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

0479459 - ÚTIA 2018 RIV ES eng C - Conference Paper (international conference)
Guy, Tatiana Valentine - Ruman, Marko - Hůla, František - Kárný, Miroslav
Experimental Performance of Deliberation-Aware Responder in Multi-Proposer Ultimatum Game.
Proceedings of the NIPS 2016 Workshop on Imperfect Decision Makers. Cambridge: JMLR, 2017 - (Guy, T.; Kárný, M.; Rios-Insua, D.; Wolpert, D.), s. 51-60. Proceedings of Machine Learning Research, volume 58. E-ISSN 1938-7228.
[NIPS 2016 Workshop on Imperfect Decision Makers. Barcelona (ES), 09.12.2016-09.12.2016]
R&D Projects: GA ČR GA16-09848S
Institutional support: RVO:67985556
Keywords : decision making * deliberation effort * Markov decision process * ultimatum game
OECD category: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
http://library.utia.cas.cz/separaty/2047/AS/guy-0479459.pdf

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.
Permanent Link: http://hdl.handle.net/11104/0275505