I describe two traditions of philosophical accounts of evidence: one characterizes the notion in terms of signs of success, the other characterizes the notion in terms of conditions of success. The best examples of the former rely on the probability calculus, and have the virtues of generality and theoretical simplicity. The best examples of the latter describe the features of evidence which scientists appeal to in practice, which include general features of methods, such as quality and relevance, and general features (...) of evidence, such as patterns in data, concordance with other evidence, and believability of the evidence. Two infamous episodes from biomedical research help to illustrate these features. Philosophical characterization of these latter features—conditions of success—has the virtue of potential relevance to, and descriptive accuracy of, practices of experimental scientists. (shrink)

Emergent antireductionism in biological sciences states that even though all living cells and organisms are composed of molecules, molecular wholes are characterized by emergent properties that can only be understood from the perspective of cellular and organismal levels of composition. Thus, an emergence claim (molecular wholes are characterized by emergent properties) is thought to support a form of antireductionism (properties of higher-level molecular wholes can only be understood by taking into account concepts, theories and explanations dealing with higher-level entities). I (...) argue that this argument is flawed: even if molecular wholes are characterized by emergent properties and even if many successful explanations in biology are not molecular, there is no entailment between the two claims. (shrink)

This thesis develops and defends a Peircean conception of the task of metaphysics and critically compares it with recent anti-metaphysical forms of pragmatism. Peirce characterises metaphysics in terms of its place within his hierarchical classification of the sciences. According to the classification, metaphysics depends on logic for principles and provides principles to the natural and social sciences. This arrangement of the sciences is defended by appeal to Peirce's account of philosophy as 'cenoscopy'. The dependence of the natural and social sciences (...) on cenoscopy is then argued for on the basis of Peirce's rejection of psychologism and in terms of the necessity of abductive inference. Peirce's position is then compared with recent forms of pragmatism. While it is less naturalistic, Peirce's position is defended on pragmatist grounds. An account of Peirce on truth is then developed. Peirce's account of truth in terms of an ideal limit of inquiry is defended as consistent with recent, more deflationary, approaches. The truth of 'abstract propositions' is a matter of local indefeasibility. These abstract propositions are related to the 'absolute truth', understood as a single non-abstract proposition. The truth of this proposition is then understood in terms of an identity theory. Two conceptions of Peircean metaphysics are presented. Both are 'abductive'. Their task is to explain the possibility of success in inquiry. However, only one proposal accepts the notion of an absolute truth. The 'absolutist' proposal is defended as an interpretation of Peirce and as a contemporary option for pragmatist philosophers. The thesis concludes by comparing recent anti-metaphysical arguments due to Huw Price with the Peircean position. Room for the absolutist proposal is defended by means of an account of recent exchanges between Price and Robert Brandom on dispositional modality. (shrink)

Much has been written on Claude Bernard as a relentless promoter of the experimental method in physiology. Although the paper will touch Bernard’s experimental intuitions and his experimental practice as well, its focus is slightly different. It will address the laboratory, that is, the space in which experimentation in the life sciences takes place, and it will analyze the scattered remarks that Bernard made on the topic both in his books and in his posthumously published writings. The paper is divided (...) into four parts. The introduction briefly sketches the coming into being of the physiological laboratory in the first half of the nineteenth century. The second section will give an overview of Claude Bernard’s own itinerary in physiology and his personal laboratory experience. The third part of the paper will have a look at the image of the laboratory that Claude depicted in his Introduction to Experimental Medicine. In the subsequent section and by contrast, the image of the laboratory will come into focus as it can be reconstructed from Bernard’s notebook that he kept between 1850 and 1860, the Cahier rouge. Finally, the fifth part of the paper will spotlight Claude Bernard’s comparison of the sciences and the arts and their respective practices. A brief concluding statement tries to summarize Bernard’s epistemological position toward experimentally practiced science. (shrink)

