Being at the forefront of cutting-edge research, COSMOS publishes its research studies in leading international scientific forums. In this newsletter, we spotlight two of our recent publications, each using Agent-based Models (ABMs) to better understand social media behavior. 

In the paper Developing an Agent-based Model to Minimize the Spreading of Malicious Information in Dynamic Social Networks published in the Springer Journal of Computational and Mathematical Organization Theory (CMOT), Mustafa Alassad and Nitin Agarwal look at agents in social media. In this research, the authors delve into the realm of misinformation and disinformation spread on social media platforms. They recognize the growing challenge of countering the rampant flow of false information in online spaces. 

While previous research has focused on identifying and highlighting misinformation, this paper takes a step further by exploring how to combat its spread. The researchers designed an agent-based model to simulate the dissemination of misinformation within a controlled environment. They tested various approaches to reduce the flow of false information, aiming to uncover strategies that can be implemented in real-world scenarios. The study reveals that even minimal delays, as short as a few seconds, in the spread of misinformation can significantly slow its diffusion.

Through rigorous scientific testing of the model, the paper provides recommendations for platform owners, policymakers, and developers to implement strategies that can mitigate the dissemination of false information effectively. This work highlights the potential of agent-based models in addressing critical societal challenges.

Similarly, in the conference paper An Agent-based Model of Mobs Using Theoretical Constructs of Collective Action, published at the Annual Modeling and Simulation Conference (ANNSIM 2023) in Ontario, Canada, Samer Al-khateeb, Jack Burright, Nitin Agarwal, and Rebecca Murray discuss an agent-based model for collective action.

In this different research endeavor, the authors delve into the fascinating phenomenon of mobs and collective action. Mobs, characterized by a group’s collective behavior, often exhibit both peaceful and violent actions. Understanding the dynamics and triggers behind mob formation is crucial for predicting and managing potentially dangerous situations.

This research paper employed agent-based models to simulate the behaviors and interactions within mobs. The authors drew on collective action theories to inform the rules and behaviors of individual agents in the model. By analyzing how individuals’ decisions within a mob collectively lead to particular outcomes, the researchers gain insights into predicting the behavior of mobs in real-world scenarios.

The paper showcases the potential of agent-based models to offer theoretical grounding to complex social phenomena. By unraveling the dynamics of collective actions, the research provides valuable insights that can aid decision-makers in understanding the underlying mechanisms of mob formation.

Agent-based models stand as an innovative and versatile tool for exploring complex systems and behaviors across various disciplines. The two research papers discussed in this article underscore the importance of ABMs in addressing contemporary challenges, from countering misinformation to understanding the dynamics of collective actions. These models offer a unique opportunity to test hypotheses, simulate scenarios, and gain a deeper understanding of complex phenomena that shape our world. As technology advances and interdisciplinary collaborations flourish, the role of agent-based models is poised to grow, contributing to a better understanding of our intricate reality.