arXiv subjects

Luca Pappalardo

Publications and source records attributed to Luca Pappalardo.

At least 19 recordsLinked to original sources

arXiv2026

Joint Effects of Recommender Systems and Network Structure on the Visibility of Content and Creators

Social media algorithms allocate users' visibility by ranking content within their social networks. Yet, how recommendation logic and network structure jointly shape visibility across content and creators remains largely understudied. In this work, we tackle this question through agent-based simulations using YSocial, a social media virtual twin, in which agents interact under 7 recommendation strategies and 2 network topologies. We find that recommender logic sets the visibility regime: popularity creates a reinforcement loop in which early reactions increase later exposure, concentrating visibility on a small subset of content and limiting creator visibility to those whose content enters this loop, while collaborative filtering distributes visibility broadly across the active catalogue and user base. When the follower graph shapes candidate selection, network structure changes the direction of inequality: under popularity ranking, creator-level concentration becomes comparable to global popularity, but visibility is systematically redirected toward creators who are already socially popular. Network topology modulates the magnitude of these effects without changing their qualitative ordering. These results show that visibility allocation should be evaluated across content, creators, network position, and temporal reinforcement, and that controlled simulations can help test how feed design distributes visibility before deployment.

cs.SI↗

arXiv2026

Low-Vocality Engagement Shapes Online Participation

Online participation is often measured through visible expression, especially posting, yet many consequential forms of engagement occur through less vocal actions such as liking and following. Here we study how users inhabit Bluesky by reconstructing participation profiles from more than three billion activity records produced by a near-complete sample accounting for more than 80\% of registered users. We aggregate behavior into monthly user-level observations and distinguish two dimensions that are often conflated in platform analytics: intensity, capturing how much users engage, and style, capturing how engagement is expressed across actions. We find that vocal production is highly concentrated, but low-posting behavior does not imply absence from platform participation. High-intensity engagement is most strongly associated with liking rather than posting, while posting-oriented participation is more common among low-intensity users, indicating that visibility and sustained engagement should not be conflated. Transition patterns suggest that high-intensity likers and posters could be described as attractors; network-building redirects users within the active space; whereas observed inactivity acts as a persistent boundary that selectively limits re-entry. Higher-order motifs further show that inactivity often interrupts rather than erases prior regimes, and that low-intensity liking can precede durable high-intensity engagement. These results show that online participation is structured by differentiated low-vocality practices, calling for a shift from post-centered measures of activity toward dynamic accounts of platform presence. We identify a broader challenge for computational social science: platform participation cannot be adequately understood through the behavior of vocal minorities alone.

cs.SI↗

arXiv2026

The Diversity Paradox revisited: Systemic Effects of Feedback Loops in Recommender Systems

Recommender systems shape individual choices through feedback loops in which user behavior and algorithmic recommendations coevolve over time. The systemic effects of these loops remain poorly understood, in part due to unrealistic assumptions in existing simulation studies. We propose a feedback-loop model that captures implicit feedback, periodic retraining, probabilistic adoption of recommendations, and heterogeneous recommender systems. We apply the framework on online retail and music streaming data and analyze systemic effects of the feedback loop. We find that increasing recommender adoption may lead to a progressive diversification of individual consumption, while collective demand is redistributed in model- and domain-dependent ways, often amplifying popularity concentration. Temporal analyses further reveal that apparent increases in individual diversity observed in static evaluations are illusory: when adoption is fixed and time unfolds, individual diversity consistently decreases across all models. Our results highlight the need to move beyond static evaluations and explicitly account for feedback-loop dynamics when designing recommender systems.

cs.IR↗

arXiv2026

Comparing Explanations is Not Enough, Explain the Change: New Standards are Needed to Explain Behavioral Shifts in Large Language Models

