LEADER 04415nam 22006135 450 001 9910484285403321 005 20200701214423.0 010 $a3-030-10606-3 024 7 $a10.1007/978-3-030-10606-5 035 $a(CKB)4100000007656705 035 $a(DE-He213)978-3-030-10606-5 035 $a(MiAaPQ)EBC5717914 035 $a(PPN)243770235 035 $a(EXLCZ)994100000007656705 100 $a20190219d2019 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aOpinion Dynamics and the Evolution of Social Power in Social Networks /$fby Mengbin Ye 205 $a1st ed. 2019. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2019. 215 $a1 online resource (XXIII, 209 p. 53 illus., 51 illus. in color.) 225 1 $aSpringer Theses, Recognizing Outstanding Ph.D. Research,$x2190-5053 311 $a3-030-10605-5 327 $aIntroduction -- Preliminaries -- A Novel Model for Opinion Dynamics Under Pressure to Conform -- The EPO Model?s Connections with Social Psychology Concepts -- Evolution of Social Power in Networks with Constant Topology -- Dynamic Social Networks: Exponential Forgetting of Perceived Social Power -- Modi?cation of Social Dominance in Autocratic Networks -- Nonlinear Mapping Convergence and Application to Social Power Analysis -- Continuous-Time Opinion Dynamics with Interdependent Topics -- Conclusions and Future Work. 330 $aThis book uses rigorous mathematical analysis to advance opinion dynamics models for social networks in three major directions. First, a novel model is proposed to capture how a discrepancy between an individual?s private and expressed opinions can develop due to social pressures that arise in group situations or through extremists deliberately shaping public opinion. Detailed theoretical analysis of the final opinion distribution is followed by use of the model to study Asch?s seminal experiments on conformity, and the phenomenon of pluralistic ignorance. Second, the DeGroot-Friedkin model for evolution of an individual?s social power (self-confidence) is developed in a number of directions. The key result establishes that an individual?s initial social power is forgotten exponentially fast, even when the network changes over time; eventually, an individual?s social power depends only on the (changing) network structure. Last, a model for the simultaneous discussion of multiple logically interdependent topics is proposed. To ensure that a consensus across the opinions of all individuals is achieved, it turns out that the interpersonal interactions must be weaker than an individual?s introspective cognitive process for establishing logical consistency among the topics. Otherwise, the individual may experience cognitive overload and the opinion system becomes unstable. Conclusions of interest to control engineers, social scientists, and researchers from other relevant disciplines are discussed throughout the thesis with support from both social science and control literature. 410 0$aSpringer Theses, Recognizing Outstanding Ph.D. Research,$x2190-5053 606 $aAutomatic control 606 $aMass media 606 $aCommunication 606 $aComputational complexity 606 $aGraph theory 606 $aControl and Systems Theory$3https://scigraph.springernature.com/ontologies/product-market-codes/T19010 606 $aMedia Sociology$3https://scigraph.springernature.com/ontologies/product-market-codes/X22110 606 $aComplexity$3https://scigraph.springernature.com/ontologies/product-market-codes/T11022 606 $aGraph Theory$3https://scigraph.springernature.com/ontologies/product-market-codes/M29020 615 0$aAutomatic control. 615 0$aMass media. 615 0$aCommunication. 615 0$aComputational complexity. 615 0$aGraph theory. 615 14$aControl and Systems Theory. 615 24$aMedia Sociology. 615 24$aComplexity. 615 24$aGraph Theory. 676 $a629.8 676 $a302.3015118 700 $aYe$b Mengbin$4aut$4http://id.loc.gov/vocabulary/relators/aut$01225308 906 $aBOOK 912 $a9910484285403321 996 $aOpinion Dynamics and the Evolution of Social Power in Social Networks$92845014 997 $aUNINA