Economic behavior and emotion reports in trust game interactions with fellow humans and robots.

Published: 01-09-2019| Version 1 | DOI: 10.17632/gz35m94jkn.1
Contributors:
Eric Schniter,
Timothy Shields

Description

In Schniter, Shields, and Sznycer (2019) we focus our research on key issues relevant to the topic of trust-based interactions with robots (i.e., agentic forms of artificial intelligence (AI) and automation): (i) that people may not trust robots as they do humans, and (ii) that people may react to robots with different emotions. Using laboratory experiments that model trust-based interactions, we compare trust-based investment and emotions from across three nearly identical economic games: human-human trust games, human-robot trust games, and human-robot trust games where the robot decision also affects another human. In each of these trust games, a human trustor decides how much of a ten dollar endowment to entrust to a trustee (e.g. a fellow human, a robot). The experimenter multiplies the entrusted amount by three - creating potential gains from trust. Then the trustee receives this and decides how much to reciprocate to the trustor. We explain to participants who interact with them that the robots are programmed to mimic humans: they make automated reciprocity decisions based on previously observed behaviors by humans in analogous situations. After conclusions of the trust-game interactions, we provide participants feedback about their interactions and then participants rate how much they feel various positive and negative emotions. In this companion publication, we provide the raw data generated by participants (N = 397) in the role of "Investor" (“Person 1” in the participants’ instructions) and also "Trustee" (either “Person 2” or “the Robot”, depending on condition in the participants’ instructions) in our trust game experiment. This data is organized into two parts (1) the participants’ economic behavior in the trust games and their emotion reports from the post-game questionnaire, (2) a codebook explaining participants’ data.

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