Intentional Models as Essential Scientific Tools

Abstract

In this article, I argue that the use of scientific models that attribute intentional content to complex systems bears a striking similarity to the way in which statistical descriptions are used. To demonstrate this, I compare and contrast an intentional model with a statistical model, and argue that key similarities between the two give us compelling reasons to consider both as a type of phenomenological model. I then demonstrate how intentional descriptions play an important role in scientific methodology as a type of phenomenal model, and argue that this makes them as essential as any other model of this type.

Acknowledgements

I am very grateful to Chris Eliasmith and Doreen Fraser for feedback and helpful discussions on early drafts of this manuscript. An earlier version of this paper was also presented at the 2012 Annual Congress of the Canadian Philosophical Association, where audience questions and comments were particularly insightful and helpful in shaping the paper. Lastly, I would like to thank the three anonymous referees of this journal whose comments and feedback helped to strengthen and improve the paper significantly.

Notes

[1] It should be noted that, given the prolific nature of Dennett's work, he is not always consistent regarding this point. Overall, however, the idea that intentional descriptions play a restricted role in science has been a strong undercurrent throughout Dennett's work.

[2] Given that the focus of this paper is specifically on the scientific benefits of intentional concepts as part of scientific models, I remain agnostic as to whether the everyday use of intentional language in colloquial contexts can be characterized as a phenomenological model as well. There is, however, recent evidence that even the folk psychological use of intentional idioms can be understood as something akin to the application of a model, albeit not necessarily a scientific one (Maibom 2003, 2009; Godfrey-Smith 2004, 2005). If such accounts are true, then the position I advocate here may apply to intentional descriptions more broadly.

[3] It should be noted that this particular definition is vague. It could be interpreted as saying that a phenomenological model is used as a means of characterizing directly observable feature(s) of a system, such as behavioural regularities, while saying nothing about the unobserved causes of those features/regularities. On the other hand, it could be interpreted as saying that a phenomenological model is one that only invokes entities and relations that are directly observable (while refraining from references to theoretical posits, or inferred entities). I propose that the former definition is more in-line with actual scientific usage than the latter. Consider that statistical models are commonly considered to be phenomenological models, yet they posit boundary conditions that are not directly observable features of systems. Likewise, many examples of phenomenological models knowingly invoke theoretical unobserved entities. Craver (2006, 356), for instance, argues that one can use a Ptolemaic model of the solar system as a phenomenological model if one is interested only in a predicatively adequate account of where the planets will appear in the sky. Yet such a model posits numerous unobservable theoretical entities such as equants, deferents, and epicycles (Craver 2006, 358). What makes such a model phenomenological is that it is used as a means of describing aspects of the systems that are directly observable (like where the planets will appear in the sky) while saying nothing about underlying causes, not that it only makes reference to observables entities. Many scientific examples of phenomenological models are of this sort.

[4] A note of clarification: the way in which I describe a mechanistic model suggests that I am equating ‘mechanisms’ with physical parts and operations. Yet, it might be argued that many appropriate scientific uses of the term ‘mechanism’ in the life sciences need not be characterized this way. Within psychology, for example, one might make reference to learning mechanisms without providing any story of how biological or neurological objects are interacting. These are certainly legitimate uses of the term ‘mechanism’, and my intention is not to stipulate correct usage or chastise other common uses of the term. Instead, my intention is highlight the fact that certain types of scientific models (those commonly referred to as ‘mechanistic’ models) are adopted for very particular purposes: to identify the physical implementation of a given system. As Eliasmith notes:In the case of cognitive and brain sciences, useful explanations are those that appeal to subpersonal mechanisms [understood in terms of physical parts and operations]. This is because it is precisely such explanations which provide a basis for both intervention in behaviour and the artificial reproduction of those behaviours. These mechanisms must be specific enough to allow for intervention. That is, the mechanisms must be specified in a way that relates to the measurable and manipulable properties of the system. (Eliasmith 2010, 316)If we are talking about a mechanism only at an abstract level without appealing to parts and operations, our model may tell us behavioural patterns and regularities, but provides no insight into why such regularities exist as they do or how they might change under different conditions. For this reason, some argue that scientific models which employ the more abstract usage of the term ‘mechanism’ tend to behave more as phenomenological models (Machamer, Darden, and Craver 2000; Craver 2006). In contrast, the application of mechanistic models are expressly employed for the purposes of telling us how a given system is being physically instantiated, and thus provides an account of why the system behaves as it does. It is this usage of the term ‘mechanism’, and ‘mechanistic model’, that I am appealing to for the purposes of this paper (for more details, see Machamer, Darden, and Craver 2000; Bunge 2003; Bechtel 2005, 2007, 2008; Glennan 2005; Craver 2006, 2007; Darden 2006; Thagard 2006, 2009, 2012; Bechtel and Abrahamsen 2007; Wimsatt 2007; Eliasmith 2010).

[5] Given that we use dynamical models for different purposes that those we use mechanistic models for, there is often thought to be no tension between the two types of models. For more details, see Kaplan and Bechtel (2011).

[6] It is also important to note that the attribution of intentional information to genes is not a case of mere casual anthropomorphism on the part of biologists, but is part of genuine scientific models. As Maynard Smith notes:Transcription, translation, code, redundancy, synonymous, messenger, editing, proofreading, library—these are all technical terms in biology. I am not aware of any confusions arising because their meanings are not understood. In fact, the similarities between their meanings when referring to human communication and genetics are surprisingly close. (Maynard Smith 2000, 178)

[7] For a contrasting view, see Sarkar (1996, 2000) and Weber (2005).