Towards a philosophy of modelling for humanities computing

In developing a persuasive philosophical stance on humanities computing, the first task is to relate its aspirations to the current vision of computer science. In (Paper 1), Beynon and Russ propose that an alternative science of computing is needed to bring computing and the humanities into a more fulfilling relationship. McCarty (2004) identifies a better understanding of "what modelling is" as key to making sense of humanities computing. This paper — to be read in conjunction with (McCarty 2004) — revisits McCarty's arguments in the context of the critique of traditional thinking about computing motivated by the study of Empirical Modelling (EM) (Paper 1).

Informally, McCarty's Onomasticon (McCarty 2005) may serve as an archetypal example of EM. Though it has been built using commercial spreadsheet and database software, rather than the special-purpose tools that have been developed for EM (EM-website), its development exploits the essential principles and concepts of EM. It is characteristic of this development that (to paraphrase McCarty 2005) the Onomasticon, however finely perfected, is better understood with reference to temporary states in the process of coming to know rather than a fixed structure of knowledge. In thinking of the Onomasticon in EM terms, the term model on the computer is preferred to McCarty's computational model. The principal reason for this is that the way in which EM views the semantics of the Onomasticon is quite different from what is understood by the computational semantics of the underlying computer program (cf. Cantwell Smith's discussion of semantic relations in Smith 1987). Specifically, the manner in which the EM model (the Onomasticon) represents the referent (Ovid's Metamorphoses) is that there is a repertoire of atomic interactions that the modeller can make both with the model and with its referent and that these are perceived by the modeller (McCarty) to connect the experience of the model with that of its referent.

As McCarty's careful analysis of terminology (McCarty 2004) indicates, the dynamic and provisional quality of the model argues against describing the model as a representationof its referent. For reasons discussed at length in (EM-website: 078), the terminology that William James introduced in considering relations between experience is preferred: "experience of the model knows experience of the referent". It is to be understood that the modeller will never be obliged to explain why one experience knows another experience, nor to make any claims for the objectivity of this perceived relationship. This is the essence of James's Radical Empiricism(James), that relations between experiences are themselves given in experience.

Though it is accepted usage to refer to the spreadsheet as a model of a financial situation, this is not the sense in which model is most commonly used in computer science. Expressions such as model-checking, model-based reasoning, mathematical model allude to far more abstract semantic relations that are by no means directly apprehendable in experience. When we conceive a model as a set of logical equations or constraints, the manner in which the model is experienced is outside the semantic scope. Invoking the alternative semantic framework of EM entails being more discriminating about kinds of computing activity, and motivates a reappraisal of what McCarty (2004) identifies as the "decisive criteria" for modelling by computer: complete explicitness and absolute consistency and manipulability.

Where consistency is concerned, it must be recognised that the experience a computer generates is not explicitly specified in every respect — at any rate not in the same sense that an abstract computation is explicitly specified. EM focuses on the experiential aspects of computer-based models, for which — as is appropriate for humanities computing in general — no presumption of complete explicitness and absolute consistency in informal semantics is required. Indeed, in (EM-website: 072), Beynon makes the case that the semantic framework of EM is aptly suited to dealing with situation, ignorance and nonsense ("the principle of SIN"). For this purpose, it is not the linguistic and logical frameworks supplied by Chomsky and Tarsky or the syntactic treatment of metaphor in logicist AI that are appropriate (EM-website: 050), but semantics closer in spirit to the thinking of Lakoffand Turner.

Where manipulability is concerned, it may seem that we can manipulate representations effectively using a computer because we can modify programs. The notorious difficulty of adapting conventional programs to meet new requirements is evidence that this contention cannot be taken at face value. And where "one experience knows another" is concerned, there are serious conceptual and practical objections to deeming the common debugging cycle (as in "stop execution of program P, fix line 235, recompile, run program P 'to the same point as it was before' — whoops ... I've introduced another bug — etc etc ...") to be an atomic transition in experience. In practice, manipulability is bound up with contextual and pragmatic issues that are entirely alien to the formal semantics of computation. This is consistent with McCarty's observation that "manipulation ... requires something that can be handled [in] a time-frame sufficiently brief that the emphasis falls on the process rather than its product" (McCarty 2004). For this purpose, the notion that "the experience of adjusting the computer model should know the experience of adjusting the interpretation of the referent" is precisely what is required.

The decisive emphasis of EM is on what is known in immediate experience, and what in William James's terms is associated with "the most intimate conjunctive relation .... that experienced between terms that form states of mind" (James 44-45). Within this apparently limited frame of "what experience knows another in-the-now" all kinds of conception of model are possible through assuming different kinds of context, observation and agency. This is the very subject of James's Radical Empiricism. James develops the story of knowledge to deal with expectations of what has not been experienced — knowledge that transcends direct experience. The rich quality of engagement with past and future experience that this demands is well-represented in EM, both in the characteristic inflection of the "what if?" interaction, and the capacity to replay the entire process of construction as one might in exposing the sequence by which the cells of a spreadsheet came to be defined. This facility, frequently exploited in presenting EMmodels, captures the aspiration for modelling identified by Dening — "[that we may] return to the past the past's own present, a present with all the possibilities still in it, with all the consequences of actions still unknown".

In appreciating the shift of perspective in EMfully, it is vital to distinguish the semantics given in experience in a state-of-mind from semantics based on behaviours (as in program semantics (Smith 1987)) — even when these are guided by experience (as in Turner's treatment of narrative (Turner), and CantwellSmith's discussion of "the process semantics" (Smith 1987)). This is evidenced by the diversity of contexts behind the wide range of applications for EM (EM-website), and the associated diversity of models. As is illustrated in (Paper 2), EM can be used to generate just such rich varieties of model — analogy, experiment, simulation, map, diagram, representation — as are catalogued in McCarty (2004). This diversity is enabled precisely because an EMmodel is identified by a state and a body of latent anticipated interactions that can be more or less familiar and significant to the modeller, or any other human interpreter, and in this way serves as an interactive environment whose meaning is constrained only by the imagination. This delivers more than is envisaged by Minskyor Naur in respect of constructed models: beyond the confirmation of a theory, a place for "blind variation"in the sense of Vincenti — interaction "without complete or adequate guidance" potentially leading to discovery.

Several intriguing philosophical connections identified by McCarty (2005) are ripe for further scholarship and exploration. The suggestive links between EMthinking and the phenomenology of Polanyiand Heideggerecho the phenomenological interpretations of software development offered by Winograd and Flores, but also argue against invoking such interpretations in relation to traditional software practice. Of crucial importance in ensuring the universality of the concept of modelling, and embracing activities that involve creation and discovery, is the ontological status of the model, the referent and the relation between them. The idea of an EM model as a construal invokes Vaihinger's "as if": neither true nor false, but as construed for the purpose in hand. Such a stance even underwrites propositions such as "our constructions continue to work, no matter how violent the changes in scientific opinion may be" (cf. McCarty 2005) that might be seen as authorising absolute claims for EM models as physical artefacts. This outlook accords with James's contention that "subjectivity and objectivity are affairs not of what an experience is aboriginally made of, but of its classification" (James141), and his perspective on the difficulties of understanding the direct products of experience: "But how the experiences ever get themselves made, or why their characters and relations are just such as appear, we can not begin to understand " (James132-133). The pragmatic importance of this ontological stance for humanities computing is that it helps to dispel the mystique that surrounds high art and hard science: a mystique that is the pretext for divisive absolute partitions in experience.

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