Skip to main content

Social Epistemology and Validation in Agent-Based Social Simulation

Abstract

The literature in agent-based social simulation suggests that a model is validated when it is shown to ‘successfully’, ‘adequately’ or ‘satisfactorily’ represent the target phenomenon. The notion of ‘successful’, ‘adequate’ or ‘satisfactory’ representation, however, is both underspecified and difficult to generalise, in part, because practitioners use a multiplicity of criteria to judge representation, some of which are not entirely dependent on the testing of a computational model during validation processes. This article argues that practitioners should address social epistemology to achieve a deeper understanding of how warrants for belief in the adequacy of representation are produced. Two fundamental social processes for validation: interpretation and commensuration, are discussed to justify this claim. The analysis is advanced with a twofold aim. First, it shows that the conceptualisation of validation could greatly benefit from incorporating elements of social epistemology, for the criteria used to judge adequacy of representation are influenced by the social, cognitive and physical organisation of social simulation. Second, it evidences that standardisation tools such as protocols and frameworks fall short in accounting for key elements of social epistemology that affect different instances of validation processes.

This is a preview of subscription content, access via your institution.

Notes

  1. 1.

    While confirmation covers both verification and validation, each process is, in principle, linked to different problems of interpretation. Verification is usually defined as the evaluation of whether the implemented computational model does what it is supposed to do. This prescription is understood in agent-based social simulation either in terms of conforming to the conceptual model (Edmonds, 2000; Rand & Rust, 2011) or, more generally, to the intention of the modeller (Gilbert & Troitzsch, 2005; David, 2013). As such, issues of interpretation during verification processes are more related to questions about the epistemology of measurement (e.g. how are magnitudes or types of data represented by or incorporated into computational models), instrumentation (e.g. how can models provide indirect knowledge of the phenomenon of interest) or standardisation and systematisation (e.g. how prior knowledge is accounted for by tools such as model frameworks or metamodels).

  2. 2.

    This separation has prevented practitioners from inquiring into the nature and status of simulated data, which, as the literature in the philosophy of simulation evidences (Barberousse & Vorms, 2014; Lusk, 2016; Parker, 2020), might be fundamental to understand how warrants for belief in adequacy of representation are produced.

  3. 3.

    The literature in the philosophy of simulation has a more complex approach to the representational nature of simple models and, therefore, to their validation (e.g., Grüne-Yanoff, 2009; Knuuttila, 2011; Nguyen, 2020; Weinsberg, 2013; Ylikoski & Aydinonat, 2014).

  4. 4.

    In fact, segregation is presented in the literature as a higher order mechanism that operates indistinctly across domains where clustering patterns emerge at the population level, e.g. classrooms, workplaces, online networks.

  5. 5.

    The article also offers an interesting account of why dynamics of accreditation neglect Sakoda’s pioneering contribution to the study of clustering dynamics.

  6. 6.

    There seems to be a difference between interpretation and commensuration when it comes to their effect on the process of confirmation. As mentioned above, problems of interpretation are different, depending on whether the interest is on verification or validation. Conversely, the effect of commensuration seems to be more diffuse and might require knowledge about the goals of the modeller to be made sense of. Those authors that acknowledge that commensuration could be used both for verification and validation (e.g. Axelrod, 1997; Wilensky & Rand, 2007) do not elaborate on the reasons for which a researcher might choose one or the other or whether, in practice, the distinction is so clear-cut.

  7. 7.

    Commensuration in docking is, in part, more challenging than in replication, for the term encompasses a more diverse set of activities. North and Macal (2002), for example, use the term ‘docking’ to describe an exercise in which they compare implementations of the beer game, originally, a system dynamics model, in three different platforms: Mathematica, Re-past and Swarm. This exercise, however, significantly differs from that of (Axtell et al., 1996).

  8. 8.

