Papers

Many complex systems involve group interactions, which can readily be represented by higher-order structures such as hypergraphs. However, when observing a system's dynamical evolution, establishing whether that evolution actually results from higher-order interactions, or whether pairwise interactions suffice to explain the data, remains a challenge. Even in the simple case of a temporal sequence of nodes visited during a diffusion process, such a task is intricate. Indeed, group interactions leave their traces as memory on the observed nodes, while path dependence in a purely pairwise process can mimic that same footprint without any group structure. To resolve this, we propose an inference pipeline based on a hidden Markov model (HMM) that tests, directly from an observed sequence of nodes carrying no group labels, whether higher-order interactions are required to describe the dynamics. The HMM latent states represent higher-order interactions, seen as hyperedges of an unobserved hypergraph, so that their detection becomes a model selection problem between the HMM class and a class of null models without higher-order interactions. We first validate this approach on synthetic node sequences generated from random walks on graphs and hypergraphs, showing that it recovers both the number and the composition of the underlying hyperedges, while correctly rejecting them when the generating process is pairwise. As a second step, we consider a validation based on empirical data, namely empirical trajectories of ball possession in football matches. This application shows that the pipeline goes beyond providing statistical evidence of the presence of higher-order interactions, and yields interpretable underlying structures: the recovered hyperedges map directly onto recognizable tactical team formations.

This dissertation discusses the distribution of city sizes using censuses of the Brazilian Institute for Geography and Statistics (IBGE) spanning 150 years, from 1872 to 2022, as well as the allometric scaling relation with the 2022 census data. The Gompertz distribution is fitted to the population size of less populated cities, while the Zipf power law is used to represent the cities having larger populations, with threshold varying between 20,216 and 102,857 inhabitants. The data analysis shows that the Gompertz-Zipf distribution can describe the data across all censuses, with the Gompertz parameter $B$ oscillating between $-2.39\times10^{-5}$ and $-1.49\times10^{-5}$, indicating periods with faster decreases and periods with slower decreases in the number of cities within the less populated segment as population increases, while the Zipf exponent ranges from $a=3.61\pm0.09$ in 1872 to $a=2.28\pm0.02$ in 2022. The Gini coefficient $G$ and the Zanardi index $Z$ are used to quantify the inequality in the city population distribution, whose obtained values are $G=0.439$ and $Z=-0.002$ for the 1872 census and $G=0.733$ and $Z=0.018$ for the 2022 dataset, while the Pietra index $p$ and the Kolkata index $k$, associated with the most populated cities, are $p=31.893\%$ and $k=65.894\%$ for 1872, and $p=57.395\%$ and $k=78.342\%$ for 2022. These results reveal a high concentration of population in larger cities, with $G$ increasing from 1872 to 2022 and $Z$ becoming positive after 1900, indicating a greater concentration of population in the group of more populated cities than in the group of less populated cities. Finally, urban allometry is also examined, generating an exponent $b=0.835$ for the 2022 data, showing that the scaling relation between area and population holds for urbanized area but not for total administrative area, indicating that scaling is associated with spatial occupation.

A group does not usually consult all its members before every decision. This paper develops a quantitative framework for measuring how widely members are consulted in decision making within a group. Two kinds of records are considered: the number of members consulted for each decision and the number of decisions in which each member is consulted. From these records, two participation indices are defined, and their equality is proved analytically. A third index measures the fraction of decisions for which at least a specified percentage of members are consulted. The distribution of consultation levels is described using a discrete maximum-entropy model from statistical physics. Tail probabilities are used to obtain bounds for the main participation index. This study examines aggregate survey data from two groups. Their observed characteristics are broadly consistent with the patterns found across the full survey. Using binomial and Poisson calculations, the future probability that all members will be consulted for a decision is examined. Inequality in consultation among members is examined through Lorenz curves, the Gini coefficient and a normalised Shannon participation measure. The member-decision structure is represented as a bipartite network. Controlled simulations show that the same overall level of consultation can have different distributions among the members. The framework measures both the overall breadth of consultation and how consultation is distributed among the members. It therefore provides a quantitative sociophysical description of one measurable aspect of democracy in decision making within a group.

