scholarly journals Memory-Based Reduced Modelling and Data-Based Estimation of Opinion Spreading

2021 ◽  
Vol 31 (1) ◽  
Author(s):  
Niklas Wulkow ◽  
Péter Koltai ◽  
Christof Schütte
Keyword(s):  
Interaction Network ◽  
Opinion Dynamics ◽  
Agent Based ◽  
Agent Interaction ◽  
Network Topologies ◽  
Projection Formalism ◽  
Nonlinear Autoregressive Model ◽  
The Individual ◽  
Nonlinear Autoregressive ◽  
With Memory

AbstractWe investigate opinion dynamics based on an agent-based model and are interested in predicting the evolution of the percentages of the entire agent population that share an opinion. Since these opinion percentages can be seen as an aggregated observation of the full system state, the individual opinions of each agent, we view this in the framework of the Mori–Zwanzig projection formalism. More specifically, we show how to estimate a nonlinear autoregressive model (NAR) with memory from data given by a time series of opinion percentages, and discuss its prediction capacities for various specific topologies of the agent interaction network. We demonstrate that the inclusion of memory terms significantly improves the prediction quality on examples with different network topologies.

scholarly journals An agent-based model of multi-dimensional opinion dynamics and opinion alignment

2020 ◽  
Author(s):  
Simon Schweighofer ◽  
David Garcia ◽  
Frank Schweitzer
Keyword(s):  
Resistance To Change ◽  
Dimensional Space ◽  
Opinion Dynamics ◽  
Emergent Properties ◽  
Agent Based Model ◽  
Agent Based ◽  
Ideological Dimension ◽  
The Individual ◽  
Dissonance Theory ◽  
Proximity Voting

It is known that individual opinions on different policy issues often align to a dominant ideological dimension (e.g. ``left'' vs. ``right'') and become increasingly polarized. We provide an agent-based model that reproduces these two stylized facts as emergent properties of an opinion dynamics in a multi-dimensional space of continuous opinions. The mechanisms for the change of agents' opinions in this multi-dimensional space are derived from cognitive dissonance theory and structural balance theory. We test assumptions from proximity voting and from directional voting regarding their ability to reproduce the expected emerging properties. We further study how the emotional involvement of agents, i.e. their individual resistance to change opinions, impacts the dynamics. We identify two regimes for the global and the individual alignment of opinions. If the affective involvement is high and shows a large variance across agents, this fosters the emergence of a dominant ideological dimension. Agents align their opinions along this dimension in opposite directions, i.e. create a state of polarization.

scholarly journals Opinion Expression Dynamics in Social Media Chat Groups: An Integrated Quasi-Experimental and Agent-Based Model Approach

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Siyuan Ma ◽  
Hongzhong Zhang
Keyword(s):  
Social Media ◽  
Peer Support ◽  
Online Communication ◽  
Opinion Dynamics ◽  
Agent Based Model ◽  
Agent Based ◽  
Individual Level ◽  
Opinion Expression ◽  
Quasi Experimental ◽  
The Individual

Social media chat groups, such as WeChat and WhatsApp groups, are widely applied in online communication. This research has conducted two studies to examine the individual level and collective level’s opinion dynamics in those groups. The opinion dynamic is driven by two variables, people’s perceived peer support and willingness of opinion expression. The perceived peer support influences the willingness of opinion expression, and the willingness influences the dynamics of real opinion-expression. First, the quasi-experimental study recruited twenty-five participants as the observation group and found that decreasing perceived peer support would significantly increase individuals’ expression willingness to protect his/her opinion. To generalize the individual level findings to a collective level, the second study treated the social media chat groups as an undirected fully-connected social network and simulated people’s opinion expression dynamics with an agent-based model. The simulation indicated that (1) with the help of increased willingness of opinion expression, the minority opinion supporters as a collective did not fall silent but continue to express themselves and (2) increasing willingness of opinion expression would maintain the existence of minority opinion but could not help the minority reverse to the majority.