    Commensuration in abstract models is not as problematic, for it can rely on loose criteria of resemblance or plausibility. Different segregation models, for example, could be simply commensurated in their ability produce clustering at the macro level.

  9. 9.

    This claim has particularly interesting implications when discussed in the context of qualitative research, for some authors in this tradition deny the possibility to qualitatively quantify social phenomena (Lincoln and Guba, 1985).

  10. 10.

    Beliefs about what constitutes a good explanation are far from standard in agent-based social simulation, reflecting a more general disagreement about this topic in the philosophy of science. Consensus is not widespread even for some basic scientific values, such as prediction (see, for example, the discussion between Epstein (2008), Thompson & Derr (2009) and Troitzsch, (2009)).

  11. 11.

    Similarly, for example, to public sociology (Burawoy, 2005), a strand within mainstream sociology that has the engagement of non-academic audiences as a criterion of ‘success’.

References

  1. Ahrweiler, P., & Gilbert, N. (2005). Caffè Nero: The evaluation of social simulation. Journal of Artificial Societies and Social Simulation, 8(4). http://jasss.soc.surrey.ac.uk/8/4/14.html.

  2. Angus, S., & Hassani-Mahmooei, B. (2015). “Anarchy” reigns: A quantitative analysis of agent-based modelling publication practices in JASSS, 2001-2012. Journal of Artificial Societies and Social Simulation, 18(4). http://jasss.soc.surrey.ac.uk/18/4/16.html.

  3. Ankeny, R., & Leonelli, S. (2011). What’s so special about model organisms? Studies in History and Philosophy of Science Part A, 42(2), 313–323.

    Article  Google Scholar 

  4. Anzola, D. (2019a). Disagreement in discipline-building processes. Synthese. https://doi.org/10.1007/s11229-019-02438-9.

  5. Anzola, D. (2019b). Knowledge transfer in agent-based computational social science. Studies in History and Philosophy of Science Part A, 77, 29–38.

    Article  Google Scholar 

  6. Anzola, D. (2021). Capturing the representational and the experimental in the modelling of artificial societies. European Journal for Philosophy of Science. https://doi.org/10.1007/s13194-021-00382-5.

  7. Anzola, D., & Rodri̇guez-Cȧrdenas, D. (2018). A model of cultural transmission by direct instruction: An exercise on replication and extension. Cognitive Systems Research, 52, 450–465.

    Article  Google Scholar 

  8. Axelrod, R. (1995). The convergence and stability of cultures: Local convergence and global polarization. Santa Fe Institute working paper No. 95-03-028. https://www.santafe.edu/research/results/working-papers/the-convergence-and-stability-of-cultures-local-co.

  9. Axelrod, R. (1997). Advancing the art of simulation in the social sciences. In R. Conte, R. Hegselmann, & P. Terna (Eds.) Simulating social phenomena. Berlin: Springer.

  10. Axtell, R., Axelrod, R., Epstein, J., & Cohen, M. (1996). Aligning simulation models: A case study and results. Computational &, Mathematical Organization Theory, 1(2), 123–141.

    Article  Google Scholar 

  11. Aydinonat, E. (2018). The diversity of models as a means to better explanations in economics. Journal of Economic Methodology, 25(3), 237–251.

    Article  Google Scholar 

  12. Bakar, N., & Selamat, A. (2018). Agent systems verification: Systematic literature review and mapping. Applied Intelligence, 48(5), 1251–1274.

    Article  Google Scholar 

  13. Baker, M. (2016). 1,500 Scientists lift the lid on reproducibility. Nature, 533, 452–454.

    Article  Google Scholar 

  14. Balci, O. (2003). Verification, validation, and certification of modeling and simulation applications. In S Chick, P. Sȧnchez, D. Ferrin, & D. Morrice (Eds.) Proceedings of the 2003 Winter simulation conference. IEEE: New Orleans.

  15. Barberousse, A., & Vorms, M. (2014). About the warrants of computer-based empirical knowledge. Synthese, 191(15), 3595–3620.

