0479459 20240103214701.320171012235959.9 10 s. E cav_un_epca*0479516Proceedings of the NIPS 2016 Workshop on Imperfect Decision Makers51-60CambridgeJMLR2017GuyTatiana ValentineKárnýMiroslavRios-InsuaD.WolpertD. H. decision making deliberation effort Markov decision process ultimatum game cav_un_auth*0101092 Adaptivní systémy Department of Adaptive Systems AS AS Department of Adaptive Systems 25% Guy Tatiana Valentine UTIA-B Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0333672 Adaptivní systémy Department of Adaptive Systems AS AS Department of Adaptive Systems 25% Ruman Marko UTIA-B SK Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0333671 Adaptivní systémy Department of Adaptive Systems AS AS Department of Adaptive Systems 25% Hůla František UTIA-B CZ Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0101124 Adaptivní systémy Department of Adaptive Systems AS AS Department of Adaptive Systems 25% Kárný Miroslav UTIA-B Ústav teorie informace a automatizace AV ČR, v. v. i. http://library.utia.cas.cz/separaty/2047/AS/guy-0479459.pdf cav_un_auth*0331019 GA16-09848S GA ČR 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. cav_un_auth*0351361 NIPS 2016 Workshop on Imperfect Decision Makers 20161209 20161209 Barcelona ES BC 10000 10200 10201 2018 4 PR RVO:67985556 http://hdl.handle.net/11104/0275505 S 2017 SCOPUS PUBMED WOS cav_un_epca*0479516 Proceedings of the NIPS 2016 Workshop on Imperfect Decision Makers 1938-7228 51 60 Cambridge JMLR 2017 Proceedings of Machine Learning Research volume 58 Guy Tatiana Valentine 340 340 Kárný Miroslav 340 Rios-Insua D. 340 Wolpert D. H.