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Capturing the representational and the experimental in the modelling of artificial societies

Abstract

Even though the philosophy of simulation is intended as a comprehensive reflection about the practice of computer simulation in contemporary science, its output has been disproportionately shaped by research on equation-based simulation in the physical and climate sciences. Hence, the particularities of alternative practices of computer simulation in other scientific domains are not sufficiently accounted for in the current philosophy of simulation literature. This article centres on agent-based social simulation, a relatively established type of simulation in the social sciences, to exemplify this claim. The analysis advanced has a twofold goal. First, it shows that the philosophy of agent-based social simulation, mostly developed by practitioners themselves, is, on one hand, heavily influenced by the methodological features of agent-based modelling and by the loose and fragmented character of social theory and, on the other hand, distinctively shaped by contrasting views of what it implies to do social research with virtual or artificial societies. Second, it suggests ways in which cross-fertilisation could enrich the philosophical understanding of computer simulation both in agent-based social simulation and in the philosophy of simulation.

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Fig. 1

Notes

  1. 1.

    Much of this discussion precedes the consolidation of the contemporary philosophy of simulation. This might be a factor hindering cross-fertilisation.

  2. 2.

    ‘Practical’ is used here in the sense of regularly leaning towards model-building rather than philosophical reflection, not in the sense of developing empirical models. The question about whether models should be empirically calibrated has fostered what is, arguably, the most important theoretical disagreement in the agent-based social simulation community (Anzola, 2019a; Sun et al., 2016).

  3. 3.

    Unless it is claimed that the entire social world can be reduced to individual decision-making.

  4. 4.

    It is important to clarify that ‘place’, in opposition to ‘space’, has more to do with the meaning and value than with the physical embeddedness of social practices.

  5. 5.

    The epistemic opacity of computer simulation is a major factor in the conceptualisation of this sense of surprise (although practitioners of agent-based social simulation have not discussed the issue as extensively as philosophers of simulation). It is partly what made the concept of emergence popular in agent-based social simulation (Gilbert, 1995) and, curiously, also one reason for which some social scientists do not fully trust the results of agent-based models (Lehtinen & Kuorikoski, 2007; Waldherr & Wijermans, 2013).

  6. 6.

    Theoretically, because the micro-macro dualism is, arguably, the most important theoretical distinction in social science (Alexander & Giesen, 1987); methodologically, because agent-based modelling is, perhaps, the type of computer simulation that is better equipped to deal with emergence, due to the possibility to explicitly model entities and interactions (Davidsson et al., 2017).

  7. 7.

    For example, based on concerns about computation, some large-scales model e.g., the popular TRANSIMS (Smith et al., 1995) model, deliberately trade off features, such as agents’ reactivity for population size. In turn, Netlogo’s processing power upper bound is determined by the way the software incorporates Java.

  8. 8.

    Interestingly, Schelling’s model is extremely popular in the philosophy of simulation literature that discusses the usefulness of simple models.

  9. 9.

    Conceptually, the notion of benchmark is more consistent with a logic of justification than of discovery. To an extent, the entire dual evaluation scheme is devised this way, since both verification and validation are formulated as success terms. Furthermore, knowledge and benchmarks proper experience completely different certification processes and are also not contested in the same way. Benchmarks could be deliberately and institutionally established and, at the same time, cannot really be falsified.

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Anzola, D. Capturing the representational and the experimental in the modelling of artificial societies. Euro Jnl Phil Sci 11, 63 (2021). https://doi.org/10.1007/s13194-021-00382-5

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Keywords

  • Computer simulation
  • Agent-based modelling
  • Artificial societies
  • Representation
  • Experimentation
  • Verification - validation