Abstract
In this paper, we explore an approach for developing an initial agent population that is suitable for integrating two component agent based models, representing conceptually the same agents. For some models the structure of the initial population is an important aspect of the model. When integrating two (or more) models that represent the same agents, we require a single integrated agent population (or unique mappings between the two populations). Obtaining such is not straightforward if we wish to preserve important structural characteristics of the component populations. We describe here a methodology inspired by work in constructing synthetic populations which are structurally similar to a real population. The approach uses the Iterative Proportional Fitting Procedure (IPFP) to combine two different data sets in a way that preserves the structure of each. We apply our approach to a specific case study and evaluate the quality of the resulting integrated population.
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Notes
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We describe the procedure for two components, but multi-dimensional IPFP can be used for cases with three or four components.
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This is further discussed in Sect. 4.
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Exact adherence modulo rounding errors. We must of course round agent numbers obtained from percentages to be whole numbers.
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As mentioned, IPFP rounding introduces errors. We rounded by taking the floor values which gave populations of around 3% less than 4000. This choice is not critical for us, however for a detailed discussion of rounding issues, see [11].
References
Balmer, M., Raney, B., Nagel, K.: Adjustment of activity timing and duration in an agent-based traffic flow simulation. In: Timmermans, H.J.P. (ed.) Progress in Activity-Based Analysis, pp. 91–114. Elsevier, Oxford, UK (2005)
Beckman, R.J., Baggerly, K.A., McKay, M.D.: Creating synthetic baseline populations. Transportation research part a: policy and practice 30(6), 415–429 (1996)
Billari, F., Aparicio Diaz, B., Fent, T., Prskawetz, A., Prskawetz, A.: The “wedding-ring” an agent-based marriage model based on social interaction. Demogr. Res. 17(3), 59–82 (2007)
Bishop, Y.M.M., Fienberg, S.E., Holland, P.W.: Discrete Multivariate Analysis: Theory and Practice. MIT, Cambridge (1975)
Dahmann, J.S., Kuhl, F.S., Weatherly, R.M.: Standards for simulation: as simple as possible but not simpler the high level architecture for simulation. Simulation 71(6), 378–387 (1998)
Deming, W.E., Stephan, F.F.: On a least squares adjustment of a sampled frequency table when the expected marginal totals are known. Ann. Math. Stat. 11(4), 427–444 (1940)
Dunham, J.B.: An agent-based spatially explicit epidemiological model in MASON. J. Artif. Soc. Soc. Simul. 9(1), 3 (2005)
Fienberg, S.E.: An iterative procedure for estimation in contingency tables. Ann. Math. Stat. 41(3), 907–917 (1970)
Harland, K., Heppenstall, A., Smith, D., Birkin, M.: Creating realistic synthetic populations at varying spatial scales: a comparative critique of population synthesis techniques. J. Artif. Soc. Soc. Simul. 15(1), 1 (2012)
Kirill, M., Axhausen, K.W.: Hierarchical IPF: generating a synthetic population for Switzerland. In: 51st Congress of the European Regional Science Association, January 2011
Lovelace, R., Ballas, D.: Truncate, replicate, sample: a method for creating integer weights for spatial microsimulation. Comput. Environ. Urban Syst. 41, 1–11 (2013)
Lovelace, R., Ballas, D., Birkin, M.H., van Leeuwen, E.: Evaluating the performance of iterative proportional fitting for spatial microsimulation: new tests for an established technique. J. Artif. Soc. Soc. Simul. 18(2), 21 (2015)
Moore, R.V., Tindall, I.: An overview of the open modelling interface and environment (the OpenMI). Environ. Sci. Pol. 8, 279–286 (2005)
Namazi-Rad, M.-R., Huynh, N., Barthelemy, J., Perez, P.: Synthetic population initialization and evolution-agent-based modelling of population aging and household transitions. In: Dam, H.K., Pitt, J., Xu, Y., Governatori, G., Ito, T. (eds.) PRIMA 2014. LNCS (LNAI), vol. 8861, pp. 182–189. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-13191-7_15
Namazi-Rad, M.R., Mokhtarian, P., Perez, P.: Generating a dynamic synthetic population - using an age-structured two-sex model for household dynamics. PLoS ONE 9(4), 94761 (2014)
Noble, J., Silverman, E., Bijak, J., Rossiter, S., Evandrou, M., Bullock, S., Vlachantoni, A., Falkingham, J.: Linked lives: the utility of an agent-based approach to modeling partnership and household formation in the context of social care. In: Winter Simulation Conference (WSC), Berlin, Germany, pp. 93:1–93:12. IEEE, 9–12 December 2012
Silverman, E., Bijak, J., Hilton, J., Cao, V.D., Noble, J.: When demography met social simulation: a tale of two modelling approaches. Artif. Soc. Soc. Simul. 16(4), 9 (2013)
Singh, D., Padgham, L.: Opensim: a framework for integrating agent-based models and simulation components. In ECAI 2014–21st European Conference on Artificial Intelligence, Prague, Czech Republic, pp. 837–842, 18–22 August 2014
Tanton, R., Vidyattama, Y., Nepal, B., McNamara, J.: Small area estimation using a reweighting algorithm. J. Roy. Stat. Soc. Ser. A (Stat. Soc.) 174(4), 931–951 (2011)
Voas, D., Williamson, P.: An evaluation of the combinatorial optimisation approach to the creation of synthetic microdata. Int. J. Population Geogr. 6(5), 349–366 (2000)
Waddell, P.: Urbansim: modeling urban development for land use, transportation and environmental planning. J. Am. Plann. Assoc. 68(3), 297–314 (2002)
Walker, E., Nowacki, A.S.: Understanding equivalence and noninferiority testing. J. Gener. Intern. Med. 26(2), 192–196 (2011)
Wickramasinghe, B.N., Singh, D., Padgham, L.: Synchronising agent populations when combining agent-based simulations. In: Spring Simulation Multiconference, Alexandria, USA (2015)
Williamson, P., Birkin, M., Rees, P.H.: The estimation of population microdata by using data from small area statistics and samples of anonymised records. Environ. Plann. A 30(5), 785–816 (1998)
Ye, X., Konduri, K., Pendyala, R.M., Sana, B.: A methodology to match distributions of both household and person attributes in the generation of synthetic populations. In: 88th Annual Meeting of the Transportation Research Board (2009). 9600(206)
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This research was funded in part by the Australian Research Council, SJB Urban and the (Melbourne) Metropolitan Planning Authority through Linkage Project grant LP130100008.
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Wickramasinghe, B.N., Singh, D., Padgham, L. (2017). Heuristic Data Merging for Constructing Initial Agent Populations. In: Sukthankar, G., Rodriguez-Aguilar, J. (eds) Autonomous Agents and Multiagent Systems. AAMAS 2017. Lecture Notes in Computer Science(), vol 10643. Springer, Cham. https://doi.org/10.1007/978-3-319-71679-4_12
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