Papers

Residential and social sorting along political lines structure Americans' daily interactions, often limiting encounters with opposing viewpoints. Here, we combine high-resolution anonymized mobility data from 11 major U.S. metropolitan areas with precinct-level voting results to quantify and examine individuals' experienced partisan segregation in urban areas. We find that residential context provides a strong baseline for political exposure, but experienced partisan segregation is also shaped by everyday mobility behavior, since individuals living in similar residential environments can experience different levels of partisan segregation depending on their daily routines. In particular, individuals who travel farther encounter more politically diverse environments, especially in highly segregated areas, indicating that mobility range moderates the relationship between residential and experienced segregation, while exploring a wider range of places does not reduce partisan segregation and may even reinforce it. Moreover, place segregation largely reflects political geography, yet categories of points of interest have distinct segregation patterns, with community places and social businesses tending to attract more left-leaning visitors, reflecting visitation patterns rather than local accessibility alone. Our findings show that experienced partisan segregation extends beyond residential geography and is also associated with the mobility behavior and visitation patterns that structure people's daily lives.

Emergent coordinated behaviors of AI agents are starting to present critical safety risks. A key phenomenon driving these behaviors is the rapid formation and spread of beliefs about the world, and mechanistic understanding is crucial for collective alignment. To this end, we introduce the Flag Game, a toy model for studying the mechanisms of collective belief formation. Concretely, a hidden country flag defines the ground truth, and each bounded agent directly observes only a private crop but can exchange beliefs and weigh social evidence from peers. Despite its simplicity, the Flag Game reproduces rich collective phenomenology: non-monotonic scaling of performance with population size, accuracy gains from social-awareness prompting and team diversity, and strong effects of organizational structure. In particular, we identify that collective belief collapse at small population sizes turns into collective belief polarization as the population grows. This polarization causes the performance decline at large population sizes, but creates diversity in collective beliefs. Finally, we dissect the mechanisms underlying collective belief collapse and polarization with two complementary approaches. We first introduce social circuit attribution, a technique to predict which agent, and what view, matters most to collective dynamics, and verify its predictions by causal interventions on agents, tracing how agent patching changes collective outcomes. However, the efficacy of causal interventions on agents decreases as the population grows. We therefore develop a statistical mechanical theory for larger populations and verify that it matches the empirical phase diagram. Together, these results take a first step toward mechanistic swarm interpretability, a science of how the properties of individual agents and their communication give rise to emergent collective behavior.

Rapid algorithmic developments have accelerated the sampling of redistricting ensembles (balanced graph partitions), yet evaluating rare events and sampling complex target measures remains a core challenge due to the high-dimensional and combinatorial nature of the phase space. We address a prerequisite for stratified sampling on this space: constructing and diagnosing candidate strata with suitable coverage and overlap. We build a grammar on observed plans by clustering districts into representative “letters” and using them to form plan-level “words.” A partition of unity over these words gives a soft assignment of plans to strata and allows us to estimate stratum masses and an overlap-induced flux matrix. We demonstrate this computational pipeline using real-world congressional redistricting data from Connecticut and examine how strata learned from one target distribution behave under related distributions. The resulting construction provides a foundation for future stratified sampling on spaces of redistricting plans or balanced graph partitions. We do not implement a complete stratified sampler here; evaluating whether the proposed strata improve sampling efficiency or reduce estimator variance is left for future work.

Conversational recommender systems~(CRS) enable users to express preferences, constraints, and feedback through natural language interaction. However, existing CRS resources are concentrated in English and other high-resource languages, leaving Bangla and code-mixed Bangla–English settings underrepresented. To address this gap, we introduce BanglaShop-CRS, a large-scale user-centric synthetic Bangla conversational recommendation dataset grounded in real e-commerce behavior. It contains 27,178 multi-turn dialogues, 274,802 utterances, and 3.6M tokens across 10 product domains. Our generation pipeline incorporates user purchase histories, positive and negative feedback, and review texts to maintain consistency between dialogue content and user preferences. We evaluate BanglaShop-CRS under catalog-constrained and open-vocabulary recommendation protocols. Results show that dialogue context improves recommendation quality, while fine-tuning yields further gains across models. Human evaluation by five native Bangla-speaking annotators confirms the fluency, informativeness, logicality, and coherence of the dialogues, with significant agreement across all dimensions. Factual-grounding evaluation further shows stronger alignment with correct than shuffled user records, with substantial inter-annotator agreement ($\kappa=0.65$) and comparable human and GPT-5.1 judgments. BanglaShop-CRS provides a scalable benchmark for advancing conversational recommendation in Bangla.

