Bio:

Eugene is currently a 4th year Ph.D. candidate at NUS Institute of Operations Research and Analytics. He obtained his Bachelor of Science with Honours (Highest Distinction) in Applied Mathematics from the National University of Singapore. His research interests focus on network analysis, graph theory and their applications in operations research and he is advised by Professor Andrew Lim, Assistant Professor Soh Yong Sheng and Adjunct Assistant Professor Prasanta Bhattacharya.

Abstract:

Evaluating the impact of policy interventions on respondents who are embedded in a social network is often challenging due to the presence of network interference within the treatment groups, as well as between treatment and non-treatment groups. These network interferences stem from interaction and discussion among respondent and this could reform new opinions, beliefs and behaviors in the process. We first propose a novel empirical strategy that combines network sampling based on the identification of independent sets with a stochastic actor-oriented model (SAOM) to infer the direct and net effects of a policy. By assigning respondents from an independent set to the treatment, we are able to block direct spillover of the treatment among the treated respondents for an extended period of time, during which the direct effect of the treatment can be isolated from the associated network interference. Next, we quantify the effect of these social dynamics on the broader opinion towards a new policy. Based on different sampling strategies, we control the degree of discussion among the population and quantify 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 and a simulation-based evaluation of a fictitious policy implementation on generated network and real-life network datasets. Our results highlight the role of network sampling techniques in influencing the evaluation of policy effects. The findings from our work have the potential to help researchers and policymakers with planning, designing, and anticipating policy responses in a networked society.