Pragmatism has played only a small role in the half century and more of the science‐and‐religion dialogue, in part because pragmatism was at a low ebb in the 1950s. Even though Jamesean pragmatism in particular is experiencing a resurgence, owing partly to the work of Rorty and Putnam, it remains inconspicuous in the dialogue. Excepting artificial intelligence and artificial life, computer science also has not played a large role in the dialogue. Recent research into the foundations of object‐oriented programming, however, (...) shows this increasingly pervasive practice possesses an implicit pragmatist epistemology. Although science will have to become more computational, it will have to come to terms with both object‐oriented computing and its implicit pragmatism, which in turn supports the conclusion that we have fresh warrant for recasting the science‐and‐religion dialogue in Jamesean pragmatist terms. Some preliminary consequences of such a recasting of the dialogue are explored. (shrink)

We claim that adjustable parameters play a crucial role in building and applying simulation models. We analyze that role and illustrate our findings using examples from equations of state in thermodynamics. In building simulation models, two types of experiments, namely, simulation and classical experiments, interact in a feedback loop, in which model parameters are adjusted. A critical discussion of how adjustable parameters function shows that they are boon and bane of simulation. They help to enlarge the scope of simulation far (...) beyond what can be determined by theoretical knowledge, but at the same time undercut the epistemic value of simulation models. (shrink)

This book addresses key conceptual issues relating to the modern scientific and engineering use of computer simulations. It analyses a broad set of questions, from the nature of computer simulations to their epistemological power, including the many scientific, social and ethics implications of using computer simulations. The book is written in an easily accessible narrative, one that weaves together philosophical questions and scientific technicalities. It will thus appeal equally to all academic scientists, engineers, and researchers in industry interested in questions (...) related to the general practice of computer simulations. (shrink)

As a working hypothesis for philosophy of science, the unity of science thesis has been decisively challenged in all its standard formulations; it cannot be assumed that the sciences presuppose an orderly world, that they are united by the goal of systematically describing and explaining this order, or that they rely on distinctively scientific methodologies which, properly applied, produce domain-specific results that converge on a single coherent and comprehensive system of knowledge. I first delineate the scope of arguments against global (...) unity theses. However implausible old-style global unity theses may now seem, I argue that unifying strategies of a more local and contingent nature do play an important role in scientific inquiry. This is particularly clear in archaeology where, to establish evidential claims of any kind, practitioners must exploit a range of inter-field and inter-theory connections. At the same time, the robustness of these evidential claims depends on significant disunity between the sciences from which archaeologists draw background assumptions and auxiliary hypotheses. This juxtaposition of unity with disunity poses a challenge to standard positions in the debate about scientific unity. (shrink)

In the late 1990s, computational technology had advanced sufficiently that astrophysicists were able to construct reasonably high resolution computer simulations of the Local Group of galaxies. These simulations indicated there should be around 250 small satellite galaxies orbiting the Milky Way and Andromeda. In the real Local Group, however, only around 40 satellites had been observed, and only twenty or so more have been discovered since then. Despite this discrepancy in numbers, claims have been made in recent years that the (...) ‘missing satellites problem’ has been solved. Using the examples of the constructed luminosity curve, and hydrodynamic simulations, this paper explores how simulations are used in conjunction with observation to ‘solve’ the missing satellites problem. It is suggested that the simulated universes have sufficient complexity to be understood as worlds in their own right, ones that can be measured and observed. By demonstrating that these virtual worlds are sufficiently ‘realistic’ with respect to observations, astrophysicists are able to make a robust argument for the existence of ‘dark’, non-observable astrophysical objects. Observational and simulated data are combined to demonstrate the plausibility—a term that develops more ontologically meaningful connotations—of both the existence and the maintenance of dark satellites. It is thus through the conflation of the real and virtual worlds, the blending of simulation data with empirical data, that the missing satellites problem is ‘solved’. (shrink)

The promise of Newtonian science to create a universal precise explanation of all phenomena seems to be out-dated. “Cutting through complexity” may kill potential solutions. The complexity of real phenomena should be accepted and at best tamed by appropriate techniques. Complexity, a recent megatrend in the sciences, may effectuate another scientific revolution.

