Abstract
Analysis of the adequacy of engineering design methods, as well as analysis of the utility of concepts of function often invoked in these methods, is a neglected topic in both philosophy of technology and in engineering proper. In this paper, I present an approach—dubbed an explanationist perspective—for assessing the adequacy of function-based design methods. Engineering design is often intertwined with explanation, for instance, in reverse engineering and subsequent redesign, knowledge base-assisted designing, and diagnostic reasoning. I argue that the presented approach is useful for validating function-based design methods with respect to their explanatory elements and that it supports assessment of the explanatory and design utility of “function”, and the different conceptualizations thereof, as used in such engineering design methods. I deploy two key desiderata from the explanation literature to assess the viability of function-based design methods: explanatorily relevant difference-making factors and counterfactual understanding defined in terms of what-if-things-had-been-different questions. I explicate the approach and its merits in terms of two case studies drawn from the engineering functional modeling literature: reverse engineering and redesign and malfunction analysis. I close the paper by discussing ramifications of the presented approach for the philosophy of design and the philosophy of explanation.
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Notes
There are no in-depth analyses developed in engineering to assess the viability of function-based design methods and to make comparative judgments between such methods. The relevance of key concepts figuring in these methods is simply assumed, lacking a systematic theoretical underpinning. As Maier and Fadel (2009) observe in the context of function-based designing: “there is no theory to guide us as to the proper use of function in design […] function has in effect been accepted as the de facto fundamental concept in design without theoretical justification” (p. 17). The successes of design methods are measured in terms of notions like efficiency, ease-of-use, and repeatability of design outcomes in design experiments and in terms of industrial applications (which are few). Although they are relevant criteria, validation of design methods also requires (more) rigorous systematic analyses of key notions and comparative assessments across methods. I take up this project here, focusing on the key notions of “function” and “explanation”.
Some of the contributors in this special issue suggest that engineers use specific concepts of function for specific tasks (e.g., Goel 2013; Vermaas 2013; Vermaas and Eckert 2013), but detailed investigation of the task-dependent utility of notions of function is marked as a future research topic:
this perspective leads in turn to the questions what tasks there are in engineering for which functional descriptions may be useful, and what constraints functional descriptions should meet in order to be actually useful […] reflection on functional description then leads to novel research questions on the relation between specific functional descriptions and specific tasks for which they can be used (Vermaas and Eckert 2013, p. 189).
The term “archetypical” here refers to “most common”; the three concepts of function are not meant to be exhaustive. For instance, some engineers use function to refer to intentional behaviors of agents (cf. van Eck 2010), and others have recently explored the idea that function might be a Wittgensteinian family resemblance concept (Carrara et al. 2011). In reverse engineering analyses, function refers to actual or expected behavior without the normative connotation “desired.”
Behavior function descriptions thus refer to the complete behaviors involved, including features like thermal and acoustic energy flows, whereas effect functions refer to subsets of these behaviors, i.e., desired effects.
In design methodologies that advance effect and/or purpose functions, the concept of behavior is typically introduced as well alongside function. By invoking behavior descriptions of technical artifacts alongside functional descriptions, physical conservation laws are taken into account.
Single functional decomposition models in which different concepts of function are described are rare in engineering.
Redesign differs from innovative designing as understood by Stone and Wood (2000): in innovative designing form/component solutions for functions are not considered in the initial conceptual phase of designing. To-be-designed and developed artifacts are specified solely in terms of their desired functions. Only after functional specification are potential solutions considered.
This is not to say that the explanandum in reverse engineering explanation, or mechanistic explanation in general, cannot be described in contrastive fashion. For instance, the request for explanation may concern why an artifact x exhibits behavior b with value y rather than behavior b with value z. However, this is a different contrast than the one drawn in the explanandum of why artifact x does not exhibit behavior b rather than displaying behavior b.
This is the most straightforward scenario. If there are backup systems that are intended to prevent malfunction, and modern technology is replete with them, failing backup systems should be referred to as well, of course, in explaining system-level malfunctions.
I use the term functional explanation to refer to models/explanations in which only reference to some mechanistic aspects is made and others are omitted. I take my reading of functional explanation/model to be equivalent to what others have called a “mechanism sketch” (Machamer et al. 2000; Craver 2007). I acknowledge that, strictly speaking, there is a distinction between a model and an explanation: A model needs to be applied in a given explanatory context in order to produce an explanation (Glennan 2005; Weisberg 2007; Matthewson and Calcott 2011). My arguments, however, do not hinge on this distinction. Finally, I view all models, both complete mechanistic ones, functional ones and everything in between, as abstractions. In some papers, mechanisms taken as things in the world are distinguished from and compared with mechanism sketches taken as abstractions. As Overton (2011) rightly argues, it is awkward to compare ontic conceptualizations of mechanisms with epistemic conceptualizations of models/abstractions.
