Abstract
This chapter reports on an exercise in replicating the analysis of outputs from 20,000 runs of a social simulation of biodiversity incentivisation (FEARLUS-SPOMM) as part of the MIRACLE project. Typically, replication refers to reconstructing the model used to generate the output from the description thereof, but for larger-scale studies, the output analysis itself may be difficult to replicate even when given the original output files. Tools for analysing simulation output data do not facilitate kee** records of what can be a lengthy and complicated process. We provide an outline design for a tool to address this issue, and make some recommendations based on the experience with this exercise.
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References
Edmonds, B., Hales, D.: Replication, replication and replication: some hard lessons from model alignment. J Artif Soc Soc Simulat 6(4), 11 (2003). http://jasss.soc.surrey.ac.uk/6/4/11.html
Hales, D., Rouchier, J., Edmonds, B.: Model-to-model analysis. J Artif Soc Soc Simulat 6(4), 5 (2003). http://jasss.soc.surrey.ac.uk/6/4/5.html
Roucher, J., Cioffi-Revilla, C., Polhill, J.G., Takadama, K.: Progress in model-to-model analysis. J. Artif. Soc. Soc. Simulat. 11(2), 8 (2008). http://jasss.soc.surrey.ac.uk/11/2/8.html
Schmolke, A., Thorbek, P., DeAngelis, D.L., Grimm, V.: Ecological models supporting environmental decision making: a strategy for the future. Trends Ecol. Evol. 25(8), 479–486 (2010)
Gimona, A., Polhill, J.G.: Exploring robustness of biodiversity policy with a coupled metacommunity and agent-based model. J. Land Use Sci. 6(2-3), 175–193 (2011)
Polhill, J.G., Gimona, A., Gotts, N.M.: Nonlinearities in biodiversity incentive schemes: A study using an integrated age-based and metacommunity model. Environ. Model. Software 45, 74–91 (2013)
Boero, R., Squazzoni, F.: Does empirical embeddedness matter? Methodological issues on agent-based models for analytical social science. J Artif Soc Soc Simulat 8(4), 6 (2005). http://jasss.soc.surrey.ac.uk/8/4/6.html
Parker, D. C., Barton, NM., Dawson, T., Filatova, T., **, X., Lee, J.-S., Polhill, J. G., Robinson, K. and Voinov, A.: The MIRACLE project: a community library to archive and document analysis methods for output from agent-based models. Association of American Geographers 2015 Annual Meeting, April 21–25, 2015, Chicago, IL (2015)
Hastie, T.J., Tibshirani, R.J.: Generalized Additive Models. Chapman & Hall/CRC, Boca Raton, FL (1990)
Akaike, H.: A new look at the statistical model identification. IEEE Trans. Automat. Contr. 19(6), 716–723 (1974)
Breiman, L., Friedman, K., Olshen, R.A., Stone, C.J.: Classification and Regression Trees. Chapman & Hall/CRC, Boca Raton, FL (1984)
Wolstencroft, K., Haines, R., Fellows, D., Williams, A., Withers, D., Owen, S., Soiland-Reyes, S., Dunlop, I., Nenadic, A., Fisher, P., Bhagat, J., Belhajjame, K., Bacall, F., Hardisty, A., Nieva de la Hidalga, A., Balcazar Vargas, M.P., Sufi, S., Goble, C.: The Taverna workflow suite: designing and executing workflows of Web Services on the desktop, web or in the cloud. Nucleic Acids Res. 41(W1), W557–W561 (2013)
Moreau, L., Missier, P. (eds.): PROV-DM: The PROV Data Model. W3C Recommendation 30 April 2013. http://www.w3.org/TR/2013/REC-prov-dm-20130430/. (2013)
Ivanyi, M., Bocsi, R., Gulyas, L., Kozma, V., Legendi, R.: The Multi-Agent Simulation Suite. 22nd Conference on Artificial Intelligence (AAAI-07), Vancouver, British Colombia, 22–26 July 2007. (2007)
Acknowledgements
This work was funded by a number of agencies under the Digging into Data Challenge (Third Round) and by the Scottish Government Rural Affairs and the Environment Portfolio Strategic Research Theme 1 (Ecosystem Services). Computing facilities have been provided by Compute Canada and Sharcnet.
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Polhill, G., Milazzo, L., Dawson, T., Gimona, A., Parker, D. (2017). Lessons Learned Replicating the Analysis of Outputs from a Social Simulation of Biodiversity Incentivisation. In: Jager, W., Verbrugge, R., Flache, A., de Roo, G., Hoogduin, L., Hemelrijk, C. (eds) Advances in Social Simulation 2015. Advances in Intelligent Systems and Computing, vol 528. Springer, Cham. https://doi.org/10.1007/978-3-319-47253-9_32
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