Working papers

(with Terne Sasha Thorn Jakobsen and Robert Böhm)

This study proposes a simple method to address annotator bias in crowdsourced data without requiring a large number of annotators or instances, by asking annotators to report their beliefs about others’ judgements rather than providing direct judgements. In two experiments involving Democrats and Republicans (n=1,590), this approach consistently reduced bias, offering potential to improve AI generalizability and reduce harm to underrepresented groups.

(with Jolien Cremers, Benjamin Kohler, Benjamin Frank Maier, Stine Nymann Eriksen, Johanna Einsiedler, Frederik Kolby Christensen, Sune Lehmann, David Dreyer Lassen, Laust Hvas Mortensen)

This paper introduces a registry-based, multi-layer, and temporal network of the entire Danish population (7.2 million individuals) from 2008-2021, mapping relationships through family, households, neighborhoods, colleagues, and classmates. We present key properties of this multiplex network, show how to efficiently compute measures like shortest paths, and release a Python package for efficient bipartite network analysis.

(with Lykke S. Christensen, Mikkel H. Gandil & Hans H. Sievertsen)

We study the role of manipulation beyond preference to gain admissions to schools and show how the design of admission affects such manipulation and the consequences for admission.

(with Martin B. Busch)
We examine how individuals form expectations about societal behavior through statistical inference and social connections, focusing on the effects of sampling bias and uncertainty. Our study characterizes equilibrium in strategic settings, exploring how sophistication in understanding these biases influences behavior and population dynamics.

(with Emil Chrisander)

We investigate why students in Danish college admissions misreport preferences, finding that omissions are mostly due to pessimistic admission beliefs and influenced by personality, with minimal negative impact. Our findings suggest that assuming truthful reporting biases demand estimates, highlighting implications for modeling in strategy-proof admission mechanisms.

(with Emil Chrisander)

We propose two mechanisms for college admissions that allow voluntary disclosure of private information to address fairness and welfare, demonstrating improved outcomes when public and private student information weakly correlate and processing costs are low, supported by empirical evidence from Denmark.

(with Mikkel H. Gandil)

Scandinavian welfare states provide free primary education, but in this study from Denmark we show property values reflect school socioeconomic status, impacting equal access. Property prices adjust within three years, and low-income families must sacrifice 7-10% of consumption to access better schools, indicating centralized funding alone doesn’t ensure equal access.