Uncertain opinion formation based on the bounded confidence model

Opinion formation is well used to investigate a consensus or several clusters among the opinions of a group of interaction agents. This study proposes several bounded confidence models to discuss the uncertain opinion formation. In the proposed models, the agents’ various tolerances (zero-tolerance, partial tolerance and complete tolerance) on the uncertain opinions are firstly identified. Then, the relevant communication regimes are given to determine the confidence set, and the updated opinions are further calculated. Finally, we explore the influences of various types of agents and self-support on the average number of clusters through simulation analysis.

[1]  Francisco Herrera,et al.  Consensus under a fuzzy context: Taxonomy, analysis framework AFRYCA and experimental case of study , 2014, Inf. Fusion.

[2]  Jan Lorenz,et al.  Continuous Opinion Dynamics under Bounded Confidence: A Survey , 2007, 0707.1762.

[3]  Evguenii V. Kurmyshev,et al.  Dynamics of bounded confidence opinion in heterogeneous social networks: concord against partial antagonism , 2011, ArXiv.

[4]  R. Axelrod The Dissemination of Culture , 1997 .

[5]  Lotfi A. Zadeh,et al.  The Concepts of a Linguistic Variable and its Application to Approximate Reasoning , 1975 .

[6]  Diemo Urbig,et al.  Opinion Dynamics: the Effect of the Number of Peers Met at Once , 2008, J. Artif. Soc. Soc. Simul..

[7]  Krassimir T. Atanassov,et al.  Intuitionistic fuzzy sets , 1986 .

[8]  S. Redner,et al.  Dynamics of majority rule in two-state interacting spin systems. , 2003, Physical review letters.

[9]  Guillaume Deffuant,et al.  Mixing beliefs among interacting agents , 2000, Adv. Complex Syst..

[10]  Dongwon Lim,et al.  Opinion Formation in the Digital Divide , 2014, J. Artif. Soc. Soc. Simul..

[11]  Yilun Shang,et al.  An agent based model for opinion dynamics with random confidence threshold , 2014, Commun. Nonlinear Sci. Numer. Simul..

[12]  B. Latané,et al.  Statistical mechanics of social impact. , 1992, Physical review. A, Atomic, molecular, and optical physics.

[13]  Sascha Kurz,et al.  On the Hegselmann–Krause conjecture in opinion dynamics , 2014, ArXiv.

[14]  Gerard Weisbuch Bounded confidence and social networks , 2004 .

[15]  Eduardo Conde,et al.  A linear optimization problem to derive relative weights using an interval judgement matrix , 2010, Eur. J. Oper. Res..

[16]  Witold Pedrycz,et al.  A review of soft consensus models in a fuzzy environment , 2014, Inf. Fusion.

[17]  Masoud Asadpour,et al.  Opinion Formation by Informed Agents , 2015 .

[18]  Zeshui Xu,et al.  Qualitative decision making with correlation coefficients of hesitant fuzzy linguistic term sets , 2015, Knowl. Based Syst..

[19]  V. Latora,et al.  VECTOR OPINION DYNAMICS IN A BOUNDED CONFIDENCE CONSENSUS MODEL , 2005, physics/0504017.

[20]  Rainer Hegselmann,et al.  Opinion dynamics and bounded confidence: models, analysis and simulation , 2002, J. Artif. Soc. Soc. Simul..

[21]  Jian-Bo Yang,et al.  Interval weight generation approaches based on consistency test and interval comparison matrices , 2005, Appl. Math. Comput..

[22]  J. Kacprzyk,et al.  A ‘soft’ measure of consensus in the setting of partial (fuzzy) preferences , 1988 .

[23]  V. Torra,et al.  A framework for linguistic logic programming , 2010 .