Epistemic Communities under Active Inference

Abstract

The spread of ideas is a fundamental concern of today’s news ecology. Understanding the dynamics of the spread of information and its co-option by interested parties is of critical importance. Research on this topic has shown that individuals tend to cluster in echo-chambers and are driven by confirmation bias. In this paper, we leverage the active inference framework to provide an in silico model of confirmation bias and its effect on echo-chamber formation. We build a model based on active inference, where agents tend to sample information in order to justify their own view of reality, which eventually leads to them to have a high degree of certainty about their own beliefs. We show that, once agents have reached a certain level of certainty about their beliefs, it becomes very difficult to get them to change their views. This system of self-confirming beliefs is upheld and reinforced by the evolving relationship between an agent’s beliefs and observations, which over time will continue to provide evidence for their ingrained ideas about the world. The epistemic communities that are consolidated by these shared beliefs, in turn, tend to produce perceptions of reality that reinforce those shared beliefs. We provide an active inference account of this community formation mechanism. We postulate that agents are driven by the epistemic value that they obtain from sampling or observing the behaviours of other agents. Inspired by digital social networks like Twitter, we build a generative model in which agents generate observable social claims or posts (e.g., ‘tweets’) while reading the socially observable claims of other agents that lend support to one of two mutually exclusive abstract topics. Agents can choose which other agent they pay attention to at each timestep, and crucially who they attend to and what they choose to read influences their beliefs about the world. Agents also assess their local network’s perspective, influencing which kinds of posts they expect to see other agents making. The model was built and simulated using the freely available Python package pymdp. The proposed active inference model can reproduce the formation of echo-chambers over social networks, and gives us insight into the cognitive processes that lead to this phenomenon.

Cognition as Morphological/Morphogenetic Embodied Computation In Vivo

“…In a Bayesian setting, social cognition is addressed through interpersonal inference and niche construction as enactive and distributed inference. These lines of inquiry range from the nature of dyadic interactions to the spread of ideas over communities, [ 110 , 111 ]. The mathematics challenge : Mathematical results show that human thinking cannot be computational in the standard sense, as shown by Cooper [ 6 , 7 , 8 , 9 , 54 , 76 , 112 , 113 ].…”

“…In a Bayesian setting, social cognition is addressed through interpersonal inference and niche construction as enactive and distributed inference. These lines of inquiry range from the nature of dyadic interactions to the spread of ideas over communities, [ 110 , 111 ].…”

Social Media Platforms: Trading with Prediction Error Minimization for Your Attention

“…In this regard, individuals are also drawn to the platforms because they allow them to craft personas which are broadcast to curated audiences , reinforcing an image of themselves they could not achieve in real life [Tremblay et al, 2021]. Through a mix of epistemic value and confirmation bias, individuals feel like they have much to gain by tending to social media [Albarracin et al, 2022].…”

The nature of beliefs and believing

“…In extreme cases, agents can then fall into "echo chambers, " in which their beliefs no longer align with any other interpretation of the world, outside that of their own group. We can see this phenomenon at play with misinformation on social media, spreading extremely fast in echo-chambers, and leading to radicalization (Albarracin et al, 2022).…”