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Generative Models

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Abstract

Generative models have been proposed as a new type of non-representational scientific models recently. A generative model is characterized with the capacity of producing new models on the basis of the existing one. The current accounts do not explain sufficiently the mechanism of the generative capacity of a generative model. I attempt to accomplish this task in this paper. I outline two antecedent accounts of generative models. I point out that both types of generative models function to generate new homogenous models in the sense that the latter is a straightforward derivative of the former, both of which share many similar features. Unfortunately, both accounts are implicit about the generative capacity of generative models. Using a case study, I articulate that a two-staged process of abstraction and idealization in modeling may contribute to the generative capacity of a scientific model. I also demonstrate that this two-staged process may go beyond the capacity of generating new homogenous models to generating new heterogeneous models.

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Notes

  1. According to Rice, idealization is a process that distorts the model as a whole, viz., idealizations are inevitably pervasive system-wide distortions. Rice argues that it is implausible to idealize a specific feature of the target phenomenon without affecting other features in the process of modeling.

  2. Though philosophers claim that generative models are not meant for representation, some philosophers (such as Peschard 2011) argue that a generative model must be representational to serve a constructive role. What is worth pointing out here is that the proponents of generative models (including Peschard) think that the epistemic role of a generative model lies not in representation but in its capacity of model construction.

  3. I thank an anonymous reviewer for pressing me on this question.

  4. According to Peschard, a generative model can produce new models and target systems only if it has a representational target in the first place, which serves as a basis for the new models and target systems to emerge. The representational target is required to be coordinated, through mathematics, to a generative model in the process of constructing new model-target.

  5. Peschard does not provide an explicit definition for ‘coupling parameters’. However, she states that a coupling parameter is “a controllable parameter of the system” (2011, p. 346). The way this term is used in her case study suggests that a coupling parameter is a parameter used to coordinate the value in the model with the feature in the target system. Once the relevant values of the coupling parameters have been determined, a generative model “can function autonomously as a source of inferences about [its] target” (Peschard 2011, p. 345)

  6. Peschard characterizes the generative constructive use of models as: Firstly, “the target of the model constructed is different from that of the model that is used.” (p. 350). Secondly, “the target of the new model is the result of an extension or transformation of the original target.” (p. 350).

  7. Fagan (2016) maintains that hESCs are non-classical model organisms paralleling the classical model organisms in terms of the small size, high fecundity, short generation time, and rapid development.

  8. In short, abstraction is omission whereas idealization is distortion. This difference is also adopted by Jones (2005). Although Arnon Levy (2018) holds that abstraction should not be tied too close to truth and idealization should be sufficiently differentiated from error, he maintains that the core idea of Jones (2005) in the distinction between abstraction and idealization is correct. In this paper, I shall follow Jones (2005) and Levy (2018) and maintain that abstraction should be distinguished from idealization.

  9. The generative capacity argued in this paper will not be affected by such distinction.

  10. A cautionary note is needed here. I do not deny that many models in synthetic biology are used by the scientists for representation. My discussion here is limited to the use of a set of diverse generative models to construct new synthetic models. We shall remember that generative models and representational models are not taken as mutually exclusive in the pioneering works of Peschard (2011) and Fagan (2016). I do not contend that the diverse models used to construct a synthetic biological model cannot be representational.

  11. Schyfter (2012) dubs the heterogeneity of a synthetic biological system as displaying “a concomitant ontological ‘messiness’” according to which a synthetic biological system fits imperfectly into several categories of natural kinds.

  12. For example, Shou et al. (2007) mixed cell populations to engineer a synthetic ecological community and used it as an ecological model for the robustness analysis. Datla et al. (2017), in a similar research, constructed a synthetic system that plays the role as an ecological model for the study of the evolution of species. In their influential work on the construction of synthetic transcriptional regulators, Elowitz and Leibler (2000) aver that a synthetic biological system constructed from diverse models can serve as a model leading to the construction of new cellular behaviors and to an enhanced understanding of naturally occurring systems.

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  1. Xiamen University Malaysia, Jalan Sunsuria, Bandar Sunsuria, 43900, Sepang, Selangor, Malaysia

    Sim-Hui Tee

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Tee, SH. Generative Models. Erkenn 88, 23–41 (2023). https://doi.org/10.1007/s10670-020-00338-w

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