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

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.

[1]  Burian,et al.  Psa 1994 : Proceedings of the 1994 Biennial Meeting of the Philosophy of Science Association , 1990 .

[2]  Giuseppe Primiero,et al.  Trust and distrust in contradictory information transmission , 2017, Appl. Netw. Sci..

[3]  Corinna Elsenbroich,et al.  Explanation in Agent-Based Modelling: Functions, Causality or Mechanisms? , 2012, J. Artif. Soc. Soc. Simul..

[4]  Charles M. Macal,et al.  Managing Business Complexity: Discovering Strategic Solutions with Agent-Based Modeling and Simulation , 2007 .

[5]  M. Macy,et al.  FROM FACTORS TO ACTORS: Computational Sociology and Agent-Based Modeling , 2002 .

[6]  Till Grüne-Yanoff,et al.  The explanatory potential of artificial societies , 2009, Synthese.

[7]  L. Floridi A DEFENCE OF CONSTRUCTIONISM: PHILOSOPHY AS CONCEPTUAL ENGINEERING , 2011 .

[8]  Eric Winsberg,et al.  Simulated Experiments: Methodology for a Virtual World , 2003, Philosophy of Science.

[9]  Viola Schiaffonati,et al.  Stretching the Traditional Notion of Experiment in Computing: Explorative Experiments , 2015, Science and Engineering Ethics.

[10]  Francesco Guala,et al.  Models, Simulations, and Experiments , 2002 .

[11]  Cyrille Imbert,et al.  Computer simulations as experiments , 2009, Synthese.

[12]  Charles M. Macal,et al.  Managing Business Complexity: Discovering Strategic Solutions with Agent-Based Modeling and Simulation , 2007 .

[13]  Eric Winsberg,et al.  Models of Success Versus the Success of Models: Reliability without Truth , 2006, Synthese.

[14]  Eran Tal,et al.  From data to phenomena and back again: computer-simulated signatures , 2011, Synthese.

[15]  Steven F. Railsback,et al.  Agent-Based and Individual-Based Modeling: A Practical Introduction , 2011 .

[16]  Paul Humphreys,et al.  Extending Ourselves: Computational Science, Empiricism, and Scientific Method , 2004 .

[17]  Andrew Crooks,et al.  Introduction to Agent-Based Modelling , 2012 .

[18]  Paul Humphreys,et al.  Computational science and scientific method , 1995, Minds and Machines.

[19]  Stephan Hartmann,et al.  The World as a Process , 1996 .

[20]  Eric Winsberg,et al.  Science in the Age of Computer Simulation , 2010 .

[21]  Paul Humphreys,et al.  The philosophical novelty of computer simulation methods , 2009, Synthese.

[22]  Roman Frigg,et al.  The philosophy of simulation: hot new issues or same old stew? , 2009, Synthese.

[23]  Margaret Morrison,et al.  Models, measurement and computer simulation: the changing face of experimentation , 2009 .