Multiplayer social dilemmas on hypergraphs show a variety of emergent phenomena, which has been attributed to group interactions. Here we test whether pairwise dynamics can reproduce them as well. We derive the mesoscopic Langevin equation of a testbed model and find the operator content of absorbing phase transitions: a directed-percolation line, a tricritical point, and a first-order region whose spinodal is the explosive transition. In our analysis, three-player games only set the values of the Landau coefficients. We construct a strictly pairwise model with short-lived memory, in which every event involves at most two entities, that reproduces these coefficients term by term and has exactly the same mean-field fixed points. Simulations confirm that the two dynamics share mean-field critical and tricritical scaling and the size of the critical mass, and that a purely three-player game on a two-dimensional triangular lattice lies in the directed-percolation class. For these games, the explosive transition and the critical mass therefore do not require group interactions, and the critical behavior is that of the pairwise theory.

Social norms are a fundamental component of society, shaping how we communicate, cooperate, and avoid conflict. Despite their ubiquitous presence and profound influence on behaviour, questions remain about how they spread from person to person and whether they are adopted. Using an evolutionary game-theoretic model, we study whether simple or complex contagion processes characterise how social norms spread. Crucially, contagion type is endogenous and emerges from the model. We also vary cultural conformism, the cost of following a norm, and network topology to assess the generality of our results. We show that social norms in our model can spread through simple contagion when a new norm is low in cost and cultural conformism is low, while norms spread through complex contagion when the new norm is costly and cultural conformism is high.

When an agent leaves a spatial network, its replacement removes old contacts and may transport population to a new location. The frequency of replacement alone therefore does not determine how turnover changes spatial organization. We study diffusing agents joined by persistent attractive contacts, with the total pull on each agent bounded. At fixed population, successors appear at the departure position, within a surrounding disk, or anywhere in the system. We ask whether small differences between dense and dilute regions grow or fade. The theory separates an exactly calculable diffusion and replacement contribution from an approximate attraction contribution obtained by averaging the contact network. Paired simulations find similar attraction contributions under the three rules at all nine sampled wavelengths. Changing only the replacement location reverses the broadest tested pattern: it grows with replacement at the departure position but fades under both displaced rules. An independent radius scan traces this change from growth to decay. Under displaced replacement, intermediate wavelengths grow between sampled long and short scales with decay. Rates measured in the earliest finite window are compatible with the prediction at long and intermediate wavelengths; discrepancies are already present at short wavelengths. Replacement distance thus helps determine which spatial density contrasts are initially amplified.

Climate-intensified hurricanes disrupt urban mobility, yet whether recovery is equitable across the rural-urban gradient is unclear. Analyzing device-based mobility for Hurricanes Helene and Milton, we find local activity restarted within approximately 4.5 days in both storms, whereas reconnection to the outside world took 5 days in coastal-urban Florida but 11 in inland-rural Appalachia. The gap lies in external reconnection, not local restart, implying planners should target rural isolation.

Simple aggregation of independent responses ("wisdom of crowds") generally improves collective performance through statistical facilitation, but interaction among members can sometimes hinder group outcomes. However, under certain circumstances, coordination studies show that interacting groups can outperform their individual members and nominal groups, and interaction can also be more efficient than independence. We present a reservoir computing model to investigate how interaction can enhance joint performance in terms of accuracy and efficiency. Echo state networks were trained independently to identify Japanese speakers from vowel recordings and tested jointly with and without coupled feedback. Model pairs reproduced the dyadic advantage reported by Bahrami et al. (2010) with interaction via pooled-feedback, but not when independent responses were aggregated together. Model dynamics revealed that interaction was beneficial whereas aggregated outputs were prone to diverging from runaway feedback over time. In terms of efficiency, larger groups of smaller interacting models outperformed smaller groups of larger models while equating for total amount of group computation. Our results offer a formal framework for explaining when and why interaction enhances collective intelligence.

