Recent philosophy of science has witnessed a shift in focus, in that significantly more consideration is given to how scientists employ models. Attending to the role of models in scientific practice leads to new questions about the representational roles of models, the purpose of idealizations, why multiple models are used for the same phenomenon, and many more besides. In this paper, I suggest that these themes resonate with central topics in feminist epistemology, in particular prominent versions of feminist empiricism, (...) and that model-based science and feminist epistemology each has crucial resources to offer the other's project. (shrink)

Autonomist accounts of cognitive science suggest that cognitive model building and theory construction (can or should) proceed independently of findings in neuroscience. Common functionalist justifications of autonomy rely on there being relatively few constraints between neural structure and cognitive function (e.g., Weiskopf, 2011). In contrast, an integrative mechanistic perspective stresses the mutual constraining of structure and function (e.g., Piccinini & Craver, 2011; Povich, 2015). In this paper, I show how model-based cognitive neuroscience (MBCN) epitomizes the integrative (...) mechanistic perspective and concentrates the most revolutionary elements of the cognitive neuroscience revolution (Boone & Piccinini, 2016). I also show how the prominent subset account of functional realization supports the integrative mechanistic perspective I take on MBCN and use it to clarify the intralevel and interlevel components of integration. (shrink)

Scientific models share one central characteristic with fiction: their relation to the physical world is ambiguous. It is often unclear whether an element in a model represents something in the world or presents an artifact of model building. Fiction, too, can resemble our world to varying degrees. However, we assign a different epistemic function to scientific representations. As artifacts of human activity, how are scientific representations allowing us to make inferences about real phenomena? In reply to this concern, (...) philosophers of science have started analyzing scientific representations in terms of fictionalization strategies. Many arguments center on a dyadic relation between the model and its target system, focusing on structural resemblances and “as if” scenarios. This chapter provides a different approach. It looks more closely at model building to analyze the interpretative strategies dealing with the representational limits of models. How do we interpret ambiguous elements in models? Moreover, how do we determine the validity of model-based inferences to information that is not an explicit part of a representational structure? I argue that the problem of ambiguous inference emerges from two features of representations, namely their hybridity and incompleteness. To distinguish between fictional and non-fictional elements in scientific models my suggestion is to look at the integrative strategies that link a particular model to other methods in an ongoing research context. To exemplify this idea, I examine protein modeling through X-ray crystallography as a pivotal method in biochemistry. (shrink)

Among the many functions of models, explanation is central to the functioning and aims of science. However, the discussions surrounding modeling and explanation in philosophy have largely remained separate from each other. This chapter seeks to bridge the gap by focusing on the puzzle of model-based explanation, asking how different philosophical accounts answer the following question: if idealizations and fictions introduce falsehoods into models, how can idealized and fictional models provide true explanations? The chapter provides a selective (...) and critical overview of the available strategies for solving this puzzle, mainly focusing on idealized models and how they explain. (shrink)

Mechanistic explanations satisfy widely held norms of explanation: the ability to manipulate and answer counterfactual questions about the explanandum phenomenon. A currently debated issue is whether any nonmechanistic explanations can satisfy these explanatory norms. Weiskopf argues that the models of object recognition and categorization, JIM, SUSTAIN, and ALCOVE, are not mechanistic yet satisfy these norms of explanation. In this article I argue that these models are mechanism sketches. My argument applies recent research using model-based functional magnetic resonance imaging, (...) a novel neuroimaging method whose significance for current debates on psychological models and mechanistic explanation has yet to be explored. (shrink)

Now that complex Agent-Based Models and computer simulations spread over economics and social sciences - as in most sciences of complex systems -, epistemological puzzles (re)emerge. We introduce new epistemological tools so as to show to what precise extent each author is right when he focuses on some empirical, instrumental or conceptual significance of his model or simulation. By distinguishing between models and simulations, between types of models, between types of computer simulations and between types of empiricity, section (...) 2 gives conceptual tools to explain the rationale of the diverse epistemological positions presented in section 1. Finally, we claim that a careful attention to the real multiplicity of denotational powers of symbols at stake and then to the implicit routes of references operated by models and computer simulations is necessary to determine, in each case, the proper epistemic status and credibility of a given model and/or simulation. (shrink)

This paper introduces and defends an account of model-based science that I dub model pluralism. I argue that despite a growing awareness in the philosophy of science literature of the multiplicity, diversity, and richness of models and modeling practices, more radical conclusions follow from this recognition than have previously been inferred. Going against the tendency within the literature to generalize from single models, I explicate and defend the following two core theses: any successful analysis of (...) models must target sets of models, their multiplicity of functions within science, and their scientific context and history and for almost any aspect x of phenomenon y, scientists require multiple models to achieve scientific goal z. (shrink)

In this article I give a critical evaluation of the use and limitations of null-model-based hypothesis testing as a research strategy in the biological sciences. According to this strategy, the null model based on a randomization procedure provides an appropriate null hypothesis stating that the existence of a pattern is the result of random processes or can be expected by chance alone, and proponents of other hypotheses should first try to reject this null hypothesis in order (...) to demonstrate their own hypotheses. Using as an example the controversy over the use of null hypotheses and null models in species co-occurrence studies, I argue that null-model-based hypothesis testing fails to work as a proper analog to traditional statistical null-hypothesis testing as used in well-controlled experimental research, and that the random process hypothesis should not be privileged as a null hypothesis. Instead, the possible use of the null model resides in its role of providing a way to challenge scientists’ commonsense judgments about how a seemingly unusual pattern could have come to be. Despite this possible use, null-model-based hypothesis testing still carries certain limitations, and it should not be regarded as an obligation for biologists who are interested in explaining patterns in nature to first conduct such a test before pursuing their own hypotheses. (shrink)

It has been argued that our currently most satisfactory social epistemology of science can’t account for science that is based on artificial intelligence (AI) because this social epistemology requires trust between scientists that can take full responsibility for the research tools they use, and scientists can’t take full responsibility for the AI tools they use since these systems are epistemically opaque. I think this argument overlooks that much AI-based science can be done without opaque models, (...) and that agents can take full responsibility for the systems they use even if these systems are opaque. Requiring that an agent fully understand how a system works is an untenably strong condition for that agent to take full responsibility for the system and risks absolving AI developers from responsibility for their products. AI-based science need not create trust-related social epistemological problems if we keep in mind that what makes both individual scientists and their use of AI systems trustworthy isn’t full transparency of the internal processing but their adherence to social and institutional norms that ensure that scientific claims can be trusted. (shrink)