Computational modeling plays an increasingly important explanatory role in cases where we investigate systems or problems that exceed our native epistemic capacities. One clear case where technological enhancement is indispensable involves the study of complex systems.1 However, even in contexts where the number of parameters and interactions that define a problem is small, simple systems sometimes exhibit non-linear features which computational models can illustrate and track. In recent decades, computational models have been proposed as a way to assist us in (...) understanding emergent phenomena. (shrink)

The idea that ambiguity can be productive in data science remains controversial. Efforts to make scientific publications and data intelligible to computers generally assume that accommodating multiple meanings for words, known as polysemy, undermines reasoning and communication. This assumption has nonetheless been contested by historians, philosophers, and social scientists, who have applied qualitative research methods to demonstrate the generative and strategic value of polysemy. Recent quantitative results from linguistics have also shown how polysemy can actually improve the efficiency of human (...) communication. I present a new conceptual typology based on a synthesis of prior research about the aims, norms, and circumstances under which polysemy arises and is evaluated. The typology supports a contextual pluralist view of polysemy’s value for scientific research practices: polysemy does both substantial positive and negative work in science, but its utility is context-sensitive in ways that are often overlooked by the norms people have formulated to regulate its use, including prior scholars researching polysemy. I also propose that historical patterns in the use of partial synonyms, i.e. terms with overlapping meanings, provide an especially promising phenomenon for integrative research addressing these issues. (shrink)

This paper combines naturalized metaphysics and a philosophical reflection on a recently evolving interdisciplinary branch of quantum chemistry, ab initio molecular dynamics. Bridging the gaps among chemistry, physics, and computer science, this cutting-edge research field explores the structure and dynamics of complex molecular many-body systems through computer simulations. These simulations are allegedly crafted solely by the laws of fundamental physics, and are explicitly designed to capture nature as closely as possible. The models and algorithms employed, however, involve many approximations and (...) significant degrees of idealization of their target systems. Therefore, for philosophers of science the pivotal question of whether relying only on the fundamental laws of physics supports a reductionist or realist stance arises. One conceivable answer to this question is that the irreducible approximations and idealizations support rather anti-realist positions. After reviewing an influential attitude in the philosophy of computer simulations and the debate concerning scientific realism, I offer a fair interpretation of such ab initio modelling in quantum chemistry within a naturalistic metaphysical framework that gives rise to a specific type of ontic structural realism. (shrink)

Where should computer simulations be located on the ‘usual methodological map’ which distinguishes experiment from theory? Specifically, do simulations ultimately qualify as experiments or as thought experiments? Ever since Galison raised that question, a passionate debate has developed, pushing many issues to the forefront of discussions concerning the epistemology and methodology of computer simulation. This review article illuminates the positions in that debate, evaluates the discourse and gives an outlook on questions that have not yet been addressed.

My aim in this paper is to give a philosophical analysis of the relationship between contingently available technology and the knowledge that it makes possible. My concern is with what specific subjects can know in practice, given their particular conditions, especially available technology, rather than what can be known “in principle” by a hypothetical entity like Laplace’s Demon. The argument has two parts. In the first, I’ll construct a novel account of epistemic possibility that incorporates two pragmatic conditions: responsibility and (...) practicability. For example, whether subjects can gain knowledge depends in some circumstances on whether they have the capability of gathering relevant evidence. In turn, the possibility of undertaking such investigative activities depends in part on factors like ethical constraints, economical realities, and available technology. In the second part of the paper, I’ll introduce “technological possibility” to analyze the set of actions made possible by available technology. To help motivate the problem and later test my proposal, I’ll focus on a specific historical case, one of the earliest uses of digital electronic computers in a scientific investigation. I conclude that the epistemic possibility of gaining access to scientific knowledge about certain subjects depends (in some cases) on the technological possibility for making responsible investigations. (shrink)

