Applying the agent-based social impact theory model to the bullying phenomenon in K–12 classrooms

Abstract

Bullying is a widespread phenomenon, occurring in many countries; however, until recently, knowledge of it was limited due to the lack of access to information. Also, most parents of K-12 students failed to see it as an important issue. Now, it is widely agreed upon that bullying is common and detrimental. Use of media outlets to disseminate this message is helping to gradually chip away at this previously ignored problem. Hence, bullying behavior is difficult to detect immediately and prevent. This study uses a simulation model based on the social impact theory to study the changing roles of a class of 40 students during a semester long period. The students in the class were divided into three roles: the bullies, the victims and the bystanders. The model also defines a new criterion, the ''bullying index,'' designed to shed light on the underlying dynamics of bullying through experimental design. Regression results show that the percentage of students bullied in school lessened significantly when the environment changed. The parental and persuasiveness constants were significant, while the impact of counseling was not, even after intensive counseling. This means that we should separate bullies', bystanders' and victims' education to prevent bullying behaviors.

Agent-Based Modeling of Rumor Propagation Using Expected Integrated Mean Squared Error Optimal Design

“…In this study, we extended Social Impact Theory as a dynamic instead of a static model by representing individuals' impact over time for simulating rumor propagation. This theory has been applied to a wide variety of phenomena, including language change [24], social loafing [25], student STEM selection [26], and bullying [27]. According to Latané et al [28] and Nettle [24], the relationship between distance and social impact is critical to the model; however, environmental biases are not considered.…”

“…Based on the hypothesis that environmental bias can affect the strength of the impact when models are applied in specific situations, Allen and Davis [26] slightly extended Nettle's [24] model by incorporating the environmental bias parameter. In addition, Tseng, Chen, Yu, and Wang [27] added the element of weight with the agent's strength to Allen and Davis [26] in order to classify groups of students while studying bullying in school.…”

“…Macy and Willer [32] proposed the following four key assumptions of ABM: agents are autonomous, they are independent, they follow simple rules, and they are adaptive and backward-looking. ABMs have been widely applied in many fields such as the physical sciences, biology, business [33,34], education theory [26,27], and the social sciences [29,35,36]. Liu and Chen [6] examined rumor propagation in a scale-free network via agent-based modeling (NetLogo software) based on the SIR model.…”

“…According to Latané [13], people with high levels of knowledge, educational background, and emotional "strength" will have the most influence on the target person. In this study, we proposed to extend Social Impact Theory [24,26,27] to explore rumor spreading. The proposed equation based on Social Impact Theory is as follows:…”

“…In this experiment, the conditions of the model can be changed based on the following parameters: (1) persuasive-constant "a" ranged from 0.5 to 0.75 [26]; (2) environmental bias in the workplace was set between 0.9 and 1.1 to ensure the stability of the model [24]. (3) The leader or supervisor "L" is referred to as a teacher or counselor in [26,27]. (4) We measured social network usage during working hours in a range from 0.1 to 0.9 or 10% to 90% of the total communication in the workplace, and the distance between the two agents "d i " and "d j ".…”