Human-Algorithm Interaction: Algorithmic Pricing in Hybrid Laboratory Markets

Hans-Theo Normann () and Martin Sternberg ()
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Hans-Theo Normann: Heinrich Heine University, Düsseldorf
Martin Sternberg: Max Planck Institute for Research on Collective Goods, Bonn

No 2021_11, Discussion Paper Series of the Max Planck Institute for Research on Collective Goods from Max Planck Institute for Research on Collective Goods

Abstract: This paper investigates pricing in laboratory markets when human players interact with an algorithm. We compare the degree of competition when exclusively humans interact to the case of one firm delegating its decisions to an algorithm, an n-player generalization of tit-for-tat. We further vary whether participants know about the presence of the algorithm. When one of three firms in a market is an algorithm, we observe significantly higher prices compared to human-only markets. Firms employing an algorithm earn significantly less profit than their rivals. (Un)certainty about the actual presence of an algorithm does not significantly affect collusion, although humans seem to perceive algorithms as more disruptive.

Keywords: algorithms; collusion; human-computer interaction; laboratory experiments (search for similar items in EconPapers)
JEL-codes: C90 L41 (search for similar items in EconPapers)
Date: 2021-05-06, Revised 2022-04-13
New Economics Papers: this item is included in nep-bec, nep-com, nep-exp, nep-law and nep-ore
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Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:mpg:wpaper:2021_11

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