How many compensatory seats are needed to ensure (full) proportionality in two-tier electoral systems with multi-member constituencies? This question is answered by estimating the discrepancy between seats and votes rigorously, probabilistically and empirically. In 1919, Pólya showed that the Jefferson/D'Hondt method favours large parties. This seat surplus accumulates across constituencies. In some countries (e.g., Portugal, Poland, Spain and Turkey), this accumulation directly leads to quantifiable disproportionality in the final parliament. Elsewhere compensatory seats counteract this accumulated seat surplus. Using a logic-first approach, closed formulas for the required number of compensatory seats are derived given regional and national apportionment methods, the number of relevant parties $n$ and the size of the largest party $p$. If there are $c$ constituencies, it is derived logically that a two-tier system built on local D'Hondt apportionment in multi-member constituencies needs around $\frac{c}{2}(n-\frac{1}{p})$ compensatory seats to ensure proportionality. In words, that is half a compensatory seat per constituency times a number that increases with the number of parties and the size of the largest party. The validity of the derived formulas is analysed empirically using the extensive recent CLEA dataset. Many quantities do not depend much on the total parliament sizes, which means that the variance can be reduced by artificially varying the house sizes. Portugal, Poland, Spain and Turkey all employ a one-tier D'Hondt system and the analysis shows that if they were to add compensatory seats to the system, reasonable numbers would be 40, 75, 75 and 120, assuming that no other properties of the systems change.

Crises and population responses can form coupled dynamical systems, with crisis conditions shaping protective behaviours and collective responses altering the crisis trajectory. Existing models rarely capture both evolving crisis conditions and the reinforcement-dependent spread of competing behaviours. We propose a complex contagion with feedback (CCF) model that couples competing complex contagion with crisis dynamics through bidirectional feedback. Agents stochastically switch between competing states according to social reinforcement and adoption complexity, which is influenced by crisis conditions and information campaigns, while population-level behavioural change feeds back into the crisis. We formulate and analyse the model for a well-mixed population, characterising its equilibria and associated stability conditions. As a case study for model validation, we integrate CCF model with a census-calibrated agent-based model of COVID-19 transmission in Australia to represent social distancing adoption and discontinuation. We compare the CCF model with an existing opinion dynamics model and find that, despite using fewer parameters, it achieves comparable performance in reproducing recurrent infection waves. The resulting framework is parsimonious, analytically tractable, and can be adapted to different types of crises.

Environmental systems are characterized by complex spatial interactions that cannot be fully described through pairwise relationships or local uncertainty measures. We propose a unified framework combining higher-order information theory and topological data analysis to characterize the organization and uncertainty of environmental networks. Spatial entities, represented by monitoring stations or municipalities, are embedded into a Delaunay simplicial complex, and O-information is used to quantify redundancy and synergy among neighboring triplets. The resulting field of higher-order interactions is analyzed through persistent homology, enabling the identification of topological structures that remain stable across interaction scales. The methodology is applied to both an air-quality monitoring network based on weekly \(\mathrm{NO_2}\) and \(\mathrm{O_3}\) observations and a multi-hazard territorial assessment. We show that regions exhibiting strong O-information and persistent topological structures correspond to robust environmental patterns, whereas areas characterized by heterogeneous regimes and rapidly varying interactions display increased uncertainty. Building on these results, we introduce a topological uncertainty framework that integrates simplex divergence, higher-order interactions, and topological uncertainty. Our results demonstrate that uncertainty can be interpreted not only as statistical variability but also as the instability of the underlying information topology. By integrating O-information and persistent homology within a common spatial framework, the proposed approach provides a new methodology for detecting robust higher-order structures and topologically uncertain regions in environmental and multi-hazard systems.