Urban venues such as restaurants, shops, bars and parks host the daily life of a city, and their collective rise and fall signals how vibrant a neighbourhood is. The usual assumption is that a venue's fortunes follow its neighbourhood's, so that how wealthy an area is and how many people live there will decide a venue's performance. Yet whether there is a common performance trajectory shared by venues over time is underexplored. In this study, we use 5.5 years of Foursquare data for about 10^4 venues in London and Barcelona. We found six distinguishable shapes recur in both cities, that are (1) Fall, (2) Rise, (3) Rise & Fall, (4) Steady, (5) Ephemeral (an early hit that loses popularity fast) and (6) Underdogs (a slow starter that gains popularity late). However, no recovery shape appears, that is neither city yields a U-shaped fall-and-rise group. Additionally, we found that deprivation and population density are not indicative which shape a venue follows. We also discovered that how early a shape can be recognized depends on when its distinctive segment occurs, not a shape's complexity. Proximity between venues, however, does predict how well-performing a venue is. We uncovered a mixing pattern where venues of the same shape tend to locate together more often than chance within roughly 200 to 600 metres. Finally, our qualitative reading of two contrasting districts, one rising in London and one falling in Barcelona, show that comparable socioeconomic conditions but divergent gentrification histories can generate vastly different popularity trajectories. All in all, our results altogether point at that urban vibrancy emerges at the block- and street- level, and social and historical contexts matter in terms of the characteristics of such patterns.

Artificial intelligence (AI) increasingly participates in communication, decision-making, and collective action, making the social organisation of human-machine interaction central to Hybrid Intelligence (HI). This chapter reviews the behavioural sociology of humans and machines through an HI lens, examining how interaction and feedback shape the combination of human and artificial capabilities. Drawing on sociology, social psychology, human-computer interaction, organisational studies, collective intelligence, and science and technology studies, it traces processes from individual social perception to cooperation in hybrid groups and organisational and institutional arrangements. Particular attention is given to how machine participation alters familiar mechanisms: social categorisation attaches expectations to designed identities, reciprocity may involve unequal social obligations, and algorithmic recommendations acquire authority through organisational practices. The chapter distinguishes observable machine behaviour from human interpretations of intention, understanding, and moral responsibility. It also considers how behavioural effects can extend beyond direct human-machine encounters, while separating established findings from proposed longer-term consequences. It argues that the benefits of HI depend on how roles, incentives, reliance, and accountability are organised, and identifies priorities for empirical research on these evolving sociotechnical arrangements.

The second quantum revolution is a revolution of information. Advances in quantum computation, communication, and sensing offer new capabilities while raising questions about how quantum infrastructures will be organised, accessed, and governed. We argue that their significance for governance lies not in transforming political principles, but in how their technical and infrastructural requirements may affect existing relationships of authority, responsibility, legitimacy, and power. This paper develops a quantum governance lens that connects governance scholarship with quantum information science, examining interactions between technical design, institutional arrangements, and infrastructural dependencies. We apply this lens to five areas: quantum computation, quantum key distribution, Quantum Byzantine Agreement, blind quantum computation, and dual-use quantum sensing. These applications illustrate how technical capabilities may influence access, trust, privacy, oversight, and accountability, while demonstrating that technical guarantees cannot resolve institutional questions independently. We conclude that quantum technologies do not necessarily introduce unique governance challenges or require entirely new governance frameworks. Rather, they provide a context in which established questions of legitimacy, accountability, and equity must be reconsidered in relation to emerging technological capabilities and institutional choices.

Pedestrians in a crowd largely walk in small groups, dyads above all, and modelling how these groups arrange themselves is a step towards realistic models of the crowd as a whole. A dyad walks side by side in a dilute crowd and one behind the other in a dense one, an abreast to in-file transition recently measured in real life across the full range of naturally occurring crowd conditions (van der Laan and Corbetta, PLOS ONE 2026). The cost-function models of dyad formation by Zanlungo and coworkers (Phys. Rev. E 2014; EPL 2015) hold the pair abreast and have no in-file minimum at any finite density. Building on them, we propose and analyse a qualitative model in which the transition appears as an effective density parameter grows: the two formations become minima that coexist over a window of densities and exchange stability within it. We quantify this transition by computing the Orientation Log-Odds $\Pi_\text{OLO}$ as predicted by the model. This paper is a first step towards quantitative models of dyads and of their formation transitions.