    Article  Google Scholar 

  16. Becker, J., Niehaves, B., & Klose, K. (2005). A framework for epistemological perspectives on simulation. Journal of Artificial Societies and Social Simulation, 8(4). http://jasss.soc.surrey.ac.uk/8/4/1.html.

  17. Beisbart, C., & Saam, N. (Eds.) (2019). Computer simulation validation. Springer.

  18. Benenson, I., & Hatna, E. (2011). Minority-majority relations in the Schelling model of residential dynamics. Geographical Analysis, 43(3), 287–305.

    Article  Google Scholar 

  19. Bird, A. (2014). When is there a group that knows? Distributed cognition, scientific knowledge, and the social epistemic subject. In J. Lackey (Ed.) Essays in collective epistemology. Oxford: Oxford University Press.

  20. Boghossian, P. (2011). Epistemic relativism defended. In A. Goldman D. Whitcomb (Eds.) Social epistemology. Oxford: Oxford University Press.

  21. ten Broeke, G., van Voorn, G., & Ligtenberg, A. (2016). Which sensitivity analysis method should I use for my agent-based model? Journal of Artificial Societies and Social Simulation 19(1). http://jasss.soc.surrey.ac.uk/19/1/5.htmlhttp://jasss.soc.surrey.ac.uk/19/1/5.html.

  22. Bruch, E., & Mare, R. (2006). Neighborhood choice and neighborhood change. American Journal of Sociology, 112(3), 667–709.

    Article  Google Scholar 

  23. Bruch, E., & Mare, R. (2009). Segregation dynamics. In P. Hedström P. Bearman (Eds.) The Oxford handbook of analytical sociology. Oxford: Oxford University Press.

  24. Burawoy, M. (2005). For public sociology. American Sociological Review, 70, 4–28.

    Article  Google Scholar 

  25. Burman, L., Reed, R., & Alm, J. (2010). A call for replication studies. Public Finance Review, 38(6), 787–793.

    Article  Google Scholar 

  26. Chattoe-Brown, E. (2013). Why sociology should use agent based modelling. Sociological Research Online, 18(3). http://www.socresonline.org.uk/18/3/3.html.

  27. Christensen, D. (2007). Epistemology of disagreement: The good news. Philosophical Review, 116(2), 187–217.

    Article  Google Scholar 

  28. Cioffi-Revilla, C. (2014). Introduction to computational social science. Berlin: Springer.

    Book  Google Scholar 

  29. Clark, W. (1991). Residential preferences and neighborhood racial segregation: A test of the Schelling segregation model. Demography, 28(1), 1–19.

    Article  Google Scholar 

  30. Clark, W., & Fossett, M. (2008). Understanding the social context of the Schelling segregation model. Proceedings of the National Academy of Sciences of the United States of America, 105(11), 4109–4114.

    Article  Google Scholar 

  31. Collins, H. (1992). Changing order. Chicago: The University of Chicago Press.

    Google Scholar 

  32. Conte, R. (2009). From simulation to theory (and backward). In F Squazzoni (Ed.) Epistemological aspects of computer simulation in the social sciences. Berlin: Springer.

  33. Crooks, A. (2010). Constructing and implementing an agent-based model of residential segregation through vector GIS. International Journal of Geographical Information Science, 24(5), 661–675.

    Article  Google Scholar 

  34. David, N. (2013). Validating simulations. In B. Edmonds R. Meyer (Eds.) Simulating social complexity, understanding complex systems. Berlin: Springer.

  35. Edmonds, B. (2000). The use of models - making MABS more informative. In S. Moss P. Davidsson (Eds.) Multi-agent-based simulation. Berlin: Springer.

