Worlds of Agents: Prospects of Agent-Based Modeling for Communication Research

Abstract

Agent-based computational models create virtual laboratories in which to formalize and simulate dynamic, multi-level theories of communication. They allow the systematic development of thought experiments, and they improve our understanding of the generative mechanisms that underlie patterns observed in empirical data. Simulation models help explore hypothetical and unprecedented scenarios, serving as powerful hypothesis generators for future theoretical and empirical research. This Special Issue showcases a collection of studies that demonstrate the analytical potential and methodological contribution of agent-based modeling (ABM) for media and communication research. In this introduction, we highlight five major benefits of this modeling approach to communication scholarship: (1) formalization, (2) understanding, (3) explanation, (4) prediction, and (5) exploration. We then present the four studies of this special issue, which contribute methodologically and theoretically to diverse key areas of communication: the emergence of meanings; political deliberation; information diffusion; and media use and social influence. We conclude with outlining future perspectives of ABM in communication research.

An Overview of Innovative Approaches to Support Timely and Agile Health Communication Research and Practice

“…Given that communication is a complex, multilevel phenomenon, the application of system science methods (a broad class of analytical approaches that aim to uncover the behavior of complex systems [ 59 ]) could help further health communication research and practice. For example, agent-based modeling could advance understanding of mechanisms underlying emergent patterns at the group and population level, offer explanations for empirically observed patterns, and generate predictions for hypothetical or unprecedented scenarios [ 60 ]. Simulation models were widely used during the COVID-19 pandemic to forecast the trajectory of infections and assess the impact of specific policies, allowing policymakers and public health officials to make more informed decisions about mitigation measures [ 60 ]; creating analogous models to help health communication practitioners develop more effective communication plans could be similarly beneficial.…”

“…Agent-based modeling is a method for modeling and simulating complex systems based on theoretical principles that uses data for calibration purposes and conceptual validation [ 60 ]. The models consist of a collection of agents (with given attributes) that interact with each other in a given environment and follow a set of rules that encode behavioral mechanisms [ 60 ]. During the simulation, key parameters are systematically tuned to determine the extent to which these changes affect the results and overall dynamics of the system [ 60 ].…”

A Systems Approach to Studying Online Communities

“…The second advance is in software and statistical approaches for doing large-scale and cross-community work. This includes approaches like multilevel modeling in statistics, computational text analysis tools like topic modeling and sentiment analysis (Boumans & Trilling, 2016;Jacobi et al, 2016), event-based network analysis techniques like relational event modeling and processual communication networks (Pilny et al, 2020;Schecter et al, 2018), and agent-based modeling and other simulation-based analyses (Waldherr et al, 2021), an advance discussed in more detail below.…”

“…While earlier software like cellular automata (Wolfram, 1984) was incredibly simple due to a lack of computational power, modern software like Mesa (Kazil et al, 2020) or NetLogo (Wilensky, 1999) makes it possible to create much more complex and realistic agents and environments. Waldherr et al (2021) argue that greater adoption of ABMs would benefit communication research for many reasons, including formalization, explanation, and exploration. Formalization refers to the benefits that come from explicitly encoding a theory's predictions into computer code.…”

“…Formalization refers to the benefits that come from explicitly encoding a theory's predictions into computer code. Waldherr et al (2021) argue that this can help to identify ambiguities and blind spots in theories. Explanation refers to how ABMs can be used to test communication theories.…”