A dynamic programming approach for nation building problems

Recent attention has been given to quantitative methods for studying Nation Building problems. A nation's economic, political, and social structures constitute large and complex dynamic systems. This leads to the construction of large and computationally intensive Nation Building simulation models, especially when a high level of detail and validity are important. We consider a Markov Decision Process model for the Nation Building problem and attempt a dynamic programming solution approach. However DP algorithms are subject to the "curse of dimensionality". This is especially problematic since the models we consider are of large size and high dimensionality. We propose an algorithm that focuses on a local decision rule for the area of a Nation Building model's state space around the target nation's actual state. This process progresses in an online fashion; as the actual state transitions, a new local decision rule is computed. Decisions are chosen to maximize an infinite horizon discounted reward criteria that considers both short and long-term gains. Short term gains can be described exactly by the local model. Long term gains, which must be considered to avoid myopic behavior of local decisions, are approximated as fixed costs locally.