Free Information Disrupts Even Bayesian Crowds
Fuente:
arXiv
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866908933408423936 |
|---|---|
| author | Stein, Jonas Cruz, Shannon Grossi, Davide Testori, Martina |
| author_facet | Stein, Jonas Cruz, Shannon Grossi, Davide Testori, Martina |
| contents | A core tenet underpinning the conception of contemporary information networks, such as social media platforms, is that users should not be constrained in the amount of information they can freely and willingly exchange with one another about a given topic. By means of a computational agent-based model, we show how even in groups of truth-seeking and cooperative agents with perfect information-processing abilities, unconstrained information exchange may lead to detrimental effects on the correctness of the group's beliefs. If unconstrained information exchange can be detrimental even among such idealized agents, it is prudent to assume it can also be so in practice. We therefore argue that constraints on information flow should be carefully considered in the design of communication networks with substantial societal impact, such as social media platforms. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_01838 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Free Information Disrupts Even Bayesian Crowds Stein, Jonas Cruz, Shannon Grossi, Davide Testori, Martina Multiagent Systems Theoretical Economics Physics and Society A core tenet underpinning the conception of contemporary information networks, such as social media platforms, is that users should not be constrained in the amount of information they can freely and willingly exchange with one another about a given topic. By means of a computational agent-based model, we show how even in groups of truth-seeking and cooperative agents with perfect information-processing abilities, unconstrained information exchange may lead to detrimental effects on the correctness of the group's beliefs. If unconstrained information exchange can be detrimental even among such idealized agents, it is prudent to assume it can also be so in practice. We therefore argue that constraints on information flow should be carefully considered in the design of communication networks with substantial societal impact, such as social media platforms. |
| title | Free Information Disrupts Even Bayesian Crowds |
| topic | Multiagent Systems Theoretical Economics Physics and Society |
| url | https://arxiv.org/abs/2604.01838 |