This paper argues that Ludwik Fleck’s concepts of thought collectives and proto-ideas are surprisingly topical to tackle some conceptual challenges in analyzing contemporary innovation. The objective of this paper is twofold: First, it strives to establish Ludwik Fleck as an important classic on the map of innovation analysis. A systematic comparison with Thomas Kuhn’s work on paradigms, a concept highly influential in various branches of innovation studies, suggests a number of pronounced yet under-researched advantages of a Fleckian perspective in the (...) context of technological change and innovation. Secondly, the paper links these advantages to some recent changes in the organization of innovation. Due to the rising pervasiveness of Information and Communication Technologies (ICTs), configurational innovation has become commonplace that cuts across the boundaries of established trajectories of knowledge generation. Fleck’s graded understanding of the closedness of thought collectives and his weak notion of incommensurability provide powerful metaphors to grasp the peculiarities of configurational innovation. (shrink)

Computer simulation has become important in ecological modeling, but there have been few assessments on how complex simulation models differ from more traditional analytic models. In Part I of this paper, I review the challenges faced in complex ecological modeling and how models have been used to gain theoretical purchase for understanding natural systems. I compare the use of traditional analytic simulation models and point how that the two methods require different kinds of practical engagement. I examine a case study (...) of three models from the insect resistance literature in transgenic crops to illustrate and explore differences in analytic and computer simulation models. I argue that analyzing simulation models has been often inappropriately managed with expectations derived from handling analytic models. In Part II, I look at simulation as a hermeneutic practice. I argue that simulation models are a practice or techné. I the explore five aspects of philosophical hermeneutics that may be useful in complex ecological simulation: (1) an openness to multiple perspectives allowing multiple levels of scientific pluralism, (2) the hermeneutic circle, a back and forth in active communication among both modelers and ecologists; (3) the recognition of human factors and the nature of human practices as such, including recognizing the role of judgments and choices in the modeling enterprise; (4) the importance of play in modeling; (5) the non-closed nature of hermeneutic engagement, continued dialogue, and recognizing the situatedness, incompleteness, and tentative nature of simulation models. (shrink)

Philosophers have traditionally addressed the issue of scientific unification in terms of theoretical reduction. Reductive models, however, cannot explain the occurrence of unification in areas of science where successful reductions are hard to find. The goal of this essay is to analyse a concrete example of integration in biology—the developmental synthesis—and to generalize it into a model of scientific unification, according to which two fields are in the process of being unified when they become explanatorily relevant to each other. I (...) conclude by suggesting that this methodological conception of unity, which is independent of the debate on the metaphysical foundations of science, is closely connected to the notion of interdisciplinarity. 1 Introduction2 Some Troubles with Theory Reduction3 Interfield and Mechanistic Unification4 Foundations of the Developmental Synthesis5 Explanatory Relevance6 Concluding Remarks. (shrink)

Responsible innovation is gathering momentum as an academic and policy debate linking science and society. Advocates of RI in research policy argue that scientific research should be opened up at an early stage so that many actors and issues can steer innovation trajectories. If this is done, they suggest, new technologies will be more responsible in different ways, better aligned with what society wants, and mistakes of the past will be avoided. This paper analyses the dynamics of RI in policy (...) and practice and makes recommendations for future development. More specifically, we draw on the theory of ‘trading zones’ developed by Peter Galison and use it to analyse two related processes: the development and inclusion of RI in research policy at the UK’s Engineering and Physical Sciences Research Council ; the implementation of RI in relation to the Stratospheric Particle Injection for Climate Engineering project. Our analysis reveals an RI trading zone comprised of three quasi-autonomous traditions of the research domain – applied science, social science and research policy. It also shows how language and expertise are linking and coordinating these traditions in ways shaped by local conditions and the wider context of research. Building on such insights, we argue that a sensible goal for RI policy and practice at this stage is better local coordination of those involved and we suggest ways how this might be achieved. (shrink)