Computer models and simulations have become, since the 1960s, an essential instrument for scientific inquiry and political decision making in several fields, from climate to life and social sciences. Philosophical reflection has mainly focused on the ontological status of the computational modeling, on its epistemological validity and on the research practices it entails. But in computational sciences, the work on models and simulations are only two steps of a longer and richer process where operations on data are as important as, (...) and even more time and energy-consuming than modeling itself. Drawing on two study cases—computational embryology and computational epidemiology—this article contributes to filling the gap by focusing on the operations of producing and re-using data in computational sciences. The different phases of the scientific and artisanal work of modelers include data collection, aggregation, homogenization, assemblage, analysis and visualization. The article deconstructs the ideas that data are self-evident informational aggregates and that data-driven approaches are exempted from theoretical work. More importantly, the paper stresses the fact that data are constructed and theory laden not only in their fabrication, but also in their reusing. (shrink)

Philosophers of science diverge on the question what drives the growth of scientific knowledge. Most of the twentieth century was dominated by the notion that theories propel that growth whereas experiments play secondary roles of operating within the theoretical framework or testing theoretical predictions. New experimentalism, a school of thought pioneered by Ian Hacking in the early 1980s, challenged this view by arguing that theory-free exploratory experimentation may in many cases effectively probe nature and potentially spawn higher evidence-based theories. Because (...) theories are often powerless to envisage workings of complex biological systems, theory-independent experimentation is common in the life sciences. Some such experiments are triggered by compelling observation, others are prompted by innovative techniques or instruments, whereas different investigations query big data to identify regularities and underlying organizing principles. A distinct fourth type of experiments is motivated by a major question. Here I describe two question-guided experimental discoveries in biochemistry: the cyclic adenosine monophosphate mediator of hormone action and the ubiquitin-mediated system of protein degradation. Lacking underlying theories, antecedent data bases, or new techniques, the sole guides of the two discoveries were respective substantial questions. Both research projects were similarly instigated by theory-free exploratory experimentation and continued in alternating phases of results-based interim working hypotheses, their examination by experiment, provisional hypotheses again, and so on. These two cases designate theory-free, question-guided, stepwise biochemical investigations as a distinct subtype of the new experimentalism mode of scientific enquiry. (shrink)

In the late 19th century, physiologists such as David Ferrier, Eduard Hitzig, and Hermann Munk argued that cerebral brain functions are localized in discrete structures. By the early 20th century, this became the dominant position. However, another prominent physiologist, Friedrich Goltz, rejected theories of cerebral localization and argued against these physiologists until his death in 1902. I argue in this paper that previous historical accounts have failed to comprehend why Goltz rejected cerebral localization. I show that Goltz adhered to a (...) falsificationist methodology, and I reconstruct how he designed his experiments and weighted different kinds of evidence. I then draw on the exploratory experimentation literature from recent philosophy of science to trace one root of the debate to differences in how the German localizers designed their experiments and reasoned about evidence. While Goltz designed his experiments to test hypotheses about the functions of predetermined cerebral structures, the localizers explored new functions and structures in the process of constructing new theories. I argue that the localizers relied on untested background conjectures to justify their inferences about functional organization. These background conjectures collapsed a distinction between phenomena they produced direct evidence for (localized symptoms) and what they reached conclusions about (localized functions). (shrink)

The life sciences are said to be in the midst of a replication crisis because a majority of published results are irreproducible, and scientists rarely replicate existing data. Here I argue that point 2 of this assessment is flawed because there is a hitherto unidentified form of replication in the experimental life sciences, which I call ‘microreplications’. Using a case study from biochemistry, I illustrate how MRs depend on a key element of experimentation, namely, experimental controls. I end by reflecting (...) on what MRs mean for the broader debate about the replication crisis. (shrink)

Observation and experiment as categories for analysing scientific practice have a long pedigree in writings on science. There has, however, been little attempt to delineate observation and experiment with respect to analysing scientific practice; in particular, scientific experimentation, in a systematic manner. Someone who has presented a systematic account of observation and experiment as categories for analysing scientific experimentation is Ian Hacking. In this paper, I present a detailed analysis of Hacking’s observation versus experiment account. Using a range of cases (...) from various fields of scientific enquiry, I argue that the observation versus experiment account is not an adequate framework for delineating scientific experimentation in a systematic manner. (shrink)

