This joint research project of applied and theoretical psychology and scientific computing applies agent-based modeling (ABM) techniques to explore how cognitive factors contribute to the spread of information in filter bubbles. ABM is a method to computationally model individual behaviors and to assess how these affect others as well as the environment. This innovative project is the first to investigate the spread of information in filter bubbles from a cognitive perspective by using a rigorous scientific computing approach. We merge agent-based modeling with ACT-R, a cognitive architecture that simulates the cognitive process of each agent and apply mathematical simulation and optimization to our model to make quantitative statements about the distribution of outcomes (e.g., radicalization of beliefs in groups) under different parameter settings. We also determine optimal parameter settings (e.g., optimal belief distributions in groups to avoid radicalization).
