arXiv subjects

Yamir Moreno

Publications and source records attributed to Yamir Moreno.

At least 19 recordsLinked to original sources

arXiv2026

Peer Review at Capacity: An editor's view

From the editor's desk, the pressure on peer review is no longer an abstract concern. It appears in the growing number of invitations needed to secure an appropriate report, the repeated reliance on a small group of dependable colleagues, and the duplication of work when manuscripts move between journals. The problem is not simply that science has too few potential reviewers. It is that reviewing labor is unevenly distributed, weakly recognized, and organized through a publication architecture built for a smaller system.

physics.soc-ph↗

arXiv2026

Bursty Arrivals, Smooth Sojourns: Non-Poissonian Temporal Dynamics in a Logistics Warehouse

Warehouses are central nodes in logistics networks: they buffer material flows, synchronize heterogeneous actors, and absorb temporal mismatches between inbound and outbound operations. Yet most warehouse analyses still rely on aggregate performance indicators or on queueing assumptions in which event timing is stationary and approximately memoryless. Here we use one month of high-resolution pallet-level data from a large Spanish warehouse to characterize arrivals, departures, and outbound residence times from a statistical-physics perspective. Inter-arrival and inter-departure times are strongly heterogeneous and compatible with heavy-tailed, non-Poissonian behavior, whereas outbound sojourn times are more naturally described by a log-normal distribution, suggesting constrained service mechanisms with a characteristic operational scale. Disaggregation by logistics flow reveals systematic differences in burstiness, memory, and distributional similarity. A renewal-based aging analysis uncovers recurrent weekly accumulation and clearance cycles in the outbound buffer zone. Finally, a Little's-Law-inspired activity--sojourn scaling identifies two operational regimes: a near-linear baseline under regular turnover and a reproducible off-baseline branch associated with weekend accumulation and Monday dispatches. These results provide a compact diagnostic framework for temporal complexity in warehouse operations and show how limited but high-resolution industrial data can reveal operational structure invisible to aggregate throughput statistics.

physics.soc-ph↗

arXiv2026

The Informational Cost of Structure: Representational Complexity in Networked Dynamical Systems

How much information is required to represent a dynamical system in terms of an interaction structure and an evolution rule? We address this question using algorithmic information theory. We introduce Representational Complexity, the excess description length of a structure-plus-rule model relative to the shortest possible description of the dynamics itself. This intrinsic description defines a universal lower bound: no exact structural representation can be more concise. If arbitrary rules are allowed, graphs, hypergraphs, and other formalisms can all reach this bound by shifting information between structure and dynamics, so expressiveness alone cannot distinguish them. Meaningful differences arise only when scientific modeling restricts the admissible structures and rules. Within this setting, we identify conditions under which graph and hypergraph descriptions are informationally equivalent, and show how graph-preferred, hypergraph-preferred, and mixed regimes can emerge when those conditions are relaxed. Because Kolmogorov complexity is not computable, we complement the formal results with explicit description-length estimates. Our framework reframes the choice of network representation as a question of informational cost and mechanistic transparency rather than universal expressive power.

cs.IT↗

arXiv2026

Collective cooperation without individual fidelity in LLM agents

Large language models (LLMs) are increasingly used as agents in simulations of social systems, yet it remains unclear when their behavior can be interpreted as a faithful proxy for human decision-making. Here we test LLM agents against a direct empirical benchmark: a large-scale networked Prisoner's Dilemma experiment with human participants. Using the same interaction protocol, payoff structure, and network topologies, we compare nine open-weight LLMs with the human data. The selected model reproduces several macro-level features of cooperation dynamics, including the early decline and later stabilization of cooperation. This aggregate agreement, however, does not extend uniformly to finer levels of behavior. LLM populations underestimate individual-level heterogeneity and generate conditional cooperation patterns that differ from those observed in humans. Adding a fraction of random agents improves some aspects of micro-level agreement, but does not remove the mismatch in decision rules. These findings reveal a macro--micro dissociation in LLM-based social agents: collective outcomes can appear human-like even when the underlying behavioral distributions and mechanisms are not. They suggest that validating LLM agents as human surrogates requires comparisons across aggregate dynamics, individual heterogeneity, and context-dependent decision rules, rather than outcome-level agreement alone.