ArgumentIn the theory-dominated view of scientific experimentation, all relations of theory and experiment are taken on a par; namely, that experiments are performed solely to ascertain the conclusions of scientific theories. As a result, different aspects of experimentation and of the relations of theory to experiment remain undifferentiated. This in turn fosters a notion of theory-ladenness of experimentation (TLE) that is toocoarse-grainedto accurately describe the relations of theory and experiment in scientific practice. By contrast, in this article, I suggest that (...) TLE should be understood as anumbrella conceptthat has different senses. To this end, I introduce a three-fold distinction among the theories of high-energy particle physics (HEP) as background theories, model theories, and phenomenological models. Drawing on this categorization, I contrast two types of experimentation, namely, “theory-driven” and “exploratory” experiments, and I distinguish between the “weak” and “strong” senses of TLE in the context of scattering experiments from the history of HEP. This distinction enables identifying the exploratory character of the deep-inelastic electron-proton scattering experiments – performed at the Stanford Linear Accelerator Center (SLAC) between the years 1967 and 1973 – thereby shedding light on a crucial phase of the history of HEP, namely, the discovery of “scaling,” which was the decisive step towards the construction of quantum chromo-dynamics as a gauge theory of strong interactions. (shrink)

Exploratory inquiry has difficulty attracting research funding because funding agencies have little sense of how to detect good science in exploratory contexts. After documenting and explaining the focus on hypothesis testing among a variety of institutions responsible for distinguishing between good and bad science, I analyze the NIH grant review process. I argue that a good explanation for the focus on hypothesis testing—at least at the level of science funding agencies—is the fact that hypothesis-driven research is relatively easy to appraise. (...) I then explore one method by which we might gauge the epistemic merits of different styles of inquiry. (shrink)

In this article, I argue that genomic programs are not substitutes for multi-causal molecular mechanistic explanations of inheritance, but abstract representations of the same sort as mechanism schemas already described in the philosophical literature. On this account, the program analogy is not reductionistic and does not ignore or underestimate the active contribution of epigenetic elements to phenotypes and development. Rather, genomic program representations specifically highlight the genomic determinants of inheritance and their organizational features at work in the wider context of (...) the mechanisms of genome expression. (shrink)

Exploratory experiments are widely characterized as experiments that do not test hypotheses. Experiments that do test hypotheses are characterized as confirmatory experiments. Philosophers have pointed out that research programmes can be both confirmatory and exploratory. However, these definitions preclude single experiments being characterized as both exploratory and confirmatory; how can an experiment test and not test a hypothesis? Given the intuition that some experiments are exploratory, some are confirmatory, and some are both, a recharacterization of the relationship between exploratory and (...) confirmatory experimentation is needed. I discuss ‘phase IV’ trials to show what this recharacterization could look like. Phase IV trials can be exploratory and confirmatory insofar as they concurrently test hypotheses and explore for unforeseen phenomena. Even if it is uncontroversial that a single experiment can have multiple aims, the recharacterization of the relationship between exploratory and confirmatory experimentation is still required for these aims to be held together coherently. I offer an alternative characterization of the relationship between exploratory and confirmatory experimentation where the former remains a distinct kind of experimentation but is not characterized as non-hypothesis-testing. (shrink)

Whether we live in a world of autonomous things, or a world of interconnected processes in constant flux, is an ancient philosophical debate. Modern biology provides decisive reasons for embracing the latter view. How does one understand the practices and outputs of science in such a dynamic, ever-changing world - and particularly in an emergency situation such as the COVID-19 pandemic, where scientific knowledge has been regarded as bedrock for decisive social interventions? We argue that key to answering this question (...) is to consider the role of the activity of reification within the research process. Reification consists in the identification of more or less stable features of the flux, and treating these as constituting stable things. As we illustrate with reference to biological and biomedical research on COVID-19, reification is a necessary component of any process of inquiry and comes in at least two forms: means reification, when researchers create objects meant to capture features of the world, or phenomena, in order to be able to study them; and target reification, when researchers infer an understanding of phenomena from an investigation of the epistemic objects created to study them. We note that both objects and phenomena are dynamic processes and argue that have no reason to assume that changes in objects and phenomena track one another. We conclude that failure to acknowledge these forms of reification and their epistemic role in scientific inquiry can have dire consequences for how the resulting knowledge is interpreted and used. (shrink)