  36. Edmonds, B., & Hales, D. (2003). Replication, replication and replication: Some hard lessons from model alignment. Journal of Artificial Societies and Social Simulation, 6(4). http://jasss.soc.surrey.ac.uk/6/4/11.htmlhttp://jasss.soc.surrey.ac.uk/6/4/11.html.

  37. Edmonds, B., & Moss, S. (2005). From KISS to KIDS — An ‘anti-simplistic’ modelling approach. In P Davidsson, B Logan, & K Takadama (Eds.) Multi-agent and multi-agent-based simulation. Berlin: Springer.

  38. Edmonds, B., Le, Page C, Bithell, M., Chattoe-Brown, E., Grimm, V., Meyer, R., Montañola-Sales, C., Ormerod, P., Root, H., & Squazzoni, F. (2019). Different modelling purposes. Journal of Artificial Societies and Social Simulation, 22(3). http://jasss.soc.surrey.ac.uk/22/3/6.html.

  39. Epstein, J. (1999). Agent-based computational models and generative social science. Complexity, 4(5), 41–60.

    Article  Google Scholar 

  40. Epstein, J. (2008). Why model? Journal of Artificial Societies and Social Simulation, 11(4). http://jasss.soc.surrey.ac.uk/11/4/12.html.

  41. Epstein, J., & Axtell, R. (1996). Growing artificial societies. Cambridge: MIT press.

    Book  Google Scholar 

  42. Fossett, M., & Dietrich, D. (2009). Effects of city size, shape, and form, and neighborhood size and shape in agent-based models of residential segregation: Are Schelling-style preference effects robust? Environment and Planning B: Planning and Design, 36(1), 149–169.

    Article  Google Scholar 

  43. Frigg, R., & Nguyen, J. (2017). Models and representation. In L. Magnani T. Bertolotti (Eds.) Springer Handbook of Model-Based Science. Berlin: Springer.

  44. Galán, J., & Izquierdo, L. (2005). Appearances can be deceiving: Lessons learned re-implementing Axelrod’s ‘evolutionary approach to norms’. Journal of Artificial Societies and Social Simulation, 8(3). http://jasss.soc.surrey.ac.uk/8/3/2.html.

  45. Ghorbani, A., Bots, P., Dignum, V., & Dijkema, G. (2013). MAIA: A framework for developing agent-based social simulations. Journal of Artificial Societies and Social Simulation, 16(2). http://jasss.soc.surrey.ac.uk/16/2/9.html.

  46. Giddens, A. (1984). The constitution of society. Berkeley: University of California Press.

    Google Scholar 

  47. Gilbert, N. (2003). Varieties of emergence. In C. Macal D. Sallach (Eds.) Proceedings of the agent 2002 conference on social agents. Chicago: Argonne National Laboratory.

  48. Gilbert, N. (2010). Editor’s introduction: Computational social science. In N Gilbert (Ed.) Computational social science. London: Sage.

  49. Gilbert, N., & Troitzsch, K. (2005). Simulation for the social scientist. Glasgow: Open University Press.

    Google Scholar 

  50. Graebner, C. (2018). How to relate models to reality? An epistemological framework for the validation and verification of computational models. Journal of Artificial Societies and Social Simulation, 21(3). http://jasss.soc.surrey.ac.uk/21/3/8.html.

  51. Grimm, V., Berger, U., Bastiansen, F., Eliassen, S., Ginot, V., Giske, J., Goss-Custard, J., Grand, T., Heinz, S., Huse, G., Huth, A., Jepsen, J., Jørgensen, C., Mooij, W., Müller, B., Pe’er, G., Piou, C., Railsback, S., Robbins, A., ..., DeAngelis, D. (2006). A standard protocol for describing individual-based and agent-based models. Ecological Modelling, 198(1-2), 115–126.

    Article  Google Scholar 

  52. Grimm, V., Berger, U., DeAngelis, D., Polhill, G., Giske, J., & Railsback, S. (2010). The ODD protocol: A review and first update. Ecological Modelling, 221(23), 2760–2768.

