Models and Perspectives

Explanations constructed in interdisciplinary research may be conceptu-alized as models. Models have many functions in science, one of which is the explanation of phenomena of interest. The nature of explanation by models is an unsettled issue, with current debates focusing on whether

representational accuracy is required ( Frigg 2010 ; Kaplan and Craver 2011 ), whether such explanations must be causal ( Woodward 2003 ; Rice 2015 ), and the role of similarity relations between explanatory models and their targets ( Weisberg 2013 ; Parker 2015 ). The diversity of expla-nations across the sciences renders it unlikely that univocal answers on these points will be forthcoming. But one general claim about models in science does hold quite broadly: models and their roles are influenced by model users’ purposes. If those purposes include explanation, then users’ ideas about explanation inform their practices of model construc-tion as well as resulting products. Methods of constructing explanatory models, and norms that guide these methods and constrain resultant products, vary widely across specializations. Explanatory models are, in this way, localized to the goals, methods, and standards of particular scientific specializations. In interdisciplinary research aimed at expla-nation, models or modeling resources from multiple specializations are needed to construct an explanatory model adequate to the purpose. So the explanatory challenge for interdisciplinary research can be refor-mulated in terms of divergent explanatory models , with the latter term referring to an epistemic product constructed for explanatory purposes by members of a specialization. ¹⁰

The idea of explanatory models localized to scientific specializations is related to the notion of diverse perspectives. A perspective, in the every-day sense of the term, is a stance from which an object appears to one or more observers, roughly synonymous with “a point of view.” From differ-ent perspectives the same object may presdiffer-ent very differdiffer-ent, even incom-patible, appearances. The content of a perspective can be articulated as an indexical statement: “This is how it is from here.” In art, the term has a more precise technical meaning, associated with strategies for real-istically rendering three-dimensional scenes in two-dimensional media ( Giere 2006 ; van Fraassen 2008 ). Visual perspective is characterized by having an origin, orientation, grain, marginal distortion, occlusion, and spatial distortion. The origin, traditionally, is the painter’s or viewer’s eye, while orientation is the direction in which that eye is looking—the angle of the gaze. Grain is the level of detail permitted by a perspective, ranging from rough to fine. “Marginal distortion” refers to the limits of the mode of viewing in a particular perspective. Occlusion is related; this term refers to focusing on some features so as to block or shadow others, which are thereby occluded in that perspective. Systematic spatial distor-tion is another hallmark of visual perspective: relative sizes of objects are represented differently than in reality (e.g., projection in two-dimensional maps of the earth).

These features of visual perspective have analogs in scientific model-ing. Briefly, the origin corresponds to the user, orientation to features of the user’s context, grain to the degree of abstraction, marginal distortion to the limits of the model, occlusion to the selection of some features of

the target at the expense of others, and spatial distortion to idealization.

Perspective in the everyday sense is also relevant to models in science. In philosophical discussions of scientific modeling, a model is taken to offer a (partial, often idealized) representation of a target object ( Giere 1988 ; Morgan and Morrison 1999; Weisberg 2013 ). This partiality and contex-tual selectivity can be characterized as perspectival. For example, Mitchell and Gronenborn assert that, in scientific modeling, “what is represented and what is left out are usually tailored to meet some explanatory or pragmatic goal. This type of selection encodes what is sometimes referred to as a ‘perspective’” ( Mitchell and Gronenborn 2017 , 707). Models of real-world targets do not, and arguably cannot, accurately represent those targets in every respect ( Teller 2001 ). The features represented, and the degree of accuracy required, are chosen in light of model users’ pur-poses. Such selective, partial representation is what makes models useful.

In this sense, models are inherently perspectival. Their perspectival aspect permits multiple models of a single worldly target, incompatible with one another and yet all scientifically legitimate.

This situation is the starting point for debate about perspectival real-ism. But the perspectival view of models also applies to the explanatory challenge for interdisciplinary research. Different specializations produce, among their other results, explanatory models of phenomena of interest.