Large-scale foundation models exhibit behavioral shifts when subjected to interventions such as scaling, fine-tuning, reinforcement learning with human feedback, or in-context learning. Current explainability methods are structurally ill-suited to explain these shifts, because they either treat models as static objects, as traditional eXplainable AI (XAI) approaches do, or merely compare independent explanations across different checkpoints of a model. As a result, these approaches fail to explain the functional transition between two model instances in which a certain behavior has shifted following an intervention. This gap creates significant governance risks across jurisdictions including the EU AI Act, US state legislation, and Chinese AI regulations, which require documenting causal chains for substantial system modifications. This position paper argues that explaining behavioral shifts in large language models requires a principled approach that treats the shift itself as the primary object of explanation: namely, one that explains how and why an intervention transforms a reference model into an updated model with different behavior. To support this claim, we introduce Comparative XAI (XAI$_\Delta$), a novel XAI paradigm aimed at explaining the difference between two model checkpoints where a behavior has shifted, together with a set of desiderata specifying what XAI$_\Delta$ explainers and explanations must satisfy, including comparability, validity, actionability, and monitoring, with the goal of grounding model auditing in explicit, measurable requirements. Finally, we provide preliminary evidence suggesting the need for XAI$_\Delta$ in practice through illustrative experiments, compiling the resulting findings into a transition report directly usable for governance and incident documentation.

cs.AI↗

arXiv2025

A Simulation Framework for Studying Systemic Effects of Feedback Loops in Recommender Systems

Recommender systems continuously interact with users, creating feedback loops that shape both individual behavior and collective market dynamics. This paper introduces a simulation framework to model these loops in online retail environments, where recommenders are periodically retrained on evolving user-item interactions. Using the Amazon e-Commerce dataset, we analyze how different recommendation algorithms influence diversity, purchase concentration, and user homogenization over time. Results reveal a systematic trade-off: while the feedback loop increases individual diversity, it simultaneously reduces collective diversity and concentrates demand on a few popular items. Moreover, for some recommender systems, the feedback loop increases user homogenization over time, making user purchase profiles increasingly similar. These findings underscore the need for recommender designs that balance personalization with long-term diversity.

cs.IR↗

arXiv2025

A computational framework for quantifying route diversification in road networks

The structure of road networks impacts various urban dynamics, from traffic congestion to environmental sustainability and access to essential services. Recent studies reveal that most roads are underutilized, faster alternative routes are often overlooked, and traffic is typically concentrated on a few corridors. In this article, we examine how road network structure, and in particular the presence of mobility attractors (e.g., highways and ring roads), shapes the counterpart to traffic concentration: route diversification. To this end, we introduce DiverCity, a measure that quantifies the extent to which traffic can potentially be distributed across multiple, loosely overlapping near-shortest routes. Analyzing 56 diverse global cities, we find that DiverCity is influenced by network characteristics and is associated with traffic efficiency. Within cities, DiverCity increases with distance from the city center before stabilizing in the periphery, but declines in the proximity of mobility attractors. We demonstrate that strategic speed limit adjustments on mobility attractors can increase DiverCity while preserving travel efficiency. We isolate the complex interplay between mobility attractors and DiverCity through simulations in a controlled setting, confirming the patterns observed in real-world cities. DiverCity provides a practical tool for urban planners and policymakers to optimize road network design and balance route diversification, efficiency, and sustainability. We provide an interactive platform (https://divercitymaps.github.io) to visualize the spatial distribution of DiverCity across all considered cities.

physics.soc-ph↗

arXiv2025

The Urban Impact of AI: Modeling Feedback Loops in Next-Venue Recommendation

Next-venue recommender systems are increasingly embedded in location-based services, shaping individual mobility decisions in urban environments. While their predictive accuracy has been extensively studied, less attention has been paid to their systemic impact on urban dynamics. In this work, we introduce a simulation framework to model the human-AI feedback loop underpinning next-venue recommendation, capturing how algorithmic suggestions influence individual behavior, which in turn reshapes the data used to retrain the models. Our simulations, grounded in real-world mobility data, systematically explore the effects of algorithmic adoption across a range of recommendation strategies. We find that while recommender systems consistently increase individual-level diversity in visited venues, they may simultaneously amplify collective inequality by concentrating visits on a limited subset of popular places. This divergence extends to the structure of social co-location networks, revealing broader implications for urban accessibility and spatial segregation. Our framework operationalizes the feedback loop in next-venue recommendation and offers a novel lens through which to assess the societal impact of AI-assisted mobility-providing a computational tool to anticipate future risks, evaluate regulatory interventions, and inform the design of ethic algorithmic systems.

