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The communication of participants to identify an acceptable bargaining outcome in the Nash bargaining game is all about fairness norms. Participants introduce fairness norms which yield a better outcome for themselves in order to convince the other participant of their bargaining proposal. Typically, these fairness norms are in line with theoretical predictions, which support a wide variety of different but fair outcomes the participants can choose from. In this experiment, we play two treatments of the Nash bargaining game: in one treatment, the participants play a dictator game prior to bargaining, and in the other treatment they do not. We find that participants who have not played the dictator game intensively discuss the outcome of the game and come to solutions closer to the equal split of the pie the longer they chat. This effect vanishes as soon as the participants have previous experience from a dictator game: instead of chatting, they establish the fairness norm introduced in the dictator game. Remarkably, if the dictator is unfair in the dictator game, he also gets a higher share of the pie in the Nash bargaining game. [ABSTRACT FROM AUTHOR]

Our study analyzes theories of learning for strategic interactions in networks. Participants played two of the 2 × 2 games used by Selten and Chmura [1]. Every participant played against four neighbors. As a distinct aspect our experimental design allows players to choose different strategies against each different neighbor. The games were played in two network structures: a lattice and a circle. We analyze our results with respect to three aspects. We first compare our results with the predictions of five different equilibrium concepts (Nash equilibrium, quantal response equilibrium, action-sampling equilibrium, payoff-sampling equilibrium, and impulse balance equilibrium) which represent the long-run equilibrium of a learning process. Secondly, we relate our results to four different learning models (impulse-matching learning, action-sampling learning, self-tuning EWA, and reinforcement learning) which are based on the (behavioral) round-by-round learning process. At last, we compare the data with the experimental results of Selten and Chmura [1]. One main result is that the majority of players choose the same strategy against each neighbor. As other results, we observe an order of predictive success for the equilibrium concepts that is different from the order shown by Selten and Chmura and an order of predictive success for the learning models that is only slightly different from the order shown in a recent paper by Chmura, Goerg and Selten [2]. [ABSTRACT FROM AUTHOR]

Previous research has typically focused on distribution problems that emerge in the domain of gains. Only a few studies have distinguished between games played in the domain of gains from games in the domain of losses, even though, for example, prospect theory predicts differences between behavior in both domains. In this study, we experimentally analyze players' behavior in dictator and ultimatum games when they need to divide a monetary loss and then compare this to behavior when players have to divide a monetary gain. We find that players treat gains and losses differently in that they are less generous in games over losses and react differently to prior experiences. Players in the dictator game become more selfish after they have had the experience of playing an ultimatum game first. [ABSTRACT FROM AUTHOR]