Transportation agencies in coastal urban areas face a significant challenge to enhance the long-term resilience of their networks to flooding and storm surge events exacerbated by sea level rise. The problem of sea-level rise adaptation is characterized by deep uncertainty that makes it complex to assess the value of adaptation investments. To enable informed adaptation decisions, the present study created a dynamic stochastic modeling framework based on the theoretical underpinnings of complex adaptive systems that integrates: (i) stochastic simulation of sea-level rise stressors based on the data obtained from downscaled climate studies pertaining to future projections of sea-level and precipitation; (ii) dynamic modeling of roadway conditions by considering regular decay of roadways, as well as structural damages caused by storm surge events; and (iii) a decision-theoretic modeling of agency infrastructure management and adaptation processes based on cognitive psychology, bounded rationality, and regret theories. In this framework, resilience is examined based on trend changes in the network performance measures (e.g., life cycle costs and performance). The created framework and model were tested in a case study related to the road network of the city of Miami-Beach, which global assessments rank first iv among the world's urban areas most exposed to sea-level rise risks. The results indicated that: (i) SLR Adaptation investment and life cycle costs of roadway infrastructure are negatively correlated. In addition, it was shown that the sensitivity of network’s life cycle cost to actual sea-level rise scenario decreases when adaptation investment increases. These finding emphasize the importance of proactive improvement of the network resilience to alleviate the long-term costs of sea-level rise. (ii) When funding is sufficient for all required adaptation actions, mid-term adaptation planning yields lower life cycle cost. When funding is insufficient, aggregated investment in long-term adaptation planning intervals yields lower network LCC. These findings imply that different adaptation planning approaches should be taken for different levels of adaptation investment. (iii) The agency’s perception of SLR and risk attitude do not have significant effect on life cycle cost of roadway networks. Hence, implementation of adaptation action based on any perception of sea-level rise and risk attitude can significantly reduce the life cycle costs of roadway networks under the impacts of SLR. (iv) The devised performance target has negative correlation with life cycle cost of a roadway network affected by SLR impacts. Therefore, compromising the network performance condition will never result in lower life cycle costs.