    Article  Google Scholar 

  53. Grimm, V., Railsback, S., Vincenot, C., Berger, U., Gallagher, C., DeAngelis, D., Edmonds, B., Ge, J., Giske, J., Groeneveld, J., Johnston, A., Milles, A., Nabe-Nielsen, J., Polhill, G., Radchuk, V., Rohwäder, M.S., Stillman, R., Thiele, J., & Ayllón, D. (2020). The ODD protocol for describing agent-based and other simulation models: A second update to improve clarity, replication, and structural realism. Journal of Artificial Societies and Social Simulation, 23(2). http://jasss.soc.surrey.ac.uk/23/2/7.html.

  54. Grüne-Yanoff, T. (2009). Learning from minimal economic models. Erkenntnis, 70(1), 81–99.

    Article  Google Scholar 

  55. Grüne-Yanoff, T., & Marchionni, C. (2018). Modeling model selection in model pluralism. Journal of Economic Methodology, 25(3), 265–275.

    Article  Google Scholar 

  56. Hegselmann, R. (2017). Thomas C. Schelling and James M. Sakoda: The intellectual, technical, and social history of a model. Journal of Artificial Societies and Social Simulation, 20(3). http://jasss.soc.surrey.ac.uk/20/3/15.html.

  57. Huang, Q., Parker, D., Filatova, T., & Sun, S. (2014). A review of urban residential choice models using Agent-Based modeling. Environment and Planning B: Planning and Design, 41(4), 661–689.

    Article  Google Scholar 

  58. Janssen, M., Alessa, L., Barton, M., Bergin, S., & Lee, A. (2008). Towards a community framework for agent-based modelling. Journal of Artificial Societies and Social Simulation, 11(2). http://jasss.soc.surrey.ac.uk/11/2/6.html.

  59. Jebeile, J., & Ardourel, V. (2019). Verification and validation of simulations against holism. Minds and Machines, 29(1), 149–168.

    Article  Google Scholar 

  60. Jebeile, J., & Barberousse, A. (2016). Empirical agreement in model validation. Studies in History and Philosophy of Science Part A, 56, 168–174.

    Article  Google Scholar 

  61. Kerr, N., MacCoun, R., & Kramer, G. (1996). Bias in judgment: Comparing individuals and groups. Psychological Review, 103(4), 687–719.

    Article  Google Scholar 

  62. Kitcher, P. (1993). The advancement of science. Oxford: Oxford University Press.

    Google Scholar 

  63. Kitcher, P. (2011). Science in a democratic society. New York: Prometheus.

    Book  Google Scholar 

  64. Knuuttila, T. (2011). Modelling and representing: An artefactual approach to model-based representation. Studies in History and Philosophy of Science Part A, 42(2), 262–271.

    Article  Google Scholar 

  65. Laatabi, A., Marilleau, N., Nguyen-Huu, T., Hbid, H., & Ait Babram, M. (2018). ODD+ 2D: An ODD based protocol for mapping data to empirical ABMs. Journal of Artificial Societies and Social Simulation, 21(2). http://jasss.soc.surrey.ac.uk/21/2/9.html.

  66. Lackey, J. (2011). Testimony: Acquiring knowledge from others. In A. Goldman D. Whitcomb (Eds.) Social epistemology. Oxford: Oxford University Press.

  67. Lee, J.S., Filatova, T., Ligmann-Zielinska, A., Hassani-Mahmooei, B., Stonedahl, F., Lorscheid, I., Voinov, A., Polhill, G., Sun, Z., & Parker, D. (2015). The complexities of agent-based modeling output analysis. Journal of Artificial Societies and Social Simulation, 18(4). http://jasss.soc.surrey.ac.uk/18/4/4.htmlhttp://jasss.soc.surrey.ac.uk/18/4/4.html.