Several philosophers of science claim that scientific toy models afford knowledge of possibility, but answers to the question of why toy models can be expected to competently play this role are scarce. The main line of reply is that toy models support possibility claims insofar as they are credible. I raise a challenge for this credibility-thesis, drawing on a familiar problem for imagination-based modal epistemologies, and argue that it remains unanswered in the current literature. The credibility-thesis has a (...) long way to go if it is to account for the epistemic merits of toy models. (shrink)

This essay develops a new conceptual framework of science and engineering ethics education based on virtue ethics and positive psychology. Virtue ethicists and positive psychologists have argued that current rule-based moral philosophy, psychology, and education cannot effectively promote students’ moral motivation for actual moral behavior and may even lead to negative outcomes, such as moral schizophrenia. They have suggested that their own theoretical framework of virtue ethics and positive psychology can contribute to the effective promotion of motivation (...) for self-improvement by connecting the notion of morality and eudaimonic happiness. Thus this essay attempts to apply virtue ethics and positive psychology to science and engineering ethics education and to develop a new conceptual framework for more effective education. In addition to the conceptual-level work, this essay suggests two possible educational methods: moral modeling and involvement in actual moral activity in science and engineering ethics classes, based on the conceptual framework. (shrink)

In the past few years, social epistemologists have developed several formal models of the social organisation of science. While their robustness and representational adequacy has been analysed at length, the function of these models has begun to be discussed in more general terms only recently. In this article, I will interpret many of the current formal models of the scientific community as representing the latest development of what I will call the ‘Kuhnian project’. These models share with Kuhn a (...) number of questions about the relation between individuals and communities. At the same time, they also inherit some of Kuhn’s problematic characterisations of the scientific community. In particular, current models of the social organisation of science represent the scientific community as essentially value-free. This may put into question both their representational adequacy and their normative ambitions. In the end, it will be shown that the discussion on the formal models of the scientific community may contribute in fruitful ways to the ongoing debates on value judgements in science. (shrink)

The geosciences include a wide spectrum of disciplines ranging from paleontology to climate science, and involve studies of a vast range of spatial and temporal scales, from the deep-time history of microbial life to the future of a system no less immense and complex than the entire Earth. Modeling is thus a central and indispensable tool across the geosciences. Here, we review both the history and current state of model-based inquiry in the geosciences. Research in these fields (...) makes use of a wide variety of models, such as conceptual, physical, and numerical models, and more specifically cellular automata, artificial neural networks, agent-based models, coupled models, and hierarchical models. We note the increasing demands to incorporate biological and social systems into geoscience modeling, challenging the traditional boundaries of these fields. Understanding and articulating the many different sources of scientific uncertainty – and finding tools and methods to address them – has been at the forefront of most research in geoscience modeling. We discuss not only structuralmodel uncertainties, parameter uncertainties, and solution uncertainties, but also the diverse sources of uncertainty arising from the complex nature of geoscience systems themselves. Without an examination of the geosciences, our philosophies of science and our understanding of the nature of model-based science are incomplete. (shrink)

On the long and well-worn road of many, but justifiable attempts of human to discover his origin, his trajectory as a species, and a suitable understanding consciousness, his system allowing the connection to the environment and to his own organism, the concepts and models of philosophy enunciated or experienced by millennia, meet today with modern science concepts of physics and of science of information. Based on recent discoveries of quantum physics and astrophysics, revealing a new understanding of (...) our environment and starting from some philosophical concepts on information of matter and of living structures, this work discusses the dynamics of information within the frame of the Informational Model of Consciousness as an informational system of the human body, connected both to the environment and to the body itself, to control the adaptation for survival. It is shown that consciousness is actually an informational projection in the mind of seven informational subsystems, three of which forming the operative system of consciousness for the short-term adaptation, and other three forming the programmed operating system, dedicated to the maintenance of body and to the long-term survival of species, showing various inputs and outputs of information. The seventh subsystem is the information pole, connecting the organism with the external information, especially related to the extra-sensorial properties of the mind, the human body appearing as a bipolar info-matter structure, managed by the brain. The received information is progressively integrated into the informational system of the organism, which absorbs and emanates information as a reactive system for adaptation, able to operate both with matter-related (codified) and non-matter related (virtual) information. As both connections with external and internal environment (body itself) can be described in terms of information, this model opens the gate to investigate consciousness by means of the tools of the information science, offering also answers to the philosophic “mind-body” problem and to the “hard” problem and showing correspondences with some ancient philosophies. (shrink)

Empiricist modal epistemologies can be attractive, but are often limited in the range of modal knowledge they manage to secure. In this paper, I argue that one such account – similarity-based modal empiricism – can be extended to also cover justification of many scientifically interesting possibility claims. Drawing on recent work on modelling in the philosophy of science, I suggest that scientific modelling is usefully seen as the creation and investigation of relevantly similar epistemic counterparts of real target (...) systems. On the basis of experiential knowledge of what is actually the case with the models, one can draw justified conclusions about what is de re possible for the target systems. (shrink)

The evidence-based medicine (EBM) movement is touted as a new paradigm in medical education and practice, a description that carries with it an enthusiasm for science that has not been seen since logical positivism flourished (circa 1920–1950). At the same time, the term ‘‘evidence-based medicine’’ has a ring of obviousness to it, as few physicians, one suspects, would claim that they do not attempt to base their clinical decision-making on available evidence. However, the apparent obviousness of EBM (...) can and should be challenged on the grounds of how ‘evidence’ has been problematised in the philosophy of science. EBM enthusiasm, it follows, ought to be tempered. The post-positivist, feminist, and phenomenological philosophies of science that are examined in this paper contest the seemingly unproblematic nature of evidence that underlies EBM by emphasizing different features of the social nature of science. The appeal to the authority of evidence that characterizes evidence-based practices does not increase objectivity but rather obscures the subjective elements that inescapably enter all forms of human inquiry. The seeming common sense of EBM only occurs because of its assumed removal from the social context of medical practice. In the current age where the institutional power of medicine is suspect, a model that represents biomedicine as politically disinterested or merely scientific should give pause. (shrink)

