Opinion evolution influenced by informed agents

Fan, Kangqi; Pedrycz, Witold

doi:10.1016/j.physa.2016.06.110

Cited by 34 publications

(18 citation statements)

References 46 publications

(122 reference statements)

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“…In order to focus on the impact of factors (CF7) and (CF3) on the emerging coevolutionary dynamics, we study fully connected networks [43]. The impact of the selection rule (I) (CF7) is assessed in terms of 4 different probabilistic selection rules (Definition 2, Table 1).…”

Section: Estimating the Dynamics Of Network Co-evolutionmentioning

confidence: 99%

Intelligent Agents in Co-Evolving Knowledge Networks

2021

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We extend the agent-based models for knowledge diffusion in networks, restricted to random mindless interactions and to “frozen” (static) networks, in order to take into account intelligent agents and network co-evolution. Intelligent agents make decisions under bounded rationality. This is the key distinction of intelligent interacting agents compared to mindless colliding molecules, involved in the usual diffusion mechanism resulting from accidental collisions. The co-evolution of link weights and knowledge levels is modeled at the local microscopic level of “agent-to-agent” interaction. Our network co-evolution model is actually a “learning mechanism”, where weight updates depend on the previous values of both weights and knowledge levels. The goal of our work is to explore the impact of (a) the intelligence of the agents, modeled by the selection-decision rule for knowledge acquisition, (b) the innovation rate of the agents, (c) the number of “top innovators” and (d) the network size. We find that rational intelligent agents transform the network into a “centralized world”, reducing the entropy of their selections-decisions for knowledge acquisition. In addition, we find that the average knowledge, as well as the “knowledge inequality”, grow exponentially.

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Section: Estimating the Dynamics Of Network Co-evolutionmentioning

confidence: 99%

Intelligent Agents in Co-Evolving Knowledge Networks

2021

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“…Promoters influence individuals towards the promoting attitude ψ + = 1, while at the same time, Adversaries influence individuals towards the adverse attitude ψ − = −1 Promoters and adversaries are purposely disseminating targeted messages, beliefs, values and behaviors, influencing the individuals of the network so that their attitude (of the individuals) to become eventually close to the "target" attitude ψ target , which is equal to ψ + = 1 (if the influential agent is a "promoter") or equal to ψ − = −1 (if the influential agent is an "adversary"). We assume that "promoters" (ψ + = 1) and "adversaries" (ψ − = −1) have always the same attitude, like "informed" or "stubborn" agents [50][51][52][53].…”

Section: The Scenarios Of Organizational Conflictmentioning

confidence: 99%

Promoters versus Adversaries of Change: Agent-Based Modeling of Organizational Conflict in Co-Evolving Networks

2020

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The social adoption of change is usually hard because in reality, forces opposing the social adoption of change manifest. This situation of organizational conflict corresponds to the case where two competing groups of influential agents (“promoters” versus “adversaries” of change) operate concurrently within the same organizational network. We model and explore the co-evolution of interpersonal ties and attitudes in the presence of conflict, taking into account explicitly the microscopic “agent-to-agent” interactions. In this perspective, we propose a new ties-attitudes co-evolution model where the diffusion of attitudes depends on the weights and the evolution of weights is formulated as a “learning mechanism” (weight updates depend on the previous values of both weights and attitudes). As a result, the co-evolution is intrinsic/endogenous. We simulate representative scenarios of conflict in 4 real organizational networks. In order to formulate structural balance in directed networks, we extended Heider’s definition of balance considering directed triangles. The evolution of balance involves two stages: first, negative links pop up disorderly and destroy balance, but after some time, as new negative links are formed, a “new” balance is re-established. This “new” balance is emerging concurrently with the polarization of attitudes or domination of one attitude. Moreover, same-minded agents are positively linked and different-minded agents are negatively-linked. This macroscopic self-organization of the system is due only to agent-to-agent interactions, involving feedbacks on weight updates at the local microscopic level.

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“…This assumption is not common in other similar researches studying the impact of informed agents. 12–15 Logically, they will gradually change their proclaimed opinion in small magnitudes and will not adopt an extreme opinion.…”

Section: Two Competing Groups Of Informed Agents (Tcia) Modelmentioning

confidence: 99%

“…All the previous works reviewed are commonly limited to studying the effect of one group of informed agents on the opinion formation process, 12–15 while in real-life situations we can depict the existence of more than one competing force; each force tries to shift public opinion towards its purpose. The most recurrent situation is the presence of two predominant competing forces in a society, where each force intends to persuade the majority of the society with their target opinion or alternative.…”

Section: Introductionmentioning

confidence: 99%

Measuring the role of two competing groups of informed agents in opinion formation

2018

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For politicians, to promote intended messages to different groups of individuals, they could employ strategic individuals called “informed agents.” The aim of this article is to explore and measure the impact of two competing groups of informed agents on opinion dynamics within a society exposed to two extreme opinions. Thus, an agent-based model is developed as an extension to the bounded confidence model by assuming the existence of two groups of informed agents. The impact of these agents with respect to their social characteristics, such as, their size in the society, how tolerant they are, their self-weight and attitudes about others’ opinions is explored. Different assumptions about the initial opinion distributions and their effect are also investigated. Due to the difficulty of observing a real society, social simulation experiments are constructed based on artificial societies. The simulations conducted resulted in some interesting findings. With no dominating group of the two informed agents, the society will be ended up concentrated around a moderate position. On the other hand, with significant difference between the two group sizes, the larger group will polarize the population towards its opinion. However, this conclusion will not apply if the population is skewed towards the other opinion. In such case, the larger group will only succeed to turn some of the society to be more moderate. In a society skewed towards extreme opinion, dominant informed agents adopting the other extreme will not be able to shift the society towards their opinion. Finally, in radical societies informed agents could turn most of the society to be extremists.

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Opinion evolution influenced by informed agents

Cited by 34 publications

References 46 publications

Intelligent Agents in Co-Evolving Knowledge Networks

Intelligent Agents in Co-Evolving Knowledge Networks

Promoters versus Adversaries of Change: Agent-Based Modeling of Organizational Conflict in Co-Evolving Networks

Measuring the role of two competing groups of informed agents in opinion formation

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