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We base our analyses on a rich data set that we collected in an incenitivzed field study over a period of three years between 2016 and 2019. Specifically, at the beginning of each semester, we invited first-semester economics students from a large German University to participate in our study. Most important for the current paper, the field study includes an incentivized one-shot sequential prisoners’ dilemma along the lines presented before, allowing us to elicit participants’ revealed preferences through their behavior instead of observing mere hypothetical survey responses. We show the exact instructions in the Appendix B. We elicited field study participants’ behavior using the strategy method. This is, every participant needed to define an action conditional on the choices of the trustor, providing us the unique opportunity to observe consequences of counterfactual choices that trustors do not actually make. In addition to the incentivized game, the field study comprises a broad set of survey items on students’ demographics, socio-economic background, cognitive abilities, personality traits, and experimental tasks. Overall there are 49 distinct questions. Overall, we collected 3,624 individual observations that make up our raw data set. The raw data set required considerable preprocessing due to fragmentation. After cleansing the raw data, we are left with 1051 observations.
algorithmic discrimination, social welfare, economics, game theory, feedback loops, artificial intelligence, machine learning
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