Cloud computing (ClC) has become a more popular computer paradigm in the preceding few years. Quality of Service (QoS) is becoming a crucial issue in service alteration because of the rapid growth in the number of cloud services. When evaluating cloud service functioning using several performance measures, the issue becomes more complex and non-trivial. It is therefore quite difficult and crucial for consumers to choose the best cloud service. The user's choices are provided in a quantifiable manner in the current (...) methods for choosing cloud services. Hence, this study attempts to achieve this objective through construction. decision-making framework so-called cloud services evaluator (CSsEv). The main indicator and its sub-indicators are formed as nodes at levels(n) in tree soft sets (TSSs). Thereafter Single Value Neutrosophic Sets (SVNSs) as branch of neutrosophic sets which conjunction with the Multi-Criteria Decision Making (MCDM) technique to facilitate analysis and evaluation process for the available Cloud services providers. Hence, entropy is employed to obtain indicators and sub_indicators weights and Complex Proportional Assessment utilizes these weights to facilitate the decision process of selecting optimal ClSPs. (shrink)

This manuscript is about how to start from relying on science to be more certain about Christianity. This is because science is so pervasive in our everyday life that we expect that it works almost every time. However, when it comes to Christianity, we need to have faith because most of the time God does not appear to respond to us. Therefore, we feel uncertain about beliefs in Christianity. Instead of being uncertain, this manuscript tries to find a (...) way so that we are more certain about our beliefs in Christianity. We borrow our method to be certain from science. Science faces a similar situation as in believing in Christianity because its knowledge is fallible. Scientists themselves cannot be certain that the scientific knowledge is true all the time. So, they rely on a method called hypothesis testing to make their decisions to believe or not in scientific knowledge. We borrow this method to believe in Christianity. Therefore, we need evidence to make our decision of belief. We mention some evidence from past miracles to help us to make decisions. These include Eucharistic miracles, Marian apparitions, incorruptible corpses, etc. After we decide to believe in Christianity, we work out a theory of Christianity based on the salvation plan of God. We try to substantiate the principles in the theory by referencing testimonies from the Bible and from evidence of miracles that happened. We show that these principles are related to models and experiments that we formulate based on our beliefs in Christianity. Having obtained an understanding of Christianity like a (historical) science, we formulate our practice as a Christian. (shrink)

Despite an enormous philosophical literature on models in science, surprisingly little has been written about data models and how they are constructed. In this paper, I examine the case of how paleodiversity data models are constructed from the fossil data. In particular, I show how paleontologists are using various model-based techniques to correct the data. Drawing on this research, I argue for the following related theses: first, the ‘purity’ of a data model is not a measure (...) of its epistemic reliability. Instead it is the fidelity of the data that matters. Second, the fidelity of a data model in capturing the signal of interest is a matter of degree. Third, the fidelity of a data model can be improved ‘vicariously’, such as through the use of post hoc model-based correction techniques. And, fourth, data models, like theoretical models, should be assessed as adequate for particular purposes. (shrink)

Undoubtedly the Penrose-Hameroff Orch OR model may be considered as a good theory for describing information processing mechanisms and holistic phenomena in the human brain, but it doesn’t give us satisfactory explanation of human perception. In this work a new approach explaining our perception is introduced, which is in good agreement with Orch OR model and other mainstream science theories such as string theory, loop quantum gravity and holographic principle. It is shown that human perception cannot be (...) explained in the terms of elementary particles and we should introduce new indivisible holistic objects with geometry based on smooth infinitesimal analysis - elastic membranes. The example of such a membrane is our Universe which is an indivisible whole. It is shown that our perception may be considered as the result of elastic oscillations of two dimensional (2D) elastic membranes with closed topology embedded in our bodies. Only one elastic membrane responsible for its perceptions will correspond to the selected organism, but there may be other membranes, even at the cell level. In other words, reality may be considered as the process of time evolution of holistic energetically very weak macro objects - elastic membranes with the geometry based on smooth infinitesimal analysis. An embedded membrane in this multidimensional world will look different for the external and internal observers: from the outside it will look like a material object with smooth infinitesimal geometry, while from the inside our Universe-like space-time fabric. When interacting with elementary particles and other membranes, a membrane will transform their energy into its elastic energy (a new form of energy) - the energy of stretching of the infinitesimal segments. The theory postulates that these elastic deformations will not be observable from the point of view of the internal observer. Heisenberg’s uncertainty principle will work in this physics only from the point of view of the internal observer. For the external observer each embedded elastic membrane may be stretched and even a very small region will become observable. For example, living organisms play the role of internal observers of the Universe, and at the same time they serve as external observers for 2D membranes embedded into our Universe. We can observe our 2D self-membranes through our perceptions, which are encoded in elastic oscillations of the elastic membrane. According to the theory, elastic membranes occupy energetically favorable positions around microtubules involved into Orch OR. The theory not only gives us a really multidimensional holistic picture of reality, but it also provides us with a new method for understanding such phenomena as perception, self-awareness and will. (shrink)

Detailed examinations of scientific practice have revealed that the use of idealized models in the sciences is pervasive. These models play a central role in not only the investigation and prediction of phenomena, but in their received scientific explanations as well. This has led philosophers of science to begin revising the traditional philosophical accounts of scientific explanation in order to make sense of this practice. These new model-based accounts of scientific explanation, however, raise a number of key (...) questions: Can the fictions and falsehoods inherent in the modeling practice do real explanatory work? Do some highly abstract and mathematical models exhibit a noncausal form of scientific explanation? How can one distinguish an exploratory "how-possibly" model explanation from a genuine "how-actually" model explanation? Do modelers face tradeoffs such that a model that is optimized for yielding explanatory insight, for example, might fail to be the most predictively accurate, and vice versa? This chapter explores the various answers that have been given to these questions. (shrink)

The aim of this paper is to discuss the “Framework for M&S with Agents” (FMSA) proposed by Zeigler et al. [2000, 2009] in regard to the diverse epistemological aims of agent simulations in social sciences. We first show that there surely are great similitudes, hence that the aim to emulate a universal “automated modeler agent” opens new ways of interactions between these two domains of M&S with agents. E.g., it can be shown that the multi-level conception at the core of (...) the FMSA is similar in both contexts: notions of “levels of system specifi cation”, “behavior of models”, “simulator”and “endomorphic agents” can be partially translated in the terms linked to the “denotational hierarchy” (DH) recently introduced in a multi-level centered epistemology of M&S. Second, we suggest considering the question of “credibility” of agent M&S in social sciences when we do not try to emulate but only to simulate target systems. Whereas a stringent and standardized treatment of the heterogeneous internal relations (in the DH) between systems of formalisms is the key problem and the essential challenge in the scope of Agent M&S driven engineering, it is urgent too to address the problem of the external relations (and of the external validity, hence of the epistemic power and credibility) of such levels of formalisms in the specific domains of agent M&S in social sciences, especially when we intend to introduce the concepts of activity tracking. (shrink)

