Dynamic Social Networks (DSNs) are advanced and constantly changing relations between people and in such situations, efficient and precise decision-making is the essential but difficult requirement. Global models with a traditional implementation usually sense to fail because of overhead calculations and slow reaction time. In this paper, the authors propose a Neighborhood-Driven Decision Maker (N-DM) model based on the principles of neighborhood theory of cellular automata. The suggested method leverages local metrics-edging locations which are 1) a locational gauge 2) cash 3) volatility of the neighborhood ( \(\varDelta N\) ). When used against the datasets Email-Eu-core and High-School Contact Network, the model shows great consistency between the local volatility and the time influence. The findings are that N-DM tends to expand decision coverage up to 25% as compared to conventional influencer-based strategies and identifies important actors earlier in the network development lifecycle. Our results can be used to validate the feasibility of neighborhood centric scalable, interpretable, flexible decision-making paradigm in dynamic situations.