The article analyzes the conception of a trading zone as a space of action and belief coordination. P. Galison proposed the conception based on anthropological and linguistic analogies. The article reviews the anthropological analogies aimed at building up the conception and the legitimacy of their use. The conclusion is that the analogies used are not accurate enough. If the tribes interacting in trading zones have a common history, material culture, and practices, they can hardly have significant differences. If they are (...) not in possession of all these characteristics, they are unlikely to remind us of different groups of scientists who participate in common researches. The article also contains the hypothesis that acceptance of a common habitus is a condition subject to which the scientists can arrange the mutual understanding space. It issues new challenges to the scientific community, as all people related to university education also accept the scientists’ habitus. (shrink)

Disciplinary Non-Consolidation. On the Origins of Medieval Archaeology in the 1920s and the 1930s. This article investigates the roots of the sub-discipline medieval archaeology that emerged in German-speaking universities in the 1950s and 1960s. In the 1930s, research practices crucial for the formation of medieval archaeology, such as the investigation of medieval castles and peasant houses, became more prominent in the humanities, especially in the context of völkisch research. After the Nazis took power in Germany, they encouraged such research because (...) it built a scientific basis for their nationalist policy. This politically motivated funding did not result in a new discipline, in contrast to research fields such as prehistory and folklore studies. In this article, I propose two explanations for why medieval archaeology did not emerge as an interdisciplinary research field in the 1930s and 1940s, even though the course was set for its development. First, for archaeologists, art historians, and regional medieval historians, research objects such as medieval castles were semantically too indeterminate. Archaeologists would investigate a castle as a building completely destroyed and buried under rubble, while art historians would be interested in its building technique, and regional medieval historians in its written record. Second, disciplines that were important for the creation of medieval archaeology, such as prehistoric archaeology, art history, and regional medieval history, structurally did not allow for the emergence of an interdisciplinary research field in the 1930s. In particular, prehistoric archaeology, which was crucial for the development of medieval archaeology, itself was not fully institutionalized at universities in the 1930s. This institutionalization process prevented the emergence and development of an interdisciplinary research field such as medieval archaeology. To demonstrate this argument, I draw on two examples of investigations of castles, one in Nazi Germany and the other in the German-speaking part of Switzerland. (shrink)

Like other mathematically intensive sciences, economics is becoming increasingly computerized. Despite the extent of the computation, however, there is very little true simulation. Simple computation is a form of theory articulation, whereas true simulation is analogous to an experimental procedure. Successful computation is faithful to an underlying mathematical model, whereas successful simulation directly mimics a process or a system. The computer is seen as a legitimate tool in economics only when traditional analytical solutions cannot be derived, i.e., only as a (...) purely computational aid. We argue that true simulation is seldom practiced because it does not fit the conception of understanding inherent in mainstream economics. According to this conception, understanding is constituted by analytical derivation from a set of fundamental economic axioms. We articulate this conception using the concept of economists' perfect model. Since the deductive links between the assumptions and the consequences are not transparent in ‘bottom‐up’ generative microsimulations, microsimulations cannot correspond to the perfect model and economists do not therefore consider them viable candidates for generating theories that enhance economic understanding. (shrink)

This article combines the idea of the active interview with insights from science studies and suggests that some concepts from science studies, like boundary objects and trading zones, should be utilized to understand and facilitate the production and analysis of data in a transcultural interview. This is illustrated by examples from interviews that the author conducted with women computer science students and faculty in a university in Malaysia. The article argues that the understanding of, as well as the performance of (...) the transcultural interview might benefit from the highly pragmatic character of scientific investigations, focusing on using locally available resources to produce knowledge; and that this in turn may enhance our capacity to do feminist research. (shrink)

Es soll ein Beitrag zur epistemischen Charakterisierung von Computersimulationen als jenseits von Experiment und Theorie geleistet werden. Es wird argumentiert, dass die in der Simulationstechnik eingesetzten Verfahren nicht numerische Lösungen liefern, sondern deren Dynamik mittels generativer Mechanismen imitieren. Die Computersimulationen in der Klimatologie werden als systematisches wie historisches Fallbeispiel behandelt. Erst "Simulationsexperimente" gestatten es, mittels Modellen eine Dynamik zu imitieren, ohne deren Grundgleichungen zu "lösen". /// Computer simulations will be characterized in epistemic respect as a method between experiment and theory. (...) It will be argued that simulations do not provide numerical solutions, rather they use generative mechanisms to imitate a dynamics. Climate science will be considered as a case both systematically and historically. Only simulation experiments allow to build models that imitate a dynamics without solving the relevant equations. (shrink)