Under what conditions does machine learning (ML) model opacity inhibit the possibility of explaining and understanding phenomena? In this article, I argue that nonepistemic values give shape to the ML opacity problem even if we keep researcher interests fixed. Treating ML models as an instance of doing model-based science to explain and understand phenomena reveals that there is (i) an external opacity problem, where the presence of inductive risk imposes higher standards on externally validating models, and (...) (ii) an internal opacity problem, where greater inductive risk demands a higher level of transparency regarding the inferences the model makes. (shrink)

In an ever-changing world, when we search for answers on our present challenges, it can be tricky to extrapolate past realities when concerning science-based issues. Climate change, public health or artificial intelligence embody issues on how scientific evidence is often challenged, as false beliefs could drive the design of public policies and legislation. Therefore , how can we foresee if science can tip the scales of political legislation? In this article, we outline how models of historical cases (...) can be used to predict and understand how scientific evidence can influence the emergence (or fall-back) of science based-legislation. We also present frameworks on how to use past episodes of History of Science and Alternative History insights to build epistemic models, based on previous successful approaches on the spread of scientific misinformation. These models will help the accuracy of the design of eventual alternative realities, that can come insightful on present decision making methodologies. (shrink)

The Emergic Cognitive Model (ECM) is a unified computational model of visual filling-in based on the Emergic Network architecture. The Emergic Network was designed to help realize systems undergoing continuous change. In this thesis, eight different filling-in phenomena are demonstrated under a regime of continuous eye movement (and under static eye conditions as well). -/- ECM indirectly demonstrates the power of unification inherent with Emergic Networks when cognition is decomposed according to finer-grained functions supporting change. These can (...) interact to raise additional emergent behaviours via cognitive re-use, hence the Emergic prefix throughout. Nevertheless, the model is robust and parameter free. Differential re-use occurs in the nature of model interaction with a particular testing paradigm. -/- ECM has a novel decomposition due to the requirements of handling motion and of supporting unified modelling via finer functional grains. The breadth of phenomenal behaviour covered is largely to lend credence to our novel decomposition. -/- The Emergic Network architecture is a hybrid between classical connectionism and classical computationalism that facilitates the construction of unified cognitive models. It helps cutting up of functionalism into finer-grains distributed over space (by harnessing massive recurrence) and over time (by harnessing continuous change), yet simplifies by using standard computer code to focus on the interaction of information flows. Thus while the structure of the network looks neurocentric, the dynamics are best understood in flowcentric terms. Surprisingly, dynamic system analysis (as usually understood) is not involved. An Emergic Network is engineered much like straightforward software or hardware systems that deal with continuously varying inputs. Ultimately, this thesis addresses the problem of reduction and induction over complex systems, and the Emergic Network architecture is merely a tool to assist in this epistemic endeavour. -/- ECM is strictly a sensory model and apart from perception, yet it is informed by phenomenology. It addresses the attribution problem of how much of a phenomenon is best explained at a sensory level of analysis, rather than at a perceptual one. As the causal information flows are stable under eye movement, we hypothesize that they are the locus of consciousness, howsoever it is ultimately realized. (shrink)

The debates on the scientificity of social sciences in general, and sociology in particular, are recurring. From the original methodenstreitat the end the 19th Century to the contemporary controversy on the legitimacy of “regional epistemologies”, a same set of interrogations reappears. Are social sciences really scientific? And if so, are they sciences like other sciences? How should we conceive “research programs” Lakatos (1978) or “research traditions” for Laudan (1977) able to produce advancement of knowledge in the field of social and (...) human phenomena? Is the progress of knowledge in social sciences similar to the one generally observed in natural sciences? Is it possible to evaluate the relative merits of each one of these research programs? -/- These debates are important vectors of social and intellectual polarization. The historical divide between the positivist and the hermeneutics poles precedes the structure of the contemporary debate around the epistemic space of social sciences, It is not only a question of renewing the opposition between a monist view of sciences (e.g. McIntyre, 1996) and a dualistic one (e.g. Geertz, 1973) or even a trialist view of sciences (e.g. Lepenies, 1985). It is also a question of asserting dichotomies transformed into framework (including when it is a question of exceeding them): nature-culture, nomothetic-idiographic, models-narrative, structure-history, cause-reason, explanation-comprehension. -/- In this short introduction, we provide, in section 2, a first overview of this epistemological debate in social science. Section 3 proposes a different standpoint on the same questions, by introducing both ontological and methodological aspects in this basic epistemological debate. Namely, following (Hollis, 1994) oppositions of the explanation - understanding, causes - meaning, etc., types discussed in the firsts ection are comparatively examined together with oppositions of the structure - action, holism - individualism types. This allows us to discus show multi-agent design, by integrating various dimensions and standpoints in the same framework (Phan, Amblard, 2007, Chapters 1, 5, 14) can help us to shift these boundaries, and to bypass these oppositions. As model building and ontology design are at the core of this process (Phan, Amblard, 2007, Chapter 12), Section4 discusses various issues of the art of modelling, starting both from economists’ and sociologists’ current standpoints. (shrink)

In this paper I propose an account of representation for scientific models based on Kendall Walton’s ‘make-believe’ theory of representation in art. I first set out the problem of scientific representation and respond to a recent argument due to Craig Callender and Jonathan Cohen, which aims to show that the problem may be easily dismissed. I then introduce my account of models as props in games of make-believe and show how it offers a solution to the problem. Finally, I (...) demonstrate an important advantage my account has over other theories of scientific representation. All existing theories analyse scientific representation in terms of relations, such as similarity or denotation. By contrast, my account does not take representation in modelling to be essentially relational. For this reason, it can accommodate a group of models often ignored in discussions of scientific representation, namely models which are representational but which represent no actual object. (shrink)

The incompleteness of set theory ZF C leads one to look for natural nonconservative extensions of ZF C in which one can prove statements independent of ZF C which appear to be “true”. One approach has been to add large cardinal axioms.Or, one can investigate second-order expansions like Kelley-Morse class theory, KM or Tarski-Grothendieck set theory T G or It is a nonconservative extension of ZF C and is obtained from other axiomatic set theories by the inclusion of Tarski’s axiom (...) which implies the existence of inaccessible cardinals. See also related set theory with a filter quantifier ZF (aa). In this paper we look at a set theory NC# ∞# , based on bivalent gyper infinitary logic with restricted Modus Ponens Rule In this paper we deal with set theory NC# ∞# based on bivalent gyper infinitary logic with Restricted Modus Ponens Rule. Nonconservative extensions of the canonical internal set theories IST and HST are proposed. (shrink)

