Bootstrapping Knowledge About Social Phenomena Using Simulation Models

There are considerable difficulties in the way of the development of useful and reliable simulation models of social phenomena, including that any simulation necessarily includes many assumptions that are not directly supported by evidence. Despite these difficulties, many still hope to develop quite general models of social phenomena. This paper argues that such hopes are ill-founded, in other words that there will be no short-cut to useful and reliable simulation models. However this paper argues that there is a way forward, that simulation modelling can be used to "boot-strap" useful knowledge about social phenomena. If each bit of simulation work can result in the rejection of some of the possible processes in observed social phenomena, even if this is about a very specific social context, then this can be used as part of a process of gradually refining our knowledge about such processes in the form of simulation models. Such a boot-strapping process will only be possible if simulation models are more carefully judged, that is a greater selective pressure is applied. In particular models which are just an analogy of social processes in computational form should be treated as "personal" rather than "scientific" knowledge. Such analogical models are useful for informing the intuition of its developers and users, but do not help the community of social simulators and social scientists to "boot-strap" reliable social knowledge. However, it is argued that both participatory modelling and evidence-based modelling can play a useful part in this process. Some kinds of simulation model are discussed with respect to their suitability for the boot-strapping of social knowledge. The knowledge that results is likely to be of a more context-specific, conditional and mundane nature than many social scientists hope for.

[1]  B. Edmonds,et al.  Replication, Replication and Replication: Some hard lessons from model alignment , 2003, J. Artif. Soc. Soc. Simul..

[2]  David Firth,et al.  Exit polling in a cold climate: the BBC–ITV experience in Britain in 2005 , 2008 .

[3]  N. Gilbert.  A Simulation of the Structure of Academic Science , 1997 .

[4]  S. Toulmin.  The collective use and evolution of concepts , 1972 .

[5]  D. Helbing.  Traffic and related self-driven many-particle systems , 2000, cond-mat/0012229.

[6]  Bruce Edmonds,et al.  Towards Good Social Science , 2005, J. Artif. Soc. Soc. Simul..

[7]  Robert L. Axtell,et al.  Aligning simulation models: A case study and results , 1996, Comput. Math. Organ. Theory.

[8]  Guillaume Deffuant,et al.  Probability Distribution Dynamics Explaining Agent Model Convergence to Extremism , 2008 .

[9]  Bruce Edmonds,et al.  The Use of Models - Making MABS Actually Work , 2000 .

[10]  Bruce Edmonds,et al.  The Importance of Representing Cognitive Processes in Multi-Agent Models , 2001, ICANN.

[11]  S. Schweber.  Science Without Laws , 2009, Perspectives in biology and medicine.

[12]  Guillaume Deffuant,et al.  How can extremism prevail? A study based on the relative agreement interaction model , 2002, J. Artif. Soc. Soc. Simul..

[13]  Ronald N. Giere,et al.  Science without laws , 1999 .

[14]  Kathleen M. Carley,et al.  The nature of the social agent , 1994 .

[15]  Maurice Grinberg,et al.  Simulating Context Effects in Problem Solving with AMBR , 2001, CONTEXT.

[16]  Edward Fullbrook,et al.  The crisis in economics : the post-autistic economics movement : the first 600 days , 2003 .

[17]  Guillaume Deffuant,et al.  Comparing Extremism Propagation Patterns in Continuous Opinion Models , 2006, J. Artif. Soc. Soc. Simul..

[18]  Bruce Edmonds,et al.  Errors and Artefacts in Agent-Based Modelling , 2009, J. Artif. Soc. Soc. Simul..

[19]  J. Gareth Polhill,et al.  Using the ODD Protocol for Describing Three Agent-Based Social Simulation Models of Land-Use Change , 2008, J. Artif. Soc. Soc. Simul..

[20]  S. Moss,et al.  A smart automated macroeconometric forecasting system , 1994 .

[21]  Imre Lakatos,et al.  Criticism and the Growth of Knowledge , 1972 .

[22]  D. Hull,et al.  Science as a Process: An Evolutionary Account of the Social and Conceptual Development of Science, David L. Hull. 1988. The University of Chicago Press, Chicago, IL. 608 pages. ISBN: 0-226-35060-4. $39.95 , 1989 .

[23]  Margaret Morrison,et al.  Models as Mediators , 1999 .

[24]  Olivier Barreteau,et al.  Our Companion Modelling Approach , 2003, J. Artif. Soc. Soc. Simul..