Traditional electoral analyses often rely on aggregate sociopolitical indicators or on macroscopic models grounded in statistical physics that treat election data at national level; however, these models have rarely been applied to local-level voting data, where the analytical complexity is substantially higher. In this study, we propose a framework rooted in statistical physics and complex network theory to investigate the fine-grained architecture of voting behaviour at the local scale. Leveraging a granular dataset from the 2024 municipal and 2025 regional elections in Bari, Italy, we represent the urban electoral landscape as a complex network, in which polling sections are modelled as nodes connected by links that encode statistically significant correlations based on vote expression. For each coalition participating in the elections, community detection reveals geographically proximal clusters that transcend administrative boundaries. Furthermore, in order to quantify whether a coalition exhibits territorially homogeneous voting behaviour or, instead, is fragmented into distinct voting blocs, we adapt latent-ideology estimation and the information-theoretic Shannon entropy $H$ to the intra-coalition scale. Our results indicate that winning coalitions exhibit significantly higher territorial fragmentation, and these findings suggest that, at the local level, electoral success is not driven by the maintenance of geographically uniform consensus rather than by the capacity to aggregate diverse and non-homogeneous voting blocs. This scalable, data-driven framework moves beyond simple geographic accounting, providing a robust tool for uncovering the structural dynamics of political idea clustering and coalition fragmentation in complex urban environments.

Collective discovery often requires the integration of partially overlapping knowledge, yet the beneficial level of overlap and the role of background topology remain unclear. We model individual knowledge as connected subnetworks of a common knowledge space and distinguish internal synergy, in which integration shortens paths between concepts, from external synergy, in which an unknown concept connects to the shared and all individual-specific regions. Internal synergy peaked at intermediate overlap across random, scale-free, modular, and small-world backgrounds, reflecting a balance between complementarity and common ground. External synergy was strongly topology dependent and was most pronounced when small-world structure maintained overlapping local boundaries. Rewiring separated the total supply of synergistic nodes from their concentration at the accessible boundary; moderate rewiring could increase structural opportunities while dispersing them among more external alternatives and reducing their discovery. Group-size effects on internal synergy depended on topology, whereas strict higher-order external synergy declined as its all-region criterion became more restrictive. Collective discovery therefore depends jointly on knowledge overlap and on how complementary knowledge regions are organized within the surrounding knowledge space.

Can the terminal macroscopic outcome of a many-body system be inferred from its microscopic initial data without simulating the full trajectory? Rather than construct such a shortcut, we address a more fundamental question for Gaussian microscopic inputs: can any fixed Wiener-Hermite degree retain a nonvanishing fraction of the variance of the terminal outcome as the system grows? We consider homogeneous systems with independent Gaussian disorder in which all microscopic coordinates are symmetry-equivalent, the terminal event is monotone in each disorder variable, and a uniform disorder shift is exactly equivalent to a control-field shift with a size-independent conversion factor. Using forward and inverse Gaussian influence bounds together with a Gaussian Russo formula, we derive a directly measurable criterion that is both necessary and sufficient for noise sensitivity. Specifically, the correlation between the original and coordinate-perturbed terminal outcomes vanishes asymptotically for every fixed nonzero level of coordinatewise noise if and only if the slope of the outcome probability with respect to the control field at the balanced threshold grows more slowly than the square root of the system volume. Event-driven simulations of the three-dimensional driven random-field Ising model up to linear size 192 find that both the normalized response and the correlations between perturbed samples decrease overall, consistent with the noise-sensitive regime at finite size. For spatial Stag-Hunt dynamics with prescribed seeds, the criterion generalizes through an effective number of influential coordinates. Separately, simulations of a path-dependent best-response game show, over the sizes studied, that the terminal equilibrium can depend on the update schedule while still carrying substantial finite-order predictive information.