The notion of exploratory modeling constitutes a powerful heuristic tool for historical-epistemological analysis and especially for studying concept formation. I will show this by means of a case study from the history of particle physics: the formation of the concept of “strangeness” in the early 1950s at the interface of theory and experiment. Strangeness emerged from a broad space of possibilities opened up by exploratory modeling by authors working in communication and competition, and constructing both new questions and new answers. (...) A systematic focus on exploratory modeling also helps compensate a bias towards the “right” developments still often present in historical investigations of theoretical work. (shrink)

The aim of this work is to elucidate John F. W. Herschel’s distinctive contribution to nineteenth-century British inductivism by exploring his understanding of experimental methods. Drawing on both his explicit discussion of experiment in his Preliminary Discourse on Natural Philosophy and his published account of experiments he conducted in the domain of electromagnetism, I argue that the most basic principle underlying Herschel’s epistemology of experiment is that experiment enables a particular kind of lower-level experimental understanding of phenomena. Experimental practices provide (...) knowledge of a particular phenomenon as a genuine effect produced under precise material conditions whose connections with other phenomena can be traced by variations in experimental parameters. The orienting concern of experimental inquiry seems to be the production of a secure understanding of phenomena even if it has no direct theoretical significance. Insofar as one can generate this lower-level understanding, it can function both as a fertile source for explanatory principles about phenomena and as a body of evidence against which one can test the adequacy of an explanatory hypothesis. This project provides evidence of Herschel’s inductivism not merely as a philosophical approach to understanding the sciences but as a methodological commitment in scientific practice. (shrink)

According to Joshua Alexander, philosophers use intuitions routinely as a form of evidence to test philosophical theories but experimental philosophy demonstrates that these intuitions are unreliable and unrepresentative.1 According to Herman Cappelen, philosophers never use intuitions as evidence (despite the vacuous sentential leader ‘intuitively’) and experimental philosophy lacks a rationale for its much-touted existence.2 That two books are diametrically opposed on methodology in philosophy is not noteworthy. But eyebrows might be raised at such contradictory accounts of the phenomenology of philosophical (...) inquiry. What is it that (analytic) philosophers do? Why is it so difficult to achieve consensus on the professional activities in which we engage? (shrink)

Along three measurements at the Large Hadron Collider (LHC), a high energy particle accelerator, we analyze procedures and consequences of exploratory experimentation (EE). While all of these measurements fulfill the requirements of EE: probing new parameter spaces, being void of a target theory and applying a broad range of experimental methods, we identify epistemic differences and suggest a classification of EE. We distinguish classes of EE according to their respective goals: the exploration where an established global theory cannot provide the (...) details of a local phenomenon, exploration of an astonishing discovery and exploration to find a new entity. We find that these classes also differ with respect to the existence of an identifiable target and their impact on the background theory. The characteristics distinguish EE from other kinds of experimentation, even though these different kinds have not yet been systematically studied. The formal rigor and precision of LHC physics facilitates to analyze concept formation in its early state. In particular we emphasize the importance for nil–results for conceptualization and argue that conceptualization can also be achieved from nil–results only. (shrink)

-/- The aim of this paper is to grasp the relevant distinctions between various ways in which models and simulations in Artificial Intelligence (AI) relate to cognitive phenomena. In order to get a systematic picture, a taxonomy is developed that is based on the coordinates of formal versus material analogies and theory-guided versus pre-theoretic models in science. These distinctions have parallels in the computational versus mimetic aspects and in analytic versus exploratory types of computer simulation. The proposed taxonomy cuts across (...) the traditional dichotomies between symbolic and embodied AI, general intelligence and symbol and intelligence and cognitive simulation and human/non-human-like AI. -/- According to the taxonomy proposed here, one can distinguish between four distinct general approaches that figured prominently in early and classical AI, and that have partly developed into distinct research programs: first, phenomenal simulations (e.g., Turing’s “imitation game”); second, simulations that explore general-level formal isomorphisms in pursuit of a general theory of intelligence (e.g., logic-based AI); third, simulations as exploratory material models that serve to develop theoretical accounts of cognitive processes (e.g., Marr’s stages of visual processing and classical connectionism); and fourth, simulations as strictly formal models of a theory of computation that postulates cognitive processes to be isomorphic with computational processes (strong symbolic AI). -/- In continuation of pragmatic views of the modes of modeling and simulating world affairs, this taxonomy of approaches to modeling in AI helps to elucidate how available computational concepts and simulational resources contribute to the modes of representation and theory development in AI research—and what made that research program uniquely dependent on them. (shrink)

