A dynamic programming approach for nation building problems

Gregory Tauer, Rakesh Nagi, Moises Sudit

Research output: Contribution to conferencePaperpeer-review

Abstract

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.

Original languageEnglish (US)
StatePublished - Jan 1 2011
Externally publishedYes
Event61st Annual Conference and Expo of the Institute of Industrial Engineers - Reno, NV, United States
Duration: May 21 2011May 25 2011

Other

Other61st Annual Conference and Expo of the Institute of Industrial Engineers
CountryUnited States
CityReno, NV
Period5/21/115/25/11

Keywords

  • Approximate dynamic programming
  • Markov decision processes
  • Nation building

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering

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