41 research outputs found
General scores for accessibility and inequality measures in urban areas
In the last decades, the acceleration of urban growth has led to an
unprecedented level of urban interactions and interdependence. This situation
calls for a significant effort among the scientific community to come up with
engaging and meaningful visualizations and accessible scenario simulation
engines. The present paper gives a contribution in this direction by providing
general methods to evaluate accessibility in cities based on public
transportation data. Through the notion of isochrones, the accessibility
quantities proposed measure the performance of transport systems at connecting
places and people in urban systems. Then we introduce scores rank cities
according to their overall accessibility. We highlight significant inequalities
in the distribution of these measures across the population, which are found to
be strikingly similar across various urban environments. Our results are
released through the interactive platform: www.citychrone.org, aimed at
providing the community at large with a useful tool for awareness and
decision-making
Towards novelty-driven recommender systems
Abstract We get recommendations about everything and in a pervasive way. Recommender systems act like compasses for our journey in complex conceptual spaces and we more and more rely on recommendations to ground most of our decisions. Despite their extraordinary efficiency and reliability, recommender systems are far from being flawless. They display instead serious drawbacks that might seriously reduce our open-mindedness and our capacity of experiencing diversity and possibly conflicting views. In this paper, we carefully investigate the very foundations of recommendation algorithms in order to identify the determinants of what could be the next generation of recommender systems. We postulate that it is possible to overcome the limitations of current recommender systems, by getting inspiration from the way in which people seek for novelties and give value to new experiences. From this perspective, the notion of adjacent possible seems a relevant one to redesign recommender systems in a way that better aligns with the natural inclination of human beings towards new and pleasant experiences. We claim that this new generation of recommenders could help in overcoming the pitfalls of current technologies, namely the tendency towards a lack of diversity, polarization, the emergence of echo-chambers and misinformation
Maximum Entropy Approach for the Prediction of Urban Mobility Patterns
The science of cities is a relatively new and interdisciplinary topic. It
borrows techniques from agent-based modeling, stochastic processes, and partial
differential equations. However, how the cities rise and fall, how they evolve,
and the mechanisms responsible for these phenomena are still open questions.
Scientists have only recently started to develop forecasting tools, despite
their importance in urban planning, transportation planning, and epidemic
spreading modeling. Here, we build a fully interpretable statistical model
that, incorporating only the minimum number of constraints, can predict
different phenomena arising in the city. Using data on the movements of
car-sharing vehicles in different Italian cities, we infer a model using the
Maximum Entropy (MaxEnt) principle. With it, we describe the activity in
different city zones and apply it to activity forecasting and anomaly detection
(e.g., strikes, and bad weather conditions). We compare our method with
different models explicitly made for forecasting: SARIMA models and Deep
Learning Models. We find that MaxEnt models are highly predictive,
outperforming SARIMAs and having similar results as a Neural Network. These
results show how relevant statistical inference can be in building a robust and
general model describing urban systems phenomena. This article identifies the
significant observables for processes happening in the city, with the
perspective of a deeper understanding of the fundamental forces driving its
dynamics.Comment: 14 pages, 7 figure
Efficient team structures in an open-ended cooperative creativity experiment
Understanding how to best form teams to perform creative tasks is a fascinating although elusive problem. Here we propose an experimental setting for studying the performances of a population of individuals committed to an open-ended cooperative creativity task, namely the construction of LEGO artworks. The real-time parallel monitoring of the growth of the artworks and the structure and composition of the dynamically working teams allow identifying the key ingredients of successful teams. Large teams composed of committed and influential people are more effectively building. Also, there exists an optimal fraction of weak ties in the working teams, i.e., an optimal ratio exploit/explore that maximizes the building efficiency.Creativity is progressively acknowledged as the main driver for progress in all sectors of humankind{ extquoteright}s activities: arts, science, technology, business, and social policies. Nowadays, many creative processes rely on many actors collectively contributing to an outcome. The same is true when groups of people collaborate in the solution of a complex problem. Despite the critical importance of collective actions in human endeavors, few works have tackled this topic extensively and quantitatively. Here we report about an experimental setting to single out some of the key determinants of efficient teams committed to an open-ended creative task. In this experiment, dynamically forming teams were challenged to create several artworks using LEGO bricks. The growth rate of the artworks, the dynamical network of social interactions, and the interaction patterns between the participants and the artworks were monitored in parallel. The experiment revealed that larger working teams are building at faster rates and that higher commitment leads to higher growth rates. Even more importantly, there exists an optimal number of weak ties in the social network of creators that maximizes the growth rate. Finally, the presence of influencers within the working team dramatically enhances the building efficiency. The generality of the approach makes it suitable for application in very different settings, both physical and online, whenever a creative collective outcome is required
Complex delay dynamics on railway networks: from universal laws to realistic modelling
Railways are a key infrastructure for any modern country. The reliability and
resilience of this peculiar transportation system may be challenged by
different shocks such as disruptions, strikes and adverse weather conditions.
