arXiv subjects

Giacomo Livan

Publications and source records attributed to Giacomo Livan.

At least 37 records · Page 2Linked to original sources

arXiv2021

Interdisciplinary researchers attain better performance in funding

Interdisciplinary research is fundamental when it comes to tackling complex problems in our highly interlinked world, and is on the rise globally. Yet, it is unclear why--in an increasingly competitive academic environment--one should pursue an interdisciplinary career given its recent negative press. Several studies have indeed shown that interdisciplinary research often achieves lower impact compared to more specialized work, and is less likely to attract funding. We seek to reconcile such evidence by analyzing a dataset of 44,419 research grants awarded between 2006 and 2018 from the seven national research councils in the UK. We compared the research performance of researchers with an interdisciplinary funding track record with those who have a specialized profile. We found that the former dominates the network of academic collaborations, both in terms of centrality and knowledge brokerage; but such a competitive advantage does not immediately translate into impact. Indeed, by means of a matched pair experimental design, we found that researchers who transcend between disciplines on average achieve lower impacts in their publications than the subject specialists in the short run, but eventually outperform them in funding performance, both in terms of volume and value. Our results suggest that launching an interdisciplinary career may require more time and persistence to overcome extra challenges, but can pave the way for a more successful endeavour.

cs.DL↗

arXiv2021

Macroeconomic forecasting with statistically validated knowledge graphs

This study leverages narrative from global newspapers to construct theme-based knowledge graphs about world events, demonstrating that features extracted from such graphs improve forecasts of industrial production in three large economies compared to a number of benchmarks. Our analysis relies on a filtering methodology that extracts "backbones" of statistically significant edges from large graph data sets. We find that changes in the eigenvector centrality of nodes in such backbones capture shifts in relative importance between different themes significantly better than graph similarity measures. We supplement our results with an interpretability analysis, showing that the theme categories "disease" and "economic" have the strongest predictive power during the time period that we consider. Our work serves as a blueprint for the construction of parsimonious - yet informative - theme-based knowledge graphs to monitor in real time the evolution of relevant phenomena in socio-economic systems.

physics.soc-ph↗

arXiv2021

Macroeconomic forecasting through news, emotions and narrative

This study proposes a new method of incorporating emotions from newspaper articles into macroeconomic forecasts, attempting to forecast industrial production and consumer prices leveraging narrative and sentiment from global newspapers. For the most part, existing research includes positive and negative tone only to improve macroeconomic forecasts, focusing predominantly on large economies such as the US. These works use mainly anglophone sources of narrative, thus not capturing the entire complexity of the multitude of emotions contained in global news articles. This study expands the existing body of research by incorporating a wide array of emotions from newspapers around the world - extracted from the Global Database of Events, Language and Tone (GDELT) - into macroeconomic forecasts. We present a thematic data filtering methodology based on a bi-directional long short term memory neural network (Bi-LSTM) for extracting emotion scores from GDELT and demonstrate its effectiveness by comparing results for filtered and unfiltered data. We model industrial production and consumer prices across a diverse range of economies using an autoregressive framework, and find that including emotions from global newspapers significantly improves forecasts compared to three autoregressive benchmark models. We complement our forecasts with an interpretability analysis on distinct groups of emotions and find that emotions associated with happiness and anger have the strongest predictive power for the variables we predict.

cs.CY↗

arXiv2020

The Impact Of Noise And Topology On Opinion Dynamics In Social Networks

We investigate the impact of noise and topology on opinion diversity in social networks. We do so by extending well-established models of opinion dynamics to a stochastic setting where agents are subject both to assimilative forces by their local social interactions, as well as to idiosyncratic factors preventing their population from reaching consensus. We model the latter to account for both scenarios where noise is entirely exogenous to peer influence and cases where it is instead endogenous, arising from the agents' desire to maintain some uniqueness in their opinions. We derive a general analytical expression for opinion diversity, which holds for any network and depends on the network's topology through its spectral properties alone. Using this expression, we find that opinion diversity decreases as communities and clusters are broken down. We test our predictions against data describing empirical influence networks between major news outlets and find that incorporating our measure in linear models for the sentiment expressed by such sources on a variety of topics yields a notable improvement in terms of explanatory power.

cs.SI↗

arXiv2020

Correspondence between temporal correlations in time series, inverse problems, and the Spherical Model

