A hybrid search algorithm with heuristics for resource allocation problem
Information Sciences: an International Journal
| Hi-index | 0.00 |
One of the most difficult aspects in mathematical modeling and simulation is developing data to drive models and learning. This is particularly difficult when the subject involves intangible variables and concepts such as stress and perceptions that are difficult to ascribe a quantitative value to. This paper provides a description of how qualitative data collected during in depth phenomenological interviews with subject matter experts can be used to drive models. It also provides a case study of insurgency warfare and coalition and Afghan National Government performance during the last ten years. The U.S. government has spent more than $300 billion on the war in Afghanistan. Despite the employment of these resources, the goal of creating stability in the country has not been achieved. Twenty U.S. Army officers with six or more months of experience in Afghanistan were selected by random choice from a specific group. The participants were then interviewed to determine the meaning of their experiences in fighting an insurgency. Data analysis included organizing responses by question to identify the frequency of trends, patterns, and themes; and development of textural and structural descriptions of resource allocation and stability within the context of this study. Data was then transformed to create look-up tables that can be used to model, calibrate, and ascribe quantitative values to various variables in a dynamic insurgency model. A proof of concept model was then created to demonstrate the potential utility and power behind a model that combines the qualities of quantitative mathematical science and qualitative research methodology.