This dissertation examines aspects of the interplay between computing and scientific practice. The appropriate foundational framework for such an endeavour is rather real computability than the classical computability theory. This is so because physical sciences, engineering, and applied mathematics mostly employ functions defined in continuous domains. But, contrary to the case of computation over natural numbers, there is no universally accepted framework for real computation; rather, there are two incompatible approaches --computable analysis and BSS model--, both claiming to formalise (...) algorithmic computation and to offer foundations for scientific computing. -/- The dissertation consists of three parts. In the first part, we examine what notion of 'algorithmic computation' underlies each approach and how it is respectively formalised. It is argued that the very existence of the two rival frameworks indicates that 'algorithm' is not one unique concept in mathematics, but it is used in more than one way. We test this hypothesis for consistency with mathematical practice as well as with key foundational works that aim to define the term. As a result, new connections between certain subfields of mathematics and computer science are drawn, and a distinction between 'algorithms' and 'effective procedures' is proposed. -/- In the second part, we focus on the second goal of the two rival approaches to real computation; namely, to provide foundations for scientific computing. We examine both frameworks in detail, what idealisations they employ, and how they relate to floating-point arithmetic systems used in real computers. We explore limitations and advantages of both frameworks, and answer questions about which one is preferable for computational modelling and which one for addressing general computability issues. -/- In the third part, analog computing and its relation to analogue (physical) modelling in science are investigated. Based on some paradigmatic cases of the former, a certain view about the nature of computation is defended, and the indispensable role of representation in it is emphasized and accounted for. We also propose a novel account of the distinction between analog and digital computation and, based on it, we compare analog computational modelling to physical modelling. It is concluded that the two practices, despite their apparent similarities, are orthogonal. (shrink)

Chronic kidney disease (CKD) is a global health problem with high morbidity and mortality rate, and it induces other diseases. There are no obvious incidental effects during the starting periods of CKD, patients routinely disregard to see the sickness. Early disclosure of CKD enables patients to seek helpful treatment to improve the development of this disease. AI models can effectively assist clinical with achieving this objective on account of their fast and exact affirmation execution. In this appraisal, proposed a Logistic (...) relapse framework for diagnosing CKD. Proposed calculation like NAIVE BAYES , DECISION TREE , KSTAR , LOGISITIC , AND SVM and look at these calculation and get the most noteworthy precision .AI store, which has an enormous number of missing qualities. Missing characteristics are for the most part found, taking everything into account, clinical conditions since patients might miss a couple of assessments for various reasons. By separating the misjudgements delivered by the set up models and proposed a fused model that unites determined backslide and sporadic woods by using perceptron. (shrink)

This article aims to unify all scientific theories based on the concept of “intrinsicality of nature”, including the fundamental theories in physics. First, the general property within existing natural phenomena, say “intrinsicality of nature”, was deduced as “logicality” and "imperfectness". Then, the identical intrinsicality was deduced out for the science to unify all scientific theories. Finally, with this intrinsicality and the novelties of consciousness, the unification of physics theories, say Theory of Everything, was framed by a physical (...) class='Hi'>model of more fundamental element (MFE), which had managed to cover the concepts of quantum mechanics, general relativity, and the physics of consciousness altogether, to test this model of unification. Since the MFE is only a theoretical model, two methods were introduced for its experimental verification. While its application to explain yet unanswered questions in science was tested, and further verification of this model in a lab by measuring consciousness signals was discussed. (shrink)

Agent-​​based modeling is showing great promise in the social sciences. However, two misconceptions about the relation between social macroproperties and microproperties afflict agent-based models. These lead current models to systematically ignore factors relevant to the properties they intend to model, and to overlook a wide range of model designs. Correcting for these brings painful trade-​​offs, but has the potential to transform the utility of such models.

We live in a world of crowds and corporations, artworks and artifacts, legislatures and languages, money and markets. These are all social objects — they are made, at least in part, by people and by communities. But what exactly are these things? How are they made, and what is the role of people in making them? In The Ant Trap, Brian Epstein rewrites our understanding of the nature of the social world and the foundations of the social sciences. Epstein explains (...) and challenges the three prevailing traditions about how the social world is made. One tradition takes the social world to be built out of people, much as traffic is built out of cars. A second tradition also takes people to be the building blocks of the social world, but focuses on thoughts and attitudes we have toward one another. And a third tradition takes the social world to be a collective projection onto the physical world. Epstein shows that these share critical flaws. Most fundamentally, all three traditions overestimate the role of people in building the social world: they are overly anthropocentric. Epstein starts from scratch, bringing the resources of contemporary metaphysics to bear. In the place of traditional theories, he introduces a model based on a new distinction between the grounds and the anchors of social facts. Epstein illustrates the model with a study of the nature of law, and shows how to interpret the prevailing traditions about the social world. Then he turns to social groups, and to what it means for a group to take an action or have an intention. Contrary to the overwhelming consensus, these often depend on more than the actions and intentions of group members. (shrink)

Many biological investigations are organized around a small group of species, often referred to as ‘model organisms’, such as the fruit fly Drosophila melanogaster. The terms ‘model’ and ‘modelling’ also occur in biology in association with mathematical and mechanistic theorizing, as in the Lotka–Volterra model of predator-prey dynamics. What is the relation between theoretical models and model organisms? Are these models in the same sense? We offer an account on which the two practices are shown to (...) have different epistemic characters. Theoretical modelling is grounded in explicit and known analogies between model and target. By contrast, inferences from model organisms are empirical extrapolations. Often such extrapolation is based on shared ancestry, sometimes in conjunction with other empirical information. One implication is that such inferences are unique to biology, whereas theoretical models are common across many disciplines. We close by discussing the diversity of uses to which model organisms are put, suggesting how these relate to our overall account. 1 Introduction2 Volterra and Theoretical Modelling3 Drosophila as a Model Organism4 Generalizing from Work on Model Organisms5 Phylogenetic Inference and Model Organisms6 Further Roles of Model Organisms6.1 Preparative experimentation6.2 Model organisms as paradigms6.3 Model organisms as theoretical models6.4 Inspiration for engineers6.5 Anchoring a research community7 Conclusion. (shrink)

