Epistemic Communities under Active Inference
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 confir...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-03-01
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| Series: | Entropy |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1099-4300/24/4/476 |
| _version_ | 1797434782802509824 |
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| author | Mahault Albarracin Daphne Demekas Maxwell J. D. Ramstead Conor Heins |
| author_facet | Mahault Albarracin Daphne Demekas Maxwell J. D. Ramstead Conor Heins |
| author_sort | Mahault Albarracin |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T10:37:00Z |
| format | Article |
| id | doaj.art-285604604675407ca022dfc64106df8b |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-09T10:37:00Z |
| publishDate | 2022-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-285604604675407ca022dfc64106df8b2023-12-01T20:50:05ZengMDPI AGEntropy1099-43002022-03-0124447610.3390/e24040476Epistemic Communities under Active InferenceMahault Albarracin0Daphne Demekas1Maxwell J. D. Ramstead2Conor Heins3Department of Cognitive Computing, Université du Québec a Montreal, Montreal, QC H2K 4M1, CanadaDepartment of Computing, Imperial College London, London SW7 5NH, UKVERSES Labs, Los Angeles, CA 90016, USAVERSES Labs, Los Angeles, CA 90016, USAThe 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.https://www.mdpi.com/1099-4300/24/4/476epistemic communitysocial mediaactive inferenceopinion dynamics |
| spellingShingle | Mahault Albarracin Daphne Demekas Maxwell J. D. Ramstead Conor Heins Epistemic Communities under Active Inference Entropy epistemic community social media active inference opinion dynamics |
| title | Epistemic Communities under Active Inference |
| title_full | Epistemic Communities under Active Inference |
| title_fullStr | Epistemic Communities under Active Inference |
| title_full_unstemmed | Epistemic Communities under Active Inference |
| title_short | Epistemic Communities under Active Inference |
| title_sort | epistemic communities under active inference |
| topic | epistemic community social media active inference opinion dynamics |
| url | https://www.mdpi.com/1099-4300/24/4/476 |
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