Publikationen von Marcus Buckmann

Katsikopoulos, K. V., Şimşek, Ö., Buckmann, M., & Gigerenzer, G. (2022). Transparent modeling of influenza incidence: Big data or a single data point from psychological theory? International Journal of Forecasting, 38(2), 613–619. https://doi.org/10.1016/j.ijforecast.2020.12.006

Katsikopoulos, K. V., Şimşek, Ö., Buckmann, M., & Gigerenzer, G. (2022). Reply to commentaries on "Transparent modelling of influenza incidence": Recency heuristics and psychological AI. International Journal of Forecasting, 38(2), 630–634. https://doi.org/10.1016/j.ijforecast.2021.10.011

Buckmann, M., & Şimşek, Ö. (2016). Decision heuristics for comparison: How good are they? Proceedings of Machine Learning Research, 58, 1–11.

Şimşek, Ö., & Buckmann, M. (2016). On learning decision heuristics. Proceedings of Machine Learning Research, 58, 75–85.

Buckmann, M., Gaschler, R., Hofer, S., Loeben, D., Frensch, P. A., & Brock, O. (2015). Learning to explore the structure of kinematic objects in a virtual environment. Frontiers in Psychology, 6, Article 374. https://doi.org/10.3389/fpsyg.2015.00374

Şimşek, Ö., & Buckmann, M. (2015). Learning from small samples: An analysis of simple decision heuristics. In C. Cortes, N. D. Lawrence, D. D. Lee, M. Sugiyama, & R. Garnett (Eds.), Advances in neural information processing systems (NIPS 2015) [Online version] (Vol. 28, pp. 3141–3149). Curran Associates.

Fific, M., & Buckmann, M. (2013). Stopping Rule Selection (SRS) theory applied to deferred decision making. In M. Knauff, M. Pauen, N. Sebanz, & I. Wachsmuth (Eds.), Cooperative minds: Social interaction and group dynamics. Proceedings of the 35th Annual Conference of the Cognitive Science Society (pp. 2273–2278). Cognitive Science Society.

Şimşek, Ö., & Buckmann, M. (2017). Learning From Small Samples: An Analysis of Simple Decision Heuristics. Presented at Meeting of the Scientific Advisory Board 2017.

Buckmann, M. (2014). An empirical analysis of the performance of simple decision heuristics in dynamic environments [Masterarbeit, Humboldt-Universität zu Berlin].