Abstract
In the last thirty years, agent-based modelling has become a well-known technique for studying and simulating dynamical systems. Still, there are some open issues to be addressed. One of these is the substantial absence of studies about the sensitivity to initial conditions, that is the effect of small variations at the beginning of simulation on the macro-level behaviour of the model. The goal of this preliminary work is to explore how a single modification on one agent affects the evolution of the simulation. Through the analysis of two deterministic models (a simple market model and Reynolds’ flocking model), we obtain two main results. First, we observe that the impact of the variation of a single initial condition on the simulation behaviour is high in both models. Second, there is evidence of an at least qualitative relation between some general agent-based model settings (numerosity of agents in the model and rate of connections between agents) and the sensitivity to the modified initial condition. We conclude that at least some significant classes of agent-based models are affected by a high sensitivity to initial conditions that have a negative effect on the predictive power of simulations.
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References
Abar, S., Theodoropoulos, G.K., Lemarinier, P., O’Hare, G.M.P.: Agent based modelling and simulation tools: a review of the state-of-art software. Comput. Sci. Rev. 24, 13–33 (2017). https://doi.org/10.1016/j.cosrev.2017.03.001
Barabási, A.L., Albert, R.: Emergence of scaling in random networks. Science 286, 509–512 (1999). https://doi.org/10.1126/science.286.5439.509
Bonabeau, E.: Agent-based modeling: Methods and techniques for simulating human systems. Proc. Natl. Acad. Sci. U.S.A. 99, 7280–7287 (2002). https://doi.org/10.1073/pnas.082080899
Boschetti, F., Grigg, N.J., Enting, I.: Modelling = conditional prediction. Ecol. Complex. 8, 86–91 (2011). https://doi.org/10.1016/j.ecocom.2010.06.001
Castellano, C., Fortunato, S., Loreto, V.: Statistical physics of social dynamics. Rev. Mod. Phys. 81, 591 (2009). https://doi.org/10.1103/RevModPhys.81.591
Edmonds, B.: Different modelling purposes. In: Edmonds, B., Meyer, R. (eds.) Simulating Social Complexity. UCS, pp. 39–58. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66948-9_4
Epstein, J.M.: Why model? JASSS (2008). https://doi.org/10.1016/s0168-9525(01)02382-4
Klein, D., Marx, J., Fischbach, K.: Agent-based modeling in social science, history, and philosophy. An introduction. Hist. Soc. Res. (2018). https://doi.org/10.12759/hsr.43.2018.1.7-27
Railsback, S., Grimm, V.: Agent-Based and Individual-Based Modeling: A Practical Introduction. Princeton University Press (2011)
Raimbault, J., Cottineau, C., Le Texier, M., Le Néchet, F., Reuillon, R.: Space matters: extending sensitivity analysis to initial spatial conditions in geosimulation models. JASSS (2019). https://doi.org/10.18564/jasss.4136
Reynolds, C.W.: Flocks, herds, and schools. In: Computers & Graphics (1987)
Thiele, J.C., Kurth, W., Grimm, V.: Facilitating parameter estimation and sensitivity analysis of agent-based models: a cookbook using NetLogo and R. JASSS (2014). https://doi.org/10.18564/jasss.2503
Wilensky, U., Rand, W.: An Introduction to Agent-Based Modeling: Modeling Natural, Social, and Engineered Complex Systems with NetLogo. MIT Press (2015)
Wilensky, U.: NetLogo (1999). http://ccl.northwestern.edu/netlogo/. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL
Wilensky, U.: NetLogo Flocking model (1998). http://ccl.northwestern.edu/netlogo/models/Flocking. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL
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Bertolotti, F., Locoro, A., Mari, L. (2020). Sensitivity to Initial Conditions in Agent-Based Models. In: Bassiliades, N., Chalkiadakis, G., de Jonge, D. (eds) Multi-Agent Systems and Agreement Technologies. EUMAS AT 2020 2020. Lecture Notes in Computer Science(), vol 12520. Springer, Cham. https://doi.org/10.1007/978-3-030-66412-1_32
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DOI: https://doi.org/10.1007/978-3-030-66412-1_32
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