Es soll ein Beitrag zur epistemischen Charakterisierung von Computersimulationen als jenseits von Experiment und Theorie geleistet werden. Es wird argumentiert, dass die in der Simulationstechnik eingesetzten Verfahren nicht numerische Lösungen liefern, sondern deren Dynamik mittels generativer Mechanismen imitieren. Die Computersimulationen in der Klimatologie werden als systematisches wie historisches Fallbeispiel behandelt. Erst "Simulationsexperimente" gestatten es, mittels Modellen eine Dynamik zu imitieren, ohne deren Grundgleichungen zu "lösen". /// Computer simulations will be characterized in epistemic respect as a method between experiment and theory. (...) It will be argued that simulations do not provide numerical solutions, rather they use generative mechanisms to imitate a dynamics. Climate science will be considered as a case both systematically and historically. Only simulation experiments allow to build models that imitate a dynamics without solving the relevant equations. (shrink)

This paper explores the relationship between epistemic integrity, virtue, and authority by offering a virtue epistemological reading of the defences of non-scientific beliefs, practices, and traditions in the writings of Paul Feyerabend. I argue that there was a robust epistemic rationale for those defences and that it can inform contemporary reflection on the epistemic authority of the sciences. Two common explanations of the purpose of those defences are rejected as lacking textual support. A third “pluralist” reading is judged more persuasive, (...) but found to be incomplete, owing to a failure to accommodate Feyerabend’s focus upon the integrity of scientists and the authority of science. I therefore suggest that the defences are more fully understood as defences of the epistemic integrity of scientists that take the form of critical exposures of failures by scientists to act with integrity. An appeal is made to contemporary virtue epistemology that clarifies Feyerabend’s implicit association of epistemic... (shrink)

Philosophy of biology, perhaps more than any other philosophy of science, is a discipline in flux. What counts as consensus and key arguments in certain areas changes rapidly.The publication of Contemporary Debates in Philosophy of Biology (2010 Wiley-Blackwell) is reviewed and is used as a catalyst to a discussion of the recent expansion of subjects and perspectives in the philosophy of biology as well as their diverse epistemological and methodological commitments.

Research communication in interdisciplinary research projects requires a way of demarcation of theory and knowledge that is easy to communicate, is inconsequential for the framework of concepts, results, and procedures within existing scientific disciplines, and abstains from trying to resolve the dispute between (neo)positivists and constructivists. A simple way of demarcation starts from the notion of language-independent and language-dependent reality. Currently, what passes for knowledge (“news”) and myth (“fake news”) depends, besides on sheer volume and frequency of the messages, increasingly (...) on the internal consistency of (computer) language-dependent reality and decreasingly on language-independent reality. All language is instruction, and knowledge is to know which instructions (that is, theory) are predictive of a result, state, or situation in language-independent reality. Any theory that doesn’t reduce outcome space, or contains one or more empirically/physically impossible instructions, or produces wrong predictions, or falls short of demonstration is not knowledge. (shrink)

In this paper, I analyze how recent calls to internationalize publication behavior affect research practices at an automotive engineering department in Germany. Automotive engineering is a field with traditionally rather scarce publication activity and strong connections to industry. Substantial authority to define suitable research problems and ways of organizing knowledge production on a daily basis was therefore reserved for local academic elites as well as corporate partners. However, as engineers are increasingly expected to prove their performance through publishing in international (...) peer-reviewed journals, the judgment as to what should be considered “interesting” or “robust” research is partly relegated to a gradually globalizing community of academic peers. This generates a variety of tensions with established ways of coordinating epistemic work at the department studied here. For example, the thematic interests of journals tend to exert a disruptive centrifugal pull in the context of an otherwise highly modular research culture, and possibilities to publish in international venues are unequally distributed across individual research projects. But while department members agree that there is a lack of fit between current practices and new expectations towards their publishing behavior, their opinions about the conclusions that should be drawn differ significantly. Some researchers argue that profound organizational changes are necessary to foster the academic rigor of German engineering research. Others believe that evaluation criteria should simply be adapted. This situation is arguably characteristic for research areas with a traditionally strong orientation to local stakeholders, and it suggests a need for more deliberative, participatory approaches to research evaluation in such fields. (shrink)

