Collective Intelligence

Collective intelligence is the capacity of groups to show better performance than their members, in a range of domains like decision-making, estimation and forecasting. Our group focuses on the mechanisms underlying optimal information processing in groups featuring human and machine agents.

Relevant publications:

Galesic, M., Barkoczi, D., Berdahl, A., Biro, D., Carbone, G., Giannoccaro, I., Goldstone, R., Gonzalez, C., Kandler, A., Kao, A., Kendal, R., Kline, M., Lee, E., Massari, G., Mesoudi, A., Olsson, H., Pescetelli, N., Sloman, S., Smaldino, P., & Stein, D. (2023). Beyond collective intelligence: Collective adaptation. Journal of The Royal Society Interface, 20(200).

Brinkmann, L., Cebrian, M., & Pescetelli, N. (2023). Adversarial Dynamics in Centralized Versus Decentralized Intelligent Systems. Topics in Cognitive Science.

Pescetelli N, Reichert P, Rutherford A. A variational-autoencoder approach to solve the hidden profile task in hybrid human-machine teams (2022) A variational-autoencoder approach to solve the hidden profile task in hybrid human-machine teams. PLOS ONE 17(8): e0272168.

Pescetelli, N., Rutherford, A., Rahwan, I. (2021) Modularity and composite diversity affect the collective gathering of information online. Nature Communications 12, 3195