In this paper we employ methods from Statistical Mechanics to model temporal correlations in time series. We put forward a methodology based on the Maximum Entropy principle to generate ensembles of time series constrained to preserve part of the temporal structure of an empirical time series of interest. We show that a constraint on the lag-one autocorrelation can be fully handled analytically, and corresponds to the well known Spherical Model of a ferromagnet. We then extend such a model to include constraints on more complex temporal correlations by means of perturbation theory, showing that this leads to substantial improvements in capturing the lag-one autocorrelation in the variance. We apply our approach on synthetic data, and illustrate how it can be used to formulate expectations on the future values of a data generating process.

cond-mat.stat-mech↗

arXiv2020

Maximum Entropy approach to multivariate time series randomization

Natural and social multivariate systems are commonly studied through sets of simultaneous and time-spaced measurements of the observables that drive their dynamics, i.e., through sets of time series. Typically, this is done via hypothesis testing: the statistical properties of the empirical time series are tested against those expected under a suitable null hypothesis. This is a very challenging task in complex interacting systems, where statistical stability is often poor due to lack of stationarity and ergodicity. Here, we describe an unsupervised, data-driven framework to perform hypothesis testing in such situations. This consists of a statistical mechanical approach - analogous to the configuration model for networked systems - for ensembles of time series designed to preserve, on average, some of the statistical properties observed on an empirical set of time series. We showcase its possible applications with a case study on financial portfolio selection.

q-fin.ST↗

arXiv2020

A network perspective on intermedia agenda-setting

In Communication Theory, intermedia agenda-setting refers to the influence that different news sources may have on each other, and how this subsequently affects the breadth of information that is presented to the public. Several studies have attempted to quantify the impact of intermedia agenda-setting in specific countries or contexts, but a large-scale, data-driven investigation is still lacking. Here, we operationalise intermedia agenda-setting by putting forward a methodology to infer networks of influence between different news sources on a given topic, and apply it on a large dataset of news articles published by globally and locally prominent news organisations in 2016. We find influence to be significantly topic-dependent, with the same news sources acting as agenda-setters (i.e., central nodes) with respect to certain topics and as followers (i.e., peripheral nodes) with respect to others. At the same time, we find that the influence networks associated with most topics exhibit small world properties, which we find to play a significant role towards the overall diversity of sentiment expressed about the topic by the news sources in the network. In particular, we find clustering and density of influence networks to act as competing forces in this respect, with the former increasing and the latter reducing diversity.

physics.soc-ph↗

arXiv2020

When facts fail: Bias, polarisation and truth in social networks

Online social networks provide users with unprecedented opportunities to engage with diverse opinions. At the same time, they enable confirmation bias on large scales by empowering individuals to self-select narratives they want to be exposed to. A precise understanding of such tradeoffs is still largely missing. We introduce a social learning model where most participants in a network update their beliefs unbiasedly based on new information, while a minority of participants reject information that is incongruent with their preexisting beliefs. This simple mechanism generates permanent opinion polarization and cascade dynamics, and accounts for the aforementioned tradeoff between confirmation bias and social connectivity through analytic results. We investigate the model's predictions empirically using US county-level data on the impact of Internet access on the formation of beliefs about global warming. We conclude by discussing policy implications of our model, highlighting the downsides of debunking and suggesting alternative strategies to contrast misinformation.

physics.soc-ph↗

arXiv2019

Don't follow the leader: How ranking performance reduces meritocracy

In the name of meritocracy, modern economies devote increasing amounts of resources to quantifying and ranking the performance of individuals and organisations. Rankings send out powerful signals, which lead to identify the actions of top performers as the `best practices' that others should also adopt. However, several studies have shown that the imitation of best practices often leads to a drop in performance. So, should those lagging behind in a ranking imitate top performers or should they instead pursue a strategy of their own? I tackle this question by numerically simulating a stylised model of a society whose agents seek to climb a ranking either by imitating the actions of top performers or by randomly trying out different actions, i.e., via serendipity. The model gives rise to a rich phenomenology, showing that the imitation of top performers increases welfare overall, but at the cost of higher inequality. Indeed, the imitation of top performers turns out to be a self-defeating strategy that consolidates the early advantage of a few lucky - and not necessarily talented - winners, leading to a very unequal, homogenised, and effectively non-meritocratic society. Conversely, serendipity favours meritocratic outcomes and prevents rankings from freezing.

physics.soc-ph↗

arXiv2019

Achieving competitive advantage in academia through early career coauthorship with top scientists

