Opinion influence and evolution in social networks: a Markovian agents model

In this paper, the effect on collective opinions of filtering algorithms managed by social network platforms is modeled and investigated. A stochastic multi-agent model for opinion dynamics is proposed, that accounts for a centralized tuning of the strength of interaction between individuals. The evolution of each individual opinion is described by a Markov chain, whose transition rates are affected by the opinions of the neighbors through influence parameters. The properties of this model are studied in a general setting as well as in interesting special cases. A general result is that the overall model of the social network behaves like a high-dimensional Markov chain, which is viable to Monte Carlo simulation. Under the assumption of identical agents and unbiased influence, it is shown that the influence intensity affects the variance, but not the expectation, of the number of individuals sharing a certain opinion. Moreover, a detailed analysis is carried out for the so-called Peer Assembly, which describes the evolution of binary opinions in a completely connected graph of identical agents. It is shown that the Peer Assembly can be lumped into a birth-death chain that can be given a complete analytical characterization. Both analytical results and simulation experiments are used to highlight the emergence of particular collective behaviours, e.g. consensus and herding, depending on the centralized tuning of the influence parameters.Comment: Revised version (May 2018

Path-tracking of a tractor-trailer vehicle along rectilinear and circular paths: A Lyapunov-based approach

Published versio

Statistical Analysis of Winter Sulphur Dioxide Concentration Data in Vienna

The paper describes two nonlinear regression models, applied to winter daily SO2 concentration data and to the corresponding meteorological data from the metropolitan area of Vienna. The first model accounts for the role of wind speed and temperature (a proxy for emissions due to residential heating) on average SO2 concentration in the area. The second regression has an additional wind direction input and tries to point out the contribution by the industrial emissions (located primarily near the south-eastern border of the area) to concentration in the most polluted subarea. Both models offer a satisfactory fitting performance (e.g., correlations around 0.85 between observed and regression values). However, since model validation is a critical point for regressions, sensitivity tests of model fitting performance are carried out by using various data sets for the estimation of regression coefficients. One of such tests points out that there is an "optimal length" of the data set to be used, namely neither a too short set nor a set including "too past" data offer a satisfactory fitting quality

Real-Time Control of Sulphur Dioxide Emissions from an Industrial Area

Real-time emission control is an air quality policy which is alternative to permanent emission reduction. In general terms, it consists of controlling emission only when a forthcoming episode is forecast. Thus, control costs are lower than costs due to permanent abatement. The natural application is a case characterized by a limited number of polluting sources. In more specific terms, a real-time emission control scheme consists of the following operations at the beginning of each time interval (hour,say): (i) Collect current concentration and meteorological measures by a monitoring network. (ii) Forecast future values of relevant local meteorological variables. (iii) On the basis of information about current concentration values, forecast meteorology and scheduled emissions predict future concentrations. (iv) If future concentrations exceed some reference level, reduce the scheduled emissions. The paper describes a case study [application of scheme (i)-(iv)] to sulphur dioxide pollution from the industrial area in the Venetian lagoon region. The general characteristics are the following: The meteorological predictors [step (ii)] are simple stochastic mathematical predictors. The concentration predictor [step (iii)] is based on a complex forecast algorithm (Kalman predictor). It is derived from the "stochastic version" of the numerical solution of the advection- diffusion partial differential equation. The control policy [step(iv)] is assumed to consist of mixing with cleaner fuel under the constraint of maintaining the production scheduled by each polluting plant. The results of the case study are supplied as cost-effectiveness curves (cost versus effectiveness of the control action)

Modeling vaccination rollouts, SARS-CoV-2 variants and the requirement for non-pharmaceutical interventions in Italy

Despite progress in clinical care for patients with coronavirus disease 2019 (COVID-19)1, population-wide interventions are still crucial to manage the pandemic, which has been aggravated by the emergence of new, highly transmissible variants. In this study, we combined the SIDARTHE model2, which predicts the spread of SARS-CoV-2 infections, with a new data-based model that projects new cases onto casualties and healthcare system costs. Based on the Italian case study, we outline several scenarios: mass vaccination campaigns with different paces, different transmission rates due to new variants and different enforced countermeasures, including the alternation of opening and closure phases. Our results demonstrate that non-pharmaceutical interventions (NPIs) have a higher effect on the epidemic evolution than vaccination alone, advocating for the need to keep NPIs in place during the first phase of the vaccination campaign. Our model predicts that, from April 2021 to January 2022, in a scenario with no vaccine rollout and weak NPIs (R = 1.27), as many as 298,000 deaths associated with COVID-19 could occur. However, fast vaccination rollouts could reduce mortality to as few as 51,000 deaths. Implementation of restrictive NPIs (R = 0.9) could reduce COVID-19 deaths to 30,000 without vaccinating the population and to 18,000 with a fast rollout of vaccines. We also show that, if intermittent open\u2013close strategies are adopted, implementing a closing phase first could reduce deaths (from 47,000 to 27,000 with slow vaccine rollout) and healthcare system costs, without substantive aggravation of socioeconomic losses

Almost sure stability of discrete-time Markov Jump Linear Systems

This paper deals with transient analysis and almost sure stability for discrete-time Markov Jump Linear System (MJLS). The expectation of sojourn time and activation number of any mode, and switching number between any two modes of discrete-time MJLS are presented firstly. Then a result on transient behavior analysis of discrete-time MJLS is given. Finally a new deterministically testable condition for the exponential almost sure stability of discrete-time MJLS is proposed

Comparison of two novel MRAS strategies for identifying parameters in permanent magnet synchronous motors

Two Model Reference Adaptive System (MRAS) estimators are developed for identifying the parameters of permanent magnet synchronous motors (PMSM) based on Lyapunov stability theorem and Popov stability criterion, respectively. The proposed estimators only need online detection of currents, voltages and rotor rotation speed, and are effective in the estimation of stator resistance, inductance and rotor flux-linkage simultaneously. Their performances are compared and verified through simulations and experiments. It shows that the two estimators are simple and have good robustness against parameter variation and are accurate in parameter tracking. However, the estimator based on Popov stability criterion, which can overcome the parameter variation in a practical system, is superior in terms of response speed and convergence speed since there are both proportional and integral units in the estimator in contrast to only one integral unit in the estimator based on Lyapunov stability theorem. In addition, there is no need of the expert experience which is required in designing a Lyapunov function

Effect of social influence on a two-party election: A Markovian multiagent model

In digital social networks, the filtering algorithms employed by the platform management to sieve the contents shared among the users can alter the social influence intensity. In this paper, a Markov multi-agent model of opinion dynamics is used to analyze possible opinion manipulation under apparently neutral interventions on the influence intensity. We consider a two-party election whose voters, modeled as heterogeneous agents, are connected in a social network with arbitrary topology. The equations describing the variance of the vote share, both in transient and steady state, are derived. The key is the solution of the second-order marginalization problem under the form of a numerically tractable characterization of pairwise joint probabilities of the voters' opinions. In particular, these probabilities are computed by means of a Lyapunov-like matrix differential equation driven by first-order moments. This result is used to answer some important questions, like the possible nonmonotonic effect of the influence intensity on the vote volatility and the interplay of topology and individuals' stubborness to determine the electoral balance between two parties