This paper offers a contribution to debates around integrative aspects of systems biology and engages with issues related to the circumstances under which physicists look at biological problems. We use oral history as one of the methodological tools to gather the empirical material, conducting interviews with physicists working in systems biology. The interviews were conducted at several institutions in Brazil, Germany, Israel and the U.S. Biological research has been increasingly dependent on computational methods, high-throughput technologies, and multidisciplinary skills. Quantitative scientists (...) are joining biological departments and collaborations between physicists and biologists are particularly vigorous. This state of affairs raises a number of questions, such as: What are the circumstances under which physicists approach biological problems in systems biology? What kind of interdisciplinary challenges must be tackled? The paper suggests that, concerning physicists’ move to work on biological systems, there are common reasons to move, the transition must be understood in terms of degrees, physicists have a rationale for simplifying systems, and distinct conceptions of model and modeling strategies are recurrent. We identified problems regarding linguistic clarity and integration of epistemological aims. We conclude that cultural unconformities within the systems biology community have important consequences to the flow of scientific knowledge. (shrink)

This paper offers a contribution to debates around integrative aspects of systems biology and engages with issues related to the circumstances under which physicists look at biological problems. We use oral history as one of the methodological tools to gather the empirical material, conducting interviews with physicists working in systems biology. The interviews were conducted at several institutions in Brazil, Germany, Israel and the U.S. Biological research has been increasingly dependent on computational methods, high-throughput technologies, and multidisciplinary skills. Quantitative scientists (...) are joining biological departments and collaborations between physicists and biologists are particularly vigorous. This state of affairs raises a number of questions, such as: What are the circumstances under which physicists approach biological problems in systems biology? What kind of interdisciplinary challenges must be tackled? The paper suggests that, concerning physicists’ move to work on biological systems, there are common reasons to move, the transition must be understood in terms of degrees, physicists have a rationale for simplifying systems, and distinct conceptions of model and modeling strategies are recurrent. We identified problems regarding linguistic clarity and integration of epistemological aims. We conclude that cultural unconformities within the systems biology community have important consequences to the flow of scientific knowledge. (shrink)

It is often said that computer simulations generate new knowledge about the empirical world in the same way experiments do. My aim is to make sense of such a claim. I first show that the similarities between computer simulations and experiments do not allow them to generate new knowledge but invite the simulationist to interact with simulations in an experimental manner. I contend that, nevertheless, computer simulations and experiments yield new knowledge under the same epistemic circumstances, independently of any features (...) they may share. (shrink)

Some philosophers see an analogy between simulation and experiment. But, once we acknowledge some similarities between computer simulations and experiments, can we conclude from them that simulations generate empirical knowledge, as experiments do? In this paper, I argue that the similarities between simulation and experiment give the scientist at most the illusion that she is conducting an experiment, but cannot seriously ground the analogy. However, it does not follow that experiments are always epistemologically superior to simulations. I analyze the cases (...) when simulations and experiments equally yield new empirical knowledge. (shrink)

In many scientific fields, today’s practices of empirical enquiry rely heavily on the production of images that display the investigated phenomena. And while scientific images of phenomena have been important for a long time, what is striking now is that scientists have found ways to visualize such widely different types of phenomena. In the past twenty or thirty years, we have become accustomed to seeing images of galaxies, of cells, of the human brain but also of blood flow or of (...) turbulent fluids. This success in making images relies first on the set of imaging devices that have flourished in the twentieth century, instruments such as radio-telescopes, electronic microscopes, MRI and many more. Without these... (shrink)