In recent analyses of mechanistic explanation, it is frequently claimed that mechanistic explanations are explanatorily superior to other explanation types such as: (i) covering law explanations (Machamer et al. 2000; Bechtel and Abrahamson 2005), (ii) dynamical explanations (Kaplan and Craver 2011), and (iii) functional explanations (Craver 2007; Piccinini and Craver 2011). Laws are hard to find in the life sciences, where scientists invoke “mechanism” talk to explain phenomena, and statements of laws/generalities leave unexplained what produces these generalities (e.g., Bechtel and Abrahamson 2005; Craver 2007); dynamical explanations, at best, provide elaborate descriptions of phenomena to be explained, whereas mechanistic explanations track the mechanisms that produce them (e.g., Kaplan and Craver 2011); and functional explanations leave out relevant mechanistic details, which are included in mechanistic explanations. These perspectives have not gone unchallenged however. It has been argued, for instance, that pragmatic laws, in fact, figure in mechanistic explanations (e.g., Leuridan 2010), and that in some explanation-seeking contexts, functional explanations have more explanatory power than mechanistic ones since the former describe the explanatory relevant factors, whereas the latter include explanatorily irrelevant details (van Eck and Weber 2014).
Constitutive relevance is explicated in terms of an adapted version of Woodward’s theory since Woodward advances his manipulability theory as an account of causal explanation, while constitutive relevance is explicitly defined as a non-causal relationship (cf. Craver 2007, pp. 153–154; Craver and Bechtel 2007, pp. 552–554). The mutual manipulability account has not gone uncontested however (cf. Harbecke 2010; Leuridan 2012; Soom 2012). Since these criticisms have no bearing on the analysis presented in this paper, I leave them here aside.
Completeness and mutual manipulability are independent in the sense that the identification of mutual manipulability relationships, of course, does not guarantee that all these relationships are identified.
At least, the explanatory power of functional models is limited; the richer/more complete a model is, the better. Functional models are incomplete mechanistic ones (Piccinini and Craver 2011).
In my terminology, an abstract mechanistic model is the same as a functional model. What aspects are omitted in abstract mechanistic/functional models may differ from case to case.
There is an asymmetry between the two criteria. As far as I know, no one has stipulated abstraction as a general account of explanatory power. Rather, the point has been made that abstraction holds in certain contexts. This is different in the case of completeness. Craver has repeatedly argued (2007; Kaplan and Craver 2011) that it is always the case that the more complete a model is, the more explanatory power it has.
Abstraction is understood by Levy and Bechtel (2013) as the omission of detail without misrepresenting or distorting the workings of mechanism.
The notion of monitoring the effects of single component removals on overall behaviors of technical systems as is done in reverse engineering (cf. Section 2), corresponds to the bottom-up condition of the mutual manipulability account of changing the overall behavior by intervening to change an entity’s activity (cf. Section 3.1).
I am not claiming that the models they consider fail to have any explanatory power. I think they do, and in some contexts of capacity explanation, these are the only models we have, yet adding, whenever possible, richness to such models by specifying specific entities and their specific causal roles is from an explanatory power point of view well-advised. I rather think that abstract models have the most explanatory power in the context of explaining the same capacities of different target systems, for instance, gene expression in bacteria and yeast (cf. Levy and Bechtel 2013) or, say, power transmission in different types of automobiles. There, one does need to abstract from the specific entities implementing causal roles since these structural features will differ across target systems. Abstract models, thus, meet a “generality” desideratum.
Local richness is met in FIL by constructing, after malfunction analysis, behavioral simulation models of malfunctioning components (Bell et al. 2007).
Note that behavior and effect descriptions of function describe, in different ways, the contributions of components to mechanisms of which they are a part. The distinction between behavior and effect function, thus, is not to be conflated with the distinction between a mechanism description and a description of a mechanisms’ overall activity. Neither is the behavior-effect function distinction to be conflated with the distinction between “isolated” and “contextual” descriptions of an entity’s activity (Craver 2001): isolated descriptions describe activities without taking into account the mechanisms in which they are situated while contextual descriptions describe activities in terms of the mechanistic contexts in which they are situated and to which they contribute. Both behavior and effect functions are of the contextual variety, describing contributions of components to the mechanisms of which they are a part.
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I thank three anonymous referees for valuable comments.
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van Eck, D. Validating Function-Based Design Methods: an Explanationist Perspective. Philos. Technol. 28, 511–531 (2015). https://doi.org/10.1007/s13347-014-0168-5
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DOI: https://doi.org/10.1007/s13347-014-0168-5