  68. Lehtinen, A., & Kuorikoski, J. (2007). Computing the perfect model: Why do economists shun simulation? Philosophy of Science, 74, 304–329.

    Article  Google Scholar 

  69. Lenhard, J., & Küster, U. (2019). Reproducibility and the concept of numerical solution. Minds and Machines, 29(1), 19–36.

    Article  Google Scholar 

  70. Lenhard, J., & Winsberg, E. (2010). Holism, entrenchment, and the future of climate model pluralism. Studies in History and Philosophy of Science Part B - Studies in History and Philosophy of Modern Physics, 41(3), 253–262.

    Article  Google Scholar 

  71. Lincoln, Y., & Guba, E. (1985). Naturalistic inquiry. London: Sage.

    Book  Google Scholar 

  72. Lloyd, E. (2018). The role of “complex” empiricism in the debates about satellite data and climate models. In E. Lloyd E. Winsberg (Eds.) Climate modelling. New York: Palgrave Macmillan.

  73. Lorscheid, I., Heine, B., & Meyer, M. (2012). Opening the ‘black box’ of simulations: Increased transparency and effective communication through the systematic design of experiments. Computational & Mathematical Organization Theory, 18(1), 22–62.

    Article  Google Scholar 

  74. Lusk, G. (2016). Computer simulation and the features of novel empirical data. Studies in History and Philosophy of Science Part A, 56, 145–152.

    Article  Google Scholar 

  75. MacLeod, M. (2016). What makes interdisciplinarity difficult? Some consequences of domain specificity in interdisciplinary practice. Synthese, 1–24.

  76. Macy, M., & Sato, Y. (2002). Trust, cooperation and market formation in the US and Japan. Proceedings of the National Academy of Sciences of the United States of America, 99, 7214–7220.

    Article  Google Scholar 

  77. Macy, M., & Sato, Y. (2008). Reply to Will and Hegselmann. Journal of Artificial Societies and Social Simulation, 11(4). http://jasss.soc.surrey.ac.uk/11/4/11.html.

  78. Macy, M., & Sato, Y. (2010). The surprising success of a replication that failed. Journal of Artificial Societies and Social Simulation, 13(2). http://jasss.soc.surrey.ac.uk/13/2/9.html.

  79. Matthewson, J., & Weisberg, M. (2009). The structure of tradeoffs in model building. Synthese, 170(1), 169–190.

    Article  Google Scholar 

  80. Merton, R. (1936). The unanticipated consequences of purposive social action. American Sociological Review, 1(6), 894–904.

    Article  Google Scholar 

  81. Michell, J. (2007). Measurement. In S. Turner M. Risjord (Eds.) Philosophy of anthropology and sociology. Amsterdam: Elsevier.

  82. Morrison, M. (2015). Reconstructing reality. Oxford: Oxford University Press.

    Book  Google Scholar 

  83. Moss, S. (2008). Alternative approaches to the empirical validation of agent-based models. Journal of Artificial Societies and Social Simulation, 15(1). http://jasss.soc.surrey.ac.uk/11/1/5.html.

  84. Muelder, H., & Filatova, T. (2018). One theory - many formalizations: Testing different code implementations of the theory of planned behaviour in energy agent-based models. Journal of Artificial Societies and Social Simulation, 21(4). http://jasss.soc.surrey.ac.uk/21/4/5.html.

  85. Nguyen, J. (2020). It’s not a game: Accurate representation with toy models. The British Journal for the Philosophy of Science, 71(3), 1013–1041.

    Article  Google Scholar 

  86. North, M., & Macal, C. (2002). The beer dock: Three and a half implementations of the beer distribution game. In Swarmfest, University of Notre Dame. http://backspaces.net/sun/SCSim/BeerDock.pdf.

  87. North, M., & Macal, C. (2007). Managing business complexity. London: Oxford University Press.

    Book  Google Scholar 

  88. Oberkampf, W., & Roy, C. (2010). Verification and validation in scientific computing. Cambridge: Cambridge University Press.

