Abstract
Checking the network generated by a simulation against network data from the system being simulated holds out the promise of a fairly-strong validation. However, this poses some challenges. The nature of this task and its attended challenges are here discussed, and the outlines of a method for doing this sketched. This is illustrated using a synthetic and target network, applying increasingly detailed methods to elucidate the structure of these networks and hence make a tougher and more revealing comparison. We end with a discussion of the prospects and further challenges.
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Notes
- 1.
A companion paper by the authors has been published as part of the proceedings of the 2013 European Social Simulation Association Conference (ESSA 2013); see Abbas et al. [1].
- 2.
Excluding links from a node to itself and multiple links between pairs of nodes.
- 3.
Since \( 2^{25 \times 24 \div 2} > 10^{80} \) (which is an estimate of the number of atoms in the universe http://en.wikipedia.org/wiki/Observable_universe).
- 4.
See [19] for arguments against the Keep It Simple, Stupid (KISS) modelling approach.
- 5.
References
Abbas, S.M.A., Alam, S.J., Edmonds, B.: Towards validating social network simulations. In: Bogumił, K., Koloch, G. (eds.) Advances in Social Simulation – Proceedings of the 9th Conference of the European Social Simulation Association. Advances in Intelligent Systems and Computing, vol. 229, pp. 1–12. Springer, Heidelberg (2014)
Abbas, S.M.A.: Homophily, popularity and randomness: modelling growth of online social network. In: 12th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2013), pp. 135–142 (2013)
Alam, S.J., Edmonds, B., Meyer, R.: Identifying structural changes in networks generated from agent-based social simulation models. In: Ghose, A., Governatori, G., Sadananda, R. (eds.) PRIMA 2007. LNCS, vol. 5044, pp. 298–307. Springer, Heidelberg (2009)
Edmonds, B.: The use of models - making MABS more informative. In: Moss, S., Davidsson, P. (eds.) MABS 2000. LNCS (LNAI), vol. 1979, pp. 15–32. Springer, Heidelberg (2001)
Holzhauer, S., Krebs, F., Ernst, A.: Considering baseline homophily when generating spatial social networks for agent-based modelling. Comput. Math. Organ. Theor. 19, 128–150 (2013)
Papadopoulos, F., et al.: Popularity versus similarity in growing networks. Nature 489, 537–540 (2012)
Wasserman, S., Faust, K.: Social Network Analysis: Methods and Applications. Cambridge University Press, Cambridge (1994)
Newman, M.E.J.: Networks: An Introduction. Oxford University Press (2010)
Traud, A.L., Kelsic, E.D., Mucha, P.J., Porter, M.A.: Comparing community structure to characteristics in online collegiate social networks. SIAM Rev. 53, 526–543 (2011)
Roth, C., Gargiulo, F., Bringe, A., Hamberger, K.: Random alliance networks. Soc. Netw. 35, 394–405 (2013)
Kossinets, G., Watts, D.J.: Empirical analysis of an evolving social network. Science 311, 88–90 (2006)
Geller, A., Harrison, J.F., Revelle, M.: Growing social structure: an empirical multiagent excursion into kinship in rural North-West Frontier Province. Struct. Dyn. eJ. Anthropol. Relat. Sci. 5, 1 (2011)
Alam, S.J., Meyer, R.: Comparing two sexual mixing schemes for modelling the spread of HIV/AIDS. In: Simulating Interacting Agents and Social Phenomena, pp. 65–76 (2010)
Geller, A., Moss, S.: Growing Qawm: an evidence-driven declarative model of Afghan power structures. Adv. Complex Syst. 11, 321–335 (2007)
Krackhardt, D., Stern, R.N.: Informal networks and organizational crises : an experimental simulation. Soc. Psychol. 51, 123–140 (2011)
Watts, D.J., Strogatz, S.: Collective dynamics of ‘small world’ networks. Nature 393, 440–442 (1998)
Legendi, R.O., Gulyas, L.: Agent-based dynamic network models: validation on empirical data. In: Bogumił, K., Koloch, G. (eds.) Advances in Social Simulation – Proceedings of the 9th Conference of the European Social Simulation Association. Advances in Intelligent Systems and Computing, vol. 229, pp. 49–60. Springer, Heidelberg (2014)
Moss, S., Edmonds, B.: Sociology and simulation: statistical and qualitative cross-validation. Am. J. Sociol. 110, 1095–1131 (2005)
Edmonds, B., Moss, S.: From KISS to KIDS – An ‘Anti-simplistic’ Modelling Approach. In: Davidsson, P., Logan, B., Takadama, K. (eds.) MABS 2004. LNCS (LNAI), vol. 3415, pp. 130–144. Springer, Heidelberg (2005)
Amblard, F., Quattrociocchi, W.: Social networks and spatial distribution. In: Edmonds, B., Meyer, R. (eds.) Simulating Social Complexity, pp. 401–430, Springer, Heidelberg (2013)
Grimm, V., Polhill, G., Touza, J.: Documenting social simulation models: the ODD protocol as a standard. In: Edmonds, B., Meyer, R. (eds.) Simulating Social Complexity, pp. 117–133. Springer, Heidelberg (2013)
Acknowledgements
We are thankful to H. Van Dyke Parunak, the reviewers of this paper and the participants of the MABS 2013 workshop and the ESSA 2013 conference for their feedback and comments. This research was partially supported by the Engineering and Physical Sciences Research Council, grant number EP/H02171X/1 and also by the Economic and Social Research Council Secondary Data Analysis Initiative, grant number ES/K004549/1.
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Alam, S.J., Ali Abbas, S.M., Edmonds, B. (2014). Validating Simulated Networks: Some Lessons Learned. In: Alam, S., Parunak, H. (eds) Multi-Agent-Based Simulation XIV. MABS 2013. Lecture Notes in Computer Science(), vol 8235. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54783-6_5
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