Although true in some aspects, the suggested characterization of today’s science as a dichotomy between traditional science and data-driven science misses some of the nuance, complexity, and possibility that exists between the two positions. Part of the problem is the claim that Data Science works without theories. There are many theories behind the data that are used in science. However, for data science, the only theories that matter are those in mathematics, statistics, and computer science. In this conceptual paper, we (...) add two other philosophy of science tenets, experiments and data, to the discussion to create a more nuanced view of how data science uses theories. Following Ihde’s concept of technoscience and the incessant quest for more precision, magnification, and resolution, we argue that technology-driven science created a need for more technology-driven science, culminating in data science. Further, we adapt Hacking and Galison’s views on physics to argue that data science is also an experimental science, which uses data objects in experiments. Drawing from Heelan, we called these objects “data-objects-for-knowing”. Finally, we conclude that data science is a science to study artificially created phenomena—a science to study the data manipulated by the equations and operations of AI. It disregards the connections between data and the real world that were carefully built by the theories from other sciences. In the experiments of data science, data are the world itself. The knowledge created by data science is purposely disconnected from any theory from other sciences; it is a knowledge for the sake of itself. (shrink)

To explain their role in discovery and contrast them with theory-driven research, philosophers of science have characterized exploratory experiments in terms of what they lack: namely, that they lack direction from what have been called “local theories” of the target system or object under investigation. I argue that this is incorrect: it’s not whether or not there is direction from a local theory that matters, but instead how such a theory is used to direct an experiment that matters. Appealing to (...) contemporary exploratory experiments that involve the use of experimental techniques—specifically, examples where scientists explore the interaction of neural activity and human behavior by magnetically stimulating brains—I argue that local theories of a target system can inform auxiliary hypotheses in exploratory experiments, which direct these experiments. These examples illustrate how local theories can direct the exploration of target systems where researchers do not aim to evaluate these theories. (shrink)

1. Overview and organizing themes 2. Historical Review: Aristotle to Mill 3. Logic of method and critical responses 3.1 Logical constructionism and Operationalism 3.2. H-D as a logic of confirmation 3.3. Popper and falsificationism 3.4 Meta-methodology and the end of method 4. Statistical methods for hypothesis testing 5. Method in Practice 5.1 Creative and exploratory practices 5.2 Computer methods and the ‘third way’ of doing science 6. Discourse on scientific method 6.1 “The scientific method” in science education and as seen (...) by scientists 6.2 Privileged methods and ‘gold standards’ 6.3 Scientific method in the court room 6.4 Deviating practices 7. Conclusion Bibliography Academic Tools Other Internet Resources Related Entries . (shrink)

Experimentation represents today a ‘hot’ topic in computing. If experiments made with the support of computers, such as computer simulations, have received increasing attention from philosophers of science and technology, questions such as “what does it mean to do experiments in computer science and engineering and what are their benefits?” emerged only recently as central in the debate over the disciplinary status of the discipline. In this work we aim at showing, also by means of paradigmatic examples, how the traditional (...) notion of controlled experiment should be revised to take into account a part of the experimental practice in computing along the lines of experimentation as exploration. Taking inspiration from the discussion on exploratory experimentation in the philosophy of science—experimentation that is not theory-driven—we advance the idea of explorative experiments that, although not new, can contribute to enlarge the debate about the nature and role of experimental methods in computing. In order to further refine this concept we recast explorative experiments as socio-technical experiments, that test new technologies in their socio-technical contexts. We suggest that, when experiments are explorative, control should be intended in a posteriori form, in opposition to the a priori form that usually takes place in traditional experimental contexts. (shrink)

