In 1985, the psychologists Garold Stasser and William Titus ran what became one of the most replicated experiments in group decision-making. Four-person groups had to choose among three candidates for student body president. Each candidate was described by sixteen characteristics. The trick was in the distribution. The best candidate’s virtues were scattered across group members so that no individual could see the full picture, while a mediocre candidate’s virtues were known to everyone. The complete evidence pointed clearly to one answer. Each individual’s data pointed in the wrong direction.
When every member received all the information, 83 percent of groups chose the best candidate. When the information was distributed, 18 percent did.
This August, Anthropic ran the same test on teams of four AI agents deciding realistic questions: which candidate to hire, which investment to make, which property to buy. Shared evidence favored the wrong option. Each agent held unique, decisive private facts favoring the right one. Solving the task required an agent to recognize its private information as pivotal and press it against an apparent consensus.
A single agent handed the entire evidence base got the answer right nearly every time. Groups of agents, after discussion, got it right in 17 to 36 percent of runs for most model families.