Whether human or large language model (LLM), an agent in a discussion reads only a few of the others' contributions, bounded by cognition, context, or cost. LLM collectives can settle on a wrong consensus even when a majority starts out correct; we ask how far that reading bound alone decides the outcome. We model the bound with one number, the message capacity, which sets how many of the others' messages an agent reads, and generate the communication network from it. Over 31,824 randomized queries, we found that an 8-billion-parameter model's judgment of a claim effectively reduces to a logistic function of a weighted sum of its inbox, the update rule of a stochastic binary neuron with divisively normalized weights. From these weights and the network's degree statistics alone, the wrong consensus should become unreachable from any start once agents read, on average, fewer than 6.4 of their 31 sources. In 1,414 episodes with assigned starts the prediction failed: the correct side won in fewer than 50% of episodes from every start, and in only 28-45% when 75% of agents started correct. The failure traces to the field, the threshold that a claim's wording sets for the agent's answer before any message is read: the experimental claims' fields lay below the calibration mean, and with each claim's own field the same weights reproduce the outcomes. Reversing the wording showed that the threshold follows what a claim asserts, not whether it is true. On a second 8B model the pipeline predicts claim-dependent bistability; transition points appeared where computed, and an eight-claim calibration matched in 15 of 16 conditions. At 70B the assertion bias is not detected. Thus a collective's fate is largely set by two single-agent measurements: the threshold a claim's wording sets, and the message capacity that sets the transition point.

Labor strikes do not necessarily lead to complete institutional disruption, particularly when non-striking sectors maintain part of normal activities. We propose a minimal dynamical model to investigate how such residual activity affects the persistence of labor conflicts. The model couples strike participation, institutional response, and overall functionality through two competing mechanisms: a direct suppressive effect of residual activity on strike participation and an indirect effect arising from the reduced institutional pressure to resolve the conflict. Analytical calculations reveal a counterintuitive regime in which increasing residual activity increases, rather than decreases, stationary strike participation. We characterize this behavior through a strike-response susceptibility and derive an exact compensation threshold separating the conventional regime from a resilience-paradox regime. At the threshold, the two mechanisms exactly balance, making stationary strike participation independent of residual activity. Numerical results confirm the analytical predictions and illustrate the underlying mechanism. Our results show that institutional resilience can have an unintended dynamical consequence: by absorbing part of the disruption generated by a strike, continued operation may reduce the urgency of institutional response and thereby favor conflict persistence. This provides a simple example of how individually stabilizing processes can collectively generate counterintuitive outcomes in complex social systems.

Population-level heterogeneities, combined with temporal fluctuations in sexual partnerships, shape the structure of sexual contact networks and can substantially influence the spread of sexually transmitted infections (STIs). Traditional static network models, which assume fixed attributes of partnerships, such as count and duration, may not adequately capture the effects of partnerships on STI transmission. In contrast, agent-based dynamic network models offer a flexible framework for incorporating individual and population-level heterogeneities. We developed an agent-based dynamic network model in which partnership formation and dissolution probabilities, stratified by age, sex, and sexual orientation (including bisexual individuals), govern the formation of monogamous and concurrent partnerships and their dissolution via a duration-dependent hazard. Partnership statistics from the National Survey of Sexual Attitudes and Lifestyles (NATSAL-3) were used as model calibration targets, and Latin Hypercube Sampling (LHS) was used to generate candidate parameter combinations. Parameter estimation was performed by selecting the combination that produced the lowest Mean Squared Error (MSE) between the model outputs and the calibration targets. Our study addresses three questions: (1) how well can the observed characteristics of sexual partnerships in NATSAL-3 be reproduced using an agent-based model; (2) how does concurrency shape the structure of dynamic sexual contact networks; and (3) how do concurrent partnerships affect the dynamics of STI transmission. In this study, we find that interactions between individual characteristics such as age, sex, and sexual orientation, and partnership attributes such as count, duration, and concurrency play a critical role in shaping the population-level sexual contact network and, in turn, the dynamics of STI transmission.

Inferring network structure from dynamical observations is a fundamental inverse problem in complex systems. Existing approaches have largely focused on single-layer or pairwise interaction networks, leaving the reconstructability of multiplex systems with higher-order interactions poorly understood. Here, we show that structural identifiability in such systems is jointly governed by cross-layer spreading couplings and network heterogeneity. Building on coupled awareness-behavior dynamics, we develop a multiscale inference framework that disentangles interacting spreading processes and enables the simultaneous reconstruction of microscopic simplicial interactions and macroscopic metapopulation organization. Numerical experiments on synthetic and empirical networks demonstrate high reconstruction performance, particularly in non-absorbing dynamical regimes sustained by weak inhibitory and strong facilitatory couplings. Results reveal the mechanisms governing structural identifiability in higher-order multiplex networks and provide a general route for inverse inference in multiscale complex systems.

