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Building social networks out of cognitive blocks: factors of interest in agent-based socio-cognitive simulations

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Abstract

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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Notes

  1. When using the original variables for population and run time, we find the same directionality, no change in the standard errors or significance, and no effect on the other variables or the explained variance. The transformed scale does increase the magnitude of the observed effects.

  2. We include both measures because they are not equivalent. We did find instances where our categorical variable (Room Map) was significant when Grid Ratio by itself was not and vice versa.

  3. In our analyses, we ran Cooks D and DFFITS tests. We tested our model on the entire data and subsections of the data. The models effects were stable and we saw no evidence of problems associated with emergence.

  4. In ORA, the treatment of isolated nodes for path-based measures is customizable. We use a simple default where nodes report a ‘0’ to nodes they cannot reach.

  5. In ORA, the treatment of isolated nodes for path-based measures is customizable. We use a simple default where nodes report a ‘0’ to nodes they cannot reach.

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Acknowledgments

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.

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Authors and Affiliations

  1. The College of Information Sciences and Technology, The Pennsylvania State University, State College, USA

    Changkun Zhao, Ryan Kaulakis, Jeremiah W. Hiam & Frank E. Ritter

  2. Department of Sociology, Duke University, Durham, USA

    Jonathan H. Morgan

  3. Department of Computer Science, Tufts University, Medford, USA

    Joesph Sanford

  4. School of Computer Science, Carnegie Mellon University, Pittsburgh, USA

    Geoffrey P. Morgan

Authors
  1. Changkun Zhao
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  2. Ryan Kaulakis
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  3. Jonathan H. Morgan
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  4. Jeremiah W. Hiam
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  5. Frank E. Ritter
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Corresponding author

Correspondence to Changkun Zhao.

Appendices

Appendix 1: Simulation implementation details

To connect ACT-R to VIPER, we implemented the Telnet Agent Wrapper for ACT-R (TAWA) in Common Lisp. It supports logging in, waiting for synchronization, logging, halting, and writing results to CSV files. It also exports a number of functions that provide ways to examine the environment, speak, listen, move, and otherwise control a virtual body in VIPER.

When an ACT-R model is wrapped by TAWA, executions of model code are delayed until a privileged administrator agent signals for synchronization. Error conditions are also caught by TAWA and standard UNIX error codes are returned instead of dropping into the more standard debugger. For example, a successful run returns 0 to the parent process, while any error (e.g., network errors like the server being unreachable) returns a non-zero value. Returning error codes like this allows automated error checking in large-scale experiments.

Because memory decay and networks are strongly temporal, we paid special attention to time. To synchronize the agents, the administrator agent (which does not take part in the experiment) waits for all of the TAWA wrapped agents to finish loading and logging in. It then signals to TAWA to begin the simulation. Because TAWA delays the evaluation of the model code until synchronization, no agent experiences time before the synchronization signal. Further, all ACT-R models are set to run in real-time and for the same amount of “real-time”, so they all halt after the same perceived period. Thus, the total time experienced is the same for all agents.

Early benchmarks showed that ACT-R processes took up about 80 MB per process. We would only have been able to run about 100 processes on a single 8 GB machine before swapping would occur. To reduce the per-process footprint, a number of optimizations were implemented. Basic space reductions were achieved by using the DECLARE Lisp construct, as well as by pre-compiling the components, removing the debugger, and saving the whole system (sans the ACT-R agent model) as a system image. This reduced our per-process memory footprint somewhat, but they were not the biggest contributions towards memory usage reduction.

In SBCL Lisp 1.0.52, the “–merge-core-pages” flag was recently added. This flag enables Kernel Same Page Merging under recent versions of Linux (Arcangeli et al. 2009). This optimization flags shared areas of memory as being able to be merged unless modified. Because a significant percentage of our agents were replicated, we found that we could reduce the per-process memory footprint as low as 8 MB per process (with one shared copy of the merged pages excepted). Thus, the only activities that increase the size of this footprint are changes within individual agent models. Sharing and merging copies increases the number of agents capable, whether on single processors or HPC. Together, these optimizations permit orders of magnitude more agents to be run in a single experiment than many previous efforts, enabling larger-scale analysis than has been previously done. Such large-scale work is planned for future research.

Appendix 2: Analysis by population

2.1 Symmetric ties

2.1.1 Population: 20 agents

See Table 6.

Table 6 Symmetric network measure comparison (20 agents)
Full size table

2.1.2 Population: 40 agents

See Table 7.

Table 7 Symmetric network measure comparison (40 agents)
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2.1.3 Population: 60 agents

See Table 8.

Table 8 Symmetric network measure comparison (60 agents)
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2.2 Directed ties

2.2.1 Population: 20 agents

See Table 9.

Table 9 Direct network measure comparison (20 agents)
Full size table

2.2.2 Population: 40 agents

See Table 10.

Table 10 Symmetric network measure comparison (40 agents)
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2.2.3 Population: 60 agents

See Table 11.

Table 11 Directed network measure comparison (60 agents)
Full size table

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Zhao, C., Kaulakis, R., Morgan, J.H. et al. Building social networks out of cognitive blocks: factors of interest in agent-based socio-cognitive simulations. Comput Math Organ Theory 21, 115–149 (2015). https://doi.org/10.1007/s10588-014-9179-0

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