We quantify the long term impact that the coauthorship with established top-cited scientists has on the career of junior researchers in four different scientific disciplines. Through matched pair analysis, we find that junior researchers who coauthor work with top scientists enjoy a persistent competitive advantage throughout the rest of their careers with respect to peers with similar early career profiles. Such a competitive advantage materialises as a higher probability of repeatedly coauthoring work with top-cited scientists, and, ultimately, as a higher probability of becoming one. Notably, we find that the coauthorship with a top scientist has the strongest impact on the careers of junior researchers affiliated with less prestigious institutions. As a consequence, we argue that such institutions may hold vast amounts of untapped potential, which may be realised by improving access to top scientists.

physics.soc-ph↗

arXiv2019

Offline Biases in Online Platforms: a Study of Diversity and Homophily in Airbnb

How diverse are sharing economy platforms? Are they fair marketplaces, where all participants operate on a level playing field, or are they large-scale online aggregators of offline human biases? Often portrayed as easy-to-access digital spaces whose participants receive equal opportunities, such platforms have recently come under fire due to reports of discriminatory behaviours among their users, and have been associated with gentrification phenomena that exacerbate preexisting inequalities along racial lines. In this paper, we focus on the Airbnb sharing economy platform, and analyse the diversity of its user base across five large cities. We find it to be predominantly young, female, and white. Notably, we find this to be true even in cities with a diverse racial composition. We then introduce a method based on the statistical analysis of networks to quantify behaviours of homophily, heterophily and avoidance between Airbnb hosts and guests. Depending on cities and property types, we do find signals of such behaviours relating both to race and gender. We use these findings to provide platform design recommendations, aimed at exposing and possibly reducing the biases we detect, in support of a more inclusive growth of sharing economy platforms.

cs.SI↗

arXiv2019

A parametric approach to information filtering in complex networks: The Pólya filter

The ever increasing availability of data demands for techniques to extract relevant information from complex interacting systems, which can often be represented as weighted networks. In recent years, a number of approaches have been proposed to extract network backbones by assessing the statistical significance of links against null hypotheses of random interaction. Yet, it is well known that the growth of most real-world networks is highly non-random, as past interactions between nodes typically increase the likelihood of further interaction. Here, we propose a network filtering methodology based on a family of null hypotheses that can be calibrated to assess which links are statistically significant with respect to a given network's own heterogeneity. We design such family of null hypotheses by adapting the Pólya urn, a simple one-parameter combinatorial model driven by a self-reinforcement mechanism, to a network setting. We provide a full analytical description of our filter, and show that it retains or discards links based on a non-trivial interplay between their own local importance and the importance of the nodes they belong to. We prove that the widely used disparity filter can be recovered as a large-strength approximation for a specific value of the Pólya filter's parameter, and illustrate our findings by applying the filter to two large network datasets.

physics.soc-ph↗

arXiv2018

Reciprocity and success in academic careers

The growing importance of citation-based bibliometric indicators in shaping the prospects of academic careers incentivizes scientists to boost the numbers of citations they receive. Whereas the exploitation of self-citations has been extensively documented, the impact of reciprocated citations has not yet been studied. We study reciprocity in a citation network of authors, and compare it with the average reciprocity computed in an ensemble of null network models. We show that obtaining citations through reciprocity correlates negatively with a successful career in the long term. Nevertheless, at the aggregate level we show evidence of a steady increase in reciprocity over the years, largely fuelled by the exchange of citations between coauthors. Our results characterize the structure of author networks in a time of increasing emphasis on citation-based indicators, and we discuss their implications towards a fairer assessment of academic impact.

physics.soc-ph↗

arXiv2018

Digital Identity: The Effect of Trust and Reputation Information on User Judgement in the Sharing Economy

The Sharing Economy (SE) is a growing ecosystem focusing on peer-to-peer enterprise. In the SE the information available to assist individuals (users) in making decisions focuses predominantly on community generated trust and reputation information. However, how such information impacts user judgement is still being understood. To explore such effects, we constructed an artificial SE accommodation platform where we varied the elements related to hosts' digital identity, measuring users' perceptions and decisions to interact. Across three studies, we find that trust and reputation information increases not only the users' perceived trustworthiness, credibility, and sociability of hosts, but also the propensity to rent a private room in their home. This effect is seen when providing users both with complete profiles and profiles with partial user-selected information. Closer investigations reveal that three elements relating to the host's digital identity are sufficient to produce such positive perceptions and increased rental decisions, regardless of which three elements are presented. Our findings have relevant implications for human judgment and privacy in the SE, and question its current culture of ever increasing information-sharing.

