论文标题
动力学交换意见模型对随机网络的记忆和偏见的影响
Impact of memory and bias in kinetic exchange opinion models on random networks
论文作者
论文摘要
在这项工作中，我们考虑了动力学交换意见模型中记忆和偏见的影响。我们提出了一个模型，在该模型中，代理人记住他们与每对的最后一次相互作用的迹象。这在模型中引入了内存效应，因为过去的交互可能会影响未来的相互作用。我们还考虑了参数$ p $的影响，该参数会调节代理商更改其交互以匹配其意见的频率，从而引入了互动中的偏见。对于高价值的$ p $，一个代理更有可能开始与反对意见的代理商进行负面互动，并与同一意见的代理商进行积极的互动。该模型是在随机网络的顶部定义的，该网络的平均连接$ \ langle k \ rangle $。我们分析了$ p $和$ \ langle k \ rangle $对人口中有序和无序状态的出现的影响。我们的结果表明，关于临界现象，亚稳态状态的存在和阶参数的非单调行为，一种丰富的现象学。我们表明，随着偏见$ p $的增加，无序状态中的中性药物的比例减少。
In this work we consider the effects of memory and bias in kinetic exchange opinion models. We propose a model in which agents remember the sign of their last interaction with each one of their pairs. This introduces memory effects in the model, since past interactions can affect future ones. We have also considered the impact of a parameter $p$ that regulates how often an agent changes its interaction to match its opinion, thus introducing bias in the interactions. For high values of $p$ an agent is more likely to start having a negative interaction with an agent of opposing opinion and a positive interaction with an agent of the same opinion. The model is defined on the top of random networks with mean connectivity $\langle k \rangle$. We analyze the impact of both $p$ and $\langle k \rangle$ on the emergence of ordered and disordered states in the population. Our results suggest a rich phenomenology regarding critical phenomena, with the presence of metastable states and a non-monotonic behavior of the order parameter. We show that the fraction of neutral agents in the disordered state decreases as the bias $p$ increases.
