A template model for agent-based simulations
- Published
- Accepted
- Subject Areas
- Agents and Multi-Agent Systems, Scientific Computing and Simulation, Theory and Formal Methods
- Keywords
- agent-based modeling, ODD protocol, template model, statistical analysis, simulation output, tutorial
- Copyright
- © 2015 Fachada et al.
- Licence
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ PrePrints) and either DOI or URL of the article must be cited.
- Cite this article
- 2015. A template model for agent-based simulations. PeerJ PrePrints 3:e1278v1 https://doi.org/10.7287/peerj.preprints.1278v1
Abstract
Agent-based modeling (ABM) is a bottom-up modeling approach, where each entity of the system being modeled is uniquely represented as an independent decision-making agent. ABMs are very sensitive to implementation details. Thus, it is very easy to inadvertently introduce changes which modify model dynamics. Such problems usually arise due to the lack of transparency in model descriptions, which constrains how models are assessed, implemented and replicated. In this paper, we present a template ABM which aims to serve as a basis for a series of investigations, including, but not limited to, conceptual model specification, statistical analysis of simulation output, model comparison and model parallelization. This paper focuses on the first two aspects (conceptual model specification and statistical analysis of simulation output), also providing a canonical implementation of the template ABM, such that it serves as a complete reference to the presented model. Additionally, this study is presented in a tutorial fashion, and can be used as a road map for simulation practitioners who wish to improve the way they communicate their ABMs.
Author Comment
This is a submission to PeerJ Computer Science for review.
Supplemental Information
Table S1. PRNG seeds used for the NetLogo replications
Each seed was obtained by taking the MD5 checksum of replication number and converting the resulting hexadecimal string to a 32-bit integer (the maximum precision accepted by NetLogo).
Tables S2.1 to S2.10. Statistics and distributional analysis of the selected focal measures for n = 30 replications of the PPHPC model for all the model size and parameter set combinations
‘SW‘ refers to the p-value produced by the Shapiro-Wilk normality test. ‘Skew.‘ refers to the skewness in the distribution. ‘Hist.‘ shows an histogram of the distribution. ‘Q-Q‘ shows a Q-Q plot of the distribution.
Outputs of 30 replications for all model sizes and parameter set 1
Each text file corresponds to one replication. Columns correspond to outputs in the following order: prey population, predator population, available cell-bound food, mean prey energy, mean predator energy, mean value of the grid cells C state variable. Rows correspond to iterations.
Outputs of 30 replications for all model sizes and parameter set 2
Each text file corresponds to one replication. Columns correspond to outputs in the following order: prey population, predator population, available cell-bound food, mean prey energy, mean predator energy, mean value of the grid cells C state variable. Rows correspond to iterations.