arXiv subjects

Romualdo Pastor-Satorras

Publications and source records attributed to Romualdo Pastor-Satorras.

At least 19 recordsLinked to original sources

arXiv2026

Habituation determines spatial position in Hyphessobrycon herbertaxelrodi shoals through altered rules of interaction

Habituation plays a key role in shaping the collective behavior of moving animal groups, yet the effects of variation in habituation within groups are unknown. Here, we merge two subgroups of fish with different levels of habituation and quantitatively analyze their movement. Non-habituated fish (not familiar with their swimming environment) tend to occupy more central positions within the group and position themselves closer to neighbors, consistent with the selfish herd hypothesis. This behavior appears to be driven by adjusted effective local interactions with nearby individuals. Compared to more habituated fish, these individuals also exhibit greater coordination, shorter burst-and-coast dynamics, more efficient information transmission, and a stronger tendency to adopt follower roles. To validate the findings, we develop a machine learning tool that successfully classifies the subgroup identity of individual fish. Our study demonstrates how differences in local-scale inter-individual interactions can drive spatial assortment within groups, based on heterogeneity in their manipulated exposure to the experimental setup.

physics.bio-ph↗

arXiv2026

Phase synchronization of fish schools through spatial gaps

We investigate how sensory coupling mediates phase coherence between spatially separated, confined active systems. Using two fish schools separated by a water-filled transparent gap, we isolate vision as the primary interaction channel. Under boundary confinement, each school's center-of-mass heading exhibits noisy bistable dynamics, spontaneously switching between two antiparallel directions along the partition wall. Turning events in one school bias the orientation of the other, driving distance-dependent phase synchronization. We model this behavior using two coupled stochastic bistable oscillators whose exact solution quantitatively reproduces the empirical results. Our work establishes a precise connection between nonequilibrium active systems and coupled stochastic oscillators, offering insight into boundary-spanning information transfer in living collectives.

cond-mat.other↗

arXiv2026

Modeling individual attention dynamics on online social media

In the attention economy, understanding how individuals manage limited attention is critical. We introduce a simple model describing the decay of a user's engagement when facing multiple inputs. We analytically show that individual attention decay is determined by the overall duration of interactions, not their number or user activity. Our model is validated using data from Reddit's Change My View subreddit, where the user's attention dynamics is explicitly traceable. Despite its simplicity, our model offers a crucial microscopic perspective complementing macroscopic studies.

physics.soc-ph↗

arXiv2026

Data-Driven Stochastic Modeling of Schooling Fish: From Collective Dynamics to Individual Fluctuations

Collective motion in animal groups emerges from the interplay between individual variability and social coordination, yet connecting these scales quantitatively has remained a major challenge.Using high-resolution trajectories of schooling fish, we infer a data-driven stochastic framework that reproduces with remarkable accuracy the behavior of real fish schools. We decompose motion into two coupled components: the dynamics of the school's center of mass (or centroid), modeled as an active Brownian particle confined by the tank, and individual motions relative to that center, described by stochastic equations with data-inferred mean-field potentials and multiplicative noise. Simulations of these equations produce synthetic schools that quantitatively match real ones across multiple observables, including burst-and-coast dynamics, polarization, and spatial cohesion. This minimal, predictive framework bridges experiment and theory, showing that the collective dynamics of animal groups can be faithfully reconstructed from first principles directly from data.

cond-mat.other↗

arXiv2025

Group size effects and collective misalignment in LLM multi-agent systems

Multi-agent systems of large language models (LLMs) are rapidly expanding across domains, introducing dynamics not captured by single-agent evaluations. Yet, existing work has mostly contrasted the behavior of a single agent with that of a collective of fixed size, leaving open a central question: how does group size shape dynamics? Here, we move beyond this dichotomy and systematically explore outcomes across the full range of group sizes. We focus on multi-agent misalignment, building on recent evidence that interacting LLMs playing a simple coordination game can generate collective biases absent in individual models. First, we show that collective bias is a deeper phenomenon than previously assessed: interaction can amplify individual biases, introduce new ones, or override model-level preferences. Second, we demonstrate that group size affects the dynamics in a non-linear way, revealing model-dependent dynamical regimes. Finally, we develop a mean-field analytical approach and show that, above a critical population size, simulations converge to deterministic predictions that expose the basins of attraction of competing equilibria. These findings establish group size as a key driver of multi-agent dynamics and highlight the need to consider population-level effects when deploying LLM-based systems at scale.

