Network Effects Underlying Belief Polarization: An Agent-Based Modelling Approach

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1. OverviewBelief polarization in groups is an important challenge for global political (e.g., Levendusky, 2010) and environmental (Cook and Lewandowsky, 2016) affairs. For example, polarized partisan elites shape public beliefs despite questionable argumentation and lack of sufficient supportive evidence (Druckman, Peterson and Slothuus, 2013). Thus, investigating how polarization relates to the formation of precarious beliefs is key to counteracting developing hazards to democracy (Flynn, Nyhan and Reifler, 2017). Social media sources are frequently used to seek information about political content (e.g., Mitchell et al., 2014) and recent research has investigated the role of social media in the context of belief polarization (e.g., Flaxman, Goel and Rao, 2013). Findings illustrate that repetitive exposure towards selectively chosen information (e.g., via Facebook's algorithmically ranked News Feed) reduces consumption of information discordant with prior beliefs (Bakshy, Messing and Adamic, 2016). Collectively, these findings highlight the importance of understanding the mechanisms underlying belief polarization. Comprehending these mechanisms may help policy makers and system architects mediate the emergence of belief polarization. Moreover, it will allow to learn more about potential benefits and rationality related to the formation of diverging extremist beliefs (Levendusky, 2010; Jern, Chang and Kemp, 2014). 2. Focus and Objective The focus of the current proposal is on recent work using agent-based models (ABMs) and iterated learning (e.g., Griffiths and Kalish, 2007; Kirby, Griffiths and Smith, 2014; Cornish et al., 2017; Madsen, Bailey and Pilditch, 2018).Of particular interest is a recent paper by Madsen et al. (2018). In their agent-based simulations, the authors investigated how network structures can lead to the formation of echo chambers (ECs) even under the assumption that all agents are rational (in the sense of forming beliefs in accordance with Bayes theorem). Surprisingly, it was found that ECs emerged despite the absence of cognitive and social biases. In fact, ECs can emerge through interactions between rational agents under very simple assumptions, and their severity was found to be positively related to network size. This finding has important implications for the reviewed literature concerning belief polarization in groups and its consequences. First, it highlights minimal structural aspects (e.g., interaction between agents and network size) that are sufficient to cause the formation of ECs. Second, it suggests that in order to reduce fixation of extremist beliefs, policy makers may benefit from using system-based educational interventions rather than individual-based interventions. So far, the models provided by previous authors were an idealisation of the more complex network structures involved in belief polarization in a real-world setting (e.g., Madsen and Pilditch, 2018). Therefore, the first objective of the present research is to further explore the application of ABMs in the domain of polarization and political campaigning, introducing additional parameters accounting for more realistic settings. Building on previous findings by Madsen et al. (2018), it is thus hypothesised that ECs emerge faster and more regularly via introducing cognitive and social biases to the model. The second objective is to clarify under which circumstances either network size and information density (Madsen et al., 2018) or the agent's inductive biases (Griffiths and Kalish, 2007) relate to the formation of belief polarization. To increase ecological validity, this investigation will involve real participants following an iterated learning task. It is hypothesised that both network size and inductive biases are positively related to the emergence of belief polarization.