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Register a new userA Bayesian Framework for Opinion Updates
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Andre C. R. Martins
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Opinion Dynamics lacks a theoretical basis. In this article, I propose to use a decision-theoretic framework, based on the updating of subjective probabilities, as that basis. We will see we get a basic tool for a better understanding of the interaction between the agents in Opinion Dynamics problems and for creating new models. I will review the few existing applications of Bayesian update rules to both discrete and continuous opinion problems and show that several traditional models can be obtained as special cases or approximations from these Bayesian models. The empirical basis and useful properties of the framework will be discussed and examples of how the framework can be used to describe different problems given.
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Traditional opinion dynamics models are simple and yet, enough to explore the consequences in basic scenarios. But, to better describe problems such as polarization and extremism, we might need to include details about human biases and other cognitive characteristics. In this paper, I explain how we can describe and use mental models and assumptions of the agents using Bayesian-inspired model building. The relationship between human rationality and Bayesian methods will be explored, and we will see that Bayesian ideas can indeed be used to explain how humans reason. We will see how to use Bayesian-inspired rules using the simplest version of the Continuous Opinions and Discrete Actions (CODA) model. From that, we will explore how we can obtain update rules that include human behavioral characteristics such as confirmation bias, motivated reasoning, or our tendency to change opinions much less than we should. Keywords: Opinion dynamics, Bayesian methods, Cognition, CODA, Agent-based models
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The flow of information reaching us via the online media platforms is optimized not by the information content or relevance but by popularity and proximity to the target. This is typically performed in order to maximise platform usage. As a side effect, this introduces an algorithmic bias that is believed to enhance polarization of the societal debate. To study this phenomenon, we modify the well-known continuous opinion dynamics model of bounded confidence in order to account for the algorithmic bias and investigate its consequences. In the simplest version of the original model the pairs of discussion participants are chosen at random and their opinions get closer to each other if they are within a fixed tolerance level. We modify the selection rule of the discussion partners: there is an enhanced probability to choose individuals whose opinions are already close to each other, thus mimicking the behavior of online media which suggest interaction with similar peers. As a result we observe: a) an increased tendency towards polarization, which emerges also in conditions where the original model would predict convergence, and b) a dramatic slowing down of the speed at which the convergence at the asymptotic state is reached, which makes the system highly unstable. Polarization is augmented by a fragmented initial population.
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