    Book  Google Scholar 

  89. Parker, W. (2017). Computer simulation, measurement, and data assimilation. British Journal for the Philosophy of Science, 68(1), 273–304.

    Article  Google Scholar 

  90. Parker, W. (2020). Evidence and knowledge from computer simulation. Erkenntnis. https://doi.org/10.1007/s10670-020-00260-1.

  91. Parker, W., & Winsberg, E. (2018). Values and evidence: How models make a difference. European Journal for Philosophy of Science, 8(1), 125–142.

    Article  Google Scholar 

  92. Poile, C., & Safayeni, F. (2016). Using computational modeling for building theory: A double edged sword. Journal of Artificial Societies and Social Simulation, 19(3). http://jasss.soc.surrey.ac.uk/19/3/8.html.

  93. Popper, K. (1959). The logic of scientific discovery. New York: Basic Books.

    Google Scholar 

  94. Primiero, G. (2019). A minimalist epistemology for agent-based simulations in the artificial sciences. Minds and Machines, 29(1), 127–148.

    Article  Google Scholar 

  95. Rand, W., & Rust, R. (2011). Agent-based modeling in marketing: Guidelines for rigor. International Journal of Research in Marketing, 28(3), 181–193.

    Article  Google Scholar 

  96. Reardon, S., & O’Sullivan, D. (2004). Measures of spatial segregation. Sociological Methodology, 34(1), 121–162.

    Article  Google Scholar 

  97. Resnick, D. (2013). Ethics of science. In S. Psillos M. Curd (Eds.) The Routledge companion to philosophy of science. New York: Routledge.

  98. Richiardi, M., Leombruni, R., Saam, N., & Sonnessa, M. (2006). A common protocol for agent-based social simulation. Journal of Artificial Societies and Social Simulation, 9(1). http://jasss.soc.surrey.ac.uk/9/1/15.html.

  99. Rossiter, S., Noble, J., & Bell, K. (2010). Social simulations: Improving interdisciplinary understanding of scientific positioning and validity. Journal of Artificial Societies and Social Simulation, 13(1). http://jasss.soc.surrey.ac.uk/13/1/10.html.

  100. Rouchier, J. (2003). Re-implementation of a multi-agent model aimed at sustaining experimental economic research: The case of simulations with emerging speculation. Journal of Artificial Societies and Social Simulation, 6(4). http://jasss.soc.surrey.ac.uk/6/4/7.html.

  101. Roy, C. (2019). Errors and uncertainties: Their sources and treatment. In C. Beisbart N. Saam (Eds.) Computer simulation validation. Cham: Springer.

  102. Saam, N. (2019). Validation benchmarks and related metrics. In C. Beisbart N. Saam (Eds.) Computer simulation validation. Cham: Springer.

  103. Sakoda, J. (1971). The checkerboard model of social interaction. Journal of Mathematical Sociology, 1(1), 119–132.

    Article  Google Scholar 

  104. Sargent, R. (2013). Verification and validation of simulation models. Journal of Simulation, 7(1), 12–24.

    Article  Google Scholar 

  105. Schelling, T. (1971). Dynamic models of segregation. Journal of Mathematical Sociology, 1(2), 143–186.

    Article  Google Scholar 

  106. Squazzoni, F. (2012). Agent-based computational sociology. London: Wiley.

    Book  Google Scholar 

  107. Stanford, K. (2017). Underdetermination of scientific theory. In E Zalta (Ed.) The Stanford encyclopedia of philosophy, winter 2017 edn. https://plato.stanford.edu/entries/scientific-underdetermination/.

  108. Strevens, M. (2003). The role of the priority rule in science. The Journal of Philosophy, 100(2), 55–79.

    Article  Google Scholar 

  109. Tesfatsion, L. (2017). Modeling economic systems as locally-constructive sequential games. Journal of Economic Methodology, 24(4), 384–409.

