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.
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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).
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.
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.
The article also offers an interesting account of why dynamics of accreditation neglect Sakoda’s pioneering contribution to the study of clustering dynamics.
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.
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).
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.
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).
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)).
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’.
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Anzola, D. Social Epistemology and Validation in Agent-Based Social Simulation. Philos. Technol. (2021). https://doi.org/10.1007/s13347-021-00461-8
- Agent-based modelling
- Social epistemology