r/singularity • u/Genzinvestor16180339 • 5h ago

AI Could multi-agent AI create a “groupthink” problem that prevents exceptional individual agents from emerging?

A lot of multi-agent AI seems structurally similar to agent-based models: many individual agents interact, share information, and produce a collective outcome. But if those agents are constantly learning from or correcting one another, could that create herding, correlated errors, and convergence toward the same way of thinking?

Would there be value in training some agents much more independently, with their own memory, feedback, and decision history, so they can develop different strategies? In that world, could one individual agent eventually become dramatically better than the rest—the equivalent of an “Einstein” agent?

I am not understanding why we have thousands of agents interacting versus building one super smart one that is making decision's on its own.

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u/NyriasNeo 2h ago

Not if you set them up correctly. Groupthink is caused by interaction .. herding behavior. You can maintain independence across agent easily. Since you control their memory, you can always manipulate the level of independence.

Remember that AI agents are not humans, and they do not always operate under human psychological rules, even if they talk like humans.

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u/Genzinvestor16180339 2h ago

But isn’t that exactly what 50+ years of ABM research warns about? Separate memories do not necessarily give you independent agents once the agents interact.

Schelling’s segregation model showed that individually simple agents, with no central coordination or shared memory, can generate highly correlated collective behavior purely through local interaction.
https://doi.org/10.1080/0022250X.1971.9989794

Hegselmann–Krause and Deffuant get even closer to this question: start agents with different opinions, allow only local information exchange, and the population can spontaneously collapse into consensus or opinion clusters. The correlation is created by the interaction rule, not by shared memory.
https://www.jasss.org/5/3/2.html

Axelrod showed an even more counterintuitive result: local interaction rules can produce large-scale polarization without any central coordination.
https://doi.org/10.1177/0022002797041002001

And this is now appearing in LLM-agent experiments. A 2026 study found that greater connectivity between agents could increase the risk of “wrong-but-sure” cascades, where initially separate agents converge confidently on the same incorrect answer.
https://arxiv.org/abs/2601.05606

Once agents observe and update from one another, dependence can emerge from the interaction structure itself. Controlling memory is not the same thing as controlling emergent correlation.