Many decisions are derivatives of cognitive illusions, biases, contextual influences and are taken on limited data. Structured, knowledge-based, methodical decision-making enables some improvements. Linear models like decision trees, multi-variate methods or hierarchical models provide enablement in the context of structured decision-making. However, such models are constrained, to factor in changing future scenarios, state changes and adaptive contexts – thus impacting decision utility and future consequences. To factor in complexities like non-linearity, decentralization, interconnectedness, interdependences and multiplicity application of Decision Dynamic Networks (DDN) are observed – specifically in scenarios, that dynamically adapt themselves over time to changing and uncertain systemic triggers and state changes. This paper proposes extension to existing capabilities in decision networks by incorporating probabilistic modelling of adaptive contexts across future time slices. Proposed in this paper is a conceptualized simulated longitudinal dynamic decision network model to factor interactions among elements of decision networks and identify newer elements for consideration. Newer considerations including decisions, actions, results and the influence and interactions are moderated to optimize utility. It is premised that decision networks need to evolve over simulated time slices alongside future state changes. Maximizing the utility of decisions therefore needs to be understood over valid time slices in the future. Measure of utility over evolving longitudinal decision networks leads to discovery of a valid time which is likely to yield maximum decision utility. Time association with decision executions is imperative as proposed. CCS (Computing Classification System) CONCEPTS • Applied Computing → Artificial Intelligence (AI) → Dynamic Decision Networks (DDN) → Longitudinal analysis.