Choice of optimization routine for multi-agent models: A case of viral video marketing campaign
| Authors | |
|---|---|
| Year of publication | 2015 |
| Type | Article in Proceedings |
| Conference | 33rd International Conference Mathematical Methods in Economics Conference Proceedings |
| MU Faculty or unit | |
| 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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