An evolutionary origin of collective decision making in humans and machines
Groups of individuals can solve collective problems more accurately than any single member, by aggregating their opinions. Recent theoretical work has identified individual-level reward schemes that allow uninformed individuals to evolve collective intelligence from the bottom up, through social learning. Yet these results are restricted to linear prediction problems and simple averaging, while the decision tasks that real groups confront are often non-linear, and the institutions that aggregate opinions are seldom single-layer averages: districts elect representatives who in turn vote on policy, referees advise editors who decide on publication. Here we develop a framework for the evolut...
| Authors | Guocheng Wang, Qi Su, Joshua B. Plotkin |
|---|---|
| Primary category | physics.soc-ph |
| Published | 5 October 2026 |
| Last updated at source | 5 October 2026 |
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