Ranking with social cues: Integrating online review scores and popularity information

Online marketplaces, search engines, and databases employ aggregated social information to rank their content for users. Two ranking heuristics commonly implemented to order the available options are the average review score and item popularity-that is, the number of users who have experienced an item. These rules, although easy to implement, only partly reflect actual user preferences, as people may assign values to both average scores and popularity and trade off between the two. How do people integrate these two pieces of social information when making choices? We present two experiments in which we asked participants to choose 200 times among options drawn directly from two widely used online venues: Amazon and IMDb. The only information presented to participants was the average score and the number of reviews, which served as a proxy for popularity. We found that most people are willing to settle for items with somewhat lower average scores if they are more popular. Yet, our study uncovered substantial diversity of preferences among participants, which indicates a sizable potential for personalizing ranking schemes that rely on social information.Comment: 4 pages, 3 figures, ICWS

Competing interactions in arrested states of colloidal clays

Using experiments, theory and simulations, we show that the arrested state observed in a colloidal clay at intermediate concentrations is stabilized by the screened Coulomb repulsion (Wigner glass). Dilution experiments allow us to distinguish this high-concentration disconnected state, which melts upon addition of water, from a low-concentration gel state, which does not melt. Theoretical modelling and simulations reproduce the measured Small Angle X-Ray Scattering static structure factors and confirm the long-range electrostatic nature of the arrested structure. These findings are attributed to the different timescales controlling the competing attractive and repulsive interactions.Comment: Accepted for publication in Physical Review Letter

Arrested state of clay-water suspensions: gel or glass?

The aging of a charged colloidal system has been studied by Small Angle X-rays Scattering, in the exchanged momentum range Q=0.03 - 5 nm-1, and by Dynamic Light Scattering, at different clay concentrations (Cw =0.6 % - 2.8 %). The static structure factor, S(Q), has been determined as a function of both aging time and concentration. This is the first direct experimental evidence of the existence and evolution with aging time of two different arrested states in a single system simply obtained only by changing its volume fraction: an inhomogeneous state is reached at low concentrations, while a homogenous one is found at high concentrations.Comment: 5 pages, 2 figure

Etude expérimentale et modélisation des déplacements collectifs de piétons

Qu'elle soit composée de piétons dans une rue commerçante, de supporters quittant un stade, ou de pèlerins à La Mecque, une foule humaine constitue un système dont la dynamique collective est difficile à appréhender. Aujourd'hui, les mécanismes qui sous-tendent la dynamique des foules humaines restent peu connus et sont le plus souvent étudiés de manière qualitative. Ce travail de thèse est une analyse de ces mécanismes. En combinant observations en milieu naturel, expérimentations contrôlées et modélisation mathématique, nous avons mené une étude approfondie du comportement des piétons, de la dynamique globale d'une foule, et du lien qui unit ces deux niveaux d'observation. La réalisation d'expériences impliquant des piétons en interaction nous a permis de caractériser les propriétés du comportement d'évitement, et son rôle dans la dynamique collective. Nos résultats montrent l'existence d'un biais comportemental qui joue un rôle structurant dans le phénomène de formation de files. Nous nous sommes également intéressés aux interactions sociales qui gouvernent le comportement des piétons se déplaçant en groupe. A l'aide d'observations réalisées en milieu urbain, nous avons cherché à comprendre leur rôle dans la configuration de marche des groupes de piétons et leur influence sur l'efficacité du trafic. Enfin, nous proposons une nouvelle approche de modélisation basée sur de simples heuristiques comportementales s'appuyant sur le champ visuel des piétons. Nos travaux permettent d'envisager une meilleure évaluation du trafic piétonnier et ouvrent de nouvelles pistes de recherches pour l'étude d'autres formes de comportements collectifs dans notre société.In a wide variety of social and biological systems, many collective behaviours result from self-organized processes based on local interactions among individuals. Understanding these mechanisms comes down to establishing a link between two distinct levels of observation: the macroscopic patterns displayed at the group level, and the microscopic behaviour of individuals. This work investigates the mechanisms underlying self-organized behaviours in human crowds, such as shoppers in a commercial walkway, supporters leaving a stadium, or pilgrims in Mecca. Using empirical observations in urban environment, controlled laboratory experiments and mathematical modelling, we have studied the behaviour of pedestrians, the nature of their interactions, and the collective patterns of motion. We first conducted laboratory experiments involving interacting pedestrians. From these observations, we extracted a quantitative measurement of the interaction rules. We found the existence of a bias in pedestrian behaviour that is amplified in a collective context and shapes the lane formation phenomenon. Second, we analyzed empirical data collected in natural conditions to study the features of social interactions among people who are walking in groups. We investigated the role of these interactions in group-walking configurations, and we estimated its impact on the traffic efficiency. Finally, we elaborated a new modelling framework for pedestrian behaviour, based on simple behavioural heuristics. Our results suggest applied solutions to evaluate the traffic efficiency in urban environment and open research perspectives for the study of other collective behaviours in social systems