cs.AI↗

arXiv2025

Characterizing User Behavior: The Interplay Between Mobility Patterns and Mobile Traffic

Mobile devices have become essential for capturing human activity, and eXtended Data Records (XDRs) offer rich opportunities for detailed user behavior modeling, which is useful for designing personalized digital services. Previous studies have primarily focused on aggregated mobile traffic and mobility analyses, often neglecting individual-level insights. This paper introduces a novel approach that explores the dependency between traffic and mobility behaviors at the user level. By analyzing 13 individual features that encompass traffic patterns and various mobility aspects, we enhance the understanding of how these behaviors interact. Our advanced user modeling framework integrates traffic and mobility behaviors over time, allowing for fine-grained dependencies while maintaining population heterogeneity through user-specific signatures. Furthermore, we develop a Markov model that infers traffic behavior from mobility and vice versa, prioritizing significant dependencies while addressing privacy concerns. Using a week-long XDR dataset from 1,337,719 users across several provinces in Chile, we validate our approach, demonstrating its robustness and applicability in accurately inferring user behavior and matching mobility and traffic profiles across diverse urban contexts.

cs.NI↗

arXiv2024

Dynamic models of gentrification

The phenomenon of gentrification of an urban area is characterized by the displacement of lower-income residents due to rising living costs and an influx of wealthier individuals. This study presents an agent-based model that simulates urban gentrification through the relocation of three income groups -- low, middle, and high -- driven by living costs. The model incorporates economic and sociological theories to generate realistic neighborhood transition patterns. We introduce a temporal network-based measure to track the outflow of low-income residents and the inflow of middle- and high-income residents over time. Our experiments reveal that high-income residents trigger gentrification and that our network-based measure consistently detects gentrification patterns earlier than traditional count-based methods, potentially serving as an early detection tool in real-world scenarios. Moreover, the analysis also highlights how city density promotes gentrification. This framework offers valuable insights for understanding gentrification dynamics and informing urban planning and policy decisions.

physics.soc-ph↗

arXiv2024

Learning by Surprise: Adaptive Mitigation of Model Collapse in Large Language Models

As AI-generated content increasingly populates the web, generative AI models are at growing risk of being trained on their own outputs, a process known as AI autophagy. This feedback loop has been shown to induce model collapse, typically characterized by a loss of diversity in generated content. However, existing work offers a limited understanding of this phenomenon and relies on mitigation strategies that assume access to human-authored data. In this paper, we conduct extensive simulations across multiple datasets and LLMs to address key gaps in the study of model collapse. First, we introduce model-intrinsic measures based on next-token probability distributions, showing that model collapse corresponds to an increasing concentration of probability mass on a small set of tokens. Second, we demonstrate that model collapse is also associated with a loss of common sense, as measured by a decline in commonsense inference accuracy. Third, we identify perplexity (a measure of model "surprise") as a key driver of collapse: fine-tuning on the least "surprising" documents leads to more severe degeneration. Building on this insight, we propose a perplexity-based filtering strategy that prioritizes high-surprise documents during fine-tuning. Unlike existing approaches, our method does not require distinguishing between human-authored and AI-generated content. Across datasets and LLM families, this strategy consistently mitigates model collapse, achieving performance comparable to, and in some cases better than, human-data baselines, while substantially reducing the concentration of next-token probabilities. Overall, our results provide a unified, model-centric understanding of model collapse and suggest practical, scalable strategies for training generative AI systems in increasingly synthetic environments.

