Choice of optimization routine for multi-agent models: A case of viral video marketing campaign

Authors

KVASNIČKA Michal

Year of publication 2015
Type Article in Proceedings
Conference 33rd International Conference Mathematical Methods in Economics Conference Proceedings
MU Faculty or unit

Faculty of Economics and Administration

Citation
Field Economy
Keywords optimization; genetic algorithm; mutation hill climbing; simulation; agent-based model; social network; viral video marketing
Attached files
Description Very few agent-base computational models are optimized because the usually used optimization routine, the genetic algorithm, is extremely time-consuming. This paper explores how much precision is lost if a simpler optimization routine, mutational hill climber, is used instead. It shows on the case of a viral-video marketing model that even though the standard genetic algorithm is slightly more precise, the mutation hill climbing could be used as an approximate optimization routine for robustness check and scenario analysis.
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