傳統關於賽局的研究，一般均假設使用者為完全理性。即使用者絕對重視金錢收益，或者其他部分之收益必可轉化為金錢價值計算，並且使用者了解其邊際價值而不會要求高於此價值之收益。然而實際上要求使用者「絕對理性」並不可能，會干擾理性的因素太多，導致傳統在局理論在許多情況解釋決策過程的表現並不理想。也因此賽局理論開始對於許多方面放寬傳統假設，希望這些鬆綁可以帶來更好的決策解釋結果。
關於常見影響理性的因子之一：情緒，目前都當作一種硬性的限制式來看。例如說若重視公平性，怎使用者便會重視對方對其是善意或者惡意，對方有沒有盡可能協助我方等因素”立刻”做出回應。但實際上若決策具有重複性（例如重複賽局），人類似乎並不急於立刻反應。且此方面的研究目前使用因子過多，從意向上使用者間是善意惡意，使用者間認為對方是善意惡意到實務面上使用者的資產水準都可以當作是影響對於公平性追求程度的因子。但是這麼多因子如何找出真正具有關鍵影響能力的因子（每個人的關鍵影響因子可能也不同），研究上對於使用者間善意的討論也複雜到相信超過一般人決策時實際會思考的程度。
由於現有模式具有以上提及的缺失，本研究決定建立一個切入點完全相異的公平性模式。由類似良知的概念，藉由使用者”關心對方多少”來決定其將如何決策，並且藉由紀錄使用者實際收益與期望收益的落差累積後反應情緒來達到公平性的要求。合比較軟性的限制（累積不滿而非斷然拒絕）與全新的分析方法（同時考慮對別人與對自己）成功對於解釋具公平性賽局的決策過程有較好的解釋。

Traditional research of game theory assumes that players are rational, which means they’ll pursue monetary income, and seek for a share exactly as much as their marginal utility. However, this is not reality. It has been proved that traditional game theory sure failed explaining many situations. Under this circumstance, recent research have no other way but to neglect the “rational” idea.
One of the most common influence of rationality is emotion, and it’s so far taken as a hard restriction. For example, if the game requires to be fair, players will judge whether their opponent is having a positive or negative intension toward them and make the response immediately. It seems fine when the game has only one inning, but what about sequential games? Exist research about sequential games, sticking on how to judge their opponents intension toward them, contains too much factors and are all too complicate to be true.
With these drawbacks, this time we decided to analyze fairness in totally different a way. Starting from moral and kindness, we have the players choose how much they care about their opponent instead of reacting to others decision. And because people “endure”, set up a procedure to record their discontent (define as the gap between what they get and what best they may get) and to leave the game or to have their strategies changed were someone becomes really unhappy. Think differently and have the brand new steps, as you can find in the paragraph, our research do analyze fairness well in another way.

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