cs.CL↗

arXiv2024

Geospatial Road Cycling Race Results Data Set

The field of cycling analytics has only recently started to develop due to limited access to open data sources. Accordingly, research and data sources are very divergent, with large differences in information used across studies. To improve this, and facilitate further research in the field, we propose the publication of a data set which links thousands of professional race results from the period 2017-2023 to detailed geographic information about the courses, an essential aspect in road cycling analytics. Initial use cases are proposed, showcasing the usefulness in linking these two data sources.

cs.CY↗

arXiv2024

Mixing Individual and Collective Behaviours to Predict Out-of-Routine Mobility

Predicting human displacements is crucial for addressing various societal challenges, including urban design, traffic congestion, epidemic management, and migration dynamics. While predictive models like deep learning and Markov models offer insights into individual mobility, they often struggle with out-of-routine behaviours. Our study introduces an approach that dynamically integrates individual and collective mobility behaviours, leveraging collective intelligence to enhance prediction accuracy. Evaluating the model on millions of privacy-preserving trajectories across three US cities, we demonstrate its superior performance in predicting out-of-routine mobility, surpassing even advanced deep learning methods. Spatial analysis highlights the model's effectiveness near urban areas with a high density of points of interest, where collective behaviours strongly influence mobility. During disruptive events like the COVID-19 pandemic, our model retains predictive capabilities, unlike individual-based models. By bridging the gap between individual and collective behaviours, our approach offers transparent and accurate predictions, crucial for addressing contemporary mobility challenges.

cs.CY↗

arXiv2024

Future Directions in Human Mobility Science

We provide a brief review of human mobility science and present three key areas where we expect to see substantial advancements. We start from the mind and discuss the need to better understand how spatial cognition shapes mobility patterns. We then move to societies and argue the importance of better understanding new forms of transportation. We conclude by discussing how algorithms shape mobility behaviour and provide useful tools for modellers. Finally, we discuss how progress in these research directions may help us address some of the challenges our society faces today.

physics.soc-ph↗

arXiv2024

The traffic concentration effects of urban navigation services

The collective impact of navigation services remains unclear: while often beneficial to individual drivers, they can unintentionally reshape urban traffic patterns. We simulate their impact in Florence, Milan, and Rome (Italy), integrating GPS data, road networks, and route recommendations from leading providers. We identify a concentration effect: as adoption increases, route diversity declines, and traffic and emissions converge onto fewer roads. At full adoption, route diversity decreases by up to 14% compared to a baseline where recommendations are ignored. Moreover, navigation services reduce CO2 emissions at low adoption levels, but these benefits diminish, disappear, or even reverse beyond a city- and service-specific threshold. We replicate our experiments in an abstract setting, obtaining results consistent with those observed in real-world cities.

cs.MA↗

arXiv2024

A survey on the impacts of recommender systems on users, items, and human-AI ecosystems

Recommendation systems and assistants (in short, recommenders) influence through online platforms most actions of our daily lives, suggesting items or providing solutions based on users' preferences or requests. This survey systematically reviews, categories, and discusses the impact of recommenders in four human-AI ecosystems -- social media, online retail, urban mapping and generative AI ecosystems. Its scope is to systematise a fast-growing field in which terminologies employed to classify methodologies and outcomes are fragmented and unsystematic. This is a crucial contribution to the literature because terminologies vary substantially across disciplines and ecosystems, hindering comparison and accumulation of knowledge in the field. We follow the customary steps of qualitative systematic review, gathering 154 articles from different disciplines to develop a parsimonious taxonomy of methodologies employed (empirical, simulation, observational, controlled), outcomes observed (concentration, content degradation, discrimination, diversity, echo chamber, filter bubble, homogenisation, polarisation, radicalisation, volume), and their level of analysis (individual, item, and ecosystem). We systematically discuss substantive and methodological commonalities across ecosystems, and highlight potential avenues for future research. The survey is addressed to scholars and practitioners interested in different human-AI ecosystems, policymakers and institutional stakeholders who want to understand better the measurable outcomes of recommenders, and tech companies who wish to obtain a systematic view of the impact of their recommenders.

