Preference evolution with deceptive interactions and heterogeneous trust in bounded confidence model: A simulation analysis

Abstract The bounded confidence model is a popular tool to model the evolution of preferences and knowledge in opinion dynamics. In the bounded confidence model, it is assumed that all agents are honest to express their preferences and knowledge. However, in real-life opinion dynamics, agents often hide their true preferences, and express different preferences to different people. In this paper, we propose the evolution of preferences with deceptive interactions and heterogeneous trust in bounded confidence model, in which some agents will express three types of preferences: true preferences, communicated preferences and public preferences. In the proposed model, the communication regimes of the agents are established. Based on the established communication regimes, the true preferences, communicated preferences and public preferences of the agents are updated. Furthermore, we use an agent-based simulation to unfold the influences of the deceptive interactions and heterogeneous trust on the evolutions of preferences.

[1]  Serge Galam,et al.  Modelling rumors: the no plane Pentagon French hoax case , 2002, cond-mat/0211571.

[2]  Gang Kou,et al.  A review on trust propagation and opinion dynamics in social networks and group decision making frameworks , 2019, Inf. Sci..

[3]  Miguel A. Meléndez-Jiménez,et al.  Bounded Confidence under Preferential Flip: A Coupled Dynamics of Structural Balance and Opinions , 2016, PloS one.

[4]  Thilo Gross,et al.  Adaptive coevolutionary networks: a review , 2007, Journal of The Royal Society Interface.

[5]  Enrique Herrera-Viedma,et al.  A visual interaction consensus model for social network group decision making with trust propagation , 2017, Knowl. Based Syst..

[6]  Christian Schulze,et al.  Discretized Opinion Dynamics of the Deffuant Model on Scale-Free Networks , 2004, J. Artif. Soc. Soc. Simul..

[7]  Shui Yu,et al.  Consensus efficiency in group decision making: A comprehensive comparative study and its optimal design , 2019, Eur. J. Oper. Res..

[8]  Zhen Zhang,et al.  Managing Multigranular Linguistic Distribution Assessments in Large-Scale Multiattribute Group Decision Making , 2017, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[9]  Enrique Herrera-Viedma,et al.  Consensus Building for the Heterogeneous Large-Scale GDM With the Individual Concerns and Satisfactions , 2018, IEEE Transactions on Fuzzy Systems.

[10]  Enrique Herrera-Viedma,et al.  On dynamic consensus processes in group decision making problems , 2018, Inf. Sci..

[11]  Gang Kou,et al.  Bounded confidence opinion dynamics with opinion leaders and environmental noises , 2016, Comput. Oper. Res..

[12]  Enrique Herrera-Viedma,et al.  Integrating experts' weights generated dynamically into the consensus reaching process and its applications in managing non-cooperative behaviors , 2016, Decis. Support Syst..

[13]  Francisco Herrera,et al.  Managing consensus based on leadership in opinion dynamics , 2017, Inf. Sci..

[14]  Gang Kou,et al.  A survey on the fusion process in opinion dynamics , 2018, Inf. Fusion.

[15]  Gerardo Iñiguez,et al.  Effects of deception in social networks , 2014, Proceedings of the Royal Society B: Biological Sciences.

[16]  M. Degroot Reaching a Consensus , 1974 .

[17]  Gerardo Iñiguez,et al.  Modeling social dynamics in a collaborative environment , 2014, EPJ Data Science.

[18]  R. Holley,et al.  Ergodic Theorems for Weakly Interacting Infinite Systems and the Voter Model , 1975 .

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

[20]  Dong-Hong Ji,et al.  Neural networks for deceptive opinion spam detection: An empirical study , 2017, Inf. Sci..

[21]  S. Fortunato,et al.  Statistical physics of social dynamics , 2007, 0710.3256.

[22]  F. Chiclana,et al.  Strategic weight manipulation in multiple attribute decision making , 2018 .

[23]  Zhang-peng Tian,et al.  A two-fold feedback mechanism to support consensus-reaching in social network group decision-making , 2018, Knowl. Based Syst..

