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Open AccessArticle

Predicting Group Contribution Behaviour in a Public Goods Game from Face-to-Face Communication

1
Neuro-Information Technology group, Institute for Information Technology and Communications, Otto-von-Guericke University Magdeburg, 39106 Magdeburg, Germany
2
Halle Institute for Economic Research, 06108 Halle, Germany
3
Faculty of Economics and Management, Otto-von-Guericke University Magdeburg, 39106 Magdeburg, Germany
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(12), 2786; https://doi.org/10.3390/s19122786
Received: 9 May 2019 / Revised: 12 June 2019 / Accepted: 17 June 2019 / Published: 21 June 2019
(This article belongs to the Special Issue Sensors for Affective Computing and Sentiment Analysis)
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Abstract

Experimental economic laboratories run many studies to test theoretical predictions with actual human behaviour, including public goods games. With this experiment, participants in a group have the option to invest money in a public account or to keep it. All the invested money is multiplied and then evenly distributed. This structure incentivizes free riding, resulting in contributions to the public goods declining over time. Face-to-face Communication (FFC) diminishes free riding and thus positively affects contribution behaviour, but the question of how has remained mostly unknown. In this paper, we investigate two communication channels, aiming to explain what promotes cooperation and discourages free riding. Firstly, the facial expressions of the group in the 3-minute FFC videos are automatically analysed to predict the group behaviour towards the end of the game. The proposed automatic facial expressions analysis approach uses a new group activity descriptor and utilises random forest classification. Secondly, the contents of FFC are investigated by categorising strategy-relevant topics and using meta-data. The results show that it is possible to predict whether the group will fully contribute to the end of the games based on facial expression data from three minutes of FFC, but deeper understanding requires a larger dataset. Facial expression analysis and content analysis found that FFC and talking until the very end had a significant, positive effect on the contributions. View Full-Text
Keywords: face-to-face communications (FFC); voluntary contribution mechanism (VCM); random forest classification (RFc); facial activity descriptors (FADs); group activity descriptors (GADs); public goods game face-to-face communications (FFC); voluntary contribution mechanism (VCM); random forest classification (RFc); facial activity descriptors (FADs); group activity descriptors (GADs); public goods game
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Othman, E.; Saxen, F.; Bershadskyy, D.; Werner, P.; Al-Hamadi, A.; Weimann, J. Predicting Group Contribution Behaviour in a Public Goods Game from Face-to-Face Communication. Sensors 2019, 19, 2786.

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