Some voters conceal their opinions, causing a mismatch between visible and private opinions in polls. Conventional voter models do not account for such concealment. Here, we introduce a shy voter model that incorporates a hidden state in addition to two visible states, as an extension of the conventional majority-rule voter model. The model exhibits continuous and hybrid transitions, yielding a rich phase diagram organized by the hidden fraction, i.e., the fraction of voters in the hidden state. For a large hidden fraction, the system undergoes a hybrid transition from the ordered state to the symmetric (disordered) state, whereas for a small hidden fraction, it exhibits a continuous transition, with a tricritical point separating the two regimes. Near criticality, both the order-parameter variance and the susceptibility diverge. The hidden fraction also governs the entropy flux, a quantity that is not directly observable in real-world situations. Thus, opinion concealment qualitatively reshapes opinion dynamics, while the present framework provides a basis for further extensions toward more realistic forms of social interactions.

Learning agents, whether humans or algorithms, adapt to one another rather than to a fixed environment. Selfish learning need not produce a good outcome for the group, especially when learners face a social dilemma. The iterated prisoner's dilemma (IPD) is the classical example, but it is usually studied in populations of agents who imitate successful strategies. Here, we instead study selfish gradient ascent learners on the two-dimensional space of reactive strategies. A reactive strategy is described by two continuous traits: reciprocity, the chance of cooperating after the co-player cooperates, and forgiveness, the chance of cooperating after the co-player defects. Each learner updates its strategy in the direction that most rapidly improves its payoff against all other learners, by an amount governed by its learning rate. We show that heterogeneity in learning rates can qualitatively change the long-term outcome: a population with uniform learning rates converges to always-defect (ALLD), whereas a population with variable learning rates approaches generous-tit-for-tat (GTFT), which produces nearly optimal collective payoffs. In simulations, every population passes through a transient state of minimal forgiveness, in which the distribution of reciprocity determines the outcome. Variable learning rates promote cooperation because slow learners preserve high reciprocity until the population reaches this state. We prove sufficient conditions on the distribution of reciprocity for convergence to ALLD or to tit-for-tat (TFT). Variable learning rates also allow cooperation to recover from perturbation, which is provably impossible under uniform learning. These results suggest that purely selfish and myopic learning can produce persistent cooperation provided the population has intrinsic variability in learning rates.

Invasive alien species (IAS) generate substantial ecological and economic damages that often transcend political boundaries, creating strong incentives for coordinated management. We develop a two-region ecological-economic model of IAS control that integrates prevention and eradication measures with population dynamics characterized by logistic growth and an Allee effect. We derive analytical results on the interaction between ecological thresholds and strategic behaviour and compare outcomes under cooperation (central planner) and non-cooperation (open loop Nash equilibrium and Markovian Nash equilibrium). We show that, under realistic control costs, neither prevention nor eradication can eliminate invasive populations in finite time, and complete prevention of cross-border spread is generally not optimal. In the absence of an Allee effect, the zero-population equilibrium is unstable, implying long-run persistence of the invasive species. By contrast, the presence of an Allee effect fundamentally alters system dynamics by making extinction locally stable and creating a basin of attraction within which eradication becomes feasible. This finding highlights the economic significance of ecological thresholds and demonstrates how delays in management can shift populations beyond critical levels, after which eradication is unlikely regardless of subsequent control efforts. Numerical simulations further show that cooperative management expands the set of initial conditions leading to eradication and consistently reduces aggregate social costs relative to decentralized management. The results provide a formal rationale for rapid intervention, precautionary regulation, and stronger multilateral cooperation in IAS management, particularly in jurisdictions such as the European Union where listing and control actions may be delayed pending evidence of harm.

Belief propagation in social networks depends on how trust and distrust jointly shape interpersonal influence. We develop a network model that treats trust and distrust as distinct but statistically coupled quantities. Their joint values determine the net trust and uncertainty of each relationship, and a neighbor can influence an agent only when both threshold conditions are satisfied. We show that the correlation between trust and distrust reshapes the transition between consensus and fragmentation by changing the availability of influential neighbors. Network connectivity also affects this transition, whereas degree heterogeneity has a more limited effect when networks have comparable mean degree. Moreover, the availability of influential neighbors alone does not determine the final consensus: correlations between relational properties and network degree can select agents with different degree compositions, altering the direction and magnitude of collective belief. Finally, we introduce a comfort index that quantifies the expected availability of influential neighbors and analytically links the joint trust-distrust structure to collective dynamics. Our results establish a mechanistic connection between relational structure, effective social influence, and emergent collective belief states.

