Department of Information Science, Cornell University, Ithaca, NY 14853;
Department of Sociology, Cornell University, Ithaca, NY 14850.
Proc Natl Acad Sci U S A. 2021 Dec 14;118(50). doi: 10.1073/pnas.2102144118.
Research has documented increasing partisan division and extremist positions that are more pronounced among political elites than among voters. Attention has now begun to focus on how polarization might be attenuated. We use a general model of opinion change to see if the self-reinforcing dynamics of influence and homophily may be characterized by tipping points that make reversibility problematic. The model applies to a legislative body or other small, densely connected organization, but does not assume country-specific institutional arrangements that would obscure the identification of fundamental regularities in the phase transitions. Agents in the model have initially random locations in a multidimensional issue space consisting of membership in one of two equal-sized parties and positions on 10 issues. Agents then update their issue positions by moving closer to nearby neighbors and farther from those with whom they disagree, depending on the agents' tolerance of disagreement and strength of party identification compared to their ideological commitment to the issues. We conducted computational experiments in which we manipulated agents' tolerance for disagreement and strength of party identification. Importantly, we also introduced exogenous shocks corresponding to events that create a shared interest against a common threat (e.g., a global pandemic). Phase diagrams of political polarization reveal difficult-to-predict transitions that can be irreversible due to asymmetric hysteresis trajectories. We conclude that future empirical research needs to pay much closer attention to the identification of tipping points and the effectiveness of possible countermeasures.
研究记录了党派分歧和极端立场的不断加剧，而这些在政治精英中比在选民中更为明显。现在人们开始关注如何缓解极化现象。我们使用一种普遍的观点变化模型，来看影响和同质性的自我强化动态是否可能具有使反转变得困难的临界点。该模型适用于立法机构或其他小型、紧密连接的组织，但不假设特定于国家的制度安排，因为这些安排会掩盖相变中基本规律的识别。模型中的代理人在一个由两个大小相等的政党成员身份和 10 个问题的立场组成的多维问题空间中最初随机定位。然后，代理人根据他们对分歧的容忍度和对政党认同的强度与对问题的意识形态承诺相比，通过更接近附近的邻居和远离与他们意见不合的人来更新他们的问题立场。我们进行了计算实验，在实验中我们操纵了代理人对分歧的容忍度和对政党认同的强度。重要的是，我们还引入了外生冲击，这些冲击对应于创造共同利益以应对共同威胁的事件（例如，全球大流行）。政治极化的相图揭示了难以预测的转变，由于不对称的滞后轨迹，这些转变可能是不可逆的。我们得出的结论是，未来的实证研究需要更加密切关注临界点的识别和可能的对策的有效性。