scholarly journals A Novel Multi-Agent Model for Robustness with Component Failure and Malware Propagation in Wireless Sensor Networks

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4873
Author(s):  
Biao Xu ◽  
Minyan Lu ◽  
Hong Zhang ◽  
Cong Pan
Keyword(s):  
Wireless Sensor ◽  
Component Reliability ◽  
Agent Based ◽  
Agent Model ◽  
Cross Domain ◽  
Malware Propagation ◽  
Network Topologies ◽  
Multi Agent ◽  
Agent Characteristics ◽  
Information Domain

A wireless sensor network (WSN) is a group of sensors connected with a wireless communications infrastructure designed to monitor and send collected data to the primary server. The WSN is the cornerstone of the Internet of Things (IoT) and Industry 4.0. Robustness is an essential characteristic of WSN that enables reliable functionalities to end customers. However, existing approaches primarily focus on component reliability and malware propagation, while the robustness and security of cascading failures between the physical domain and the information domain are usually ignored. This paper proposes a cross-domain agent-based model to analyze the connectivity robustness of a system in the malware propagation process. The agent characteristics and transition rules are also described in detail. To verify the practicality of the model, three scenarios based on different network topologies are proposed. Finally, the robustness of the scenarios and the topologies are discussed.

scholarly journals Task allocation and site fidelity jointly influence foraging regulation in honeybee colonies

2017 ◽  
Vol 4 (8) ◽  
pp. 170344 ◽  
Author(s):  
Thiago Mosqueiro ◽  
Chelsea Cook ◽  
Ramon Huerta ◽  
Jürgen Gadau ◽  
Brian Smith ◽  
...  
Keyword(s):  
Site Fidelity ◽  
Collective Behaviour ◽  
Trade Off ◽  
Agent Based ◽  
Resource Collection ◽  
Group Members ◽  
Collective Foraging ◽  
The Individual ◽  
Colony Level ◽  
And Task

Variation in behaviour among group members often impacts collective outcomes. Individuals may vary both in the task that they perform and in the persistence with which they perform each task. Although both the distribution of individuals among tasks and differences among individuals in behavioural persistence can each impact collective behaviour, we do not know if and how they jointly affect collective outcomes. Here, we use a detailed computational model to examine the joint impact of colony-level distribution among tasks and behavioural persistence of individuals, specifically their fidelity to particular resource sites, on the collective trade-off between exploring for new resources and exploiting familiar ones. We developed an agent-based model of foraging honeybees, parametrized by data from five colonies, in which we simulated scouts, who search the environment for new resources, and individuals who are recruited by the scouts to the newly found resources, i.e. recruits. We varied the persistence of returning to a particular food source of both scouts and recruits and found that, for each value of persistence, there is a different optimal ratio of scouts to recruits that maximizes resource collection by the colony. Furthermore, changes to the persistence of scouts induced opposite effects from changes to the persistence of recruits on the collective foraging of the colony. The proportion of scouts that resulted in the most resources collected by the colony decreased as the persistence of recruits increased. However, this optimal proportion of scouts increased as the persistence of scouts increased. Thus, behavioural persistence and task participation can interact to impact a colony's collective behaviour in orthogonal directions. Our work provides new insights and generates new hypotheses into how variations in behaviour at both the individual and colony levels jointly impact the trade-off between exploring for new resources and exploiting familiar ones.

scholarly journals Modeling Confirmation Bias and Peer Pressure in Opinion Dynamics

2021 ◽  
Vol 9 ◽  
Author(s):  
Longzhao Liu ◽  
Xin Wang ◽  
Xuyang Chen ◽  
Shaoting Tang ◽  
Zhiming Zheng
Keyword(s):  
Public Opinion ◽  
Large Scale ◽  
Confirmation Bias ◽  
Opinion Dynamics ◽  
Peer Pressure ◽  
Homogeneous Population ◽  
Agent Based ◽  
Collective Decisions ◽  
Personal Opinion ◽  
The Impact