The scientific discovery of dark matter has been pursued in two very different methodological and epistemic contexts. A microphysical context centered around the laboratory detection of a new particle, and a macrophysical context based on the astronomical identification of a non-luminous gravitating component. The justification for the salience of methodological diversity is that these different methods are complementary ways to discovering aspects of the same entity. The weakly interacting massive particle (WIMP) made this expectation scientifically credible by connecting a particle candidate to a cosmological abundance, experimental searches, and explanations of astronomical phenomena. What happens to this connection when the WIMP loses its central position? I analyze 201,627 NASA Astrophysics Data System records to examine the organization of research following the decline of WIMP prominence. Astrophysics and high-energy physics remain jointly dominant, while WIMP-related mentions decline in both fields and successor candidates receive different degrees of attention. Concept networks show that WIMP-related research remains central across the literature, even as cross-field connections become less concentrated around it. These indicators measure research attention; they do not establish what scientists would accept as a discovery. I interpret the findings through a framework distinguishing constitutive identification, cosmic attribution, explanatory adequacy, and the cross-domain identification needed to connect them. A particle, compact-object population, or gravitational theory can resolve different parts of the inherited problem, and a partial or multicomponent discovery can leave other parts open. 'Diverging paths' describes this branching of possible resolutions.

Disinformation on online social media has been on the rise in the past years. While there is extensive literature modelling social cascades, online disinformation cascades show distinctive characteristics: they provoke debunking reactions, they spread over networks with heterogeneous connectivity, and they compete for the limited attention of users — which recommendation algorithms may partly offset. We propose a model including these ingredients and study it through Monte–Carlo simulations and mean–field approximations, on random graphs and an empirical Twitter network. We find that debunking reactions are double–edged: they reduce the reach of appealing items but allow unappealing ones to reach audiences they would not otherwise reach. Degree heterogeneity makes cascades trigger at a much lower content appeal but limits their final reach: heterogeneous networks are easy to ignite yet hard to saturate. Limited attention cancels the effects from degree heterogeneity, although the mediation of recommendation algorithms may restore them.

The collective-risk dilemma has become a paradigm for studying cooperation in a wide variety of social experiments, initiatives that generate increasingly rich datasets describing individual actions over successive decision-making processes. Beyond the purely descriptive analysis of such empirical datasets, a methodological framework is required to interpret and reproduce the observed dynamics, a scheme that may be skewed by exogenous biases. Here, we introduce a data-driven, non-parametric method for simulating individual decisions together with a validation protocol for assessing the fidelity level of the outcomes they generate. Our methodological contribution is based on an empirical heuristic ensemble: agents are represented by a finite set of behavioural heuristics, probabilities conditioned on different observable features of the game state, endowed with a hard-voting arbitration that determines their per-round final decisions. This reproducible generative framework allows for behavioural inference in different collective-risk dilemma setups: Behavioural heuristics are seeded directly from empirical data assessing conditional probabilities, without a priori assumptions; a plurality vote with uniform random tie-breaking resolves between competing heuristics each round, adding no free parameters; validation follows a pattern-oriented approach, comparing synthetic and empirical macro-indicators against single-heuristic and uniform-random baselines; synthetic data generated allow exploration of scenarios or experimental setups that have not been empirically realised; as a worked example, we instantiate our methodological approach on the xAire citizen science collective-risk dilemma experiment run in Barcelona.

