In the process of enacting or introducing a new policy, policymakers frequently consider the population's responses. These considerations are critical for effective governance. There are numerous methods to gauge the ground sentiment from a subset of the population; examples include surveys or listening to various feedback channels. Many conventional approaches implicitly assume that opinions are static; however, in reality, the population will discuss and debate these new policies among themselves, and reform new opinions in the process. In this paper, we pose the following questions: Can we quantify the effect of these social dynamics on the broader opinion towards a new policy? Given some information about the relationship network that underlies the population, how does overall opinion change post-discussion? We investigate three different settings in which the policy is revealed: respondents who do not know each other, groups of respondents who all know each other, and respondents chosen randomly. By controlling who the policy is revealed to, we control the degree of discussion among the population. We quantify how these factors affect the changes in policy beliefs via the Wasserstein distance between the empirically observed data post-discussion and its distribution pre-discussion. We also provide several numerical analyses based on generated network and real-life network datasets. Our work aims to address the challenges associated with network topology and social interactions, and provide policymakers with a quantitative lens to assess policy effectiveness in the face of resource constraints and network complexities.

翻译：在制定或推行新政策的过程中，决策者常需考量民众的反馈，这对有效治理至关重要。现有多种方法可通过人口子集来评估基层情绪，例如开展调查或听取各类反馈渠道。许多传统方法隐含假设观点是静态的；然而现实中，民众会相互讨论和辩论新政策，并在此过程中重塑观点。本文提出以下问题：我们能否量化这些社会动态对新政策整体舆论的影响？在已知人口底层关系网络部分信息的情况下，讨论后整体观点如何演变？我们研究了政策信息发布的三种不同情境：互不相识的受访者、彼此熟识的受访者群体以及随机选取的受访者。通过控制政策信息的接收对象，我们调控了人群中的讨论程度。我们借助讨论后经验观测数据与讨论前分布之间的Wasserstein距离，量化了这些因素对政策信念变化的影响。基于生成网络和真实网络数据集，我们还提供了若干数值分析。本研究旨在应对网络拓扑与社会互动相关的挑战，为决策者在资源约束和网络复杂性背景下评估政策有效性提供量化分析视角。