Experimental Performance of Deliberation-Aware Responder in Multi-Proposer Ultimatum Game

Tatiana V. Guy; Marko Ruman; František Hůla; Miroslav Kárný

Experimental Performance of Deliberation-Aware Responder in Multi-Proposer Ultimatum Game

Tatiana V. Guy, Marko Ruman, František Hůla, Miroslav Kárný

Proceedings of the NIPS 2016 Workshop on Imperfect Decision Makers, PMLR 58:51-60, 2017.

Abstract

The ultimatum game serves for studying various aspects of decision making (DM). Recently, its multi-proposer version has been modified to study the influence of deliberation costs. An optimising policy of the responder, switching between several proposers at non-negligible deliberation costs, was designed and successfully tested in a simulated environment. The policy design was done within the framework of Markov Decision Processes with rewards also allowing to model the responder’s feeling for fairness. It relies on simple Markov models of proposers, which are recursively learnt in a Bayesian way during the game course. This paper verifies, whether the gained theoretically plausible policy, suits to real-life DM. It describes experiments in which this policy was applied against human proposers. The results – with eleven groups of three independently acting proposers – confirm the soundness of this policy. It increases the responder’s economic profit due to switching between proposers, in spite of the deliberation costs and the used approximate modelling of proposers. Methodologically, it opens the possibility to learn systematically willingness of humans to spent their deliberation resources on specific DM tasks.

Cite this Paper

BibTeX


@InProceedings{pmlr-v58-guy17b,
  title = 	 {Experimental Performance of Deliberation-Aware Responder in Multi-Proposer Ultimatum Game},
  author = 	 {Guy, Tatiana V. and Ruman, Marko and Hůla, František and Kárný, Miroslav},
  booktitle = 	 {Proceedings of the NIPS 2016 Workshop on Imperfect Decision Makers},
  pages = 	 {51--60},
  year = 	 {2017},
  editor = 	 {Guy, Tatiana V. and Kárný, Miroslav and Rios-Insua, David and Wolpert, David H.},
  volume = 	 {58},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {09 Dec},
  publisher =    {PMLR},
  pdf = 	 {http://proceedings.mlr.press/v58/guy17b/guy17b.pdf},
  url = 	 {https://proceedings.mlr.press/v58/guy17b.html},
  abstract = 	 {The ultimatum game serves for studying various aspects of decision making
 (DM). Recently, its multi-proposer version has been modified to study the influence
 of deliberation costs. An optimising policy of the responder, switching between several
 proposers at non-negligible deliberation costs, was designed and successfully tested
 in a simulated environment. The policy design was done within the  framework of
 Markov Decision Processes with rewards also allowing to model the responder’s feeling
 for fairness. It relies on simple Markov models of proposers, which are recursively
 learnt in a Bayesian way during the game course. This paper verifies, whether the
 gained theoretically plausible policy, suits to real-life DM. It describes experiments
 in which this policy was applied against human proposers. The results – with eleven
 groups of three independently acting proposers – confirm the soundness of this policy.
 It increases the responder’s economic profit due to switching between proposers, in spite
 of the deliberation costs and the used approximate modelling of proposers. Methodologically,
 it opens the possibility to learn systematically willingness of humans to spent their
 deliberation resources on specific DM tasks.}
}

Endnote

%0 Conference Paper
%T Experimental Performance of Deliberation-Aware Responder in Multi-Proposer Ultimatum Game
%A Tatiana V. Guy
%A Marko Ruman
%A František Hůla
%A Miroslav Kárný
%B Proceedings of the NIPS 2016 Workshop on Imperfect Decision Makers
%C Proceedings of Machine Learning Research
%D 2017
%E Tatiana V. Guy
%E Miroslav Kárný
%E David Rios-Insua
%E David H. Wolpert	
%F pmlr-v58-guy17b
%I PMLR
%P 51--60
%U https://proceedings.mlr.press/v58/guy17b.html
%V 58
%X The ultimatum game serves for studying various aspects of decision making
 (DM). Recently, its multi-proposer version has been modified to study the influence
 of deliberation costs. An optimising policy of the responder, switching between several
 proposers at non-negligible deliberation costs, was designed and successfully tested
 in a simulated environment. The policy design was done within the  framework of
 Markov Decision Processes with rewards also allowing to model the responder’s feeling
 for fairness. It relies on simple Markov models of proposers, which are recursively
 learnt in a Bayesian way during the game course. This paper verifies, whether the
 gained theoretically plausible policy, suits to real-life DM. It describes experiments
 in which this policy was applied against human proposers. The results – with eleven
 groups of three independently acting proposers – confirm the soundness of this policy.
 It increases the responder’s economic profit due to switching between proposers, in spite
 of the deliberation costs and the used approximate modelling of proposers. Methodologically,
 it opens the possibility to learn systematically willingness of humans to spent their
 deliberation resources on specific DM tasks.

APA


Guy, T.V., Ruman, M., Hůla, F. & Kárný, M.. (2017). Experimental Performance of Deliberation-Aware Responder in Multi-Proposer Ultimatum Game. Proceedings of the NIPS 2016 Workshop on Imperfect Decision Makers, in Proceedings of Machine Learning Research 58:51-60 Available from https://proceedings.mlr.press/v58/guy17b.html.

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