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Using the Process of Norm Emergence to Model Consensus Formation

机译:使用规范出现过程建模共识形成

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Every agent in a society initially possesses a set of personal norms. Group norms emerge when agents interact with one another and exchange information in such a way that multiple agents begin to acquire the same personal norm. This emergence is the result of information transmission, social enforcement, and internalization. If a population contains a single group norm, as a result of every agent in the population acquiring the same personal norm, then it can be said that a consensus has been reached by the population. We model the formation of consensus in silico by adapting a recently developed model of norm emergence to a multi-agent simulation. A screening experiment is conducted to identify the significant parameters of our model and verify that our model is capable of producing a consensus. The experimental results show that our model can attain consensus as well as two additional states of information equilibrium. The results also indicate that both network structure and agent behavior play an important role in the formation of consensus. In addition, it is shown that the formation of consensus is sensitive to the simulation parameter settings, and certain values can prevent its formation entirely.
机译:社会中的每个代理人最初都拥有一套个人规范。当代理之间进行交互并交换信息时,组规范就会出现,从而使多个代理开始获取相同的个人规范。这种出现是信息传递,社会执行和内部化的结果。如果一个人口包含一个单一的组规范,那么由于该人口中的每个代理都获得了相同的个人规范,那么就可以说该人群已经达成共识。我们通过将最近开发的规范出现模型适应多主体仿真来模拟计算机上共识的形成。进行筛选实验以识别我们模型的重要参数,并验证我们的模型能够产生共识。实验结果表明,我们的模型可以达到共识以及信息平衡的另外两个状态。结果还表明,网络结构和代理行为在共识形成中都起着重要作用。另外,表明共识的形成对模拟参数设置敏感,并且某些值可以完全阻止其形成。

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