Confirmation bias and peer pressure are regarded as the main psychology origins of personal opinion adjustment. Each show substantial impacts on the formation of collective decisions. Nevertheless, few attempts have been made to study how the interplay between these two mechanisms affects public opinion evolution on large-scale social networks. In this paper, we propose an agent-based model of opinion dynamics which incorporates the conjugate effect of confirmation bias (characterized by the population identity scope and initiative adaptation speed) and peer pressure (described by a susceptibility threshold and passive adaptation speed). First, a counterintuitive non-monotonous phenomenon arises in the homogeneous population: the number of opinion clusters first increases and then decreases to one as the population identity scope becomes larger. We then consider heterogeneous populations where “impressionable” individuals with large susceptibility to peer pressure and “confident” individuals with small susceptibility coexist. We find that even a small fraction of impressionable individuals could help eliminate public polarization when population identity scope is relatively large. In particular, the impact of impressionable agents would be greater if these agents are hubs. More intriguingly, while impressionable individuals have randomly distributed initial opinions, most of them would finally evolve to moderates. We highlight the emergence of these “impressionable moderates” who are easily influenced, yet are important in public opinion competition, which may inspire efficient strategies in winning competitive campaigns.

Two-Degree-of-Freedom Helicopter Closed-Loop Identification through a Cascade Controller

2011 ◽  
Vol 403-408 ◽  
pp. 4649-4658 ◽  
Author(s):  
Pouya Ghalei ◽  
Alireza Fatehi ◽  
Mohamadreza Arvan
Keyword(s):  
Closed Loop ◽  
Flight Simulation ◽  
Degree Of Freedom ◽  
Input Output ◽  
Closed Loop Identification ◽  
Practical Algorithm ◽  
Nonlinear Autoregressive Model ◽  
And Control ◽  
Nonlinear Autoregressive ◽  
Two Degree Of Freedom

Input-Output data modeling using multi layer perceptron networks (MLP) for a laboratory helicopter is presented in this paper. The behavior of the two degree-of-freedom platform exemplifies a high order unstable, nonlinear system with significant cross-coupling between pitch and yaw directional motions. This paper develops a practical algorithm for identifying nonlinear autoregressive model with exogenous inputs (NARX) and nonlinear output error model (NOE) through closed loop identification. In order to collect input-output identifier pairs, a cascade state feedback (CSF) controller is introduced to stabilize the helicopter and after that the procedure of system identification is proposed. The estimated models can be utilized for nonlinear flight simulation and control and fault detection studies.

Identification of Important Proteins in Protein Interaction Network Based on SVM

2014 ◽  
Vol 644-650 ◽  
pp. 5202-5206
Author(s):  
Yan Li Zha ◽  
Wan Cheng Luo
Keyword(s):  
Protein Interaction ◽  
Interaction Network ◽  
Biological Information ◽  
Yeast Protein ◽  
Essential Proteins ◽  
Svm Model ◽  
Network Topologies ◽  
Living Organisms ◽  
Yeast Protein Interaction ◽  
Relevant Experiment

Importance of proteins are different to perform functions of cells in living organisms according to the relevant experiment results, and more essential proteins is the most important kind of proteins. There are recently many computational approaches proposed to predict essential proteins in network level through network topologies combined with biological information of proteins. However it is still hard to identify them because of limitations of topological centralities and bioinformatic sources. And more it is the challenge is to perform better with less resources. Therefore in this paper, we first examine the correlation between common topological centralities and essential proteins and choose a few particular centralities, and then to build a SVM model, names as TC-SVM, for predicting the essential proteins. The new method has been applied to a yeast protein interaction networks, which are obtained from the BioGRID database. The ten folds experimental results show that the performance of predicting essential proteins by TC-SVM is excellent.

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