The social sciences face a problem of sample non-representation, where the majority of samples consist of undergraduate students from Euro-American institutions. The problem has been identified for decades with little trend of improvement. In this paper, I trace the history of sampling theory. The dominant framework, called the design-based approach, takes random sampling as the gold standard. The idea is that a sampling procedure that is maximally uninformative prevents samplers from introducing arbitrary bias, thus preserving sample representation. I show (...) how this framework, while good in theory, faces many challenges in application. Instead, I advocate for an alternative framework, called the model-based approach to sampling, where representative samples are those balanced in composition, however they were drawn. I argue that the model-based framework is more appropriate in the social sciences because it allows for systematic assessment of imperfect samples and methodical improvement in resource-limited scientific contexts. I end with practical proposals of improving sample quality in the social sciences. (shrink)

In this short essay, I describe how our Vietnam-based continuing education program, Research Coach in Social Sciences (RCISS), supports early-career researchers to (co)publish their first international indexed publications (i.e., publications in Clarivate Web of Science [WoS] or Sco- pus-indexed journals). In developed countries, junior researchers often seek help from univer- sity professors to publish their first publication. However, given the chronic shortage of senior social sciences scholars with international publishing experience in Vietnam, along with out- dated and ill-designed (...) PhD programs, early-career researchers in the social sciences in Viet- nam often face challenges in international publishing. (shrink)

The goal of philosophy of information is to understand what information is, how it operates, and how to put it to work. But unlike ‘information’ in the technical sense of information theory, what we are interested in is meaningful information. To understand the nature and dynamics of information in this sense we have to understand meaning. What we offer here are simple computational models that show emergence of meaning and information transfer in randomized arrays of neural nets. These we take (...) to be formal instantiations of a tradition of theories of meaning as use. What they offer, we propose, is a glimpse into the origin and dynamics of at least simple forms of meaning and information transfer as properties inherent in behavioral coordination across a community. (shrink)

The classical theory of computation does not represent an adequate model of reality for simulation in the social sciences. The aim of this paper is to construct a methodological perspective that is able to conciliate the formal and empirical logic of program verification in computer science, with the interpretative and multiparadigmatic logic of the social sciences. We attempt to evaluate whether social simulation implies an additional perspective about the way one can understand the concepts of program and computation. (...) We demonstrate that the logic of social simulation implies at least two distinct types of program verifications that reflect an epistemological distinction in the kind of knowledge one can have about programs. Computer programs seem to possess a causal capability (Fetzer, 1999) and an intentional capability that scientific theories seem not to possess. This distinction is associated with two types of program verification, which we call empirical and intentional verification. We demonstrate, by this means, that computational phenomena are also intentional phenomena, and that such is particularly manifest in agent-based social simulation. Ascertaining the credibility of results in social simulation requires a focus on the identification of a new category of knowledge we can have about computer programs. This knowledge should be considered an outcome of an experimental exercise, albeit not empirical, acquired within a context of limited consensus. The perspective of intentional computation seems to be the only one possible to reflect the multiparadigmatic character of social science in terms of agent-based computational social science. We contribute, additionally, to the clarification of several questions that are found in the methodological perspectives of the discipline, such as the computational nature, the logic of program scalability, and the multiparadigmatic character of agent-based simulation in the social sciences. (shrink)

This article discusses Sven Ove Hansson’s corpus model for the influence of values (in particular, non-epistemic ones) in the hypothesis acceptance/rejection phase of scientific inquiry. This corpus model is based on Hansson’s concepts of scientific corpus and science ‘in the large sense’. I first present Hansson’s corpus model of value influence with some introductory comments about its origins, a detailed presentation of the model with a new terminology, an analysis of its limits, and an (...) appreciation of its handling of controversial non-epistemic values. I conclude that it is a very good candidate for managing value influence, because contrary to other models in the literature it is fairly simple, it provides a universal and systematic procedure for dealing with values, and it syste- matically preserves the epistemic integrity of science. I then study some difficulties associated with the model’s central feature of taking the maximum level of evidence required by the applications of a claim in order for the latter to enter the scientific corpus: the difficulty to identify this maximum requirement; the non-optimality of this requirement with respect to other less demanding requirements; the potentially detrimental consequences of the preference for false negatives over false positives on which it relies; and the issue of whether there can nevertheless be requirements higher than this maximum. I show that these issues may potentially challenge the corpus model. Finally, I call for empirical work (in particular, an investigation of scientists’ and engineers’ own normative views) in order to better assess these issues. (shrink)

Biosemiotics is a growing fi eld that investigates semiotic processes in the living realm in an attempt to combine the fi ndings of the biological sciences and semiotics. Semiotic processes are more or less what biologists have typically referred to as “ signals, ” “ codes, ”and “ information processing ”in biosystems, but these processes are here understood under the more general notion of semiosis, that is, the production, action, and interpretation of signs. Thus, biosemiotics can be seen as biology (...) interpreted as a study of living sign systems — which also means that semiosis or sign process can be seen as the very nature of life itself. In other words, biosemiotics is a field of research investigating semiotic processes (meaning, signification, communication, and habit formation in living systems) and the physicochemical preconditions for sign action and interpretation. -/- (...). (shrink)

Academia is a competitive environment. Early Career Researchers (ECRs) are limited in experience and resources and especially need achievements to secure and expand their careers. To help with these issues, this book offers a new approach for conducting research using the combination of mindsponge innovative thinking and Bayesian analytics. This is not just another analytics book. 1. A new perspective on psychological processes: Mindsponge is a novel approach for examining the human mind’s information processing mechanism. This conceptual framework is used (...) to construct models in studies. The framework is highly flexible and widely applicable for many different types of information processes. The mindsponge approach can help researchers discover interesting ideas or even formulate their very own theories when investigating psychosocial phenomena. This approach brings a fresh wind to the current landscape of social sciences and humanities (SSH). 2. Easy-to-follow analysis protocol: The Bayesian Mindsponge Framework (BMF analytics) is useful in terms of computing and visualizing power but also easy to learn and apply. Contrary to being intimidating, the Bayesian analytics section of this book is presented in a reader-friendly manner with a detailed yet clear step-by-step procedure. Examples are from published BMF articles, allowing readers to immediately practice the method and quickly create their own applications. With educational purposes in mind, the book is very suitable for ECRs who are looking to innovate their research methods. 3. Advocating for low-cost, high-quality research: Doing science can be very costly. Mindsponge innovative thinking and BMF analytics help produce impactful studies using openly available data on online repositories. This is based on the authors’ previous works and experiences. The book presents examples of employing the open R package bayesvl on secondary data from different sources. With less financial constraints, researchers can have more freedom of thought to pursue their curiosity and creativity. ECRs in low- and middle-income countries may find this aspect crucial in their careers. 4. Support and collaboration: The authors share their insights from experiences in the academic publishing system to help readers get through the processes of manuscript writing and peer-reviewing more easily. The authors are also ready to support other researchers with further inquiries and collaboration opportunities at the following website, mindsponge(dot)info. This book is for: a) ECRs whose only abundant resources are their innovation capacity and strength of will; b) Researchers in SSH who want to explore a novel approach to thinking and study conducting; c) Low- and middle-income countries’ researchers looking for a cost-effective research protocol; and, d) Innovative thinkers who want to turn their interesting thoughts into good publications. (shrink)