Each partial, limited model is constructed by researchers within a special-ization in accordance with its norms and methods for explanation. In this way, each specialization can be considered a distinct perspective within which explanatory models are constructed. The different specializations involved in interdisciplinary research contexts bring different perspec-tives to bear on the phenomenon of interest. Social scientists sometimes describe interdisciplinarity in exactly these terms. For example, Stichweh concludes his sociological study of scientific disciplines with the statement that “differentiation along disciplinary lines has the great advantage of viewing reality from radically different angles. The risks that the one-sidedness of each perspective entails are thus avoided” ( 1992 , 12). In an important subclass of interdisciplinary contexts, the goal is to construct a single explanation from these diverse perspectives. This task is under-taken when specialized explanatory models are individually inadequate to account for the phenomenon of interest. In such cases, members of differ-ent specializations must somehow combine their respective explanatory models of the phenomenon of interest. So the challenge is one of integrat-ing models constructed from distinct perspectives to create a sintegrat-ingle (“uni-fied”) explanation of the phenomenon of interest. ¹¹ Responding to this challenge requires showing how this unification is possible, explicating and articulating conditions for its success, and distinguishing the resul-tant interdisciplinary explanations from other epistemic results. The first step is showing how models from different perspectives can possibly be integrated. And here the debate over perspectival realism offers important

insights. That debate is not, of course, about explanation or interdisci-plinarity; my approach here is to repurpose insights that emerge in that debate rather than contribute to it directly. ¹² I do so by canvassing differ-ent relations between models from differdiffer-ent perspectives that emerge in the debate initiated by Giere’s (2006 ) Scientific Perspectivism .

In Scientific Perspectivism , Giere defends “perspectival realism” as a viable alternative to traditional scientific realism about theories, distinct from antirealist relativism. Giere proposes perspectivism based on the metaphor of color vision, arguing that scientific theories and observa-tions (both models, according to Giere) are perspectival in the same way as descriptions of color: they tell us what the world appears to be like from a particular standpoint. All justified realist claims by scientists are therefore qualified and conditional. Giere takes these qualifiers and con-ditions to have the form: “according to this highly confirmed theory (or reliable instrument), the world seems to be roughly such and such” (5–6).

A consequence of this perspectival account is that quite different descrip-tions of the same bit of the world are not incompatible. Rather, each describes a different way of experiencing the world, such that one is not an unqualifiedly better representation of that world than another. Dis-agreement (and normative selection of “the best” model) is possible only within a perspective. ¹³

Giere’s account contrasts with traditional scientific realism, which he characterizes in terms of the following conditional: if models constructed from different perspectives represent the world in incompatible ways, then at most one can be (approximately) true. It follows that in such cases there is direct conflict between models that must be resolved. The task then is to select, out of a set of competing alternatives, the model that is most likely correct. Traditional scientific realism thus allows for only one possible rela-tion between distinct models of the same target phenomenon: direct con-flict among competing alternatives. Giere rejects this traditional account, obviously, but does not otherwise say much about how models from dif-ferent perspectives might relate to one another. Although he allows that agents can switch perspectives and compare results among them (83–84), Giere does not discuss combining or merging different perspectives, apart from the intriguing remark that an experimental test involves the “mesh-ing” of theoretical and observational perspectives (89, 93).

In response to Giere, philosophers take a range of positions as to whether perspectivism is a genuinely realist alternative to both traditional scientific realism and to antirealism. In this debate the relation between models and the world is of primary concern, rather than between models from different perspectives. Yet ideas about this issue emerge obliquely, yielding an interesting array of results. Chakravartty (2010 , 2017 ) argues, contra Giere, that perspectivism amounts to traditional scientific realism. Building on Rueger’s (2005 ) proposal that incompatible models describe a system of interest relationally (“from this perspective it looks

as if . . .”), Chakravartty argues that real-world systems have dispositions non-perspectivally and that these are differentially revealed by different detection methods (i.e., perspectives). Apparently incompatible models are reconciled by seeing them as revealing different dispositional facts about a single target system. An explanation, according to Chakravar-tty, describes how non-perspectival facts about a real system’s structure produce the different behaviors/capacities that appear to us under differ-ent circumstances. This account of explanation posits an indirect relation between models from different perspectives; they are reconciled, or ren-dered consistent with one another, by an underlying structural descrip-tion that coordinates them. Lacking a structural explanadescrip-tion, we can still answer contrastive “what-questions” by picking out the appropriate answer among the multiple perspectives on offer. Thus, another relation among models from different perspectives is via a long conjunction of the claims made by different models about a target T , stating each method and dispositions of the target revealed by that method. Such a conjunc-tion has the form: “method x reveals a about T , and method y reveals b about T , and so on.” ¹⁴ The relation between different models included in such a conjunction is minimal; one is linked to another via the formal connective “and,” with no substantive relation posited to hold between any. I refer to this minimal connection between models from different perspectives as simple additivity .

