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Results: 26
Number of items: 26
  • Open Access
    Shalvi, S., Levine, E. E., Thielmann, I., Jayawickreme, E., Van Rooij, B., Teodorescu, K., Schurr, A., Furr, R. M., Aglioti, S. M., Zettler, I., Cohen, T. R., Pittarello, A., Barkan, R., Köbis, N., Leib, M., Mitkidis, P., Schulz, J., Dimant, E., van Kleef, G. A., ... Ritov, I. (2025). The science of honesty: A review and research agenda. Advances in Experimental Social Psychology, 72, 241-327. https://doi.org/10.1016/bs.aesp.2025.04.004
  • Starke, C., Ventura, A., Bersch, C., Cha, M., de Vreese, C., Doebler, P., Dong, M., Krämer, N., Leib, M., Peter, J., Schäfer, L., Soraperra, I., Szczuka, J., Tuchtfeld, E., Wald, R., & Köbis, N. (2024). Risks and protective measures for synthetic relationships. Nature Human Behaviour, 8(10), 1834–1836. https://doi.org/10.1038/s41562-024-02005-4
  • Open Access
    Forjan, J., Köbis, N., & Starke, C. (2024). Artificial intelligence as a weapon to fight corruption: Civil society actors on the benefits and risks of existing bottom-up approaches. In A. Mattoni (Ed.), Digital Media and Grassroots Anti-Corruption: Contexts, Platforms and Practices of Anti-Corruption Technologies Worldwide (pp. 229–249). Edward Elgar Publishing. https://doi.org/10.4337/9781802202106.00020
  • Open Access
    Soraperra, I., Köbis, N., Shalvi, S., Vogt, S., Efferson, C., & Offerman, T. (2023). A market for integrity. The use of competition to reduce bribery in education. Journal of Behavioral and Experimental Economics, 107, Article 102110. https://doi.org/10.1016/j.socec.2023.102110
  • Open Access
    Dorrough, A. R., Köbis, N., Irlenbusch, B., Shalvi, S., & Glöckner, A. (2023). Conditional bribery: Insights from incentivized experiments across 18 nations. Proceedings of the National Academy of Sciences, 120(18), Article e2209731120. https://doi.org/10.1073/pnas.2209731120
  • Köbis, N. C., Troost, M., Brandt, C. O., & Soraperra, I. (2022). Social norms of corruption in the field: social nudges on posters can help to reduce bribery. Behavioural Public Policy, 6(4), 597-624. https://doi.org/10.1017/bpp.2019.37
  • Köbis, N., Starke, C., & Rahwan, I. (2022). The promise and perils of using artificial intelligence to fight corruption. Nature Machine Intelligence, 4(5), 418–424. https://doi.org/10.1038/s42256-022-00489-1
  • Open Access
    Köbis, N. C., Starke, C., & Edward-Gill, J. (2022). The corruption risks of artificial intelligence. Transparency International. https://knowledgehub.transparency.org/product/the-corruption-risks-of-artificial-intelligence
  • Leib, M., Shalvi, S., Soraperra, I., Köbis, N., & Weisel, O. (2021, October 12). Treatment level dataset and read me file [Data set]. Universiteit van Amsterdam. https://doi.org/10.21942/uva.14731593.v3
  • Open Access
    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
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