Modeling the desired direction in a force-based model for pedestrian dynamics

We introduce an enhanced model based on the generalized centrifugal force model. Furthermore, the desired direction of pedestrians is investigated. A new approach leaning on the well-known concept of static and dynamic floor-fields in cellular automata is presented. Numerical results of the model are presented and compared with empirical data.Comment: 14 pages 11 figures, submitted to TGF'1

Information use by humans during dynamic route choice in virtual crowd evacuations

We conducted a computer-based experiment with over 450 human participants and used a Bayesian model selection approach to explore dynamic exit route choice mechanisms of individuals in simulated crowd evacuations. In contrast to previous work, we explicitly explore the use of time-dependent and time-independent information in decision-making. Our findings suggest that participants tended to base their exit choices on time-dependent information, such as differences in queue lengths and queue speeds at exits rather than on time-independent information, such as differences in exit widths or exit route length. We found weak support for similar decision-making mechanisms under a stress-inducing experimental treatment. However, under this treatment participants were less able or willing to adjust their original exit choice in the course of the evacuation. Our experiment is not a direct test of behaviour in real evacuations, but it does highlight the role different types of information and stress play in real human decision-making in a virtual environment. Our findings may be useful in identifying topics for future study on real human crowd movements or for developing more realistic agent-based simulations

High-statistics modeling of complex pedestrian avoidance scenarios

Quantitatively modeling the trajectories and behavior of pedestrians walking in crowds is an outstanding fundamental challenge deeply connected with the physics of flowing active matter, from a scientific point of view, and having societal applications entailing individual safety and comfort, from an application perspective. In this contribution, we review a pedestrian dynamics modeling approach, previously proposed by the authors, aimed at reproducing some of the statistical features of pedestrian motion. Comparing with high-statistics pedestrian dynamics measurements collected in real-life conditions (from hundreds of thousands to millions of trajectories), we modeled quantitatively the statistical features of the undisturbed motion (i.e. in absence of interactions with other pedestrians) as well as the avoidance dynamics triggered by a pedestrian incoming in the opposite direction. This was accomplished through (coupled) Langevin equations with potentials including multiple preferred velocity states and preferred paths. In this chapter we review this model, discussing some of its limitations, in view of its extension toward a more complex case: the avoidance dynamics of a single pedestrian walking through a crowd that is moving in the opposite direction. We analyze some of the challenges connected to this case and present extensions to the model capable of reproducing some features of the motion

Crowd behaviour during high-stress evacuations in an immersive virtual environment

Understanding the collective dynamics of crowd movements during stressful emergency situations is central to reducing the risk of deadly crowd disasters. Yet, their systematic experimental study remains a challenging open problem due to ethical and methodological constraints. In this paper, we demonstrate the viability of shared 3D virtual environments as an experimental platform for conducting crowd experiments with real people. In particular, we show that crowds of real human subjects moving and interacting in an immersive 3D virtual environment exhibit typical patterns of real crowds as observed in real-life crowded situations. These include the manifestation of social conventions and the emergence of self-organized patterns during egress scenarios. High-stress evacuation experiments conducted in this virtual environment reveal movements characterized by mass herding and dangerous overcrowding as they occur in crowd disasters. We describe the behavioral mechanisms at play under such extreme conditions and identify critical zones where overcrowding may occur. Furthermore, we show that herding spontaneously emerges from a density effect without the need to assume an increase of the individual tendency to imitate peers. Our experiments reveal the promise of immersive virtual environments as an ethical, cost-efficient, yet accurate platform for exploring crowd behaviour in high-risk situations with real human subjects.Comment: 17 pages, 5 figure

Traffic Instabilities in Self-Organized Pedestrian Crowds

In human crowds as well as in many animal societies, local interactions among individuals often give rise to self-organized collective organizations that offer functional benefits to the group. For instance, flows of pedestrians moving in opposite directions spontaneously segregate into lanes of uniform walking directions. This phenomenon is often referred to as a smart collective pattern, as it increases the traffic efficiency with no need of external control. However, the functional benefits of this emergent organization have never been experimentally measured, and the underlying behavioral mechanisms are poorly understood. In this work, we have studied this phenomenon under controlled laboratory conditions. We found that the traffic segregation exhibits structural instabilities characterized by the alternation of organized and disorganized states, where the lifetime of well-organized clusters of pedestrians follow a stretched exponential relaxation process. Further analysis show that the inter-pedestrian variability of comfortable walking speeds is a key variable at the origin of the observed traffic perturbations. We show that the collective benefit of the emerging pattern is maximized when all pedestrians walk at the average speed of the group. In practice, however, local interactions between slow- and fast-walking pedestrians trigger global breakdowns of organization, which reduce the collective and the individual payoff provided by the traffic segregation. This work is a step ahead toward the understanding of traffic self-organization in crowds, which turns out to be modulated by complex behavioral mechanisms that do not always maximize the group's benefits. The quantitative understanding of crowd behaviors opens the way for designing bottom-up management strategies bound to promote the emergence of efficient collective behaviors in crowds.Comment: Article published in PLoS Computational biology. Freely available here: http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.100244