交互神经网络社会中的道德基础
Moral foundations in an interacting neural networks society
摘要：
道德基础理论支持人们，在不同文化中，倾向于在道德基础上对问题进行分类时考虑少数维度。数据还显示，每个道德维度的权重统计数据与自我宣称的政治派别有关，而最近的神经科学和心理学文献又将其与认知学习风格联系起来。受这些数据的启发，我们提出了一个简单的统计力学模型，其中交互神经网络对向量进行分类，并从其社会邻居的成员那里学习他们对大量问题的平均意见。学习的目的是减少代理人之间的分歧，即使不同意。我们考虑一个由δ参数化的学习算法族，它代表了对确证（相同符号）意见的重要性。我们定义了一个序参量，用于量化具有同质学习风格的群体中意见的多样性。使用蒙特卡罗模拟和平均场近似，我们发现在一个温度下，序参量和学习参数δ之间的关系，我们将其与给定群体中社会影响的重要性联系起来。与数据一致，更强烈地依赖确证证据的群体维持的意见多样性更少。我们讨论的模型的预测，并提出可能的实验测试。
英文摘要：
The moral foundations theory supports that people, across cultures, tend to consider a small number of dimensions when classifying issues on a moral basis. The data also show that the statistics of weights attributed to each moral dimension is related to self-declared political affiliation, which in turn has been connected to cognitive learning styles by recent literature in neuroscience and psychology. Inspired by these data, we propose a simple statistical mechanics model with interacting neural networks classifying vectors and learning from members of their social neighborhood about their average opinion on a large set of issues. The purpose of learning is to reduce dissension among agents even when disagreeing. We consider a family of learning algorithms parametrized by δ, that represents the importance given to corroborating (same sign) opinions. We define an order parameter that quantifies the diversity of opinions in a group with homogeneous learning style. Using Monte Carlo simulations and a mean field approximation we find the relation between the order parameter and the learning parameter δat a temperature we associate with the importance of social influence in a given group. In concordance with data, groups that rely more strongly on corroborating evidence sustains less opinion diversity. We discuss predictions of the model and propose possible experimental tests.
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