cs.MA↗

arXiv2025

Symmetry breaking in collective decision-making through higher-order interactions

Collective decision-making is a widespread phenomenon in both biological and artificial systems, where individuals reach a consensus through social interactions. While traditional models of opinion dynamics and contagion focus on pairwise interactions, recent research emphasizes the importance of including higher-order group interactions and autonomous behavior to better reflect real-world complexity. In this work, we introduce a collective decision-making model inspired by social insects. In our framework, uncommitted agents can explore options independently and become committed, while social interactions influence these agents to prefer options already accepted by the group. Our model extends classical contagion models by incorporating multiple, mutually exclusive options and distinguishing between pairwise and higher-order social influences. Using simulations and analytical mean-field solutions, we show that higher-order interactions are essential for breaking symmetry in systems with equally valid options. We find that pairwise communication alone can cause decision deadlock, but adding group interactions allows the system to overcome stalemates and reach consensus. Our results emphasize the important roles of autonomous behavior and higher-order structures in collective decision-making. These insights could help us better understand social systems and design decision protocols for artificial swarms.

physics.soc-ph↗

arXiv2025

Non-linear inhibitory responses enhance performance in collective decision-making

The precise modulation of activity through inhibitory signals ensures that both insect colonies and neural circuits operate efficiently and adaptively, highlighting the fundamental importance of inhibition in biological systems. Modulatory signals are produced in various contexts and are known for subtly shifting the probability of receiver behaviors based on response thresholds. Here we propose a non-linear function to introduce inhibitory responsiveness in collective decision-making inspired by honeybee house-hunting. We show that, compared with usual linear functions, non-linear responses enhance final consensus and reduce deliberation time. This improvement comes at the cost of reduced accuracy in identifying the best option. Nonetheless, for value-based tasks, the benefits of faster consensus and enhanced decision-making might outweigh this drawback.

cond-mat.dis-nn↗

arXiv2025

Charting multidimensional ideological polarization across demographic groups in the United States

Has ideological polarization actually increased in the last decades, or have voters simply sorted themselves into parties matching their ideology more closely? We present a novel methodology to quantify multidimensional ideological polarization, by embedding the respondents to a wide variety of political, social, and economic topics from the American National Election Studies (ANES) into a two-dimensional ideological space. By identifying several demographic attributes of the ANES respondents, we chart how political and socio-economic groups move through the ideological space in time. We observe that income and especially racial groups align into parties, but their ideological distance has not increased over time. Instead, Democrats and Republicans have become ideologically more distant in the last 30 years: Both parties moved away from the center, at different rates. Furthermore, Democratic voters have become ideologically more heterogeneous after 2010, indicating that partisan sorting has declined in the last decade.

physics.soc-ph↗

arXiv2025

Social Network Heterogeneity Promotes Depolarization of Multidimensional Correlated Opinions

We propose the multidimensional social compass model, based on two competing key ingredients: DeGroot learning, driven by the social influence exerted across multiple topics, and the preference of individuals to maintain their initial opinions. The interplay between these two mechanisms triggers a phase transition from polarization to consensus, determined by a threshold value of social influence. We analytically study the nature of the depolarization transition and its threshold depending on the number of topics discussed, the possible correlations between initial opinions, the topology of the underlying social networks, and the correlations between the initial opinion distribution and the network's structure. Theoretical predictions are validated by running numerical simulations on both synthetic and real social networks. We rely on several simplifying assumptions to explore different scenarios, such as a mean-field approximation for high dimension, or orthogonal initial orientations. We uncover an upper critical dimension (D_c = 5 topics) for uncorrelated initial opinions, distinguishing between discontinuous and continuous phase transitions. For the simplest D = 2 case and correlated initial opinions, we found that the depolarization threshold can vanish if the underlying connectivity is heterogeneous, as predicted by perturbation theory. Such an effect is due to the presence of hubs, which promote consensus in the population. We test this hypothesis by designing a rewiring algorithm that increases the structural heterogeneity of the underlying network, showing that the depolarization threshold decreases. Finally, we demonstrate that if hubs share the same initial opinion, the depolarization dynamics is significantly hindered.

