Name
Abstract | ||
|---|---|---|
Questions concerning how one can influence an agent-based model in order to best achieve some specific goal are optimization problems. In many models, the number of possible control inputs is too large to be enumerated by computers; hence methods must be developed in order to find solutions that do not require a search of the entire solution space. Model reduction techniques are introduced and a statistical measure for model similarity is proposed. Heuristic methods can be effective in solving multi-objective optimization problems. A framework for model reduction and heuristic optimization is applied to two representative models, indicating its applicability to a wide range of agent-based models. Results from data analysis, model reduction, and algorithm performance are assessed. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.18564/jasss.2472 | JASSS-THE JOURNAL OF ARTIFICIAL SOCIETIES AND SOCIAL SIMULATION |
Keywords | DocType | Volume |
Agent-Based Modeling,Optimization,Statistical Test,Genetic Algorithms,Reduction | Journal | 17 |
Issue | ISSN | Citations |
2 | 1460-7425 | 1 |
PageRank | References | Authors |
0.35 | 0 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Matthew Oremland | 1 | 2 | 0.74 |
| Reinhard C. Laubenbacher | 2 | 91 | 11.98 |