physics.soc-ph↗

arXiv2026

The social consequences of AI delegation

A substantial body of recent work has debated whether large language models (LLMs) can serve as substitutes for human participants in behavioural research. This debate, however, captures only one direction of a rapidly changing relationship. The more consequential question is not simply whether researchers should use LLMs as human surrogates, but whether - and under what conditions - humans are beginning to use LLMs as surrogates for their own deliberation. Across domains including health, law, finance, education, and personal guidance, increasing numbers of people consult generative AI systems before, alongside, or instead of human experts, peers, or independent judgment. Although evidence for actual delegation remains uneven, this uncertainty makes the phenomenon an urgent social-scientific object of study. We argue for a research programme that treats LLMs as consequential social actors in a functional sense: systems whose outputs shape human decisions, social norms, and collective dynamics.

physics.soc-ph↗

arXiv2026

Nesting Controls Phase Transitions in Higher-Order Contagion

The organization of higher-order interactions plays a central role in shaping collective dynamics, yet a general structural principle governing contagion on hypergraphs remains lacking. Here we introduce a nesting coefficient that quantifies how lower-order interactions are embedded within higher-order ones, defining a continuum between simplicial complexes and random hypergraphs. Using a higher-order susceptible-infected-susceptible model, we show that increasing nesting lowers the activation threshold and suppresses discontinuous transitions, while weak embedding favors explosive behavior. We further demonstrate that correlations between nesting and interaction order modulate the onset of activity while only weakly affecting transition discontinuity. Analysis of synthetic and empirical networks reveals that nesting strongly predicts hysteresis, establishing it as a key structural determinant of phase transitions in higher-order systems.

physics.soc-ph↗

arXiv2026

Diversification of global food trade partners increased inequalities in the exposure to shock risks

Recent global food trade disruptions have evidenced how local shocks can cascade into global security threats. While the capacity of food systems to absorb spillovers depends heavily on its underlying trade networks, few studies quantify how their temporal evolution reshapes systemic vulnerability over time. Here, we evaluate how changes in global connectivity from 1986 to 2022 reshaped responses to production shocks. Using FAO data, we built yearly multiplex representations of the food trade system and quantified robustness through a stochastic shock-propagation model with dynamic export bans. We find that while increasing globalization intensified inter-dependencies and amplified cascades, robustness trends remain heterogeneous. Grain trade has become more decentralized and resilient to targeted shocks; conversely, Animal and Vegetable Fats exhibit growing centralization and fragility around key exporters like Indonesia and Malaysia. These structural transformations caused diverging shifts in systemic vulnerability, disproportionately threatening already vulnerable regions such as Africa and Southern Asia.

physics.soc-ph↗

arXiv2026

Evolutionary vaccination dynamics under higher-order reinforcement pressure

Vaccination games in higher-order settings remain underexplored, despite their importance in shaping opinions and collective decisions. Here, we introduce a parsimonious behavioral-epidemiological model to evaluate how peer reinforcement pressure influences vaccination uptake. The framework consists of a two-layer multiplex: an epidemic layer governed by the SIR process on a square lattice, and a behavioral layer represented by a hypergraph of triadic interactions. Individuals update their vaccination strategy via imitation, modulated by a reinforcement parameter $\alpha$ when peer support is present. We find that higher-order structure alone induces clusters of vaccinated individuals that act as protective barriers. Low but nonzero reinforcement ($\alpha \approx 0.5$) maximizes coverage and suppresses outbreaks, while both negligible ($\alpha \approx 0$) and moderate ($\alpha > 0.1$) reinforcement reduce uptake, as excessive confirmation lowers adaptability and enables non-vaccinators to re-emerge. Our work bridges complex contagion theory with evolutionary game dynamics, offering insights into how contact structure and peer reinforcement jointly shape vaccination behavior.