physics.soc-ph↗

arXiv2025

Leveraging spurious Omori-Utsu relation in the nearest-neighbor declustering method

Static and dynamic stress changes in the Earth's crust induced by an earthquake typically trigger other earthquakes. Identifying such aftershocks is an important step in seismic hazard assessment but has remained challenging, especially in cases involving natural fluid migration or anthropogenic fluid injections, which can occur with varying time scales and/or episodically, leading to strong temporal variations in earthquake occurrences. Here, we demonstrate analytically and numerically that earthquake catalogs without triggering can lead to spurious Omori-Utsu and productivity relations for the commonly used nearest-neighbor declustering method. However, we show that the robustness of the Omori-Utsu exponent on newly introduced parameters of the method allows one to determine whether indeed aftershocks are present. For a natural swarm catalog, which lacks a clear distinction between triggered and background events, we find that the catalog is dominated by aftershocks.

physics.geo-ph↗

arXiv2024

Competition between simple and complex contagion on temporal networks

Behavioral adoptions are influenced by peers in different ways. While some individuals may change after a single incoming influence, others need multiple cumulated attempts. These two mechanism, known as the simple and the complex contagions, often occur together in social phenomena alongside personal factors determining individual adoptions. Here we aim to identify which of these contagion mechanism dominate a spreading process propagated by time-varying interactions. We consider three types of spreading scenarios: ones pre-dominated by simple or complex contagion, and mixed dynamics where the dominant mechanism changes during the unfolding of the spreading process. We propose different methods to analytically identify the transitions between these three scenarios and compare them with numerical simulations. This work offers new insights into social contagion dynamics on temporal networks, without assuming prior knowledge about individual's contagion mechanism driving their adoption decisions.

physics.soc-ph↗

arXiv2024

Signatures of criticality in turning avalanches of schooling fish

Moving animal groups transmit information through propagating waves or behavioral cascades, exhibiting characteristics akin to systems near a critical point from statistical physics. Using data from freely swimming schooling fish in an experimental tank, we investigate spontaneous behavioral cascades involving turning avalanches, where large directional shifts propagate across the group. We analyze several avalanche metrics and provide a detailed picture of the dynamics associated to turning avalanches, employing tools from avalanche behavior in condensed matter physics and seismology. Our results identify power-law distributions and robust scale-free behaviour through data collapses and scaling relationships, confirming a necessary condition for criticality in fish schools. We explore the biological function of turning avalanches and link them to collective decision-making processes in selecting a new movement direction for the school. We report relevant boundary effects arising from interactions with the tank walls and influential roles of boundary individuals. Finally, spatial and temporal correlations in avalanches are explored using the concept of aftershocks from seismology, revealing clustering of avalanche events below a designated timescale and an Omori law with a faster decay rate than observed in earthquakes.

physics.bio-ph↗

arXiv2024

Selective social interactions and speed-induced leadership in schooling fish

Animals moving together in groups are believed to interact among each other with effective social forces, such as attraction, repulsion and alignment. Such forces can be inferred using 'force maps', i.e. by analysing the dependency of the acceleration of a focal individual on relevant variables. Here we introduce a force map technique suitable for the analysis of the alignment forces experienced by individuals. After validating it using an agent-based model, we apply the force map to experimental data of schooling fish. We observe signatures of an effective alignment force with faster neighbours, and an unexpected anti-alignment with slower neighbours. Instead of an explicit anti-alignment behaviour, we suggest that the observed pattern is the result of a selective attention mechanism, where fish pay less attention to slower neighbours. This mechanism implies the existence of temporal leadership interactions based on relative speeds between neighbours. We present support for this hypothesis both from agent-based modelling, as well as from exploring leader-follower relationships in the experimental data.

physics.bio-ph↗

arXiv2024

Cumulative Merging Percolation: A long-range percolation process in networks

Percolation on networks is a common framework to model a wide range of processes, from cascading failures to epidemic spreading. Standard percolation assumes short-range interactions, implying that nodes can merge into clusters only if they are nearest-neighbors. Cumulative Merging Percolation (CMP) is an new percolation process that assumes long-range interactions, such that nodes can merge into clusters even if they are topologically distant. Hence in CMP percolation clusters do not coincide with the topological connected components of the network. Previous work has shown that a specific formulation of CMP features peculiar mechanisms for the formation of the giant cluster, and allows to model different network dynamics such as recurrent epidemic processes. Here we develop a more general formulation of CMP in terms of the functional form of the cluster interaction range, showing an even richer phase transition scenario with competition of different mechanisms resulting in crossover phenomena. Our analytic predictions are confirmed by numerical simulations.