Green economic complexity provides a generalizable framework for examining countries' productive capabilities in a defined product set. We apply this framework to AI-enabling goods within the full product space, linking current specialization with adjacent diversification opportunities. Using BACI exports for 2007-2023 and 103 AI-enabling goods, we measure complexity-weighted specialization (AECI), product-level adjacent opportunities (AIAP), and average complexity-weighted relatedness of remaining candidates (AECP). In 2023, Japan leads AECI, while China leads AECP; portfolio breadth accounts for much of the variation in raw AECI. Initial raw potential is positively associated with subsequent changes in the AI-enabling export share, but its associations with changes in AECI and specialization counts are not statistically significant at the 5% level. Our contribution is a trade-based assessment of AI-enabling productive capabilities and related opportunities. The results and public dashboard provide a preliminary complement to publication and patent indicators, not a comprehensive measure of national AI performance or a validated forecast of diversification.

Road passenger transport faces acute disruption risks from flash flooding from heavy rainfall. To capture system complexity and dynamics for assessing its flood resilience, this study (a) proposes performance metrics to quantify disruption impacts from physical, operational, environmental, social, and economic dimensions, and (b) develops an integrated modelling framework to assess selected metrics by combining rainfall modelling, surface water flood modelling, and agent-based transport modelling to simulate asset-level disruptions and user behavioural responses. This methodology is demonstrated through a 1-in-30-year rainfall event in Tyne and Wear, North East England. Three behavioural response scenarios to rainfall are simulated, including a worst-case scenario, an ideal-response scenario, and an early-warning scenario. Results highlight the potential of behavioural adaptation in enhancing road flood resilience by reducing journey delays, cancellations, and economic losses. The framework and multidimensional performance metrics offer a transferable approach for stress-testing rainfall scenarios and evaluating interventions, supporting evidence-based climate-resilient transport planning.

A physics-inspired dynamical model of interpersonal communication is proposed to describe how information interpretation can influence the evolution of a relationship between two interacting systems. The model distinguishes between intended information and observed information, with the former represented as an asymptotic limit in an abstract information space. An information potential landscape with local minima and increasing potential barriers is introduced to represent stable information states and limited information accessibility. Information transmission and processing are described through a general thought-processing function, while the separation between information states determines the coupling strength between the interacting systems. The relationship dynamics is then defined from the coupling strength and the independent sentiments of the two systems. Numerical simulations demonstrate the potential landscape, information dynamics, and several relationship regimes under deterministic and fluctuating information processing. An illustrative dynamical scenario further shows that an emerging information discrepancy can lead to either relationship recovery or deterioration depending on the subsequent evolution of information separation, coupling, and individual sentiment. The model provides a simple physics-inspired framework for describing interpersonal communication as a coupled dynamical process.

Characterizing temporal interactions in social systems is challenging because social behavior can be bursty and non-stationary, violating the stationarity assumptions of many methods used to measure temporal dependence. The cross-correlogram, an existing technique used to profile neural excitations and inhibitions, offers an interpretable alternative to methods such as Granger causality or co-occurrence, as it produces a full profile of lagged dependence directly from event times rather than a single summary statistic. We adapt the cross-correlogram by integrating functional models of behavior with data-driven temporal response profiling. By characterizing how periodic structure biases traditional cross-correlograms, we propose a correction based on smooth intensity functions, specified from a known functional form or estimated empirically. This approach provides a robust, interpretable estimator of temporal dependency profiles even when collective rhythms operate on timescales that overlap those of the interactions of interest. We demonstrate theoretically and through simulation that the proposed method recovers temporal dependencies in periodic regimes, outperforming interval-jitter and Granger causality methods. Finally, we apply the method to 3.1 million event times from X (formerly Twitter) collected between 2019 and 2020, demonstrating how cross-correlograms reveal delayed temporal relationships in collective online behavior that are missed by co-occurrence measures. For a subset of television-related hashtags, recovered delays align with known broadcast schedules, providing evidence that the proposed method captures genuine temporal structure rather than artifacts of shared attention cycles.

