ept. However, it turned out not to happen that way. One-third of the proposers offered exactly half of the initial amount responder and more than 80% of them offered the responder at least 20% or more of the amount available. 10% of the offers were rejected. This result is very surprising as the proposers offer more than they have to while in one particular case where the responder was offered 20% but still rejected. Surprised and intrigued, Guth and his colleagues decided to carry out the experiment again but observed behaviours still illustrated a significant deviation from the equilibrium.
Reporting the results, Guth et al commented: “The main reason seems to be that the rational solution is not considered as socially acceptable or fair”. Chaudhuri (2008) reported that for further studying, Guth and his colleagues asked the students to participate in other much more complex versions of the game but the outcomes was very consistent, even when Camerer tried again in a different country in 2003. These results vigorously proved that not the inability to understand the game but their idea of a fair offer that matters, and indeed it can be explained by so called “social preferences”. It indicates that people are not primarily interested in maximizing their monetary payoffs in particular or his utility in general, they also pay a lot of their attention to other social norms like fairness or inequity-aversion. In other words, people tend to resist inequitable outcomes and they are willing to turn down the offer even in “at a cost to themselves” to move in the direction of a more equitable one. (Wilkinson,2008).
In an attempt to dig deeper in the competition in the ultimatum game, Roth et al (1991) carried out the experiments and observed variation between bargaining data and market data across four different locations – Pittsburgh, Ljubljana (Slovenia), Tokyo and Jerusalem. Proposers in each country were asked to split 1000 tokens, which will be converted to real money at the end of session. But now, there will be two games: the classic ultimatum games and the “multi person market behaviour” game. In the second game, proposers have to compete each other: there is n-1 proposers (n participants) who concurrently propose an offer to responder. Needless to mention, the responder has the opportunity to consider the highest offer. If there are more than one proposers offer the highest offer, one of them is randomly selected. Once again, if self-interest hypothesis was observed, in the classic ultimatum game, the proposers were expected to virtually get all gains from trade while in the market game, the situation will be reversed and the responder will get the most out of 1000 tokens. Knowing that the responder will only accept the highest offer, proposer knew that he would earn no return if his offer is less than the others. Therefore, all of the splits will be raised up as high as possible, as long as he still earn something which was considered to be fair. Theoretically, there will be a unique sub-game perfect equilibrium outcome when at least two proposers make an offer of all tokens and then the responder gets all from the trade.
Different modes of games bring back different kind of results. The first game reflects the differences in the social norm “fairness” of each society while the second was meant to test whether the story will be different when the responders have more market power. The data collected from experiment in bargaining environment was consistent with the classic game, when it deviated strictly from the perfect equilibrium and proposals are much nearer the middle of the range. In the market environment, the outcomes were observed to converge quickly to the Nash equilibrium and do not deviate once it has been achieved.
Figure 1: Game with proposer competition – Market Data
Source: Roth et al (1991:1080-1081)
From the figure 1, we can see that after few periods, all four markets in four countries reached to the sub perfect equilibrium and stay there after that. People try different strategies in the early rounds 