These events compromise the correct functioning of the system and trigger the
spreading of delays into the railway network on a daily basis. Despite their
importance, a general theoretical understanding of the underlying causes of
these disruptions is still lacking. In this work, we analyse the Italian and
German railway networks by leveraging on the train schedules and actual delay
data retrieved during the year 2015. We use {these} data to infer simple
statistical laws ruling the emergence of localized delays in different areas of
the network and we model the spreading of these delays throughout the network
by exploiting a framework inspired by epidemic spreading models. Our model
offers a fast and easy tool for the preliminary assessment of the
{effectiveness of} traffic handling policies, and of the railway {network}
criticalities.Comment: 32 pages (with appendix), 28 Figures (with appendix), 2 Table
Unsupervised inference approach to facial attractiveness
The perception of facial beauty is a complex phenomenon depending on many,
detailed and global facial features influencing each other. In the machine
learning community this problem is typically tackled as a problem of supervised
inference. However, it has been conjectured that this approach does not capture
the complexity of the phenomenon. A recent original experiment
(Ib\'a\~nez-Berganza et al., Scientific Reports 9, 8364, 2019) allowed
different human subjects to navigate the face-space and ``sculpt'' their
preferred modification of a reference facial portrait. Here we present an
unsupervised inference study of the set of sculpted facial vectors in that
experiment. We first infer minimal, interpretable, and faithful probabilistic
models (through Maximum Entropy and artificial neural networks) of the
preferred facial variations, that capture the origin of the observed
inter-subject diversity in the sculpted faces. The application of such
generative models to the supervised classification of the gender of the
sculpting subjects, reveals an astonishingly high prediction accuracy. This
result suggests that much relevant information regarding the subjects may
influence (and be elicited from) her/his facial preference criteria, in
agreement with the multiple motive theory of attractiveness proposed in
previous works.Comment: main article (10 pages, 4 figures) + supplementary information (22
pages, 10 figures). minor typos corrected. Federico Maggiore added as autho
Geometry of the energy landscape of the self-gravitating ring
We study the global geometry of the energy landscape of a simple model of a
self-gravitating system, the self-gravitating ring (SGR). This is done by
endowing the configuration space with a metric such that the dynamical
trajectories are identified with geodesics. The average curvature and curvature
fluctuations of the energy landscape are computed by means of Monte Carlo
simulations and, when possible, of a mean-field method, showing that these
global geometric quantities provide a clear geometric characterization of the
collapse phase transition occurring in the SGR as the transition from a flat
landscape at high energies to a landscape with mainly positive but fluctuating
curvature in the collapsed phase. Moreover, curvature fluctuations show a
maximum in correspondence with the energy of a possible further transition,
occurring at lower energies than the collapse one, whose existence had been
previously conjectured on the basis of a local analysis of the energy landscape
and whose effect on the usual thermodynamic quantities, if any, is extremely
weak. We also estimate the largest Lyapunov exponent of the SGR using
the geometric observables. The geometric estimate always gives the correct
order of magnitude of and is also quantitatively correct at small
energy densities and, in the limit , in the whole homogeneous
phase.Comment: 20 pages, 12 figure
How populist are parties? : measuring degrees of populism in party manifestos using supervised machine learning
Published online: 15 October 2021One of the main challenges in comparative studies on populism concerns its temporal and spatial measurements within and between a large number of parties and countries. Textual analysis has proved useful for these purposes, and automated methods can further improve research in this direction. Here, we propose a method to derive a score of parties’ levels of populism using supervised machine learning to perform textual analysis on national manifestos. We illustrate the advantages of our approach, which allows for measuring populism for a vast number of parties and countries without resource-intensive human-coding processes and provides accurate, updated information for temporal and spatial comparisons of populism. Furthermore, our method allows for obtaining a continuous score of populism, which ensures more fine-grained analyses of the party landscape while reducing the risk of arbitrary classifications. To illustrate the potential contribution of this score, we use it as a proxy for parties’ levels of populism, analyzing average trends in six European countries from the early 2000s for nearly two decades