Hybrid Collective Intelligence

Question: How will machines alter collective intelligence?

The success of humans is due to our outstanding ability to adapt to new environments. Individually, we quickly infer patterns from single or multiple observations. Collectively, we develop complex cultural artifacts (e.g., tools, institutions, norms, art, language or mental models of the world) that enable cooperation at scale. Algorithms will radically change the way humans collectively sense, learn and act in a rapidly changing environment. This theme investigates how such change might unfold.

Principal Investigator(s)

Niccolo Pescetelli

Iyad Rahwan


Agnieszka Czaplicka

Levin Brinkmann

Thomas Müller

Selected Papers

Saveski, M., Awad, E., Rahwan, I. et al. Algorithmic and human prediction of success in human collaboration from visual features. Sci Rep 11, 2756 (2021). https://doi.org/10.1038/s41598-021-81145-3

Pescetelli, N., Cebrian, M. and Rahwan, I. (2020) ‘BeeMe: Real-Time Internet Control of Situated Human Agents’, Computer, 53(8), pp. 49–58. doi: 10.1109/MC.2020.2996824

M. R. Frank, M. Cebrian, G. Pickard, I. Rahwan (2017). Validating Bayesian truth serum in large-scale online human experiments. PLOS ONE. 12(5): e0177385.

E. Awad, J.-F. Bonnefon, M. Caminada, T. Malone, I. Rahwan (2017). Experimental Assessment of Aggregation Principles in Argumentation-enabled Collective Intelligence. ACM Transactions on Internet Technology. 17:3.

Awad, E., Caminada, M. W., Pigozzi, G., Podlaszewski, M., & Rahwan, I. (2017). Pareto optimality and strategy-proofness in group argument evaluation. Journal of Logic and Computation, 27(8), 2581-2609.

E. Awad, R. Booth, F. Tohme, I. Rahwan (2017). Judgment Aggregation in Multi-Agent Argumentation. Journal of Logic and Computation. 27(1): 227-259.

I. Rahwan, D. Krasnoshtan, A. Shariff, J. F. Bonnefon (2014). Analytical reasoning task reveals limits of social learning in networks. Journal of the Royal Society Interface. 11(93).

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