How AI Aggregation Affects Knowledge
Artificial intelligence (AI) changes social learning when aggregated outputs become training data for future predictions. To study this, we extend the DeGroot model by introducing an AI aggregator that trains on population beliefs and feeds synthesized signals back to agents. We define the learning gap as the deviation of long-run beliefs from the efficient benchmark, allowing us to capture how AI aggregation affects learning. Our main result identifies a threshold in the speed of updating: when the aggregator updates too quickly, there is no positive-measure set of training weights that robustly improves learning across a broad class of environments, whereas such weights exist when updat...
| Authors | Daron Acemoglu, Tianyi Lin, Asuman Ozdaglar et al. |
|---|---|
| Primary category | econ.TH |
| Published | 6 April 2026 |
| Last updated at source | 6 April 2026 |
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