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

[25]  B. Latané,et al.  From private attitude to public opinion: A dynamic theory of social impact. , 1990 .

[26]  Yucheng Dong,et al.  The fusion process with heterogeneous preference structures in group decision making: A survey , 2015, Inf. Fusion.

[27]  Enrique Herrera-Viedma,et al.  Consensus reaching model in the complex and dynamic MAGDM problem , 2016, Knowl. Based Syst..

[28]  Peter Hegarty,et al.  The Hegselmann-Krause Dynamics for the Continuous-Agent Model and a Regular Opinion Function Do Not Always Lead to Consensus , 2015, IEEE Transactions on Automatic Control.

[29]  Yongsheng Ding,et al.  A social network-based trust-aware propagation model for P2P systems , 2013, Knowl. Based Syst..

[30]  M. Creutz Deterministic Ising dynamics , 1986 .

[31]  Gerardo Iñiguez,et al.  Dynamics of deceptive interactions in social networks , 2015, Journal of The Royal Society Interface.

[32]  Laurent Salzarulo,et al.  A Continuous Opinion Dynamics Model Based on the Principle of Meta-Contrast , 2006, J. Artif. Soc. Soc. Simul..

[33]  M. J. Oliveira,et al.  Isotropic majority-vote model on a square lattice , 1992 .

[34]  Enrique Herrera-Viedma,et al.  Multiple Attribute Strategic Weight Manipulation With Minimum Cost in a Group Decision Making Context With Interval Attribute Weights Information , 2019, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[35]  Sandeep Kumar,et al.  Predicting information diffusion probabilities in social networks: A Bayesian networks based approach , 2017, Knowl. Based Syst..

[36]  Enrique Herrera-Viedma,et al.  A Self-Management Mechanism for Noncooperative Behaviors in Large-Scale Group Consensus Reaching Processes , 2018, IEEE Transactions on Fuzzy Systems.

[37]  R. Berger A Necessary and Sufficient Condition for Reaching a Consensus Using DeGroot's Method , 1981 .

[38]  Yi Peng,et al.  Understanding influence power of opinion leaders in e-commerce networks: An opinion dynamics theory perspective , 2018, Inf. Sci..

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

[40]  S. Galam Minority opinion spreading in random geometry , 2002, cond-mat/0203553.

[41]  S. Fortunato On The Consensus Threshold For The Opinion Dynamics Of Krause–Hegselmann , 2004, cond-mat/0408648.

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

[43]  Adrian Carro,et al.  The Role of Noise and Initial Conditions in the Asymptotic Solution of a Bounded Confidence, Continuous-Opinion Model , 2012, Journal of Statistical Physics.

[44]  Enrique Herrera-Viedma,et al.  Group Decision Making with Heterogeneous Preference Structures: An Automatic Mechanism to Support Consensus Reaching , 2019, Group Decision and Negotiation.

[45]  Mahima Gupta,et al.  Consensus Building Process in Group Decision Making—An Adaptive Procedure Based on Group Dynamics , 2018, IEEE Transactions on Fuzzy Systems.

[46]  Katarzyna Sznajd-Weron,et al.  Opinion evolution in closed community , 2000, cond-mat/0101130.

[47]  Enrique Herrera-Viedma,et al.  Fuzzy Group Decision Making With Incomplete Information Guided by Social Influence , 2018, IEEE Transactions on Fuzzy Systems.

[48]  Weiwei Wang,et al.  Managing non-cooperative behaviors in consensus-based multiple attribute group decision making: An approach based on social network analysis , 2018, Knowl. Based Syst..

[49]  Zechao Li,et al.  Tracking the evolution of overlapping communities in dynamic social networks , 2018, Knowl. Based Syst..

[50]  Francisco Herrera,et al.  A Consensus Model for Large-Scale Linguistic Group Decision Making With a Feedback Recommendation Based on Clustered Personalized Individual Semantics and Opposing Consensus Groups , 2019, IEEE Transactions on Fuzzy Systems.

[51]  Gang Kou,et al.  Consensus reaching in social network group decision making: Research paradigms and challenges , 2018, Knowl. Based Syst..