cs.IR↗

arXiv2024

Popularity-based Alternative Routing

Alternative routing is crucial to minimize the environmental impact of urban transportation while enhancing road network efficiency and reducing traffic congestion. Existing methods neglect information about road popularity, possibly leading to unintended consequences such as increasing emissions and congestion. This paper introduces Polaris, an alternative routing algorithm that exploits road popularity to optimize traffic distribution and reduce CO2 emissions. Polaris leverages the novel concept of K-road layers, which mitigates the feedback loop effect where redirecting vehicles to less popular roads could increase their popularity in the future. We conduct experiments in three cities to evaluate Polaris against state-of-the-art alternative routing algorithms. Our results demonstrate that Polaris significantly reduces the overuse of highly popular road edges and traversed regulated intersections, showcasing its ability to generate efficient routes and distribute traffic more evenly. Furthermore, Polaris achieves substantial CO2 reductions, outperforming existing alternative routing strategies. Finally, we compare Polaris to an algorithm that coordinates vehicles centrally to distribute them more evenly on the road network. Our findings reveal that Polaris performs comparably well, even with much less information, highlighting its potential as an efficient and sustainable solution for urban traffic management.

cs.MA↗

arXiv2024

Human-AI Coevolution

Human-AI coevolution, defined as a process in which humans and AI algorithms continuously influence each other, increasingly characterises our society, but is understudied in artificial intelligence and complexity science literature. Recommender systems and assistants play a prominent role in human-AI coevolution, as they permeate many facets of daily life and influence human choices on online platforms. The interaction between users and AI results in a potentially endless feedback loop, wherein users' choices generate data to train AI models, which, in turn, shape subsequent user preferences. This human-AI feedback loop has peculiar characteristics compared to traditional human-machine interaction and gives rise to complex and often ``unintended'' social outcomes. This paper introduces Coevolution AI as the cornerstone for a new field of study at the intersection between AI and complexity science focused on the theoretical, empirical, and mathematical investigation of the human-AI feedback loop. In doing so, we: (i) outline the pros and cons of existing methodologies and highlight shortcomings and potential ways for capturing feedback loop mechanisms; (ii) propose a reflection at the intersection between complexity science, AI and society; (iii) provide real-world examples for different human-AI ecosystems; and (iv) illustrate challenges to the creation of such a field of study, conceptualising them at increasing levels of abstraction, i.e., technical, epistemological, legal and socio-political.

cs.AI↗

arXiv2023

Speed Limit: Obey, or Not Obey?

It is commonly expected that drivers maintain a driving speed that is lower than or around the posted speed limit, as failure to obey may result in safety risks and fines. By taking randomly selected road segments as examples, this study compares the percentages of speeding vehicles in five countries worldwide, namely, two European countries (Germany and Italy), two Asian countries (Japan and China), and one North American country (the United States). Contrary to expectations, our results show that more than 80% of drivers violate the posted speed limits in the studied road segments in Italy, Japan, and the United States. In particular, a significant portion (45.3%) of drivers in Italy exceed the posted speed limit by a substantial margin (30 km/h), while few speeding vehicles are observed in the road segment examined in China. Meanwhile, it is found that drivers on low-speed-limit roads are more likely to exceed the posted speed limit, particularly when there are fewer on-road vehicles. The comparison of different countries' speeding fines indicates that for the purpose of preventing speeding, increasing fines (as Italy has done) is less effective than enhancing supervision (as China has done). The findings remind law enforcement agencies and traffic authorities of the importance of the supervision of driver's behavior and the necessity of revisiting the rationale for the current speed limit settings.

physics.soc-ph↗