Morrison (2011 ) takes issue with Giere’s distinction between perspec-tivism and antirealism, arguing that the plethora of incompatible models of the atomic nucleus ascribe inconsistent “fundamental properties” to the target of inquiry, such that “there is no way to build on and extend the models in a cumulative way” (351). Construing alternative models of the atomic nucleus as offering different perspectives on its structure without the prospect of cumulative extension is, according to Morrison, tantamount to antirealism about atomic nuclear structure. Cumulative extension, she suggests, requires a unifying core or coherent treatment of all the different alternatives. The latter is available in other cases, such as models of turbulence, which are reconciled as different ways of elaborat-ing the same set of basic principles; they are alternative idealizations based on a shared common structure. Morrison refers to this relation between models as “complementary,” but as I argue below, that term is better reserved for a different relation. Morrison’s core idea is much like Chakravartty’s account of explanation: the different models do not directly relate to one another but are all subsumed by a more abstract or general model. Accordingly, I will refer to this relation as subsumption . Another possibility suggested by Morrison’s account, although not one she discusses, is direct cumulative interaction among models from distinct perspectives.

Chirimuuta (2016 ) offers a different criticism of Giere, arguing that the metaphor of vision obscures some resources for perspectival realism and

that the sense of touch, the operation of which involves active engage-ment and interaction with the world, is a more appropriate guiding meta-phor. Her key claim is that “scientific representations inform us about the natural world in virtue of their interactive and interested qualities” (746).

That is, scientific representations, although perspectival, can inform us about the world through interaction with it. On her “haptic” account, partiality, interestedness, and interaction are not obstacles to realism but constitutive of a realist role for perspectival representations in science. ¹⁵ Although Chirimuuta does not characterize relations between models from different perspectives per se, her account emphasizes the interactive processual nature of the model-world relation. Relations between models can be conceived in this way as well.

Massimi (2016a , 2016b ) also looks to interaction to formulate an alter-native to Giere’s perspectival realism. She argues that a scientific claim meets the criterion of “success from within,” supporting realist “success to truth” inferences, just in case that claim (i) performs adequately with respect to standards of its own, originating perspective; (ii) expresses a proposition that is in fact true; and (iii) meets standards of performance adequacy appropriate to its original context as assessed from another scientific perspective. The idea of (iii) is adapted from perspectival analy-ses of knowledge and belief, which distinguish context of use from con-text of assessment for knowledge claims. This concon-textualist distinction is adapted from that between a theorist’s perspective and that of epistemic agent S , commonly invoked in analytic epistemology. In classic thought experiments, the theorist judges that S does (not) know that p , in accor-dance with standards that the theorist justifiably takes to be appropriate to S ’s situation. ¹⁶ Perspectival theories of knowledge make these different stances part of the content of the theory itself. Massimi adapts the epis-temological distinction between context of use and context of assessment to the case of multiple scientific perspectives. Scientists can judge the ongoing performance adequacy of a knowledge claim by the standards of its originating perspective, as those standards are interpreted in their own perspective. The criterion of success from within supports a realist dis-tinction between approximately true parts of theories and “idle wheels”

(after Kitcher). Cross-perspectival assessment is unlike the other relations canvassed here, in that it is not a relation between models as such, but a mode of relating different perspectives with respect to claims made in one. Cross-perspectival assessment tracks how every single perspective fares with respect to standards of performance adequacy when assessed from the point of view of other (synchronic or diachronic) perspectives.

Massimi’s notion of a perspective as a “context of assessment” is com-patible with the idea that specializations amount to different perspectives from which explanatory models are constructed. But an explanatory model typically consists of more than one claim (and often non-linguistic elements as well). If claims are considered to be parts of models, then

cross-perspective assessment is an indirect relation, mediated by the standards of assessment constitutive of perspectives. In order for one perspective to assess the standards of performance adequacy of another perspective, the two must be sufficiently similar for the judgment to be warranted; conceptual distance blocks cross-perspectival assessment ( Massimi 2016b ). This is not a problem if different perspectives are con-ceived as stages within a historical lineage of, for example, theories of light, motion, or the atom. ¹⁷ But it does seem to block cross-perspectival assess-ment across disparate specializations, as the standards of performance-adequacy for explanation in particular are typically very different. I return to this issue in the conclusion.

This survey of positions in the recent debate over perspectival realism yields the following list of relations, which can in principle hold between models from different perspectives:

• Direct conflict ( Giere 2006 )

• Simple additivity ( Chakravartty 2010 )

• Subsumption; indirect reconciliation ( Morrison 2011 ; Chakravartty 2010 )

• Interactive process ( Chirimuuta 2016 ) • Cross-perspective assessment (Massimi 2018)

• Direct cumulative interaction (adapted from Morrison 2011 ).

The next task is to refine the above list of possible relations to articulate a conceptual framework for analyzing relations between models from different perspectives.