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Consensus among autonomous multi agent systems in public health: a simulation study on decision making under data uncertainty
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This study tested whether autonomous AI agents can agree on public health actions using incomplete or noisy outbreak data. A multi-agent system based on large language models (via Lang Chain) enabled structured, role-based discussions. Simulations showed that while consensus is possible, it’s rare. Agreement was mainly driven by the depth of reasoning—shorter data-grounded arguments led to better outcomes. Surprisingly, missing or noisy data had limited effect on consensus. However, complete data slightly improved results, especially when manipulated. Overall, AI agents can support complex health decisions, but their effectiveness hinges on their ability to reason clearly with available information.
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Public health Decision making AI agents Epidemiological simulation COVID-19 outbreak data Health policy modeling