Optimization of Agent-Based Models: Scaling Methods and Heuristic Algorithms

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.

• Publication year

  2014

• Venue

  Journal of Artificial Societies and Social Simulation

• Publication date

  2014-03-31

• Fields of study

  Computer Science, Engineering

• Identifiers
  DOI 10.18564/jasss.2472
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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