Our understanding of how geographical and social segregation interact remains limited, as relatively few studies investigate them jointly, and existing approaches often lack a framework distinguishing geographical, social, and total segregation. Additionally, large-scale individually resolved geo-social network data are rarely publicly available. We address both. Conceptually, we develop a unified framework that measures segregation in geosocial networks by comparing network models to appropriate null models and recovers the Theil index, dissimilarity index, and network modularity as special cases. Empirically, we turn to privacy-preserving aggregated relational data (ARD): we combine the Facebook Social Connectedness Index for the US with US Census and Pew data, and introduce an ARD-compatible joint geosocial intervening-opportunities model to infer link probabilities between region–group cell pairs. Applying our segregation framework, we find that social segregation predominates over geographical segregation, with notable separation for White–Black, college-degree–no-degree, and high-income–low/middle-income across both segregation types. We find increasing social homophily with geographical distance and group-specific geographical connectivity patterns, suggesting that geographical segregation may affect cross-group connectivity not only directly but also by amplifying social segregation.

Kalshi's multivariate-event architecture produces market objects on demand from exact selected legs. Across a registered seven-day interval, 190 independently validated temporal shards yield 7,611,594 unique REST MVE market tickers after excluding 5,777 boundary-overlap observations; the population was created at an average rate of 1.087 million objects per day, with strong hourly burstiness. The hierarchy is sharply compressed relative to the market-object count: 5,262,526 exact event keys, three collection keys, 83,701 selected-leg primitives, and 66,344,938 selected-leg occurrences. The largest collection accounts for 7,217,085 objects (94.82 percent), with an effective collection count of 1.11; the effective primitive count is approximately 720. REST and WebSocket are distinct observation surfaces: 1,487,330 created-notification tickers intersect the REST population in only 276,177 exact tickers. All three observed collection keys have current endpoint confirmation without establishing historical collection or rule versions. The exact structural signature yields 7,611,594 unique signatures with zero collision groups. At retrieval, 2,692,787 objects (35.38 percent) have positive cumulative volume or open interest; all observed 24-hour-volume values are zero and trade-count fields are unavailable. This is current-snapshot activity eligibility, not historical trading. The central result is that a very large on-demand market-object population is generated by a concentrated collection layer and reused primitive vocabulary; economic breadth must be measured at several hierarchical levels rather than by ticker count alone. Exact public endpoint evidence is available for 7,357,576 MVE objects, whereas only 126,806 have an exact determination-to-endpoint pair: terminal-state coverage and lifecycle-path coverage are distinct statistical objects.

Infrastructure networks essentially underlie human mobility and transport. Improving the quality of single links increases network performance locally. However, efficient transport requires high-quality connected corridors across multi-link paths that do not emerge from independent single-link upgrades. Here, we introduce a framework for evaluating the impact of jointly upgrading multiple links as inherently higher-order interactions, enabling us to quantify link synergies in complex transport networks. Two links are synergistic if an upgrade of one increases the benefit of upgrading the other, promoting upgrades of topologically complementary links along the same path while discouraging upgrades of redundant parallel links. By expressing these synergies as second-order derivatives of overall network performance, we develop an efficient computational framework to identify synergistic links that form a connected network backbone. We apply our theoretical framework by combining empirical street network and cycling demand data for Hamburg, Germany, with a perturbed utility route choice model for urban bicycle traffic. Our results reveal synergies from higher-order interactions, thereby enabling strategic infrastructure planning that goes beyond local link importance in complex transport and flow networks.

The ENTSO-E Transparency Platform is the single mandated publication point for European electricity market data, and three of its data items carry a production-type label. Commission Regulation (EU) No 543/2013 Article 16(1)(a) requires actual generation output per generation unit but does not mention production type; the label on that item is ENTSO-E's own addition through the schema, and Article 5(d) delegates the classification of production types to a Manual of Procedures. This data descriptor documents two preregistered datasets that measure what that classification does in practice, along four axes: simultaneous multi-fuel reporting, type change between capacity-register vintages, persistent misclassification without type change, and cross-product consistency between the per-unit and the aggregated series. Criteria were locked and checksummed before measurement, thresholds are derived from measured noise rather than chosen, and what did not survive is recorded: six claims are withdrawn, three of them from published versions of the datasets themselves. Across 2191 documents covering 199 generation units in three bidding zones where biomass co-firing is documented in a dated primary source, no unit carries two production types in the same document. One practical warning applies to every user of the hourly series: all of it carries curveType A03, a step curve in which a missing position means the previous value holds rather than that data are absent. A parser that sums only the points listed under-reports energy unevenly, by 22.6 percent for one Finnish unit in 2021.

