Name
Title | ||
|---|---|---|
Reducing complexity in an agent based reaction model-Benefits and limitations of simplifications in relation to run time and system level output. |
Abstract | ||
|---|---|---|
Agent based modelling is a methodology for simulating a variety of systems across a broad spectrum of fields. However, due to the complexity of the systems it is often impossible or impractical to model them at a one to one scale. In this paper we use a simple reaction rate model implemented using the FLAME framework to test the impact of common methods for reducing model complexity such as reducing scale, increasing iteration duration and reducing message overheads. We demonstrate that such approaches can have significant impact on simulation runtime albeit with increasing risk of aberrant system behaviour and errors, as the complexity of the model is reduced. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1016/j.biosystems.2016.06.002 | Biosystems |
Keywords | Field | DocType |
Agent-based computational model,Complexity,Limitations,Scale,Iterations,Runtime,Model reduction | Computer science,One-to-one,Theoretical computer science,Artificial intelligence,Computer engineering,Machine learning,System level,Overhead (business),Model complexity | Journal |
Volume | ISSN | Citations |
147 | 0303-2647 | 0 |
PageRank | References | Authors |
0.34 | 2 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| David M. Rhodes | 1 | 0 | 0.34 |
| M. Holcombe | 2 | 150 | 17.49 |
| Eva E Qwarnstrom | 3 | 3 | 1.41 |