cs.CY↗

arXiv2017

Introduction to Random Matrices - Theory and Practice

This is a book for absolute beginners. If you have heard about random matrix theory, commonly denoted RMT, but you do not know what that is, then welcome!, this is the place for you. Our aim is to provide a truly accessible introductory account of RMT for physicists and mathematicians at the beginning of their research career. We tried to write the sort of text we would have loved to read when we were beginning Ph.D. students ourselves. Our book is structured with light and short chapters, and the style is informal. The calculations we found most instructive are spelt out in full. Particular attention is paid to the numerical verification of most analytical results. Our book covers standard material - classical ensembles, orthogonal polynomial techniques, spectral densities and spacings - but also more advanced and modern topics - replica approach and free probability - that are not normally included in elementary accounts on RMT. This book is dedicated to the fond memory of Oriol Bohigas.

math-ph↗

arXiv2017

Excess reciprocity distorts reputation in online social networks

The peer-to-peer (P2P) economy relies on establishing trust in distributed networked systems, where the reliability of a user is assessed through digital peer-review processes that aggregate ratings into reputation scores. Here we present evidence of a network effect which biases digital reputation, revealing that P2P networks display exceedingly high levels of reciprocity. In fact, these are much higher than those compatible with a null assumption that preserves the empirically observed level of agreement between all pairs of nodes, and rather close to the highest levels structurally compatible with the networks' reputation landscape. This indicates that the crowdsourcing process underpinning digital reputation can be significantly distorted by the attempt of users to mutually boost reputation, or to retaliate, through the exchange of ratings. We uncover that the least active users are predominantly responsible for such reciprocity-induced bias, and that this fact can be exploited to obtain more reliable reputation estimates. Our findings are robust across different P2P platforms, including both cases where ratings are used to vote on the content produced by users and to vote on user profiles.

physics.soc-ph↗

arXiv2017

Statistical mechanics of complex economies

In the pursuit of ever increasing efficiency and growth, our economies have evolved to remarkable degrees of complexity, with nested production processes feeding each other in order to create products of greater sophistication from less sophisticated ones, down to raw materials. The engine of such an expansion have been competitive markets that, according to General Equilibrium Theory (GET), achieve efficient allocations under specific conditions. We study large random economies within the GET framework, as templates of complex economies, and we find that a non-trivial phase transition occurs: the economy freezes in a state where all production processes collapse when either the number of primary goods or the number of available technologies fall below a critical threshold. As in other examples of phase transitions in large random systems, this is an unintended consequence of the growth in complexity. Our findings suggest that the Industrial Revolution can be regarded as a sharp transition between different phases, but also imply that well developed economies can collapse if too many intermediate goods are introduced.

econ.GN↗

arXiv2015

Invariant sums of random matrices and the onset of level repulsion

We compute analytically the joint probability density of eigenvalues and the level spacing statistics for an ensemble of random matrices with interesting features. It is invariant under the standard symmetry groups (orthogonal and unitary) and yet the interaction between eigenvalues is not Vandermondian. The ensemble contains real symmetric or complex hermitian matrices $\mathbf{S}$ of the form $\mathbf{S}=\sum_{i=1}^M \langle \mathbf{O}_i \mathbf{D}_i\mathbf{O}_i^{\mathrm{T}}\rangle$ or $\mathbf{S}=\sum_{i=1}^M \langle \mathbf{U}_i \mathbf{D}_i\mathbf{U}_i^\dagger\rangle$ respectively. The diagonal matrices $\mathbf{D}_i=\mathrm{diag}\{λ_1^{(i)},\ldots,λ_N^{(i)}\}$ are constructed from real eigenvalues drawn \emph{independently} from distributions $p^{(i)}(x)$, while the matrices $\mathbf{O}_i$ and $\mathbf{U}_i$ are all orthogonal or unitary. The average $\langle\cdot\rangle$ is simultaneously performed over the symmetry group and the joint distribution of $\{λ_j^{(i)}\}$. We focus on the limits i.) $N\to\infty$ and ii.) $M\to\infty$, with $N=2$. In the limit i.), the resulting sum $\mathbf{S}$ develops level repulsion even though the original matrices do not feature it, and classical RMT universality is restored asymptotically. In the limit ii.) the spacing distribution attains scaling forms that are computed exactly: for the orthogonal case, we recover the $β=1$ Wigner's surmise, while for the unitary case an entirely new universal distribution is obtained. Our results allow to probe analytically the microscopic statistics of the sum of random matrices that become asymptotically free. We also give an interpretation of this model in terms of radial random walks in a matrix space. The analytical results are corroborated by numerical simulations.

cond-mat.stat-mech↗