A standard form of citation of this article is:
Wendel, Stephen and Dibble, Catherine (2007). 'Dynamic Agent Compression'. Journal of Artificial Societies and Social Simulation 10(2)9 <https://www.jasss.org/10/2/9.html>.
The following can be copied and pasted into a Bibtex bibliography file, for use with the LaTeX text processor:
@article{wendel2007,
title = {Dynamic Agent Compression},
author = {Wendel, Stephen and Dibble, Catherine},
journal = {Journal of Artificial Societies and Social Simulation},
ISSN = {1460-7425},
volume = {10},
number = {2},
pages = {9},
year = {2007},
URL = {https://www.jasss.org/10/2/9.html},
keywords = {Agent-Based Modeling, Scaling, Homogeneity, Compression},
abstract = {We introduce a new method for processing agents in agent-based models that significantly improves the efficiency of certain models. Dynamic Agent Compression allows agents to shift in and out of a compressed state based on their changing levels of heterogeneity. Sets of homogeneous agents are stored in compact bins, making the model more efficient in its use of memory and computational cycles. Modelers can use this increased efficiency to speed up the execution times, to conserve memory, or to scale up the complexity or number of agents in their simulations. We describe in detail an implementation of Dynamic Agent Compression that is lossless, i.e., no model detail is discarded during the compression process. We also contrast lossless compression to lossy compression, which promises greater efficiency gains yet may introduce artifacts in model behavior.
The advantages outweigh the overhead of Dynamic Agent Compression in models where agents are unevenly heterogeneous — where a set of highly heterogeneous agents are intermixed with numerous other agents that fall into broad internally homogeneous categories. Dynamic Agent Compression is not appropriate in models with few, exclusively complex, agents.},
}The following can be copied and pasted into a text file, which can then be imported into a reference database that supports imports using the RIS format, such as Reference Manager and EndNote.
TY - JOUR
TI - Dynamic Agent Compression
AU - Wendel, Stephen
AU - Dibble, Catherine
Y1 - 2007/03/31
JO - Journal of Artificial Societies and Social Simulation
SN - 1460-7425
VL - 10
IS - 2
SP - 9
UR - https://www.jasss.org/10/2/9.html
KW - Agent-Based Modeling
KW - Scaling
KW - Homogeneity
KW - Compression
N2 - We introduce a new method for processing agents in agent-based models that significantly improves the efficiency of certain models. Dynamic Agent Compression allows agents to shift in and out of a compressed state based on their changing levels of heterogeneity. Sets of homogeneous agents are stored in compact bins, making the model more efficient in its use of memory and computational cycles. Modelers can use this increased efficiency to speed up the execution times, to conserve memory, or to scale up the complexity or number of agents in their simulations. We describe in detail an implementation of Dynamic Agent Compression that is lossless, i.e., no model detail is discarded during the compression process. We also contrast lossless compression to lossy compression, which promises greater efficiency gains yet may introduce artifacts in model behavior. The advantages outweigh the overhead of Dynamic Agent Compression in models where agents are unevenly heterogeneous — where a set of highly heterogeneous agents are intermixed with numerous other agents that fall into broad internally homogeneous categories. Dynamic Agent Compression is not appropriate in models with few, exclusively complex, agents.
ER -