Recently, biologists have argued that data - driven biology fosters a new scientific methodology; namely, one that is irreducible to traditional methodologies of molecular biology defined as the discovery strategies elucidated by mechanistic philosophy. Here I show how data - driven studies can be included into the traditional mechanistic approach in two respects. On the one hand, some studies provide eliminative inferential procedures to prioritize and develop mechanistic hypotheses. On the other, different studies play an exploratory role in providing useful (...) generalizations to complement the procedure of prioritization. Overall this paper aims to shed light on the structure of contemporary research in molecular biology. (shrink)

Driven by an appreciation of the field’s early stage of development, I apply the concept of exploratory experimentation, originally put forward in the late 90s philosophy of biology, to current research in cognitive neuroscience. I concentrate on functional magnetic resonance imaging and how this wide-spread technique is used, from experimental design to data analysis. I claim that, although subject to certain significant modifications with respect to the concept’s original rendering, the exploratory character of neuroimaging experiments can be appreciated considering their (...) goals, centered on the stabilization of experimental systems for phenomenological description, and the relevance of their methodological facet. Although I do not claim that there is a specific kind of experiment that one can single out as definitely exploratory, exploration can be seen as a general trait imbuing fMRI-based experimentation. (shrink)

Computational modeling is one of the primary approaches to constructing protein–protein interfaces in the laboratory. The algorithm-driven computational protein design has been successfully applied to the construction of functional proteins with improved binding affinity and increased thermostability. It is intriguing how a computational protein modeling approach can construct and shape the reality of new functional proteins from scratch. I articulate an account of abstraction and exploration-driven strategies in this computational endeavor. I aim to show that how a computational modelling approach, (...) which is laden with mathematics and algorithms, can have a constructive force on the target protein. (shrink)

It is argued that in deterministic contexts evidence for causal relations states whether a boundary condition makes a difference or not to a phenomenon. In order to substantiate the analysis, I show that this difference/indifference making is the basic type of evidence required for eliminative induction in the tradition of Francis Bacon and John Stuart Mill. To this purpose, an account of eliminative induction is proposed with two distinguishing features: it includes a method to establish the causal irrelevance of boundary (...) conditions by means of indifference making, which is called strict method of agreement, and it introduces the notion of a background against which causal statements are evaluated. Causal statements thus become three-place-relations postulating the relevance or irrelevance of a circumstance C to the examined phenomenon P with respect to a background B of further conditions. To underline the importance of evidence in terms of difference/indifference making, I sketch two areas, in which eliminative induction is extensively used in natural and engineering sciences. One concerns exploratory experiments, the other engineering design methods. Given that a method is discussed that has been used for centuries, I make no claims to novelty in this paper, but hope that the combined discussion of several topics that are still somewhat underrepresented in the philosophy of science literature is of some merit. (shrink)

Recent claims, mainly from computer scientists, concerning a largely automated and model-free data-intensive science have been countered by critical reactions from a number of philosophers of science. The debate suffers from a lack of detail in two respects, regarding the actual methods used in data-intensive science and the specific ways in which these methods presuppose theoretical assumptions. I examine two widely-used algorithms, classificatory trees and non-parametric regression, and argue that these are theory-laden in an external sense, regarding the framing of (...) research questions, but not in an internal sense concerning the causal structure of the examined phenomenon. With respect to the novelty of data-intensive science, I draw an analogy to exploratory as opposed to theory-directed experimentation. (shrink)

I propose a framework that explicates and distinguishes the epistemic roles of data and models within empirical inquiry through consideration of their use in scientific practice. After arguing that Suppes’ characterization of data models falls short in this respect, I discuss a case of data processing within exploratory research in plant phenotyping and use it to highlight the difference between practices aimed to make data usable as evidence and practices aimed to use data to represent a specific phenomenon. I then (...) argue that whether a set of objects functions as data or models does not depend on intrinsic differences in their physical properties, level of abstraction or the degree of human intervention involved in generating them, but rather on their distinctive roles towards identifying and characterizing the targets of investigation. The paper thus proposes a characterization of data models that builds on Suppes’ attention to data practices, without however needing to posit a fixed hierarchy of data and models or a highly exclusionary definition of data models as statistical constructs. (shrink)

