Preprint (30)
2023
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Werner, T. (2023). Algorithmic cooperation. SSRN eLibrary, March 21, 2023. https://doi.org/10.2139/ssrn.4389647
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Köbis, N., Lorenz-Spreen, P., , , 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
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Dong, M., Nussberger, A.-M., Köbis, N., & (2023). Fears about AI-mediated communication are grounded in different expectations for one’s own versus others’ use. arXiv, 2305.01670 .
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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
, , , & 2022
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Rahwan, I., , , & Rutherford, A. (2022). Longitudinal complex dynamics of labour markets reveal increasing polarisation. arXiv, 2204.07073. https://doi.org/10.48550/arXiv.2204.07073
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Rahwan, I., , , , & (2022). When is it acceptable to break the rules? Knowledge representation of moral judgement based on empirical data. arXiv, 2201.07763.
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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
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Rahwan, I., & (2022). Properties of aggregation operators relevant for ethical decision making in artificial intelligence. arXiv, 2206.05160. https://doi.org/10.48550/arXiv.2206.05160
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Rahwan, I., & (2022). Ethical decision making for artificial intelligence: A social choice approach. arXiv, 2206.05160. https://doi.org/10.48550/arXiv.2206.05160
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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
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Köbis, N., , , , & (2022). Bribery across nations. Research Square, May 25, 2022. https://doi.org/10.21203/rs.3.rs-1690288/v1
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Köbis, N., , , , & (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
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Köbis, N., & (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
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Müller, T. F., Brinkmann, L., , & 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
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Ferreira, L. N., & (2022). Beyond the shortest path: The path length index as a distribution. arXiv, 2210.03216.
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von Schenk, A., Klockmann, V., , 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
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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
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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
, , , , & 2021
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Köbis, N., , & 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.
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Cebrian, M. (2021). Contact tracing: Computational bounds, limitations and implications. arXiv, 2102.13349.
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