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Simulating Social Complexity pp 763–800Cite as

Human Societies: Understanding Observed Social Phenomena

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Part of the book series: Understanding Complex Systems ((UCS))

Abstract

The chapter begins by briefly describing two contrasting simulations: the iconic system dynamics model publicised under the Limits to Growth book and a detailed model of first millennium Native American societies in the southwest of the United States. These are used to bring out the issues of abstraction, replicability, model comprehensibility, understanding vs. prediction and the extent to which simulations go beyond what is observed. All of these issues are rooted in some fundamental difficulties in the project of simulating observed societies that are then briefly discussed. Both issues and difficulties result in three “dimensions” in which simulation approaches differ. The core of the chapter is a look at 15 different possible simulation goals, both abstract and concrete, giving some examples of each and discussing them. The different inputs and results from such simulations are briefly discussed as to their importance for simulating human societies.

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Notes

  1. 1.

    This embeddedness has advantages as well, such as prior knowledge.

  2. 2.

    The intentions of the authors themselves in terms of what they thought of the simulation itself are difficult to ascertain and varied between the individuals; however, this was certainly how the work was perceived.

  3. 3.

    Or those whose vested interests may have led them to maintain the status quo concerning the desirability of continual economic growth.

  4. 4.

    For details of the wider project connected with these papers, see the Village Ecodynamics Project, http://village.anth.wsu.edu.

  5. 5.

    Although in many cases this is dressed up to look like prediction, such as the fitting to out-of-sample data. Prediction has to be for data unknown to the modeller; otherwise the model will be implicitly fitted to it.

  6. 6.

    In terms of design and implementation, if one has a good reference case in terms of observed data then one can also check one’s simulation against this.

  7. 7.

    Obviously, we suspect it can be a useful tool; otherwise we would not be bothering with it.

  8. 8.

    I.e. those who are part of or can influence the social phenomenon in question.

  9. 9.

    Folk knowledge is the set of widely held beliefs about popular psychological and social theories; this is sometimes used in a rather derogatory way even when the reliability of the academic alternatives is unclear.

  10. 10.

    This is when prediction is actually useful, for if it only gives expected values one would not need the simulation.

  11. 11.

    If a simulation is not directly related to evidence but is more a model of some ideas, then it might be simple enough to be able to test hypotheses, but these hypotheses will then be about the abstract model and not about the target phenomena.

  12. 12.

    This fact has led some to argue that such assumptions of perfect rationality should be dropped and that it might be better to adopt a more naturalistic representation of human’s cognition (Gode and Sunder 1993; Kirman 2011).

  13. 13.

    http://www.openabm.org.

  14. 14.

    Although in this particular case, it did not as the model indicated outcomes that the policy-makers preferred to ignore, being not compatible with the actions they had already decided to take.

  15. 15.

    To be precise: a possible encapsulation of a particular set of evidence on the case study.

  16. 16.

    This can either be done directly as a translation of an interview text into programmed rules or used to check that such programming is correct by comparing the resulting behaviour of an agent against what happens when the simulation is run. Thus, there is not a clear distinction between verification and validation from evidence. In a sense, this second method is verification since the programming is rejected until correct, but, on the other hand, this is part of the production of a simulation, which may only be completed later for its validation as a whole.

  17. 17.

    Unlikely with regard to the psychological or sociological evidence about the target subjects.

  18. 18.

    A “null” model is a model version where the claimed causal mechanism is eliminated to see if the resultant “effect” would have arisen as the result of background (e.g. random) mechanisms anyway.

  19. 19.

    Another option is to try all the possibilities exhaustively in a series of simulations or by using techniques such as constraint logic programming, but these are technically difficult and require a lot of computational power.

  20. 20.

    There are possible reasons why a constant value might not work, for example, when the input provides some mechanism of symmetry breaking.

  21. 21.

    There is nothing wrong with assumptions that had to be made due to constraints on resources, such as time, expertise or computing power, but it is simply disingenuous to pretend that this is sanctioned by a higher “virtue”.

  22. 22.

    However, this is a poor excuse given the ease with which a relatively complete technical paper can be archived and then cited by a journal article or report discussing the model.

  23. 23.

    Alternatively it may be because the simulation designers had not thought about what they were doing.

  24. 24.

    It is trivial to point out that a simulation has missed out some assumption or other, but this is not very useful. It is far more useful to point out how and why an assumption might be important and for which purposes.

  25. 25.

    At least, not in any of the cases we have as yet come across.

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Authors and Affiliations

  1. Centre for Policy Modelling, Manchester Metropolitan University, All Saints Campus, Oxford Road, Manchester, M15 6BH, UK

    Bruce Edmonds

  2. School of Sociology, University College Dublin, Dublin 4, Ireland

    Pablo Lucas

  3. LAMSADE, Paris-Dauphine, 75775, Paris Cedex 16, France

    Juliette Rouchier

  4. Oxford Branch, Stockholm Environment Institute, Stockholm, Sweden

    Richard Taylor

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  1. Bruce Edmonds
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Editors and Affiliations

  1. Centre for Policy Modelling, Manchester Metropolitan University, Business School, Manchester, United Kingdom

    Bruce Edmonds

  2. Centre for Policy Modelling, Manchester Metropolitan University, Business School, Manchester, United Kingdom

    Ruth Meyer

Further Reading

Further Reading

A more general and simpler introduction to varying modelling purposes can be found in Chap. 4 (Edmonds 2017). The best general introduction to social simulation is (Gilbert and Troitzsch 2005) which covers general issues and gives code examples. For a wider range of views on social simulation, the published papers from the US National Academy of Sciences colloquium on “Adaptive Agents, Intelligence, and Emergent Human Organization: Capturing Complexity through Agent-Based Modeling” (PNAS 2002) give a good cross-section of the different approaches people take to this area. It is difficult to point to further good sources as this topic is so diverse, but the Journal of Artificial Societies and Social Simulation has many accessible papers.

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Edmonds, B., Lucas, P., Rouchier, J., Taylor, R. (2017). Human Societies: Understanding Observed Social Phenomena. In: Edmonds, B., Meyer, R. (eds) Simulating Social Complexity. Understanding Complex Systems. Springer, Cham. https://doi.org/10.1007/978-3-319-66948-9_28

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