http://hdl.handle.net/20.500.11937/3 2021-02-23T23:17:25Z http://hdl.handle.net/20.500.11937/82652 A two-layer model for coevolving opinion dynamics and collective decision-making in complex social systems Zino, L.; Ye, Mengbin; Cao, M. © 2020 Author(s). Motivated by the literature on opinion dynamics and evolutionary game theory, we propose a novel mathematical framework to model the intertwined coevolution of opinions and decision-making in a complex social system. In the proposed framework, the members of a social community update their opinions and revise their actions as they learn of others' opinions shared on a communication channel and observe others' actions through an influence channel; these interactions determine a two-layer network structure. We offer an application of the proposed framework by tailoring it to study the adoption of a novel social norm, demonstrating that the model is able to capture the emergence of several real-world collective phenomena such as paradigm shifts and unpopular norms. Through the establishment of analytical conditions and Monte Carlo numerical simulations, we shed light on the role of the coupling between opinion dynamics and decision-making, and of the network structure, in shaping the emergence of complex collective behavior in social systems. 2020-01-01T00:00:00Z http://hdl.handle.net/20.500.11937/82651 Analysis of a nonlinear opinion dynamics model with biased assimilation Xia, W.; Ye, Mengbin; Liu, J.; Cao, M.; Sun, X.M. © 2020 Elsevier Ltd This paper analyzes a nonlinear opinion dynamics model which generalizes the DeGroot model by introducing a bias parameter for each individual. The original DeGroot model is recovered when the bias parameter is equal to zero. The magnitude of this parameter reflects an individual's degree of bias when assimilating new opinions, and depending on the magnitude, an individual is said to have weak, intermediate, and strong bias. The opinions of the individuals lie between 0 and 1. It is shown that for strongly connected networks, the equilibria with all elements equal identically to the extreme value 0 or 1 is locally exponentially stable, while the equilibrium with all elements equal to the neutral consensus value of 1/2 is unstable. Regions of attraction for the extreme consensus equilibria are given. For the equilibrium consisting of both extreme values 0 and 1, which corresponds to opinion polarization according to the model, it is shown that the equilibrium is unstable for all strongly connected networks if individuals all have weak bias, becomes locally exponentially stable for complete and two-island networks if individuals all have strong bias, and its stability heavily depends on the network topology when individuals have intermediate bias. Analysis on star graphs and simulations show that additional equilibria may exist where individuals form clusters. 2020-01-01T00:00:00Z http://hdl.handle.net/20.500.11937/82650 A Coevolutionary Model for Actions and Opinions in Social Networks Zino, L.; Ye, Mengbin; Cao, M. © 2020 IEEE. In complex social networks, the decision-making mechanisms behind human actions and the cognitive processes that shape opinion formation processes are often intertwined, leading to complex and varied collective emergent behavior. In this paper, we propose a mathematical model that captures such a coevolution of actions and opinions. Following a discrete-time process, each individual decides between binary actions, aiming to coordinate with the actions of other members observed on a network of interactions and taking into account their own opinion. At the same time, the opinion of each individual evolves due to the opinions shared by other members, the actions observed on the network, and, possibly, an external influence source. We provide a global convergence result for a special case of the coupled dynamics. Steady state configurations in which all the individuals take the same action are then studied, elucidating the role of the model parameters and the network structure on the collective behavior of the system. 2020-01-01T00:00:00Z http://hdl.handle.net/20.500.11937/82649 Tight bound on parameter of surplus-based averaging algorithm over balanced digraphs Kawamura, S.; Cai, K.; Ye, Mengbin; Lin, Z. © 2018, © 2018 Informa UK Limited, trading as Taylor & Francis Group. We study a continuous-time surplus-based algorithm for multi-agent average consensus, and derive a tight upper bound on the key parameter included in this algorithm that ensures convergence over strongly connected and balanced digraphs. We specialise the upper bound result to undirected (connected) graphs and unweighted cyclic digraphs; in particular, for undirected graphs the algorithm converges for arbitrary positive values of the parameter, and for cyclic digraphs the upper bound on the parameter depends only on the number of agents and may be easily calculated. Moreover, it is suggested through extensive simulation that, for the same number of agents, the upper bound for cyclic digraphs be smaller than that for other strongly connected and possibly unbalanced digraphs; this implies that as long as the parameter satisfies the upper bound for cyclic digraphs, this parameter can work for other digraphs. 2020-01-01T00:00:00Z