Publications

Alfonseca, M., Cebrian, M., Anta, A. F., Coviello, L., Abeliuk, A., & Rahwan, I. (2021). Superintelligence cannot be contained: Lessons from computability theory. Journal of Artificial Intelligence Research, 70, 65–76. https://doi.org/10.1613/jair.1.12202

Avraam, D., Obradovich, N., Pescetelli, N., Cebrian, M., & Rutherford, A. (2021). The network limits of infectious disease control via occupation-based targeting. Scientific Reports, 11, Article 22855. https://doi.org/10.1038/s41598-021-02226-x

Bassett, J., Gethmann, J., Blunk, P., Conraths, F. J., & Hövel, P. (2021). Individual-based model for the control of Bovine Viral Diarrhea spread in livestock trade networks. Journal of Theoretical Biology, 527, Article 110820. https://doi.org/10.1016/j.jtbi.2021.110820

Bassett, J., Pescetelli, N., Rutherford, A., & Cebrian, M. (2021). Time-critical decentralised situational awareness in emergencies: An adversarial biosecurity scenario. Applied Network Science, 6, Article 59. https://doi.org/10.1007/s41109-021-00402-6

Cebrian, M. (2021). The past, present and future of digital contact tracing. Nature Electronics, 4, 2–4. https://doi.org/10.1038/s41928-020-00535-z

Cebrian, M. (2021). A time-critical crowdsourced computational search for the origins of COVID-19. Nature Electronics, 4, 450–451. https://doi.org/10.1038/s41928-021-00619-4

Chen, H., Kryvasheyeu, Y., Xu, W., Huang, Y., Deng, J., Ren, S., Li, X., Rahwan, I., & Cebrian, M. (2021). Evolution of urban forms observed from space. EPJ Data Science, 10, Article 27. https://doi.org/10.1140/epjds/s13688-021-00283-w

Cuomo, R. E., Purushothaman, V. L., Li, J., Bardier, C., Nali, M., Shah, N., Obradovich, N., Yang, J., & Mackey, T. K. (2021). Characterizing self-reported tobacco, vaping, and marijuana-related tweets geolocated for California college campuses. Frontiers in Public Health, 9, Article 628812. https://doi.org/10.3389/fpubh.2021.628812

Dhaliwal, N. A., Patil, I., & Cushman, F. (2021). Reputational and cooperative benefits of third-party compensation. Organizational Behavior and Human Decision Processes, 164, 27–51. https://doi.org/10.1016/j.obhdp.2021.01.003

Dong, M., Palomo‐Vélez, G., & Wu, S. (2021). Reducing the gap between pro‐environmental disposition and behavior: The role of feeling power. Journal of Applied Social Psychology, 51(3), 262–272. https://doi.org/10.1111/jasp.12733

Dong, M., Spadaro, G., Yuan, S., Song, Y., Ye, Z., & Ren, X. (2021). Self-interest bias in the COVID-19 pandemic: A cross-cultural comparison between the United States and China. Journal of Cross-Cultural Psychology, 52(7), 663–679. https://doi.org/10.1177/00220221211025739

Dong, M., van Prooijen, J.-W., & van Lange, P. A. M. (2021). Calculating Hypocrites Effect: Moral judgments of word-deed contradictory transgressions depend on targets' competence. Journal of Theoretical Social Psychology, 5(4), 489–501. https://doi.org/10.1002/jts5.113

Ferreira, L. N., Hong, I., Rutherford, A., & Cebrian, M. (2021). The small-world network of global protests. Scientific Reports, 11, Article 19215. https://doi.org/10.1038/s41598-021-98628-y

Fowler, J. H., Hill, S. J., Levin, R., & Obradovich, N. (2021). Stay-at-home orders associate with subsequent decreases in COVID-19 cases and fatalities in the United States. PLoS ONE, 16(6), Article e0248849. https://doi.org/10.1371/journal.pone.0248849

Groh, M., Epstein, Z., Obradovich, N., Cebrian, M., & Rahwan, I. (2021). Human detection of machine-manipulated media. Communications of the ACM, 64(10), 40–47. https://doi.org/10.1145/3445972

Hong, I., Rutherford, A., & Cebrian, M. (2021). Social mobilization and polarization can create volatility in COVID‑19 pandemic control. Applied Network Science, 6, Article 11. https://doi.org/10.1007/s41109-021-00356-9

Klockmann, V., von Schenk, A., & von Siemens, F. A. (2021). Division of labor and the organization of knowledge in production: A laboratory experiment. Games and Economic Behavior, 130, 196–210. https://doi.org/10.1016/j.geb.2021.08.002

Köbis, N., Bonnefon, J.-F., & Rahwan, I. (2021). Bad machines corrupt good morals. Nature Human Behaviour, 5(6), 679–685. https://doi.org/10.1038/s41562-021-01128-2

Köbis, N. C., Doležalová, B., & Soraperra, I. (2021). Fooled twice: People cannot detect deepfakes but think they can. iScience, 24(11), Article 103364. https://doi.org/10.1016/j.isci.2021.103364

Köbis, N., & Mossink, L. D. (2021). Artificial intelligence versus Maya Angelou: Experimental evidence that people cannot differentiate AI-generated from human-written poetry. Computers in Human Behavior, 114, Article 106553. https://doi.org/10.1016/j.chb.2020.106553