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Processing and Analysing Experimental Data Using a Tensor-Based Method: Evidence from an Ultimatum Game Study

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Decision Economics: In the Tradition of Herbert A. Simon's Heritage (DCAI 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 618))

Abstract

This work investigates how newer economic behavioural research can be applied to human group behaviour and how it can be enriched using a relatively novel knowledge discovery approach. Based on an ultimatum game study conducted in the context of an extra-lab experiment, the authors propose a tensor-based method to analyse their experimental results and, therefore, to address a multi-dimensional approach. The authors prove that subjects do not behave as game theory would predict, but rather they basically prefer fair divisions of gains. This evidence confirms significant implications for theories addressing the evolution of, and the mechanisms underpinning, human group behaviour in economics, cognitive, and organizational studies.

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Notes

  1. 1.

    This is a one-shot two-stage sequential bargaining game methodologically based on studies concerning game approaches to interactions between individuals [13,14,15]. Although the ultimatum game is frequently used to describe the backward induction method of solving for a sub-game perfect Nash equilibrium for monetary payoffs maximising individuals, this bargaining game provides evidence for fairness concerns on individuals’ preferences. Indeed, there are multiple reported results of equal-split or close to equal-split outcomes from several experiments. Results from these experiments contradict the standard economic theory and have been used to argue that pro-social preferences are important in a wide range of real-world contexts (e.g., [16, 17]).

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Acknowledgements

The authors would like to thank the 314 students who took part in the experimental study conducted in 2015 for their time and valuable input. Furthermore, the authors wish to thank Nicola Mattoscio, Shu-Heng Chen, Herrade Igersheim, Carmen Pagliari, Assia Liberatore, and two anonymous reviewers for their advice and constructive criticism on earlier drafts. Finally, the authors thank the conference participants at the 13th DECON-DCAI 2016 for useful suggestions. The usual disclaimers apply.

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Authors and Affiliations

  1. Department of PPEQS – Section of Economics and Quantitative Methods, University of Chieti-Pescara, Viale Pindaro 42, 65127, Pescara, Italy

    Edgardo Bucciarelli

  2. Ministry of Economy and Finance, Rome, Italy

    Tony E. Persico

Authors
  1. Edgardo Bucciarelli
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  2. Tony E. Persico
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Correspondence to Edgardo Bucciarelli .

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Editors and Affiliations

  1. Department of Philosophical, Pedagogical, and Economic-Quantitative Sciences, Section of Economics and Quantitative Methods, University of Chieti-Pescara, Pescara, Italy

    Edgardo Bucciarelli

  2. Department of Economics, National Chengchi University, Taipei, Taiwan

    Shu-Heng Chen

  3. Departamento de Informática y Automática, Universidad de Salamanca, Salamanca, Spain

    Juan M. Corchado

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Bucciarelli, E., Persico, T.E. (2018). Processing and Analysing Experimental Data Using a Tensor-Based Method: Evidence from an Ultimatum Game Study. In: Bucciarelli, E., Chen, SH., Corchado, J. (eds) Decision Economics: In the Tradition of Herbert A. Simon's Heritage. DCAI 2017. Advances in Intelligent Systems and Computing, vol 618. Springer, Cham. https://doi.org/10.1007/978-3-319-60882-2_15

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  • DOI: https://doi.org/10.1007/978-3-319-60882-2_15

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