Inferring the size of a collective of self-propelled Vicsek particles from the random motion of a single unit

nferring the size of a collective from the motion of a few accessible units is a fundamental problem in network science and interdisciplinary physics. Here, we recognize stochasticity as the commodity traded in the units’ interactions. Drawing inspiration from the work of Einstein-Perrin-Smoluchowski on the discontinuous structure of matter, we use the random motion of one unit to identify the footprint of every other unit. Just as the Avogadro’s number can be determined from the Brownian motion of a suspended particle in a liquid, the size of the collective can be inferred from the random motion of any unit. For self-propelled Vicsek particles, we demonstrate an inverse proportionality between the diffusion coefficient of the heading of any particle and the size of the collective. We provide a rigorous method to infer the size of a collective from measurements of a few units, strengthening the link between physics and collective behavior

Using demographics toward efficient data classification in citizen science: a Bayesian approach

Public participation in scientific activities, often called citizen science, offers a possibility to collect and analyze an unprecedentedly large amount of data. However, diversity of volunteers poses a challenge to obtain accurate information when these data are aggregated. To overcome this problem, we propose a classification algorithm using Bayesian inference that harnesses diversity of volunteers to improve data accuracy. In the algorithm, each volunteer is grouped into a distinct class based on a survey regarding either their level of education or motivation to citizen science. We obtained the behavior of each class through a training set, which was then used as a prior information to estimate performance of new volunteers. By applying this approach to an existing citizen science dataset to classify images into categories, we demonstrate improvement in data accuracy, compared to the traditional majority voting. Our algorithm offers a simple, yet powerful, way to improve data accuracy under limited effort of volunteers by predicting the behavior of a class of individuals, rather than attempting at a granular description of each of them

Formation control on Jordan curves based on noisy proximity measurements

The paradigmatic formation control problem of steering a multi-agent system towards a balanced circular formation has been the subject of extensive studies in the control engineering community. Indeed, this is due to the fact that it shares several features with relevant applications such as distributed environmental monitoring or fence-patrolling. However, these applications may also present some relevant differences from the ideal setting such as the curve on which the formation must be achieved not being a circle, or the measurements being neither ideal nor as a continuous information flow. In this work, we attempt to fill this gap between theory and applications by considering the problem of steering a multi-agent system towards a balanced formation on a generic closed curve and under very restrictive assumptions on the information flow amongst the agents. We tackle this problem through an estimation and control strategy that borrows tools from interval analysis to guarantee the robustness that is required in the considered scenario

Modeling human migration under environmental change: a case study of the effect of sea level rise in Bangladesh

Sea level rise (SLR) could have catastrophic consequences worldwide. More than 600 million people currently living in coastal areas may see their livelihood at risk and choose to migrate in the near future. Predicting when, how, and where people could migrate under environmental change is critical to devise effective policy initiatives and improve our preparedness. Here, we propose a modeling framework to predict the effect of SLR on migration patterns from easily accessible geographic and demographic data. The framework adapts the radiation model to capture unwillingness or inability to migrate of affected residents, as well as return migration and cascading effects in migration patterns. We apply the mathematical model to study internal migration in Bangladesh, where we predict a complex and counterintuitive landscape of migration patterns between districts. Our predictions indicate that the impact of SLR on 816,000 people by 2050 will trigger cascading effects in migration patterns throughout the entire country. The population of each of the 64 districts will change, leading to a total variation of 1.3 million people. Migration from inundated regions in the center will trigger non-trivial patterns, including a reduction in the population of the district of the capital Dhaka.This study is part of the collaborative activities carried out under the program of the region of Murcia (Spain): “Groups of Excellence of the region of Murcia, the Fundación Séneca, Science and Technology Agency” project 19884/GERM/15 and “Call for Fellowships for Guest Researcher Stays at Universities and OPIS” project 21144/IV/19, and of the program “STAR 2018” of the University of Naples Federico II and Compagnia di San Paolo, Istituto Banco di Napoli – Fondazione, project ACROSS. M. Porfiri would like to express his gratitude to the Technical University of Cartagena for hosting him during a Sabbatical leave and to acknowledge support from the National Science Foundation under Grant number CMMI 1561134. M. Ruiz Marín would like to acknowledge support from Ministerio de Ciencia e Innovación of Spain under Grant number PID2019-107800GB-I00/ AEI / 10.13039/501100011033

Overconfident agents and evolving financial networks

In this paper, we investigate the impact of agent personality on the complex dynamics taking place in financial markets. Leveraging recent findings, we model the artificial financial market as a complex evolving network: we consider discrete dynamics for the node state variables, which are updated at each trading session, while the edge state variables, which define a network of mutual influence, evolve continuously with time. This evolution depends on the way the agents rank their trading abilities in the network. By means of extensive numerical simulations in selected scenarios, we shed light on the role of overconfident agents in shaping the emerging network topology, thus impacting on the overall market dynamics

A model-based opinion dynamics approach to tackle vaccine hesitancy

: Uncovering the mechanisms underlying the diffusion of vaccine hesitancy is crucial in fighting epidemic spreading. Toward this ambitious goal, we treat vaccine hesitancy as an opinion, whose diffusion in a social group can be shaped over time by the influence of personal beliefs, social pressure, and other exogenous actions, such as pro-vaccine campaigns. We propose a simple mathematical model that, calibrated on survey data, can predict the modification of the pre-existing individual willingness to be vaccinated and estimate the fraction of a population that is expected to adhere to an immunization program. This work paves the way for enabling tools from network control towards the simulation of different intervention plans and the design of more effective targeted pro-vaccine campaigns. Compared to traditional mass media alternatives, these model-based campaigns can exploit the structural properties of social networks to provide a potentially pivotal advantage in epidemic mitigation

Partial containment control over signed graphs

In this paper, we deal with the containment control problem in presence of antagonistic interactions. In particular, we focus on the cases in which it is not possible to contain the entire network due to a constrained number of control signals. In this scenario, we study the problem of selecting the nodes where control signals have to be injected to maximize the number of contained nodes. Leveraging graph condensations, we find a suboptimal and computationally efficient solution to this problem, which can be implemented by solving an integer linear problem. The effectiveness of the selection strategy is illustrated through representative simulations

Steering opinion dynamics via containment control

In this paper, we model the problem of influencing the opinions of groups of individuals as a containment control problem, as in many practical scenarios, the control goal is not full consensus among all the individual opinions, but rather their containment in a certain range, determined by a set of leaders. As in classical bounded confidence models, we consider individuals affected by the confirmation bias, thus tending to influence and to be influenced only if their opinions are sufficiently close. However, here we assume that the confidence level, modeled as a proximity threshold, is not constant and uniform across the individuals, as it depends on their opinions. Specifically, in an extremist society, the most radical agents (i.e., those with the most extreme opinions) have a higher appeal and are capable of influencing nodes with very diverse opinions. The opposite happens in a moderate society, where the more connected (i.e., influential) nodes are those with an average opinion. In three artificial societies, characterized by different levels of extremism, we test through extensive simulations the effectiveness of three alternative containment strategies, where leaders have to select the set of followers they try to directly influence. We found that, when the network size is small, a stochastic time-varying pinning strategy that does not rely on information on the network topology proves to be more effective than static strategies where this information is leveraged, while the opposite happens for large networks where the relevance of the topological information is prevalent