The two main traditional player statistics used in soccer are goals scored and assists, with an assist credited when a player's pass immediately leads to a teammate scoring. Attributing credit solely to the player who makes the final pass before a goal fails to account for the contributions of multiple players in the buildup to the goal. Consequently, traditional statistics provide only a limited view of player involvement. In this paper, we use multivariate Hawkes processes (MHPs) to model timestamped sequences of passes and shots, which form continuous-time networks. MHPs provide a branching mechanism where an event (a pass or shot) can be generated as a child of a previous event, which allows us to quantify each pass's contribution to a subsequent pass or shot. This approach distributes credit across multiple passes within an attacking sequence, making it applicable across a wide range of situations. Building on this framework, we propose expected goal involvements (xGI), a metric that measures a player's involvement in creating goal scoring opportunities based on their passes. This metric identifies players who play key roles in creating goal scoring opportunities even if they do not make the final pass, offering a more comprehensive measure of attacking involvement and player performance.

Social influence, the tendency of our social environment to sway our beliefs and behaviors, is often cited as a driver of population-level trends in opinions or beliefs. Many quantitative theories of social influence assume that an agent interacts with its entire social environment. This assumption is often unrealistic. For example, users of online platforms typically see posts from only a small subset of the accounts they follow at any given time. We introduce a simple probabilistic agent-based model of social influence that explicitly models unobserved attention mechanisms. The first of these latent mechanisms is selection: each agent interacts with only one other agent at a time from its social environment. The second is retention: the agent chooses whether or not to ignore the opinion of the other. Our model admits an efficient maximum-likelihood algorithm which allows us to fit it to opinion traces even when these mechanisms are unobserved. On synthetic data, omitting either latent mechanism reduces the overall strength of social influence governed by the inferred interaction kernel, which hides the evidence for the underlying nonlinearity of the social influence. We study a selection of previously analyzed empirical datasets and curate several new ones. We show that models that include the latent mechanisms are often more predictive on these datasets; indeed, they typically improve prediction far more than the choice of interaction kernel does. Modeling latent attention increases held-out predictive likelihood on 14 of these 15 datasets. Each mechanism changes the size of the inferred kernel in a predictable direction, while its effect on the kernel's shape depends on the data.

Frustrated Ising models of social networks mix ferromagnetic (agreement-seeking) and antiferromagnetic (antagonistic) couplings, and the antagonistic bonds are almost always placed independently at a fixed rate, yielding a +/-J spin glass. Real signed networks (Epinions, Slashdot, Wikipedia) instead show an excess of balanced triads, and perfectly balanced placement is gauge-equivalent to an unfrustrated ferromagnet. We compare three sign-placement conditions on a frustrated random-field Ising model of a polarised population, sharing an identical graph, field assignment, and edge-weight magnitudes: an independent baseline; a balance-structured condition whose camp membership coincides with each voter's field sign; and the same condition with camp labels randomly permuted, so triadic balance is identical by construction while the camp-field correlation is removed. On a confound-controlled Kolmogorov-Smirnov test of the sampled energy-gap distribution (simulated annealing), the aligned condition differs from the baseline at every density tested (p = 7.3e-8), while the permuted condition shows no significant difference (paired mean difference 0.003, 95% CI [-0.041, 0.046]). Field-camp alignment, not triadic balance alone, drives the effect. The qualitative direction follows from a known mapping; the contribution is a quantitative, confound-controlled measurement of the effect's size away from that limit, robust to parallel-tempering sampling, system sizes up to N = 300, and structural noise calibrated to real networks, while triadic balance alone produces no detectable effect at N = 90 and at most a small one at N = 180-300 (paired change in KS distance about 0.04, 95% CI upper bound below 0.09).

Structural measures such as the clustering coefficient and the average shortest-path length characterise how a network is organised. These measures are typically formulated for real, non-negative edge weights. A class of quantum and photonic architectures has complex edge weights instead, whose phases determine whether alternative routes interfere constructively or destructively. These architectures are also bipartite, so triangles are absent and the smallest closed cycle is a four-node square. Existing measures address complex weights and bipartite structure separately: bipartite clustering coefficients quantify clustering through four-node squares but do not contain phase information, while interferometric coefficients retain phase but are defined on triangles. In this work, we define a clustering coefficient for complex-weighted bipartite networks which reduces to the classical bipartite coefficient when the phases vanish, becomes negative when alternative routes cancel, and can be obtained for all nodes from a single sparse matrix product. We show that the phase accumulated around a square is the smallest gauge-invariant carrier of structural phase information in a bipartite network. The clustering coefficient factorises into a topological contribution and a phase contribution, making a bipartite Watts–Strogatz ensemble analytically tractable. For phases uniformly distributed on $[-\Delta,\Delta]$, the mean phase contribution is $(\sin\Delta/\Delta)^4$, independent of node, degree, and topology. We also derive closed-form expressions for the clustering coefficient, open-path visibility, and phase variance. We numerically verify these predictions and further show that phase disorder increases coherent distance.

