1,720,967 research outputs found
Kinetic Equations for Many-Agent Systems On Large Networks: Emerging Patterns and Data-Oriented Approaches
Full text linkQuesta tesi presenta vari modelli cinetici (principalmente di tipo Boltzmann) per sistemi multi-agente su reti sociali. Ogni capitolo si basa in gran parte su lavori originali pubblicati in precedenza su riviste peer-reviewed o come articolo preprint in fase di revisione al momento della stesura. La tesi è divisa in due parti. La prima è dedicata ai processi di diffusione all'interno di una popolazione, sia di una malattia infettiva canonicamente intesa, sia di una più astratta - ma comunque allarmante - come la disinformazione. La seconda parte si occupa direttamente delle reti: ci concentriamo sia sulle dinamiche in cui le reti sono la struttura sottostante all'interno della popolazione e studiamo principalmente i sistemi multi-agente che evolvono su questa struttura (statica o dinamica), sia sulle dinamiche in cui la rete stessa è modellata come un sistema multi-agente e studiamo soprattutto la sua topologia. Il nostro principale sforzo di ricerca è quello di fornire modelli che possano essere applicati a uno scenario reale: di conseguenza, completiamo il nostro studio delle proprietà analitiche dei modelli proposti ottenendo approssimazioni surrogate attraverso opportune procedure di limite di campo medio. Questo ci permette di descrivere accuratamente il comportamento dei sistemi nel tempo, l'evoluzione delle grandezze macroscopiche di interesse e, infine, di calibrare efficacemente il modello corrispondente ai dati disponibili.This thesis presents various kinetic models (mainly of Boltzmann-type) for multi-agent systems over social networks. Each chapter is largely based on original work either previously published in peer-reviewed journals or as preprint article still under review at the time of writing.
The thesis is divided into two parts. The first one is devoted to diffusion processes within a population, either of a canonically intended infectious disease or of a more abstract - but still extremely concerning - one such as misinformation. The second part deals with networks more directly: we focus either on dynamics where networks are the underlying structure within the population and we primarily study multi-agent systems that evolve over this structure (either static or dynamic), or on dynamics where the network itself is modeled after a multi-agent system and our primary focus is on its topology.
Our main research effort is to provide models that can be applied to a real-world scenario: consequently, we complete our study of the analytical properties of the proposed models by obtaining surrogate approximations via suitable mean-field limit procedures. This allows us to accurately describe the large-time behavior of the systems, the evolution of macroscopic quantities of interest and, finally, to effectively calibrate the corresponding model to available data
Spreading of fake news, competence, and learning: kinetic modeling and numerical approximation
Full text linkThe rise of social networks as the primary means of communication in almost
every country in the world has simultaneously triggered an increase in the
amount of fake news circulating online. This fact became particularly evident
during the 2016 U.S. political elections and even more so with the advent of
the COVID-19 pandemic. Several research studies have shown how the effects of
fake news dissemination can be mitigated by promoting greater competence
through lifelong learning and discussion communities, and generally rigorous
training in the scientific method and broad interdisciplinary education. The
urgent need for models that can describe the growing infodemic of fake news has
been highlighted by the current pandemic. The resulting slowdown in vaccination
campaigns due to misinformation and generally the inability of individuals to
discern the reliability of information is posing enormous risks to the
governments of many countries. In this research using the tools of kinetic
theory we describe the interaction between fake news spreading and competence
of individuals through multi-population models in which fake news spreads
analogously to an infectious disease with different impact depending on the
level of competence of individuals. The level of competence, in particular, is
subject to an evolutionary dynamic due to both social interactions between
agents and external learning dynamics. The results show how the model is able
to correctly describe the dynamics of diffusion of fake news and the important
role of competence in their containment
Breaking Consensus in Kinetic Opinion Formation Models on Graphons
No full textIn this work, we propose and investigate a strategy to prevent consensus in kinetic models for opinion formation. We consider a large interacting agent system and assume that agent interactions are driven by compromise as well as self-thinking dynamics and also modulated by an underlying static social network. This network structure is included using so-called graphons, which modulate the interaction frequency in the corresponding kinetic formulation. We then derive the corresponding limiting Fokker–Planck equation and analyze its large time behavior. This microscopic setting serves as a starting point for the proposed control strategy, which steers agents away from mean opinion and is characterized by a suitable penalization depending on the properties of the graphon. We show that this minimalist approach is very effective by analyzing the quasi-stationary solution mean-field model in a plurality of graphon structures. Several numerical experiments are also provided to show the effectiveness of the approach in preventing the formation of consensus steering the system toward a declustered state
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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