I am an experimental and behavioral economist, studying the interplay of beliefs and individual decision-making with applications to health, discrimination, and machine learning.
with Patrick Schmidt
European Economic Review, 2021, Vol. 135
We propose a simple, incentive compatible procedure based on binarized linear scoring rules to elicit beliefs about real-valued outcomes - multiple point predictions. Simultaneously eliciting multiple point predictions with linear incentives reveals the subjective probability distribution without pre-defined intervals or probabilistic statements. We show that the approach is theoretically as robust as existing methods, while adapting flexibly to different beliefs. In a laboratory experiment, we compare our procedure to the standard approach of eliciting discrete probabilities on pre-defined intervals. We find that elicitation with multiple point predictions is faster, perceived as less difficult and more consistent with a subsequent decision. We further find that multiple point predictions are more ac- curate if beliefs vary between participants. Finally, we provide experimental evidence that pre-defined intervals anchor reports.
with Arne Hosemann & Magnus Johannesson
Economics Letters, 2016, Vol. 142, 56-58.
We test in an experiment if a monetary incentive or a charity incentive can motivate people to fill in the German organ donor card and thereby increase the number of organ donors. We find that a monetary incentive significantly increases the number of organ donors whereas the charity incentive does not.
Identifying the cause of discrimination is crucial to design effective policies and to understand discrimination dynamics. Building on traditional models, this paper introduces a new explanation for discrimination: discrimination based on motivated reasoning. By systematically acquiring and processing information, individuals form motivated beliefs and consequentially discriminate based on these beliefs. Through a series of experiments, I show the existence of discrimination based on motivated reasoning and demonstrate important differences to statistical discrimination and taste-based discrimination. Finally, I demonstrate how this form of discrimination can be alleviated by limiting individuals’ scope to interpret information.
Using a unique dataset from a German health check-up provider including detailed individual questionnaire data as well as medical test data, I apply a random forest to predict several health risk factors. I evaluate the prediction performance using various metrics and find decent prediction qualities across all outcomes. By identifying the most relevant predictor variables, I compile concise and validated questionnaire tools to identify individuals’ blood pressure, blood glucose, and cholesterol levels, their risk of a coronary heart disease, whether or not they suffer from plaque or a metabolic syndrome as well as their relative fitness levels. In a second step, I compare the prediction results to physician predictions of the same patient observations. I find that the random forest outperforms the physicians if predictions are based on the same information set. When additionally providing the physicians with the random forest predictions for a particular patient observation, the physicians align with the random forest predictions. Finally, while the random forest considers various psychological scales, the physicians focus on family health history information instead.
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