Energy transition decision-making is pervaded by deep uncertainties. Computational models are helpful in addressing such uncertainties, as they can give insight into techno-economic complexity. However, models alone are insufficient, as part of the uncertainties involve justice dilemmas.

Likelihood-based network models are often fitted under links' independence and low-order constraints, while empirical networks frequently exhibit systematic higher-order structures such as triangles and wedges, characterizing the observed clustering patterns. Real-world core-periphery networks such as the interbank market or the air transportation system represent key examples, with cores displaying complex and nonlinear features. We formalize Penalized Likelihood with Structural Discrepancies (PLSD) as an inference-level correction that trades likelihood fit for agreement with targeted motifs. PLSD augments the negative log-likelihood with a penalty on standardized wedge and triangle discrepancies, yielding a controlled distortion of the likelihood surface that can be interpreted through a linear response analysis. A calibration approach for the unique tuning hyperparameter is introduced to turn off the penalization term when the model is correctly specified, thereby recovering maximum-likelihood estimation in that case. We then introduce a mixed-constraint maximum-entropy core-periphery Exponential Random Graph Model (ERGM), derive unconditional semi-closed motif formulas under Pareto-distributed core fitness, and interpret sparse-regime scaling through a big-jump mechanism for heavy-tailed distributions. We finally corroborate the PLSD inference methodology both with Monte Carlo simulations of a toy stochastic block model and by applying the novel core-periphery ERGM to weekly eMID interbank networks (2009-2015) and monthly US air traffic networks (1991-2000). We show that PLSD describes cores that are not only dense but also higher-order-rich and captures the observed heterogeneous degree distributions and the overexpression of wedges and triangles, at a link-level cost proportional to the model misspecification.

Broad run-length distributions and short-lag superdiffusion have recently been reported in football players and team centroids, prompting a collective-foraging interpretation. The mechanism linking these player- and team-level signatures remains unclear. Using SoccerMon GPS data from 66 tracked team-match records across 62 fixtures in the Norwegian women's top-flight Toppserien, we asked whether player transport is inherited from a translating team mode and what controls the lifetime of that mode. We decomposed player displacement into centroid translation and motion within the formation, then modelled the switching and termination of directional centroid runs. At longer lags, centroid translation carried an increasing share of player displacement. Run lifetimes were broad but finite: termination was highest near onset, declined with age and rose modestly later. After accounting for speed and directional persistence, collective order added only limited predictive information. At the population level, an age-dependent switching-and-termination model predicted, on held-out match dates, the early enrichment and later depletion of high-order states among surviving runs. These results link player- and team-level Lévy-like movement through a finite-lived collective mode whose age and internal state shape its survival. Long displacements need not be selected in advance; they can emerge when transient collective modes persist, reorganise and selectively survive. More broadly, trajectory statistics may record which dynamical histories survive, not only the rules by which agents move.

While there has been much discussion of whether AI systems could function as moral agents or acquire sentience, there has been very little discussion of whether AI systems could have free will. I sketch a framework for thinking about this question, inspired by Daniel Dennett's work. I argue that, to determine whether an AI system has free will, we should not look for some mysterious property, expect its underlying algorithms to be indeterministic, or ask whether the system is unpredictable. Rather, we should simply ask whether we have good explanatory reasons to view the system as an intentional agent, with the capacity for choice between alternative possibilities and control over the resulting actions. If the answer is "yes", then the system counts as having free will in a pragmatic and diagnostically useful sense.

Accounts of organizational dissolution explain survivor homogenization, temporally concentrated exit, and fragmentation into cohesive subgroups in different vocabularies. Their recurrence is the fact to be explained. A finite condition structure with a minimal dynamic extension generates all three, each in its own regime, with no actor responding to another; which conclusions survive when the restrictions defining those regimes are relaxed is then determined. With two requirement masks the union class is the only mediator between the pure types and the signature with the largest closed neighbourhood, and under positive endpoint loads it is also the unique maximizer of deficient load, so the independent derivations of fragmentation and homogenization both concern that class. With more than two masks the three roles need not coincide. Forced coincidence admits an exact characterization for every number of masks: it holds if and only if the requirement-derived join lies in the realized family and separates the endpoints. The proportion of family-pair instances meeting this condition vanishes under a uniform counting measure as the number of masks increases, and, at one actor per signature, an edge density above a stated bound excludes it. A common substrate does not make the mechanisms identical: selective attrition can hold while correlated collapse fails, and a change in exit dependence can change the population outcome while every actor's marginal lifetime law is unchanged.

