TY - JOUR
T1 - Building social networks out of cognitive blocks
T2 - factors of interest in agent-based socio-cognitive simulations
AU - Zhao, Changkun
AU - Kaulakis, Ryan
AU - Morgan, Jonathan H.
AU - Hiam, Jeremiah W.
AU - Ritter, Frank E.
AU - Sanford, Joesph
AU - Morgan, Geoffrey P.
N1 - Funding Information:
This work was supported by a grant from DTRA (HDTRA1-09-1-0054). We thank Jaeyhon Paik for help on the agent design and Jeremy Lothian for assistance with VIPER.
Publisher Copyright:
© 2014, Springer Science+Business Media New York.
PY - 2015/6/1
Y1 - 2015/6/1
N2 - This paper examines how cognitive and environmental factors influence the formation of dyadic ties. We use agent models instantiated in ACT-R that interact in a social simulation, to illustrate the effect of memory constraints on networks. We also show that environmental factors are important including population size, running time, and map configuration. To examine these relationships, we ran simulations of networks using a factorial design. Our analyses suggest three interesting conclusions: first, the tie formation of these networks approximates a logistic growth model; second, that agent memory quality (i.e., perfect or human-like) strongly alters the network’s density and structure; third, that the three environmental factors all influence both network density and some aspects of network structure. These findings suggest that meaningful variance of social network analysis measures occur in a narrow band of memory strength (the cognitive band); the threshold for defining tie criteria is important; and future simulations examining generative social networks should control and carefully report these environmental and cognitive factors.
AB - This paper examines how cognitive and environmental factors influence the formation of dyadic ties. We use agent models instantiated in ACT-R that interact in a social simulation, to illustrate the effect of memory constraints on networks. We also show that environmental factors are important including population size, running time, and map configuration. To examine these relationships, we ran simulations of networks using a factorial design. Our analyses suggest three interesting conclusions: first, the tie formation of these networks approximates a logistic growth model; second, that agent memory quality (i.e., perfect or human-like) strongly alters the network’s density and structure; third, that the three environmental factors all influence both network density and some aspects of network structure. These findings suggest that meaningful variance of social network analysis measures occur in a narrow band of memory strength (the cognitive band); the threshold for defining tie criteria is important; and future simulations examining generative social networks should control and carefully report these environmental and cognitive factors.
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U2 - 10.1007/s10588-014-9179-0
DO - 10.1007/s10588-014-9179-0
M3 - Article
AN - SCOPUS:84939971724
SN - 1381-298X
VL - 21
SP - 115
EP - 149
JO - Computational and Mathematical Organization Theory
JF - Computational and Mathematical Organization Theory
IS - 2
ER -