Problem of model usage in mechanical engineering

The concept 'model' in mechanical engineering is analyzed. Some problems of model usage at the industrial enterprise are named: ontology of elements of difficult model system; difference between the description of the model in natural language and mathematical language; the problem of fitting objects. Design of new way of feedback between a paint robot and its operator based on the use of not only virtual, but also augmented reality, requires an appeal to the ontology of models or to philosophical discourse. The difference between model description in natural and artificial languages implies the application of a semantic approach. The problem of fitting objects during the model testing in mechanical engineering can be solved during testing. The authors argue that the use of an interdisciplinary approach in comprehending complex model systems, taking into account virtual and augmented realities, allows us to clarify the conditions for using virtual 3D models in mechanical engineering. © Published under licence by IOP Publishing Ltd

Factive inferentialism and the puzzle of model-based explanation

Highly idealized models may serve various epistemic functions, notably explanation, in virtue of representing the world. Inferentialism provides a prima facie compelling characterization of what constitutes the representation relation. In this paper, I argue that what I call factive inferentialism does not provide a satisfactory solution to the puzzle of model-based — factive — explanation. In particular, I show that making explanatory counterfactual inferences is not a sufficient guide for accurate representation, factivity, or realism. I conclude by calling for a more explicit specification of model-world mismatches and properties imputation

Taking Stock of Complexity in Evaluation: A Discussion of Three Recent Publications

Arguably, the current interest in the complexity sciences has its roots in the natural sciences, often in interplay with, and enhanced by, developments in mathematics and informatics. An oft-cited reason for this interest has been the increased ability of current computing systems to deal with complex mathematics and algorithms. As complexity gains more traction in the natural sciences, so it does in the social sciences (see e.g. Castellani, 2009). Naturally, complexity has also invaded the evaluation literature since the 1990s, where it is increasingly discussed and applied (cf. Walton, 2014). For instance, the journal Evaluation has recently published a steady number of complexity-related pieces. A search within the journal on the terms ‘complexity theory’, ‘complex system’ or ‘complexity science’ yielded forty-nine articles as part of an increasing trend. Inquiries with Scopus into complexity and evaluation yielded similar results. In this article, we take stock of recent progress and discuss what complexity holds for evaluation by discussing three recent books

Semantic Realism in the Semantic Conception of Theories

Semantic realism can be characterised as the idea that scientic theories are truth-bearers, and that they are true or false in virtue of the world. This notion is often assumed, but rarely discussed in the literature. I examine how it fares in the context of the semantic view of theories and in connection with the literature on scientic representation. Making sense of semantic realism requires specifying the conditions of application of theoretical models, even for models that are not actually used, which leads to several diculties. My conclusion is that semantic realism is far more demanding than one would expect. Finally, I briey examine some pragmatist alternatives

How to use fitness landscape models for the analysis of collective decision-making: a case of theory-transfer and its limitations

There is considerable correspondence between theories and models used in biology and the social sciences. One type of model that is in use in both biology and the social sciences is the fitness landscape model. The properties of the fitness landscape model have been applied rather freely in the social domain. This is partly due to the versatility of the model, but it is also due to the difficulties of transferring a model to another domain. We will demonstrate that in order to transfer the biological fitness landscape model to the social science it needs to be substantially modified. We argue that the syntactic structure of the model can remain unaltered, whilst the semantic dimension requires considerable modification in order to fit the specific phenomena in the social sciences. We will first discuss the origin as well as the basic properties of the model. Subsequently, we will demonstrate the considerations and modifications pertaining to such a transfer by showing how and why we altered the model to analyse collective decision-making processes. We will demonstrate that the properties of the target domain allow for a transfer of the syntactic structure but don’t tolerate the semantic transfer

Informative Models: Idealization and Abstraction

Mauricio Suárez and Agnes Bolinska apply the tools of communication theory to scientific modelling in order to characterize the informational content of a scientific model. They argue that when represented as a communication channel, a model source conveys information about its target, and that such representations are therefore appropriate whenever modelling is employed for informational gain. They then extract two consequences. First, the introduction of idealizations is akin in informational terms to the introduction of noise in a signal; for in an idealization we introduce ‘extraneous’ elements into the model that have no correlate in the target. Second, abstraction in a model is informationally equivalent to equivocation in the signal; for in an abstraction we ‘neglect’ in the model certain features that obtain in the target. They then conclude becomes possible in principle to quantify idealization and abstraction in informative models, although precise absolute quantification will be difficult to achieve in practice

Informative Models: Idealization and Abstraction

Mauricio Suárez and Agnes Bolinska apply the tools of communication theory to scientific modelling in order to characterize the informational content of a scientific model. They argue that when represented as a communication channel, a model source conveys information about its target, and that such representations are therefore appropriate whenever modelling is employed for informational gain. They then extract two consequences. First, the introduction of idealizations is akin in informational terms to the introduction of noise in a signal; for in an idealization we introduce ‘extraneous’ elements into the model that have no correlate in the target. Second, abstraction in a model is informationally equivalent to equivocation in the signal; for in an abstraction we ‘neglect’ in the model certain features that obtain in the target. They then conclude that it becomes possible in principle to quantify idealization and abstraction in informative models, although precise absolute quantification will be difficult to achieve in practice

The new fiction view of models

How do models represent reality? There are two conditions that scientific models must satisfy to be representations of real systems, the aboutness condition and the epistemic condition. In this article, I critically assess the two main fictionalist theories of models as representations, the indirect fiction view and the direct fiction view, with respect to these conditions. And I develop a novel proposal, what I call ‘the new fiction view of models’. On this view, models are akin to fictional stories; they represent real-world phenomena if they stand in a denotation relation with reality; and they enable knowledge of reality via the generation of theoretical hypotheses, model–world comparisons and direct attributions

Inconsistent idealizations and inferentialism about scientific representation

Inferentialists about scientific representation hold that an apparatus’s representing a target system consists in the apparatus allowing “surrogative inferences” about the target. I argue that a serious problem for inferentialism arises from the fact that many scientific theories and models contain internal inconsistencies. Inferentialism, left unamended, implies that inconsistent scientific models have unlimited representational power, since an inconsistency permits any conclusion to be inferred. I consider a number of ways that inferentialists can respond to this challenge before suggesting my own solution. I develop an analogy to exploitable glitches in a game. Even though inconsistent representational apparatuses may in some sense allow for contradictions to be generated within them, doing so violates the intended function of the apparatus’s parts and hence violates representational “gameplay.