Summary Analogue models are actual physical setups used to model something else. They are especially useful when what we wish to investigate is difficult to observe or experiment upon due to size or distance in space or time: for example, if the thing we wish to investigate is too large, too far away, takes place on a time scale that is too long, does not yet exist or has ceased to exist. The range and variety of analogue models is (...) too extensive to attempt a survey. In this article, I describe and discuss several different analogue model experiments, the results of those model experiments, and the basis for constructing them and interpreting their results. Examples of analogue models for surface waves in lakes, for earthquakes and volcanoes in geophysics, and for black holes in general relativity, are described, with a focus on examining the bases for claims that these analogues are appropriate analogues of what they are used to investigate. A table showing three different kinds of bases for reasoning using analogue models is provided. Finally, it is shown how the examples in this article counter three common misconceptions about the use of analogue models in physics. (shrink)

The INBIOSA project brings together a group of experts across many disciplines who believe that science requires a revolutionary transformative step in order to address many of the vexing challenges presented by the world. It is INBIOSA’s purpose to enable the focused collaboration of an interdisciplinary community of original thinkers. This paper sets out the case for support for this effort. The focus of the transformative research program proposal is biology-centric. We admit that biology to date has been more (...) fact-oriented and less theoretical than physics. However, the key leverageable idea is that careful extension of the science of living systems can be more effectively applied to some of our most vexing modern problems than the prevailing scheme, derived from abstractions in physics. While these have some universal application and demonstrate computational advantages, they are not theoretically mandated for the living. A new set of mathematical abstractions derived from biology can now be similarly extended. This is made possible by leveraging new formal tools to understand abstraction and enable computability. [The latter has a much expanded meaning in our context from the one known and used in computer science and biology today, that is "by rote algorithmic means", since it is not known if a living system is computable in this sense (Mossio et al., 2009).] Two major challenges constitute the effort. The first challenge is to design an original general system of abstractions within the biological domain. The initial issue is descriptive leading to the explanatory. There has not yet been a serious formal examination of the abstractions of the biological domain. What is used today is an amalgam; much is inherited from physics (via the bridging abstractions of chemistry) and there are many new abstractions from advances in mathematics (incentivized by the need for more capable computational analyses). Interspersed are abstractions, concepts and underlying assumptions “native” to biology and distinct from the mechanical language of physics and computation as we know them. A pressing agenda should be to single out the most concrete and at the same time the most fundamental process-units in biology and to recruit them into the descriptive domain. Therefore, the first challenge is to build a coherent formal system of abstractions and operations that is truly native to living systems. Nothing will be thrown away, but many common methods will be philosophically recast, just as in physics relativity subsumed and reinterpreted Newtonian mechanics. -/- This step is required because we need a comprehensible, formal system to apply in many domains. Emphasis should be placed on the distinction between multi-perspective analysis and synthesis and on what could be the basic terms or tools needed. The second challenge is relatively simple: the actual application of this set of biology-centric ways and means to cross-disciplinary problems. In its early stages, this will seem to be a “new science”. This White Paper sets out the case of continuing support of Information and Communication Technology (ICT) for transformative research in biology and information processing centered on paradigm changes in the epistemological, ontological, mathematical and computational bases of the science of living systems. Today, curiously, living systems cannot be said to be anything more than dissipative structures organized internally by genetic information. There is not anything substantially different from abiotic systems other than the empirical nature of their robustness. We believe that there are other new and unique properties and patterns comprehensible at this bio-logical level. The report lays out a fundamental set of approaches to articulate these properties and patterns, and is composed as follows. -/- Sections 1 through 4 (preamble, introduction, motivation and major biomathematical problems) are incipient. Section 5 describes the issues affecting Integral Biomathics and Section 6 -- the aspects of the Grand Challenge we face with this project. Section 7 contemplates the effort to formalize a General Theory of Living Systems (GTLS) from what we have today. The goal is to have a formal system, equivalent to that which exists in the physics community. Here we define how to perceive the role of time in biology. Section 8 describes the initial efforts to apply this general theory of living systems in many domains, with special emphasis on crossdisciplinary problems and multiple domains spanning both “hard” and “soft” sciences. The expected result is a coherent collection of integrated mathematical techniques. Section 9 discusses the first two test cases, project proposals, of our approach. They are designed to demonstrate the ability of our approach to address “wicked problems” which span across physics, chemistry, biology, societies and societal dynamics. The solutions require integrated measurable results at multiple levels known as “grand challenges” to existing methods. Finally, Section 10 adheres to an appeal for action, advocating the necessity for further long-term support of the INBIOSA program. -/- The report is concluded with preliminary non-exclusive list of challenging research themes to address, as well as required administrative actions. The efforts described in the ten sections of this White Paper will proceed concurrently. Collectively, they describe a program that can be managed and measured as it progresses. (shrink)

Lexical semantics has a problem. As Allesandro Lenci put it, the problem is that it cannot distinguish semantic from non-semantic relationships within its data. (2008, 2014). The data it relies on are patterns of co-occurrence of lexemes within linguistic corpora. But patterns of co-occurrence can reflect either our knowledge of what the world is like or our knowledge of what words mean -- matters of fact or matters of meaning. -/- In this essay, I develop a semantic theory which draws (...) this distinction in a way which makes it discernible in lexical semantics and cognitive science research. In doing so, this theory unifies truth-functional and structuralist approaches to semantics, and provides an integrated explanation of meaning and reference. -/- I base this semantic theory on linguistic dispositions to pair words with words, and to pair words with things, both based on learned patterns of association. These dispositions manifest themselves in verbal behavior, and (in Part 2) I propose a new neurophysical model to account for these dispositions which underlie both the intensional and extensional patterns in that behavior. (shrink)