Facial-expression classifiers convert video into multivariate time series of scores with measurement error from classifiers, videos, and recording conditions. Empirical score series cannot establish whether the score channels reflect a smaller set of latent expressive processes or whether an analysis would recover those processes. We introduce the Latent Oscillator Measurement Model (LOMM), a data-generating model that separates latent dynamics, time-varying activity, and a factor-analytic observation model. The latent processes are damped, undamped, or amplifying linear oscillators. LOMM generates bounded scores or continuous indicators. Study 1 used four-fold cross-fitting with scores from 100 MAFW videos to calibrate LOMM and evaluate generated series on held-out videos. Median plausibility and coverage were 0.970 and 0.920 for LOMM, versus 0.510 and 0.370 for a calibrated static logistic-normal generator. Study 2 tested whether Dynamic Exploratory Graph Analysis (DynEGA), static EGA, GraphicalVAR, and GIMME recovered a known dimensional structure from continuous indicators generated by LOMM. At 100 observations per clip, with failed or timed-out fits counted as incorrect, correct-dimension recovery was 0.939 for DynEGA, 0.884 for static EGA, 0.777 for GraphicalVAR, and 0.176 for GIMME. Replacing the common fixed initialization with independent stationary starts for stable dimensions and bounded independent starts for amplifying dimensions reduced recovery for DynEGA, static EGA, and GraphicalVAR. LOMM provides a controlled test of whether an analysis recovers aspecified latent structure before score patterns are interpreted psychologically.

Discrete diffusion models are a prominent family for graph generation, but standard class-conditional mechanisms embed the class signal in the denoiser during training, tying the conditioning mechanism to the trained model. Classifier guidance avoids this coupling in continuous domains by steering a frozen model with a classifier's gradient, but discrete graph diffusion samples discrete edge states, so gradients cannot propagate through the sampled graph. We introduce ProtoGuide, a post-hoc, backbone-agnostic framework that recovers an analogous mechanism. At each reverse step the denoiser's per-edge output is relaxed into a differentiable soft adjacency, embedded by a frozen Siamese graph neural network, and scored against a target-class prototype and its nearest competitor; the resulting per-edge gradient, damped by a cosine schedule, is injected back into the denoiser output. All components stay frozen, so guidance is retargeted by supplying a different prototype. On five classes of real-world networks and two architecturally different backbones, EDGE and DiGress, ProtoGuide raises macro classification accuracy from 50.7% to 73.5% and from 73.6% to 83.8%, and outperforms DiGress's built-in conditional training under our configuration. Gains are largest where the unguided models are weakest, and are not uniform across classes. Per-graph coverage remains high in most settings, while distributional effects are class-dependent. A Best-of-N selection baseline matches this accuracy given enough oversampling, but at a substantial cost in graph diversity. An independently initialized classifier, a directionality test, and a few-shot analysis support target-directed steering and robustness to very small support sets.

We introduce document embedding geometry as a quantitative observable of conceptual reorganization and develop a counterfactual ablation framework for measuring how individual concepts influence the organization of scientific knowledge, providing a quantitative framework for detecting scientific revolutions. The observable is defined by the geometric perturbation induced when removing documents associated with a candidate concept from the embedding space before and after its historical emergence. Statistical validation is performed using five historical case studies spanning physics, mathematics, and machine learning: special relativity, Gödel's incompleteness theorems, the Higgs mechanism, deep learning, and the attention mechanism underlying transformer architectures. Across the historical case studies, the framework identifies measurable geometric signatures associated with conceptual reorganization, while the validation studies expose important limitations arising from document assignment and sparse historical data. These results establish embedding geometry as a medium for quantifying conceptual reorganization, providing a new approach for studying how scientific fields restructure over time.