[25]  R. Axelrod.  An Evolutionary Approach to Norms , 1986, American Political Science Review.

[26]  S. Toulmin.  The evolutionary development of natural science. , 1967, American scientist.

[27]  Bruce Edmonds,et al.  The Nature of Noise , 2009, EPOS.

[28]  Herbert A. Simon,et al.  The Failure of Armchair Economics , 1986 .

[29]  Bruce Edmonds,et al.  Open Access for Social Simulation , 2007, J. Artif. Soc. Soc. Simul..

[30]  Bruce Edmonds,et al.  Simplicity is not Truth-Indicative , 2007 .

[31]  D. Campbell.  Blind variation and selective retention in creative thought as in other knowledge processes. , 1960, Psychological review.

[32]  Sander van der Hoog,et al.  On Multi-Agent Based Simulation , 2004 .

[33]  Barton Moffatt,et al.  Distributed Cognition: , 2003 .

[34]  Marx W. Wartofsky,et al.  The Model Muddle: Proposals for an Immodest Realism , 1979 .

[35]  R. Swinburne.  OBJECTIVE KNOWLEDGE: AN EVOLUTIONARY APPROACH , 1973 .

[36]  Christian Schwabe,et al.  Chemistry and Biodiversity , 2004, Chemistry & biodiversity.

[37]  T. Broadbent,et al.  Criticism and the Growth of Knowledge , 1972 .

[38]  C. Gershenson,et al.  Philosophy and Complexity , 2007 .

[39]  T. Kuhn,et al.  The Structure of Scientific Revolutions. , 1964 .

[40]  S. Moss.  The Economics of Positive Methodology , 1993 .

[41]  Jaime Simão Sichman,et al.  Multi-Agent-Based Simulation , 2002, Lecture Notes in Computer Science.

[42]  H. Kyburg,et al.  How the laws of physics lie , 1984 .

[43]  Bruce Edmonds.  Artificial Science: A Simulation to Study the Social Processes of Science , 2005 .

[44]  B. Edmonds,et al.  Sociology and Simulation: Statistical and Qualitative Cross‐Validation1 , 2005, American Journal of Sociology.

[45]  Paul Davidsson,et al.  Multi-Agent and Multi-Agent-Based Simulation, Joint Workshop MABS 2004, New York, NY, USA, July 19, 2004, Revised Selected Papers , 2005, MABS.

[46]  B. Edmonds.  The Purpose and Place of Formal Systems in the Development of Science , 2000 .

[47]  Robert Axelrod,et al.  Advancing the art of simulation in the social sciences , 1997, Complex..

[48]  K. Popper.  Objective Knowledge: An Evolutionary Approach , 1972 .

[49]  R. Giere.  Explaining Science: A Cognitive Approach , 1991 .

[50]  José Manuel Galán,et al.  Appearances Can Be Deceiving: Lessons Learned Re-Implementing Axelrod's 'Evolutionary Approach to Norms' , 2005, J. Artif. Soc. Soc. Simul..

[51]  Bruce Edmonds,et al.  Social Simulation: Technologies, Advances and New Discoveries , 2007 .

[52]  Bruce Edmonds,et al.  From KISS to KIDS - An 'Anti-simplistic' Modelling Approach , 2004, MABS.

[53]  B. Edmonds,et al.  Computational Simulation as Theoretical Experiment , 2005 .

[54]  Bruce Edmonds,et al.  The Practical Modelling of Context‐Dependent Causal Processes – A Recasting of Robert Rosen's Thought , 2007, Chemistry & biodiversity.

[55]  G. Ferro-Luzzi.  On Evolutionary Epistemology , 1982, Current Anthropology.

[56]  R. Giere.  How Models Are Used to Represent Reality , 2004, Philosophy of Science.

[57]  B. Edmonds.  Modelling Bounded Rationality in Agent-Based Simulations Using the Evolution of Mental Models , 1999 .

[58]  Scott Moss,et al.  Alternative Approaches to the Empirical Validation of Agent-Based Models , 2007, J. Artif. Soc. Soc. Simul..

[59]  Charlotte K. Hemelrijk,et al.  Sexual Attraction and Inter-sexual Dominance among Virtual Agents , 2000, MABS.

[60]  유창조.  Naturalistic Inquiry , 2022, The SAGE Encyclopedia of Research Design.