This study aims to explore a demoethical model for sustainable development in modern society. It proposes an approach that focuses on organizing activities to improve sustainable development. Specifically, it presents a demoethical model relevant to Society 5.0 and Industry 5.0 organizations. The objective is to identify demoethical values that can drive sustainable development in the era of digitalization. Through a literature review and analysis, this study identifies key components of the demoethical model and provides practical recommendations for (...) stakeholders involved in digital transformation. The analysis of demoethical norms and phenomena, such as education, nurturing, mind, knowledge, science, and honest work, has enabled the identification of values that align with sustainable development in society. The results of the study demonstrate that the notion of a demoethical foundation for sustainability is rooted in the concept of spirituality as the basis for a new societal development scenario and its relationship with nature. The study shows that ideas about the demoethical basis of sustainability are based on the priority of spirituality as the basis of a new scenario for the development of society, as well as the integration of demographic, socio-economical, and ecological components in system-wide modeling. (shrink)

In recent decades, philosophers of science have devoted considerable efforts to understand what models represent. One popular position is that models represent fictional situations. Another position states that, though models often involve fictional elements, they represent real objects or scenarios. Though these two positions may seem to be incompatible, I believe it is possible to reconcile them. Using a threefold distinction between different signs proposed by Peirce, I develop an argument based on a proposal recently made by Kralemann (...) and Lattman (in Synthese 190:3397–3420, 2013) that shows that the two aforementioned positions can be reconciled by distinguishing different ways in which a model representation can be used. In particular, on the basis of Peirce’s distinction between icons, indices and symbols, I argue that models can sometimes function as icons, sometimes as indexes and sometimes as symbols, depending on the context in which they are considered and the use that they are developed for because they all have iconic, indexical and symbolic features. In addition, I show that conceiving models as signs enables us to develop an account of scientific representation that meets the main desiderata that Shech (in Synthese 192:3463–3485, 2015) presents. (shrink)

This study reflects on Harold Kincaid and Jule Zahle’s view that there is no necessary association between methodological individualism and agent-based models because the analysis of social phenomena in terms of the latter cannot always be regarded as an implementation of the former. Their view remains in contention with the standpoint of several philosophers of science and social scientists, including Chen and Di Iorio. Kincaid and Zahle’s main argument against such a standpoint is that agent-based simulation is (...) compatible with holistic explanations that are at odds with methodological individualism. The following study argues that Kincaid and Zahle’s conclusion remains untenable since it stems from inaccurate historical assumptions concerning the tradition of methodological individualism and the way the individualism-holism debate is understood within this tradition. (shrink)

ABSTRACT Is there something specific about modelling that distinguishes it from many other theoretical endeavours? We consider Michael Weisberg’s thesis that modelling is a form of indirect representation through a close examination of the historical roots of the Lotka–Volterra model. While Weisberg discusses only Volterra’s work, we also study Lotka’s very different design of the Lotka–Volterra model. We will argue that while there are elements of indirect representation in both Volterra’s and Lotka’s modelling approaches, they are largely due (...) to two other features of contemporary model construction processes that Weisberg does not explicitly consider: the methods-drivenness and outcome-orientedness of modelling. 1Introduction 2Modelling as Indirect Representation 3The Design of the Lotka–Volterra Model by Volterra 3.1Volterra’s method of hypothesis 3.2The construction of the Lotka–Volterra model by Volterra 4The Design of the Lotka–Volterra Model by Lotka 4.1Physical biology according to Lotka 4.2Lotka’s systems approach and the Lotka–Volterra model 5Philosophical Discussion: Strategies and Tools of Modelling 5.1Volterra’s path from the method of isolation to the method of hypothesis 5.2The template-based approach of Lotka 5.3Modelling: methods-driven and outcome-oriented 6Conclusion. (shrink)

The five studies of this special section investigate the role of models and similar representational tools in interdisciplinarity. These studies were all written by philosophers of science, who focused on interdisciplinary episodes between disciplines and sub-disciplines ranging from physics, chemistry and biology to the computational sciences, sociology and economics. The reasons we present these divergent studies in a collective form are three. First, we want to establish model-exchange as a kind of interdisciplinary event. The five case studies, which (...) are summarized in Section 2 below, show the relevance of this kind. Arguing for the relative unity of these cases will, we hope, re-orient the current debate over interdisciplinarity so as to reflect more appropriately the importance of this kind. We discuss our view of the current state of the debate in Section 3. The evidence from these cases also helps us to develop a taxonomy of interdisciplinary model exchanges in Section 4da taxonomy, we would like to add, that might be useful for the discussion of interdisciplinary exchanges beyond the context of models and their transfer. The second reason for presenting these studies together is that they provide an important source of evidence for the philosophy of science. Over the last three decades, philosophy of science has increasingly differentiated into philosophies of various disciplines. This differentiation in our view has greatly increased our understanding of the scientific practices of the respective disciplines, including the epistemological and methodological standards and conventions on which these practices are based and by which they are evaluated. But it has also made it harder to compare these practices and standards across disciplines. The studies we present here are case studies of interdisciplinary exchange: they focus on the transfer, collaborative construction or parallel use of models and similar representational tools. They therefore provide a unique opportunity to investigate various disciplinary treatments of the same or at least similar representational tools. This allows the identification and comparison of different disciplinary practices and their underlying conventions as well as of the respective normative standards of their evaluation. By tracing the paths along which models travel between disciplines and research fields we can observe to which extent discipline-external practices associated with an adopted tool are retained or replaced by disciplineinternal practices. This generates invaluable information about both disciplinarity and interdisciplinarity. We develop this argument further in Section 5. The third reason for presenting these studies in collective form is that their philosophical analysis also has important normative implications for the notion of interdisciplinarity itself. Too often in the current (non-philosophical) discourse is interdisciplinarity cast as an exclusively integrative project: interdisciplinary exchange is often claimed to be successful only if the involved disciplines become mutually more integrated as a consequence of this process. In contrast to this, many of the cases presented here show that interdisciplinary exchange can be scientifically highly successful, even if at the end of the exchange disciplinary borders remain fully intact. Indeed, borders can be fruitfully crossed without any integration across these borders. Such considerations of divergence in scientific practices and tools offer arguments against a naïve plea for unitarian or non-pluralist versions of interdisciplinarity. Disciplinary divergences may have their justifications, and attempting exchanges that require the reduction of these divergences may consequently not be justified. We pursue this argument further in Section 6. (shrink)