Is the relaxation of a modular network dictated by its modules or by the network that connects them? The answer fixes the time scales of diffusion, consensus, and synchronization, all set by the Fiedler eigenvalue. We show that, for bundled networks, the two levels act one after the other: a random walker must first escape from its module and then spread over the network that connects them. The equilibration time is the sum of these two times, the time needed to reach the base from inside a fiber and the relaxation time of the base, slowed down by the mass of the fibers. The consequences are unexpected. However large the modules are, a small-world core always imposes its own Fiedler dimension; the modules survive only as a logarithmic correction, which slows down equilibration and hides the true exponent up to sizes far beyond any real network. Effective dimensions measured on finite modular systems can therefore be systematically biased. We analytically work out all combinations of finite-dimensional and small-world bases and fibers, and compare them with numerically exact spectra of representatives of each category.

It is well recognised that field weighted citations are approximately lognormally distributed. This skewness reflects competition for attention, whereby most publications are gradually forgotten and only a minority continue to be recalled years later. Exploiting this observation, we construct a researcher-level indicator based on authorship of top 10% cited papers in All Science Journal Code (ASJC) bins and apply it to publications affiliated with Irish institutions over a typical funding cycle. Restricting attention largely to original research papers, we find that researchers authoring strictly more than one ASJC top-decile paper in a given year exhibit persistent participation in the highly cited tail of the literature in subsequent years, thus the simple track record indicator possesses predictive value. In contrast, the median researcher in cohorts of Research Ireland and European Research Council awardees selected through qualitative peer review either authors no top-decile paper or less than the median researcher in our ranking. These results demonstrate that research excellence as judged by qualitative peer review and influence on the research literature identify substantially different cohorts of researchers. In the absence of evidence, funding bodies and universities may conflate these two. While research reputation may be enhanced through many channels, highly cited papers constitute the most widely observable manifestation of international scholarly influence. Our analysis indicates that literature influence may be improved through greater use of data in track record pre-screening prior to qualitative peer review.

Stop-and-go waves pose a persistent challenge in traffic flow theory, significantly compromising highway safety, operational efficiency, and environmental sustainability. This paper mathematically derives an optimal cooperative driving policy based on the premise that connected vehicles can cooperate and assume distinct roles. In doing so, we depart from the classical assumption of uniform vehicular behavior. Through a rigorous optimization formulation constrained by linear stability conditions, it is analytically proved that the traffic system's throughput is globally maximized when a single designated "buffer" vehicle maintains an enlarged headway to absorb perturbations, enabling the remaining vehicles to form a tightly spaced, high-capacity platoon. This analytically derived optimum is confirmed via numerical spectral analysis and stability-constrained flow optimization using the Intelligent Driver Model and the Full Velocity Difference Model. The results demonstrate that this non-uniform cooperative buffering strategy yields dual benefits. It radically expands the string-stable parameter space to actively dissipate SGWs, while simultaneously achieving significantly higher traffic flow compared to both unbuffered traffic and non-cooperative Jam-Absorption Driving strategies.

The second-order Kuramoto model captures rotor dynamics relevant to synchronization in power grids. In actual power grids, violation of angle-stability limits can trigger protective actions that are not included in the model, restricting its validity to trajectories that remain within these limits. We therefore define the secured basin as the region of disturbance space comprising disturbances whose trajectories remain within the angle-stability limits throughout the transient. We then characterize the secured basin through energy barriers, quantifying their characteristic energy scale and heterogeneity across perturbation directions. Across seven power-grid models, this characterization revealed angle-stability vulnerabilities overlooked when stability is assessed solely by the final synchronization state. It also showed that the two energy-barrier measures characterizing the secured basin exhibit distinct associations with structural connectivity and dynamical parameters. This characterization further offers practical advantages, as energy thresholds derived from state-space perturbations remain effective in distinguishing secured and unsecured outcomes under short-duration power disturbances and can be estimated efficiently by concentrating simulations near the secured-basin boundary. Overall, the secured-basin framework provides a useful basis for distinguishing node-level structural and dynamical influences on transient stability in oscillator networks with synchronization constraints.

