Minds and Machines

, Volume 29, Issue 1, pp 127–148 | Cite as

A Minimalist Epistemology for Agent-Based Simulations in the Artificial Sciences

  • Giuseppe PrimieroEmail author


The epistemology of computer simulations has become a mainstream topic in the philosophy of technology. Within this large area, significant differences hold between the various types of models and simulation technologies. Agent-based and multi-agent systems simulations introduce a specific constraint on the types of agents and systems modelled. We argue that such difference is crucial and that simulation for the artificial sciences requires the formulation of its own specific epistemological principles. We present a minimally committed epistemology which relies on the methodological principles of the Philosophy of Information and requires weak assumptions on the usability of the simulation and the controllability of the model. We use these principles to provide a new definition of simulation for the context of interest.


Agent-based simulation Artificial sciences Multi-agent systems Constructionism Controllability Usability 



The author wishes to thank the participants to the Summer School On Computer Simulation Methods for engaging discussions and observations around the topics treated in this contribution and two anonymous reviewers for important critiques which have contributed improving this paper. The author was partially supported by the Project PROGRAMme ANR-17-CE38-0003-01.


  1. Barberousse, A., Franceschelli, S., & Imbert, C. (2009). Computer simulations as experiments. Synthese, 169(3), 557–574.MathSciNetCrossRefGoogle Scholar
  2. Battistelli, L., & Primiero, G. (2017). Logic-based collective decision making of binary properties in an autonomous multi-agent system. Technical report, Middlesex University London.
  3. Crooks, A. T., & Heppenstall, A. J. (2012). Introduction to agent-based modelling. In A. J. Heppenstall, A. T. Crooks, L. M. See, & M. Batty (Eds.), Agent-Based Models of Geographical Systems (pp. 85–105). Dordrecht: Springer.CrossRefGoogle Scholar
  4. Durán, J. M. (2013). A brief overview of the philosophical study of computer simulations. American Philosophical Association Newsletter on Philosophy and Computers, 13(1), 38–46.Google Scholar
  5. Elsenbroich, C. (2012). Explanation in agent-based modelling: Functions, causality or mechanisms? Journal of Artificial Societies and Social Simulation, 15(3), 1.CrossRefGoogle Scholar
  6. Epstein, J. M. (2008). Why model? Journal of Artificial Societies and Social Simulation, 11(4), 12.Google Scholar
  7. Floridi, L. (2011). A defence of constructionism: Philosophy as conceptual engineering. Metaphilosophy, 42(3), 282–304.CrossRefGoogle Scholar
  8. Frigg, R., & Reiss, J. (2009). The philosophy of simulation: hot new issues or same old stew? Synthese, 169(3), 593–613.MathSciNetCrossRefGoogle Scholar
  9. Grüne-Yanoff, T. (2009). The explanatory potential of artificial societies. Synthese, 169(3), 539–555.CrossRefGoogle Scholar
  10. Guala, F. (2002). Models, simulations, and experiments. In L. Magnani & N. J. Nersessian (Eds.), Model-Based Reasoning. Boston, MA: Springer.Google Scholar
  11. Hartmann, S. (1996). The world as a process. In R. Hegselmann, U. Mueller, & K. G. Troitzsch (Eds.), Modelling and simulation in the social sciences from the philosophy of science point of view (pp. 77–100). Dordrecht: Springer.CrossRefGoogle Scholar
  12. Humphreys, P. (1990). Computer simulations. In PSA: proceedings of the biennial meeting of the Philosophy of Science Association, 1990 (pp. 497–506).Google Scholar
  13. Humphreys, P. (1995). Computational science and scientific method. Minds and Machines, 5(4), 499–512.CrossRefGoogle Scholar
  14. Humphreys, P. (2004). Extending ourselves: Computational science, empiricism, and scientific method. Oxford: Oxford University Press.CrossRefGoogle Scholar
  15. Humphreys, P. (2009). The philosophical novelty of computer simulation methods. Synthese, 169(3), 615–626.MathSciNetCrossRefGoogle Scholar
  16. Korb, K. B., & Mascaro, S. (2009). The philosophy of computer simulation. In Logic, methodology and philosophy of science: proceedings of the thirteenth international congress (pp 306–325). Springer.Google Scholar
  17. Macy, M. W., & Willer, R. (2002). From factors to actors: Computational sociology and agent-based modeling. Annual Review of Sociology, 28, 14366.CrossRefGoogle Scholar
  18. Morrison, M. (2009). Models, measurement and computer simulation: The changing face of experimentation. Philosophical Studies, 143(1), 33–57.CrossRefGoogle Scholar
  19. North, M. J., & Macal, C. M. (2007). Managing business complexity: Discovering strategic solutions with agent-based modeling and simulation. Oxford: Oxford University Press.CrossRefGoogle Scholar
  20. Primiero, G., Raimondi, F., Bottone, M., & Tagliabue, J. (2017). Trust and distrust in contradictory information transmission. Applied Network Science, 2(1), 12.CrossRefGoogle Scholar
  21. Railsback, S. F., & Grimm, V. (2011). Agent-based and individual-based modeling: A practical introduction. Princeton: Princeton university press.zbMATHGoogle Scholar
  22. Schiaffonati, V. (2016). Stretching the traditional notion of experiment in computing: Explorative experiments. Science and Engineering Ethics, 22(3), 647–665.CrossRefGoogle Scholar
  23. Tal, E. (2011). From data to phenomena and back again: Computer-simulated signatures. Synthese, 182(1), 117–129.MathSciNetCrossRefGoogle Scholar
  24. Winsberg, E. (2003). Simulated experiments: Methodology for a virtual world. Philosophy of Science, 70(1), 105–125.CrossRefGoogle Scholar
  25. Winsberg, E. (2006). Models of success versus the success of models: Reliability without truth. Synthese, 152(1), 1–19.MathSciNetCrossRefGoogle Scholar
  26. Winsberg, E. (2010). Science in the age of computer simulation. Chicago, IL: University of Chicago Press.CrossRefGoogle Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.Department of PhilosophyUniversity of MilanMilanoItaly

Personalised recommendations