Evaluating Policy Effects Through Opinion Dynamics and Network Sampling

An essential aspect of effective policymaking is to regularly consider the population’s response or feedback towards a newly introduced policy. These can come in the form of population surveys or feedback channels, and they provide a simple way to understand the ground sentiment towards a new policy. Conventional surveying methods implicitly assume that opinions are static; in reality, opinions are often dynamic – the population will discuss and debate these newly introduced policies among themselves, and in the process form new opinions. In this paper, we pose the following set of questions: Can we understand the dynamics of opinions towards a new policy within the population? Specifically, can we quantify the evolution of opinions over the course of interaction? How are these changes affected by the topological structure of the underlying network describing the relationship among the population? We investigate these questions using a model where the policymaker is able to select a subset of population to which a policy is initially revealed to. By selecting the subset of respondents judiciously, the policymaker controls the degree of discussion that can take place among the population. Under this model, we quantify the changes in opinions between the empirically observed data post-discussion and its distribution pre-discussion, in terms of the number of selected respondents, as well as the number of connections each respondent has within the population network. We conduct a series of numerical experiments over synthetic data and real-world networks. 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.