability to provide early epidemiological alerts but also underscores the variability in strategy effectiveness based on urban layout. We prove the feasibility of our mobility-informed policies by mapping essential mobility flows to major transit stations, showing that few resources focused on specific stations yields a more effective surveillance than non-targeted approaches. This study emphasizes the critical role of integrating human behavioral patterns into epidemic management strategies to improve the preparedness and resilience of major cities against future outbreaks.

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arXiv2024

Emergence of innovations in networked populations with reputation-driven interactions

In this work we analyze how reputation-based interactions influence the emergence of innovations. To do so, we make use of a dynamic model that mimics the discovery process by which, at each time step, a pair of individuals meet and merge their knowledge to eventually result in a novel technology of higher value. The way in which these pairs are brought together is found to be crucial for achieving the highest technological level. Our results show that when the influence of reputation is weak or moderate, it induces an acceleration of the discovery process with respect to the neutral case (purely random coupling). However, an excess of reputation is clearly detrimental, because it leads to an excessive concentration of knowledge in a small set of people, which prevents a diversification of the technologies discovered and, in addition, leads to societies in which a majority of individuals lack technical capabilities.

physics.soc-ph↗

arXiv2023

Polarized opinion states in static networks driven by limited information horizons

The widespread emergence of opinion polarization is often attributed to the rise of social media and the internet, which can promote selective exposure and the formation of echo chambers. However, experimental evidence shows that exposure to opposing views through cross-cutting ties is common in both online and offline social contexts, which frequently involve long-standing personal relationships. To account for these facts, we have developed an opinion model that applies to static contact structures, in which an agent's influence over their neighbors depends on their similarity in opinion. Our findings suggest that polarization can emerge in such static structures and, driven by an increased narrow-mindedness, even in presence of non-negligible cross-cutting ties. The polarized opinion distributions generated by our model closely resemble those obtained in surveys about highly polarized issues, allowing us to categorize various issues based on their controversial nature.

physics.soc-ph↗

arXiv2023

Pathways to explosive transitions in interacting contagion dynamics

Yet often neglected, dynamical interdependencies between concomitant contagion processes can alter their intrinsic equilibria and bifurcations. A particular case of interest for disease control is the emergence of explosive transitions in epidemic dynamics coming from their interactions with other simultaneous processes. To address this problem, here we propose a framework coupling a standard epidemic dynamics with another contagion process, presenting a tunable parameter shaping the nature of its transitions. Our model retrieves well-known results in the literature, such as the existence of first-order transitions arising from the mutual cooperation of epidemics or the onset of explosive transitions when social contagions unidirectionally drive epidemics. We also reveal that negative feedback loops between simultaneous dynamical processes might suppress explosive phenomena, thus increasing systems robustness against external perturbations. Our results render a general perspective towards finding different pathways to explosive phenomena from the interaction of contagion processes.

physics.soc-ph↗

arXiv2023

Hyperedge overlap drives explosive collective behaviors in systems with higher-order interactions

Recent studies have shown that novel collective behaviors emerge in complex systems due to the presence of higher-order interactions. However, how the collective behavior of a system is influenced by the microscopic organization of its higher-order interactions remains still unexplored. In this Letter, we introduce a way to quantify the overlap among the hyperedges of a higher-order network, and we show that real-world systems exhibit different levels of hyperedge overlap. We then study models of complex contagion and synchronization of phase oscillators, finding that hyperedge overlap plays a universal role in determining the collective dynamics of very different systems. Our results demostrate that the presence of higher-order interactions alone does not guarantee abrupt transitions. Rather, explosivity and bistability require a microscopic organization of the structure with a low value of hyperedge overlap.

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