cond-mat.stat-mech↗

arXiv2023

Modeling Explosive Opinion Depolarization in Interdependent Topics

Understanding the dynamics of opinion depolarization is pivotal to reducing the political divide in our society. We propose an opinion dynamics model, which we name the social compass model, for interdependent topics represented in a polar space, where zealots holding extreme opinions are less prone to change their minds. We analytically show that the phase transition from polarization to consensus, as a function of increasing social influence, is explosive if topics are not correlated. We validate our theoretical framework through extensive numerical simulations and recover explosive depolarization also by using initial opinions from the American National Election Studies, including polarized and interdependent topics.

physics.soc-ph↗

arXiv2022

Scale-free behavioral cascades and effective leadership in schooling fish

Behavioral contagion and the presence of behavioral cascades are natural features in groups of animals showing collective motion, such as schooling fish or grazing herbivores. Here we study empirical behavioral cascades observed in fish schools defined as avalanches of consecutive large changes in the heading direction of the trajectory of fish. In terms of a minimum turning angle introduced to define a large change, avalanches are characterized by distributions of size and duration showing scale-free signatures, reminiscent of self-organized critical behavior. We observe that avalanches are generally triggered by a small number of fish, which act as effective leaders that induce large rearrangements of the group's trajectory. This observation motivates the proposal of a simple model, based in the classical Vicsek model of collective motion, in which a given individual acts as a leader subject to random heading reorientations. The model reproduces qualitatively the empirical avalanche behavior observed in real schools, and hints towards a connection between effective leadership and avalanche behavior in collective movement.

nlin.AO↗

arXiv2022

The advantage of self-protecting interventions in mitigating epidemic circulation at the community level

Protecting interventions of many types (both pharmaceutical and non-pharmaceutical) can be deployed against the spreading of a communicable disease, as the worldwide COVID-19 pandemic has dramatically shown. Here we investigate in detail the effects at the population level of interventions that provide an asymmetric protection between the people involved in a single interaction. Masks of different filtration types, either protecting mainly the wearer or the contacts of the wearer, are a prominent example of these interventions. By means of analytical calculations and extensive simulations of simple epidemic models on networks, we show that interventions protecting more efficiently the adopter (e.g the mask wearer) are more effective than interventions protecting primarily the contacts of the adopter in reducing the prevalence of the disease and the number of concurrently infected individuals ("flattening the curve"). This observation is backed up by the study of a more realistic epidemic model on an empirical network representing the patterns of contacts in the city of Portland. Our results point out that promoting wearer-protecting face masks and other self-protecting interventions, though deemed selfish and inefficient, can actually be a better strategy to efficiently curtail pandemic spreading.

physics.soc-ph↗

arXiv2022

Flocking dynamics mediated by weighted social networks

We study the effects of animal social networks with a weighted pattern of interactions on the flocking transition exhibited by models of self-organized collective motion. Considering a model representing dynamics on a one-dimensional substrate, application of a heterogeneous mean-field theory provides a phase diagram as function of the heterogeneity of the network connections and the correlations between weights and degree. In this diagram we observe two phases, one corresponding to the presence of a transition and other to a transition suppressed in an always ordered system, already observed in the non-weighted case. Interestingly, a third phase, with no transition in an always disordered state, is also obtained. These predictions, numerically recovered in computer simulations, are also fulfilled for the more realistic Vicsek model, with movement in a two-dimensional space. Additionally, we observe at finite network sizes the presence of a maximum threshold for particular weight configurations, indicating that it is possible to tune weights to achieve a maximum resilience to noise effects. Simulations in real weighted animal social networks show that, in general, the presence of weights diminishes the value of the flocking threshold, thus increasing the fragility of the flocking state. The shift in the threshold is observed to depend on the heterogeneity of the weight pattern.

physics.soc-ph↗