Emergent Discrimination and Polarization in Multi-Agent Systems
Abstract
We study theoretically how discrimination and polarization can emerge in a population of learning agents. Each agent uses an adaptive linear decision layer to map a language-model embedding of an issue to a binary opinion and maintains an evolving estimate of how much it trusts every other agent. Through pairwise interactions, agents update both their decision weights and trust estimates using an online Bayesian rule derived from teacher-student learning over a noisy channel. In-context-learning experiments with large language models qualitatively reproduce the predicted opinion and trust updates. We then assign each agent one of two arbitrary group labels, A or B, and model group bias by allowing a fraction fb of agents to shift its perceived agreement with each message by a strength b, a bias steering prejudice, depending on whether the sender and receiver share a label. We use order parameters from statistical mechanics to connect these local dynamics to collective states and transitions. Starting from uniformly random initial conditions, simulations reveal a rich phase diagram in the (b,fb) plane, comprising four collective states: spin-glass frustrated out-group favoritism, nondiscriminatory polarization, discrimination with opinion alignment, and discrimination without opinion alignment. Which state emerges depends on both the strength and prevalence of the bias. These results show how simple local learning dynamics can generate qualitatively distinct forms of polarization and discrimination, underscoring the need for population-level safety analysis of multi-agent systems. They also suggest measurable order parameters for characterizing analogous collective states in human societies.
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BibTeX
@misc{caticha2026emergent,
title = {{Emergent Discrimination and Polarization in Multi-Agent Systems}},
author = {Nestor Caticha and João Henrique Sant'Ana and Felippe Alves and Davi Bastos Costa and Mariana Mercucci and Juan P. Neirotti and Otavio Citton and Renato Vicente},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.22701},
eprint = {2610.22701},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.22701},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Emergent Discrimination and Polarization in Multi-Agent Systems
AU - Nestor Caticha
AU - João Henrique Sant'Ana
AU - Felippe Alves
AU - Davi Bastos Costa
AU - Mariana Mercucci
AU - Juan P. Neirotti
AU - Otavio Citton
AU - Renato Vicente
PY - 2026
DA - 2026/10/15
PB - arXiv
DO - 10.48550/arxiv.2610.22701
UR - https://arxiv.org/abs/2610.22701
AB - We study theoretically how discrimination and polarization can emerge in a population of learning agents. Each agent uses an adaptive linear decision layer to map a language-model embedding of an issue to a binary opinion and maintains an evolving estimate of how much it trusts every other agent. Through pairwise interactions, agents update both their decision weights and trust estimates using an online Bayesian rule derived from teacher-student learning over a noisy channel. In-context-learning experiments with large language models qualitatively reproduce the predicted opinion and trust updates. We then assign each agent one of two arbitrary group labels, A or B, and model group bias by allowing a fraction f_b of agents to shift its perceived agreement with each message by a strength b, a bias steering prejudice, depending on whether the sender and receiver share a label. We use order parameters from statistical mechanics to connect these local dynamics to collective states and transitions. Starting from uniformly random initial conditions, simulations reveal a rich phase diagram in the (b,f_b) plane, comprising four collective states: spin-glass frustrated out-group favoritism, nondiscriminatory polarization, discrimination with opinion alignment, and discrimination without opinion alignment. Which state emerges depends on both the strength and prevalence of the bias. These results show how simple local learning dynamics can generate qualitatively distinct forms of polarization and discrimination, underscoring the need for population-level safety analysis of multi-agent systems. They also suggest measurable order parameters for characterizing analogous collective states in human societies.
ER -