Nonpharmaceutical interventions (NPIs), such as mask wearing, can reduce epidemic spread, but their effectiveness depends on whether people adopt and sustain them. The incentive to adhere changes with infection risk and with the behavior of others. Yet it remains unclear how epidemiological protection and social reinforcement determine the strategic structure of infection control, and how epidemic feedback changes that structure over time. Here, we develop a socio-epidemiological model in which prevalence changes behavioral incentives while adherence changes transmission, separating private protection from source control and incorporating conformity. At a given prevalence, private protection favors adherence, source control creates opportunities for free-riding, and conformity reinforces whichever behavior is already common, generating distinct adherence games. Because adherence reduces transmission and prevalence, the population can move between these games as the epidemic unfolds. This feedback can stabilize partial adherence, create alternative behavioral norms, or generate recurrent epidemic-behavior cycles. Moreover, interventions with the same population-level effect on transmission at a given adherence level can produce different long-term outcomes when protection is distributed differently between the individual and others. Thus, voluntary infection control is a feedback-evolving game whose outcome depends not only on epidemiological efficacy but also on who is protected and how behavior is socially reinforced.

Language-model agents favor their own group because they have watched their members favor each other. The group label alone does little once the decision has a cost; what drives favoritism is observed behavior, and an individual's own record can override it. We test this in small societies with arbitrary group labels, ten rounds of point sharing, and matched one-shot decisions across fifteen OpenAI models and three Claude models, about 4,400 societies and 3.3 million audited model calls. First, the large effect of a bare group label reported in earlier work appears only when giving others points costs the agent nothing; once the agent can keep points for itself, that effect collapses on every model that shows it. Second, under a stake, interaction history becomes the main source of favoritism: the history effect is statistically positive on 13 of 15 models, reaches about 3.5-8 points out of 10 on 11, grows with the number of rounds played, and extends to labeled strangers the agent has never met. Third, with scripted histories, favoritism falls to near zero under an egalitarian norm and reverses when the agent's own group is seen favoring the other side; stronger models side with an individual's record when it conflicts with the group. Group favoritism is thus conformity to observed group behavior, carried to strangers by the label and overridden by individual reputation. The same account predicts responses to betrayal, scandal, and a free offer to change group: public reprimand repairs betrayal better than apology or restitution, allocation punishment remains confined to the offending member, and a formed group cannot be bought but can, on weaker models, be invited away.

Decisions that will shape future fusion facilities, including the production of waste, the management of tritium, the achievement of safety, and impacts on land, water, and local communities, are increasingly becoming sociotechnical in nature. For prior energy technologies, fission among them, such decisions were made top-down and met sustained public opposition. Research shows such opposition is rarely a deficit of public understanding and is instead rooted in place-specific environmental, health, sociocultural, and economic concerns and in distrust of how technologies are selected and sited. We argue that fusion developers have an opportunity to design differently, and we propose an ecology of participation: multiple, complementary modalities of engagement sustained over time, replacing the one-off public consultations now typical of large infrastructure projects. We synthesize human- and environment-centered design frameworks and introduce a framework that reinterprets opposition as a divergence between communities and technology developers in values, norms, or design characteristics. We present six case studies from our work: participatory technology assessment focus groups, participatory design workshops, immersive virtual-reality models of a fusion facility, the Global Fusion Forum platform, the Imaginary Energies speculative design platform, and Heartbeat, an arts-based sound installation. Mapping these onto the values-norms-design framework shows how each interrogates or closes a different part of the expert-public gap, and exposes two limitations of fusion public engagement efforts writ large: developers are seldom asked to articulate their own values and norms, and the environment is represented only indirectly. We close with five guiding questions for fusion researchers building their own participatory engagements.