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@article{(International Science Index):http://waset.org/publications/10006978,
  title    = {The Modulation of Self-interest Instruction on the Fair-Proposing Behavior in Ultimatum Game},
  author    = {N. S. Yen and  T. H. Yang and  W. H. Huang and  Y. F. Fang and  H. W. Cho},
  country   = {Taiwan},
  institution={National Chengchi University},
  abstract  = {Ultimatum game is an experimental paradigm to study human decision making. There are two players, a proposer and a responder, to split a fixed amount of money. According to the traditional economic theory on ultimatum game, proposer should propose the selfish offers to responder as much as possible to maximize proposer’s own outcomes. However, most evidences had showed that people chose more fair offers, hence two hypotheses – fairness favoring and strategic concern were proposed. In current study, we induced the motivation in participants to be either selfish or altruistic, and manipulated the task variables, the stake sizes (NT$100, 1000, 10000) and the share sizes (the 40%, 30%, 20%, 10% of the sum as selfish offers, and the 60%, 70%, 80%, 90% of the sum as altruistic offers), to examine the two hypotheses. The results showed that most proposers chose more fair offers with longer reaction times (RTs) no matter in choosing between the fair and selfish offers, or between the fair and altruistic offers. However, the proposers received explicit self-interest instruction chose more selfish offers accompanied with longer RTs in choosing between the fair and selfish offers. Therefore, the results supported the strategic concern hypothesis that previous proposers choosing the fair offers might be resulted from the fear of rejection by responders. Proposers would become more self-interest if the fear of being rejected is eliminated.},
    journal   = {International Journal of Social, Behavioral, Educational, Economic, Business and Industrial Engineering},  volume    = {11},
  number    = {3},
  year      = {2017},
  pages     = {727 - 731},
  ee        = {http://waset.org/publications/10006978},
  url       = {http://waset.org/Publications?p=123},
  bibsource = {http://waset.org/Publications},
  issn      = {eISSN:1307-6892},
  publisher = {World Academy of Science, Engineering and Technology},
  index     = {International Science Index 123, 2017},
}