Citing this article

A standard form of citation of this article is:

Bin, Hu and Zhang, Debing (2007). 'Cellular-Automata Based Qualitative Simulation for Nonprofit Group Behavior'. Journal of Artificial Societies and Social Simulation 10(1)1 <http://jasss.soc.surrey.ac.uk/10/1/1.html>.

The following can be copied and pasted into a Bibtex bibliography file, for use with the LaTeX text processor:

@article{bin2007,
title = {Cellular-Automata Based Qualitative Simulation for Nonprofit Group Behavior},
author = {Bin, Hu and Zhang, Debing},
journal = {Journal of Artificial Societies and Social Simulation},
ISSN = {1460-7425},
volume = {10},
number = {1},
pages = {1},
year = {2007},
URL = {http://jasss.soc.surrey.ac.uk/10/1/1.html},
keywords = {Cellular Automata; Qualitative Simulation; Group Behavior; Loyalty-Cost Equilibrium; Loyalty Gravitation; Cost Gravitation},
abstract = {A cellular automata based qualitative simulation of group behavior (referred hitherto as 'loyalty to group') will be presented by integrating QSIM (Qualitative SIMulation) and CA (Cellular Automata) modeling. First, we provide a breakdown of the structure of a group and offer an analysis of how this structure impacts behavior. The characteristics and impact had by anomalies within a group and by environmental factors are also explored. Second, we explore the transition between cause and effect (referred hitherto as the 'transition rule') and the change in behavior that is the result of this transition (referred hitherto as the 'successor behavior state'). A filter for weeding out anomalies is then proposed. The simulation engine is then used integrating all relevant data as outlined above. A concept referred to as the 'Loyalty-cost equilibrium' is presented and factored into the filter. Third, the validity of this method is tested by running the simulation using eight generalized examples. The input-output of each simulation run using these examples is consistent with what can reasonably be accepted to be true, thus demonstrating that the proposed method is valid. At this point we illustrate how the simulation is applied in context. Simulation outputs (effect on group behavior) at each time stage of two alternating changes in policy are compared to determine which policy would be the most advantageous. This demonstrates that this method serves as reliable virtual tool in the decision making difficulties of group management.},
}

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 - Cellular-Automata Based Qualitative Simulation for Nonprofit Group Behavior
AU - Bin, Hu
AU - Zhang, Debing
Y1 - 2007/01/31
JO - Journal of Artificial Societies and Social Simulation
SN - 1460-7425
VL - 10
IS - 1
SP - 1
UR - http://jasss.soc.surrey.ac.uk/10/1/1.html
KW - Cellular Automata; Qualitative Simulation; Group Behavior; Loyalty-Cost Equilibrium; Loyalty Gravitation; Cost Gravitation
N2 - A cellular automata based qualitative simulation of group behavior (referred hitherto as 'loyalty to group') will be presented by integrating QSIM (Qualitative SIMulation) and CA (Cellular Automata) modeling. First, we provide a breakdown of the structure of a group and offer an analysis of how this structure impacts behavior. The characteristics and impact had by anomalies within a group and by environmental factors are also explored. Second, we explore the transition between cause and effect (referred hitherto as the 'transition rule') and the change in behavior that is the result of this transition (referred hitherto as the 'successor behavior state'). A filter for weeding out anomalies is then proposed. The simulation engine is then used integrating all relevant data as outlined above. A concept referred to as the 'Loyalty-cost equilibrium' is presented and factored into the filter. Third, the validity of this method is tested by running the simulation using eight generalized examples. The input-output of each simulation run using these examples is consistent with what can reasonably be accepted to be true, thus demonstrating that the proposed method is valid. At this point we illustrate how the simulation is applied in context. Simulation outputs (effect on group behavior) at each time stage of two alternating changes in policy are compared to determine which policy would be the most advantageous. This demonstrates that this method serves as reliable virtual tool in the decision making difficulties of group management.
ER -