arXiv subjects

Alfonso de Miguel-Arribas

Publications and source records attributed to Alfonso de Miguel-Arribas.

8 recordsLinked to original sources

arXiv2026

Competition between evidence-based and alternative medical paradigms in non-infectious diseases

Standard epidemiological models coupled with evolutionary game theory (e.g., vaccination games) typically rely on the free-rider dilemma inherent to infectious diseases. In this paper, we present a coupled behavior-disease model where the underlying disease is non-infectious, and transmission occurs strictly at the cultural level. We model the competition between two medical paradigms, namely evidence-based medicine ($M$) and alternative medicine ($A$), using replicator dynamics driven by treatment-specific social-amplification coefficients. We show analytically that the nonlinearity of the coupled system arises from social feedback rather than from the underlying biological dynamics. Crucially, the stability analysis reveals that the system lacks isolated fixed points; instead, it converges to a one-dimensional continuous manifold of non-hyperbolic neutral equilibria, generating persistent path dependence in the cultural composition. Furthermore, we derive an explicit constant of motion that constrains each trajectory to a distinct invariant level set. Restricting this invariant to the equilibrium manifold yields a strictly monotonic selection function, uniquely determining the equilibrium selected from any given initial state. Our findings show that derivative-based cultural feedback is sufficient to generate a continuum of coexisting health paradigms, while asymmetric social amplification modifies this selection. Because transient interventions that directly alter paradigm adoption break this invariant, they shift the system to a different level set, producing persistent changes in the long-run cultural composition. The model thus reveals how societies retain a deterministic memory of prior health and treatment states, demonstrating that both misinformation shocks and targeted public health interventions can leave lasting structural changes long after the perturbation has ended.

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

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 $α$ 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 ($α\approx 0.5$) maximizes coverage and suppresses outbreaks, while both negligible ($α\approx 0$) and moderate ($α> 0.1$) 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↗

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

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

Co-evolution of cooperation and resource allocation in the advantageous environment-based spatial multi-game using adaptive control

In real-life complex systems, individuals often encounter multiple social dilemmas that cannot be effectively captured using a single-game model. Furthermore, the environment and limited resources both play a crucial role in shaping individuals' decision-making behaviors. In this study, we employ an adaptive control mechanism by which agents may benefit from their environment, thus redefining their individual fitness. Under this setting, a detailed examination of the co-evolution of individual strategies and resource allocation is carried. Through extensive simulations, we find that the advantageous environment mechanism not only significantly increases the proportion of cooperators in the system but also influences the resource distribution among individuals. Additionally, limited resources reinforce cooperative behaviors within the system while shaping the evolutionary dynamics and strategic interactions across different dilemmas. Once the system reaches equilibrium, resource distribution becomes highly imbalanced. To promote fairer resource allocation, we introduce a minimum resource guarantee mechanism. Our results show that this mechanism not only reduces disparities in resource distribution across the entire system and among individuals in different dilemmas but also significantly enhances cooperative behavior in higher resource intervals. Finally, to assess the robustness of our model, we further examine the influence of the advantageous environment on system-wide cooperation in small-world and random graph network models.

physics.soc-ph↗

arXiv2024

Interplay of epidemic spreading and vaccine uptake under complex social contagion

Modeling human behavior is essential to accurately predict epidemic spread, with behaviors like vaccine hesitancy complicating control efforts. While epidemic spread is often treated as a simple contagion, vaccine uptake may follow complex contagion dynamics, where individuals' decisions depend on multiple social contacts. Recently, the concept of complex contagion has received strong theoretical underpinnings thanks to the generalization of spreading phenomena from pairwise to higher-order interactions. Although several potential applications have been suggested, examples of complex contagions motivated by real data remain scarce. Surveys on COVID-19 vaccine hesitancy in the US suggest that vaccination attitudes may indeed depend on the vaccination status of social peers, aligning with complex contagion principles. In this work, we examine the interactions between epidemic spread, vaccination, and vaccine uptake attitudes under complex contagion. Using the SIR model with a dynamic, threshold-based vaccination campaign, we simulate scenarios on an age-structured multilayer network informed by US contact data. Our results offer insights into the role of social dynamics in shaping vaccination behavior and epidemic outcomes.

physics.soc-ph↗

arXiv2024

Prediction of social dilemmas in networked populations via graph neural networks

Human behavior presents significant challenges for data-driven approaches and machine learning, particularly in modeling the emergent and complex dynamics observed in social dilemmas. These challenges complicate the accurate prediction of strategic decision-making in structured populations, which is crucial for advancing our understanding of collective behavior. In this work, we introduce a novel approach to predicting high-dimensional collective behavior in structured populations engaged in social dilemmas. We propose a new feature extraction methodology, Topological Marginal Information Feature Extraction (TMIFE), which captures agent-level information over time. Leveraging TMIFE, we employ a graph neural network to encode networked dynamics and predict evolutionary outcomes under various social dilemma scenarios. Our approach is validated through numerical simulations and transfer learning, demonstrating its robustness and predictive accuracy. Furthermore, results from a Prisoner's Dilemma experiment involving human participants confirm that our method reliably predicts the macroscopic fraction of cooperation. These findings underscore the complexity of predicting high-dimensional behavior in structured populations and highlight the potential of graph-based machine learning techniques for this task.

physics.soc-ph↗