A rise of academic capitalism over the past four decades has been well documented within many research-intensive universities. Largely missing, however, are in-depth studies of how particularly situated academic groups manage the uncertainties that come with intermittent and fickle commercial funding streams in their daily research practice and problem choice. To capture the strategies scientists adopt under these conditions, this article provides an ethnographically detailed (and true) story about how a single project in Artificial Intelligence grew over several years from (...) a peripheral idea to the very center of an academic lab’s commercial portfolio. The analysis theorizes an epistemic form—nimble knowledge production—and documents three of its lab-level features: 1) rapid prototyping to keep sunk costs low, 2) shared search for “real world problems” rather than “theoretical” ones, and 3) nimble commitment to research problem choice. While similar forms of academic knowledge transfer have been lauded as “mode 2,” “innovative,” or “hybrid” for initiating cross-institutional collaboration and pushing science beyond disciplinary silos, this case suggests it can rely on fleeting attention to problems resistant to a quick fix. (shrink)

Stať se zabývá diskusemi o epistemologicko-metodologické roli simulací v soudobé vědě. Soustředí se nejprve na aktuálnost těchto diskusí v současné metodologii vědy a následně na její návaznost na určitou myšlenkovou tradici z osmdesátých let 20. století, kdy diskuse kolem modelování vyvolaly řadu otázek zpochybňujících tradiční pojmové distinkce, především mezi experimentem a teorií. Stať se přiklání v rámci těchto diskusí k názorům, které řadí simulace k novým a specifickým nástrojům vědy, jež také vyžadují novou a specifickou metodologii a epistemologické postavení. Pro (...) podporu této názorové pozice jsou uvedeny některé aktuální analýzy konkrétních příkladů simulací a jejich specifického postavení v moderní vědě. (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)

The problem of epistemic opacity in Artificial Intelligence is often characterised as a problem of intransparent algorithms that give rise to intransparent models. However, the degrees of transparency of an AI model should not be taken as an absolute measure of the properties of its algorithms but of the model’s degree of intelligibility to human users. Its epistemically relevant elements are to be specified on various levels above and beyond the computational one. In order to elucidate this claim, I first (...) contrast computer models and their claims to algorithm-based universality with cybernetics-style analogue models and their claims to structural isomorphism between elements of model and target system. While analogue models aim at perceptually or conceptually accessible model-target relations, computer models give rise to a specific kind of underdetermination in these relations that needs to be addressed in specific ways. I then undertake a comparison between two contemporary AI approaches that, although related, distinctly align with the above modelling paradigms and represent distinct strategies towards model intelligibility: Deep Neural Networks and Predictive Processing. I conclude that their respective degrees of epistemic transparency primarily depend on the underlying purposes of modelling, not on their computational properties. (shrink)

Syntactic and structural models specify relationships between their constituents but cannot show what outcomes their interaction would produce over time in the world. Simulation consists in iterating the states of a model, so as to produce behaviour over a period of simulated time. Iteration enables us to trace the implications and outcomes of inference rules and other assumptions implemented in the models that make up a theory. We apply this method to experiments which we treat as models of the particular (...) aspects of reality they are designed to investigate. Scientific experiments are constantly designed and re-designed in the context of implementation and use. They mediate between theoretical understanding and the practicalities of engaging with the empirical and social world. In order to model experiments we need to identify and represent features that all experiments have in common. We treat these features as parameters of a general model of experiment so that by varying these parameters different types of experiment can be modelled. (shrink)

World War II research into cryptography and computing produced methods, instruments and research communities that informed early research into artificial intelligence and semi-autonomous computing. Alan Turing and Claude Shannon in particular adapted this research into early theories and demonstrations of AI based on computers’ abilities to track, predict and compete with opponents. This formed a loosely bound collection of techniques, paradigms, and practices I call crypto-intelligence. Subsequent researchers such as Joseph Weizenbaum adapted crypto-intelligence but also reproduced aspects of its antagonistic (...) precepts. This was particularly true in the design and testing of chat bots. Here the ability to trick, fool, and deceive human and machine opponents was a premium, and practices of agent abuse were admired and rewarded. Recognizing the historical genesis of this particular variety of abuse can help researchers develop less antagonistic methodologies. (shrink)