physics.soc-ph↗

arXiv2026

Effective Graph Resistance as Cumulative Heat Dissipation

Effective graph resistance is a fundamental structural metric in network science, widely used to quantify global connectivity, compare network architectures, and assess robustness in flow-based systems. Despite its importance, current formulations rely mainly on spectral or pseudo-inverse Laplacian representations, offering limited physical insight into how structural features shape this quantity or how it can be efficiently optimized. Here, we establish an exact and physically transparent relationship between effective graph resistance and the cumulative heat dissipation generated by Laplacian diffusion dynamics. We show that the total heat dissipated during relaxation to equilibrium precisely equals the effective graph resistance. This dynamical viewpoint uncovers a natural multi-scale decomposition of the Laplacian spectrum: early-time dissipation is governed by degree-based local structure, intermediate times isolate eigenvalues below the spectral mean, and long times are dominated by the algebraic connectivity. These multi-scale properties yield continuous and interpretable strategies for modifying network structure and constructing optimized ensembles, enabling improvements that are otherwise NP-hard to achieve via combinatorial methods. Our results unify structural and dynamical perspectives on network connectivity and provide new tools for analyzing, comparing, and optimizing complex networks across domains.

physics.soc-ph↗

arXiv2025

Restoring Network Evolution from Static Structure

The dynamical evolution of complex networks underpins the structure-function relationships in natural and artificial systems. Yet, restoring a network's formation from a single static snapshot remains challenging. Here, we present a transferable machine learning framework that infers network evolutionary trajectories solely from present topology. By integrating graph neural networks with transformers, our approach unlocks a latent temporal dimension directly from the static topology. Evaluated across diverse domains, the framework achieves high transfer accuracy of up to 95.3%, demonstrating its robustness and transferability. Applied to the Drosophila brain connectome, it restores the formation times of over 2.6 million neural connections, revealing that early-forming links support essential behaviors such as mating and foraging, whereas later-forming connections underpin complex sensory and social functions. These results demonstrate that a substantial fraction of evolutionary information is encoded within static network architecture, offering a powerful, general tool for elucidating the hidden temporal dynamics of complex systems.

physics.soc-ph↗

arXiv2025

Multilayer network science: theory, methods, and applications

Multilayer network science has emerged as a central framework for analysing interconnected and interdependent complex systems. Its relevance has grown substantially with the increasing availability of rich, heterogeneous data, which makes it possible to uncover and exploit the inherently multilayered organisation of many real-world networks. In this review, we summarise recent developments in the field. On the theoretical and methodological front, we outline core concepts and survey advances in community detection, dynamical processes, temporal networks, higher-order interactions, and machine-learning-based approaches. On the application side, we discuss progress across diverse domains, including interdependent infrastructures, spreading dynamics, computational social science, economic and financial systems, ecological and climate networks, science-of-science studies, network medicine, and network neuroscience. We conclude with a forward-looking perspective, emphasizing the need for standardised datasets and software, deeper integration of temporal and higher-order structures, and a transition toward genuinely predictive models of complex systems.

physics.soc-ph↗

arXiv2025

Edge-based reputation promotes cooperation in simplicial complexes

Understanding how cooperation emerges and persists is a central challenge in the evolutionary dynamics of social and biological systems. Most prior studies have examined cooperation through pairwise interactions, yet real-world interactions often involve groups and higher-order structures. Reputation is a key mechanism for guiding strategic behavior in such contexts, but its role in higher-order networks remains underexplored. In this study, we introduce an edge-based reputation mechanism, incorporating both direct and indirect reputation, to investigate the evolution of cooperation in simplicial complexes. Our results show that coupling reputation mechanisms with higher-order network structures strongly promotes cooperation, with direct reputation exerting a stronger influence than indirect reputation. Moreover, we reveal a nonlinear interplay between network topology and reputation mechanisms, highlighting how multi-level structures shape collective outcomes. These findings provide a novel theoretical framework for understanding cooperation in complex social systems.

physics.soc-ph↗

arXiv2025

Directionality-induced jamming in multiplex networks

We study diffusion on multiplex networks with directed interlayer couplings. We demonstrate both numerically and analytically that even with undirected layers, interlayer directionality alone reproduces superdiffusion and the prime regime. We further reveal a new phenomenon, the directionality-induced jamming, whereby directed interlayer links hinder diffusion, fragmenting the system into dynamically disconnected components and preventing convergence to the steady state of the diffusion process. Via an optimization process, we show that this new regime is attainable in both toy models and real-world topologies. These findings underscore the crucial role of interlayer link directionality in shaping the emergent behavior of multiplex systems, with potential implications for the design and control of such systems.

physics.soc-ph↗

arXiv2025

Efficient Gillespie algorithms for spreading phenomena in large and heterogeneous higher-order networks

