Dong, M., Conway, J. R., Bonnefon, J.-F., Shariff, A., & Rahwan, I. (2023). A psychological model predicts fears about artificial intelligence across 20 countries and 6 domains of application. PsyArXiv, October 12, 2023. https://doi.org/10.31234/osf.io/pjvqt

Kasberger, B., Martin, S., Normann, H.-T., & Werner, T. (2023). Algorithmic cooperation. SSRN eLibrary, March 21, 2023. https://doi.org/10.2139/ssrn.4389647

Köbis, N., Lorenz-Spreen, P., Ajaj, T., Bonnefon, J.-F., Hertwig, R., & Rahwan, I. (2023). Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners. arXiv, 2306.04484 . https://doi.org/10.48550/arXiv.2306.04484

Purcell, Z. A., Dong, M., Nussberger, A.-M., Köbis, N., & Jakesch, M. (2023). Fears about AI-mediated communication are grounded in different expectations for one’s own versus others’ use. arXiv, 2305.01670 .

Starke, C., Kieslich, K., Reichert, M., & Köbis, N. (2023). Algorithms against corruption: A conjoint study on designing automated twitter posts to encourage collective action. SocArXiv, January 13, 2023. https://doi.org/10.31235/osf.io/wf45t

Althobaiti, S., Alabdulkareem, A., Hanwen Shen, J., Rahwan, I., Frank, M., Moro, E., & Rutherford, A. (2022). Longitudinal complex dynamics of labour markets reveal increasing polarisation. arXiv, 2204.07073. https://doi.org/10.48550/arXiv.2204.07073

Awad, E., Levine, S., Loreggia, A., Mattei, N., Rahwan, I., Rossi, F., Talamadupula, K., Tenenbaum, J., & Kleiman-Weiner, M. (2022). When is it acceptable to break the rules? Knowledge representation of moral judgement based on empirical data. arXiv, 2201.07763.

Ferreira, L. N. (2022). From time series to networks in R with the ts2net package. arXiv, 2208.09660. https://doi.org/10.48550/arXiv.2208.09660

Fioravant, F., Rahwan, I., & Tohmé, F. A. (2022). Properties of aggregation operators relevant for ethical decision making in artificial intelligence. arXiv, 2206.05160. https://doi.org/10.48550/arXiv.2206.05160

Fioravanti, F., Rahwan, I., & Tohmé, F. A. (2022). Ethical decision making for artificial intelligence: A social choice approach. arXiv, 2206.05160. https://doi.org/10.48550/arXiv.2206.05160

Hong, I., Ferreira, L. N., Rutherford, A., & Cebrian, M. (2022). Assessing the epidemic impact of protests during the COVID-19 pandemic. MedRxiv, October 14, 2022. https://doi.org/10.1101/2022.10.10.22280896

Köbis, N., Dorrough, A., Irlenbusch, B., Shalvi, S., & Gloeckner, A. (2022). Bribery across nations. Research Square, May 25, 2022. https://doi.org/10.21203/rs.3.rs-1690288/v1

Köbis, N., Oded, S., de Bruijn, A. L., Huang, S., & van Rooij, B. (2022). Is less more? Field evidence on the impact of anti-bribery policies on employee knowledge and corrupt behavior. SSRN eLibrary, October 21, 2022. https://doi.org/10.2139/ssrn.4255148

Lange, J., Köbis, N., & Herrmann, C. (2022). A systematic literature review and integrative framework on the link between emotions and corruption. OSF Preprints, June 15, 2022. https://doi.org/10.31219/osf.io/8uzkf

Müller, T. F., Brinkmann, L., Winters, J., & Pescetelli, N. (2022). Machine impostors avoid human detection by interrupting the formation of stable conventions: A minimal turing test. PsyArXiv, April 28, 2022. https://doi.org/10.31234/osf.io/x2yf7

Santos, L. B. L., Carvalho, L. M., Soares, G. G., Ferreira, L. N., & Sokolov, I. M. (2022). Beyond the shortest path: The path length index as a distribution. arXiv, 2210.03216.

von Schenk, A., Klockmann, V., Bonnefon, J.-F., Rahwan, I., & Köbis, N. (2022). Lie detection algorithms attract few users but vastly increase accusation rates. arXiv, December 8, 2022. https://doi.org/10.48550/arXiv.2212.04277

von Schenk, A., Klockmann, V., & Köbis, N. (2022). Social preferences towards machines and humans. SSRN eLibrary, June 24, 2022. https://doi.org/10.2139/ssrn.4145868

Yang, Q., Wei, W., Zhang, R., Pang, B., & Feng, X. (2022). Enhance ambiguous community structure via multi-strategy community related link prediction method with evolutionary process. arXiv, 2204.13301. https://doi.org/10.48550/arXiv.2204.13301

Köbis, N., Starke, C., & Rahwan, I. (2021). Artificial intelligence as an anti-corruption tool (AI-ACT): Potentials and pitfalls for top-down and bottom-up approaches. arXiv, 2102.11567.