Predicting free-riding in a public goods game: Analysis of content and dynamic facial expressions in face-to-face communication

Dmitri Bershadskyy, Ehsan Othman and Frerk Saxen

No 9/2019, IWH Discussion Papers from Halle Institute for Economic Research (IWH)

Abstract: This paper illustrates how audio-visual data from pre-play face-to-face communication can be used to identify groups which contain free-riders in a public goods experiment. It focuses on two channels over which face-to-face communication influences contributions to a public good. Firstly, the contents of the face-to-face communication are investigated by categorising specific strategic information and using simple meta-data. Secondly, a machine-learning approach to analyse facial expressions of the subjects during their communications is implemented. These approaches constitute the first of their kind, analysing content and facial expressions in face-to-face communication aiming to predict the behaviour of the subjects in a public goods game. The analysis shows that verbally mentioning to fully contribute to the public good until the very end and communicating through facial clues reduce the commonly observed end-game behaviour. The length of the face-to-face communication quantified in number of words is further a good measure to predict cooperation behaviour towards the end of the game. The obtained findings provide first insights how a priori available information can be utilised to predict free-riding behaviour in public goods games.

Keywords: automatic facial expressions recognition; content analysis; public goods experiment; face-to-face communication (search for similar items in EconPapers)
JEL-codes: C80 C92 D91 (search for similar items in EconPapers)
Date: 2019
New Economics Papers: this item is included in nep-big, nep-cbe, nep-exp and nep-pub
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:zbw:iwhdps:92019

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