    Article  Google Scholar 

  110. Thompson, N., & Derr, P. (2009). Contra Epstein, good explanations predict. Journal of Artificial Societies and Social Simulation, 12(1). http://jasss.soc.surrey.ac.uk/12/1/9.html.

  111. Troitzsch, K. (2009). Not all explanations predict satisfactorily, and not all good predictions explain. Journal of Artificial Societies and Social Simulation, 12(1). http://jasss.soc.surrey.ac.uk/12/1/10.html.

  112. Tsvetkova, M., Nilsson, O., Öhman, C, Sumpter, L., & Sumpter, D. (2016). An experimental study of segregation mechanisms. EPJ Data Science, 5(1), 4.

    Article  Google Scholar 

  113. Wagenknecht, S. (2016). A social epistemology of research groups. London: Palgrave Macmillan.

    Book  Google Scholar 

  114. Wang, Z., & Lehmann, A. (2007). A framework for verification and validation of simulation models and applications. In J.W. Park, T.G. Kim, & Y.B. Kim (Eds.) AsiaSim 2007. Berlin: Springer.

  115. Weinsberg, M. (2013). Simulation and similarity. Oxford: Oxford University Press.

    Book  Google Scholar 

  116. Wilensky, U., & Rand, W. (2007). Making models match: Replicating an agent-based model. Journal of Artificial Societies and Social Simulation, 10(4). http://jasss.soc.surrey.ac.uk/10/4/2.html.

  117. Will, O. (2009). Resolving a replication that failed: News on the Macy & Sato model. Journal of Artificial Societies and Social Simulation, 12(4). http://jasss.soc.surrey.ac.uk/12/4/11.html.

  118. Will, O., & Hegselmann, R. (2008a). A replication that failed: On the computational model in ‘Michael W. Macy and Yoshimichi Sato: Trust, cooperation and market formation in the U.S. and Japan. Proceedings of the National Academy of Sciences, May 2002’. Journal of Artificial Societies and Social Simulation, 11(3). http://jasss.soc.surrey.ac.uk/11/3/3.html.

  119. Will, O., & Hegselmann, R. (2008b). Remark on a reply. Journal of Artificial Societies and Social Simulation, 11(4). http://jasss.soc.surrey.ac.uk/11/4/13.html.

  120. Windrum, P., Fagiolo, G., & Moneta, A. (2007). Empirical validation of agent-based models: Alternatives and prospects. Journal of Artificial Societies and Social Simulation, 10(2). http://jasss.soc.surrey.ac.uk/10/2/8.html.

  121. Winsberg, E. (2001). Models, and theories: Simulations, complex physical systems and their representations. Philosophy of Science, 68(3), S442–S454.

    Article  Google Scholar 

  122. Winsberg, E. (2010). Science in the age of computer simulation. Chicago: University of Chicago Press.

    Book  Google Scholar 

  123. Winsberg, E. (2019). Computer simulations in science. In E. Zalta (Ed.) The Stanford encyclopedia of philosophy, winter 2019 edn. https://plato.stanford.edu/archives/win2019/entries/simulations-science/.

  124. Ylikoski, P., & Aydinonat, E. (2014). Understanding with theoretical models. Journal of Economic Methodology, 21(1), 19–36.

    Article  Google Scholar 

  125. Zhang, J. (2004). Residential segregation in an all-integrationist world. Journal of Economic Behavior &, Organization, 54(4), 533–550.

    Article  Google Scholar 

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to David Anzola.

Ethics declarations

Conflict of Interest

The authors declare no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Anzola, D. Social Epistemology and Validation in Agent-Based Social Simulation. Philos. Technol. (2021). https://doi.org/10.1007/s13347-021-00461-8

Download citation

Keywords

  • Validation
  • Representation
  • Agent-based modelling
  • Social epistemology
  • Interpretation
  • Commensuration