Higher-order interactions, where groups of nodes interact collectively rather than pairwisely, are central to many complex systems, from neural and ecological networks to social contagion. However, simulating dynamical processes on such higher-order structures remains computationally challenging due to the combinatorial growth of possible interactions. Here, we develop efficient and statistically exact Gillespie algorithms for Markovian spreading dynamics on large and heterogeneous hypergraphs. By incorporating phantom processes $-$events that advance time without altering the system's state$-$, we drastically reduce the computational complexity of standard algorithms ($\mathcal{O}(N^2)$), achieving up to linear scaling with system size. Relying on the susceptible-infected-susceptible model with critical mass thresholds as a benchmark, we show that the optimized algorithms outperform standard approaches by several orders of magnitude, enabling simulations of networks with millions of nodes and broad heterogeneity in both degree and interaction order. Efficient sampling methods, needed to overcome the bottlenecks imposed by either a high maximum order or number of interactions, and other dynamical processes on higher-order networks are tackled. These results establish a general framework for scalable, continuous-time simulations of higher-order contagion and related dynamical processes.

physics.soc-ph↗

arXiv2025

Contest Dynamics Between Cooperation and Exploitation

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.

physics.soc-ph↗

arXiv2025

Identifying Central Nodes in Multiplex Networks by Embracing Layer-Specific Heterogeneity via DomiRank

The robustness and resilience of complex systems are crucial for maintaining functionality amid disruptions or intentional attacks. Many such systems can be modeled as networks, where identifying structurally central nodes is essential for assessing their robustness and susceptibility to failure. Traditional centrality metrics often face challenges in identifying structurally important nodes in networks exhibiting heterogeneity at the network scale, with multilayer networks being a prime example of such networks. These metrics typically fail to balance the trade-off between capturing local layer-specific structures and integrating global multiplex connectivity. In this study, we extend DomiRank centrality, a metric that has been shown to effectively assess nodal importance across diverse monoplex topologies, to multiplex networks. Our approach combines layer-specific DomiRank calculations with a global contextualization step, incorporating multiplex-wide DomiRank scores to combine rankings. Through synthetic and real-world network studies, we demonstrate that our generalized DomiRank framework significantly improves the identification of key nodes in highly heterogeneous multiplex networks. This work advances centrality-based robustness assessments by addressing the fundamental trade-off between layer adaptability and multiplex-wide coherence.

physics.soc-ph↗

arXiv2025

Rumor propagation on hypergraphs

The rapid spread of information and rumors through social media platforms, especially in group settings, motivates the need for more sophisticated models of rumor propagation. Traditional pairwise models do not account for group interactions, a limitation that we address by proposing a higher-order rumor model based on hypergraphs. Our model incorporates a group-based annihilation mechanism, where a spreader becomes a stifler when the fraction of hyperedges aware of the rumor exceeds a threshold. Our model has two distinct subcritical behaviors: exponential and power-law decay, which can coexist depending on the heterogeneity of the hypergraph. Interestingly, our analysis reveals continuous phase transitions in both homogeneous and heterogeneous hypergraphs, challenging the idea that higher-order interactions lead to discontinuous transitions. Finally, we validate our model using empirical data from Telegram and email cascades, providing additional evidence that real-world rumor propagation tends to occur near criticality. These results open the door to a more detailed understanding of rumor dynamics in higher-order systems.

physics.soc-ph↗

arXiv2025

Predicting the critical behavior of complex dynamic systems via learning the governing mechanisms

Critical points separate distinct dynamical regimes of complex systems, often delimiting functional or macroscopic phases in which the system operates. However, the long-term prediction of critical regimes and behaviors is challenging given the narrow set of parameters from which they emerge. Here, we propose a framework to learn the rules that govern the dynamic processes of a system. The learned governing rules further refine and guide the representative learning of neural networks from a series of dynamic graphs. This combination enables knowledge-based prediction for the critical behaviors of dynamical networked systems. We evaluate the performance of our framework in predicting two typical critical behaviors in spreading dynamics on various synthetic and real-world networks. Our results show that governing rules can be learned effectively and significantly improve prediction accuracy. Our framework demonstrates a scenario for facilitating the representability of deep neural networks through learning the underlying mechanism, which aims to steer applications for predicting complex behavior that learnable physical rules can drive.

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