I propose a framework that explicates and distinguishes the epistemic roles of data and models within empirical inquiry through consideration of their use in scientific practice. After arguing that Suppes’ characterization of data models falls short in this respect, I discuss a case of data processing within exploratory research in plant phenotyping and use it to highlight the difference between practices aimed to make data usable as evidence and practices aimed to use data to represent a specific phenomenon. I then (...) argue that whether a set of objects functions as data or models does not depend on intrinsic differences in their physical properties, level of abstraction or the degree of human intervention involved in generating them, but rather on their distinctive roles towards identifying and characterizing the targets of investigation. The paper thus proposes a characterization of data models that builds on Suppes’ attention to data practices, without however needing to posit a fixed hierarchy of data and models or a highly exclusionary definition of data models as statistical constructs. (shrink)

In this paper, I propose an account that accommodates the possibility of experimentation being exploratory in cases where the procedures necessary to plan and perform an experiment are dependent on the theoretical accounts of the phenomena under investigation. The present account suggests that experimental exploration requires the implementation of an exploratory procedure that serves to extend the range of possible outcomes of an experiment, thereby enabling it to pursue its objectives. Furthermore, I argue that the present account subsumes the notion (...) of exploratory experimentation, which is often attributed in the relevant literature to the works of Friedrich Steinle and Richard Burian, as a particular type of experimental exploration carried out in the special cases where no well-formed theoretical framework of the phenomena under investigation exists. I illustrate the present account in the context of the ATLAS experiment at CERN’s Large Hadron Collider, where the long-sought Higgs boson has been discovered in 2012. I argue that the data selection procedure carried out in the ATLAS experiment illustrates an exploratory procedure in the sense suggested by the present account. I point out that this particular data selection procedure is theory-laden in that its implementation is crucially dependent on the theoretical models of high energy particle physics which the ATLAS experiment is aimed to test. However, I argue that the foregoing procedure is not driven by the above-mentioned theoretical models, but rather by a particular data selection strategy. I conclude that the ATLAS experiment illustrates that, contrary to what previous studies have suggested, there are cases of experimentation in which exploration serves to test theoretical predictions and that theory-ladenness plays an essential role in experimentation being exploratory. (shrink)

The aim of this paper is to grasp the relevant distinctions between various ways in which models and simulations in Artificial Intelligence (AI) relate to cognitive phenomena. In order to get a systematic picture, a taxonomy is developed that is based on the coordinates of formal versus material analogies and theory-guided versus pre-theoretic models in science. These distinctions have parallels in the computational versus mimetic aspects and in analytic versus exploratory types of computer simulation. The proposed taxonomy cuts across the (...) traditional dichotomies between symbolic and embodied AI, general intelligence and symbol and intelligence and cognitive simulation and human/non-human-like AI. -/- According to the taxonomy proposed here, one can distinguish between four distinct general approaches that figured prominently in early and classical AI, and that have partly developed into distinct research programs: first, phenomenal simulations (e.g., Turing’s “imitation game”); second, simulations that explore general-level formal isomorphisms in pursuit of a general theory of intelligence (e.g., logic-based AI); third, simulations as exploratory material models that serve to develop theoretical accounts of cognitive processes (e.g., Marr’s stages of visual processing and classical connectionism); and fourth, simulations as strictly formal models of a theory of computation that postulates cognitive processes to be isomorphic with computational processes (strong symbolic AI). -/- In continuation of pragmatic views of the modes of modeling and simulating world affairs, this taxonomy of approaches to modeling in AI helps to elucidate how available computational concepts and simulational resources contribute to the modes of representation and theory development in AI research—and what made that research program uniquely dependent on them. (shrink)

Traditional frameworks for evaluating scientific models have tended to downplay their exploratory function; instead they emphasize how models are inherently intended for specific phenomena and are to be judged by their ability to predict, reproduce, or explain empirical observations. By contrast, this paper argues that exploration should stand alongside explanation, prediction, and representation as a core function of scientific models. Thus, models often serve as starting points for future inquiry, as proofs of principle, as sources of potential explanations, and as (...) a tool for reassessing the suitability of the target system. This is illustrated by a case study of the varied career of reaction-diffusion models in the study of biological pattern formation, which was initiated by Alan Turing in a classic 1952 paper. Initially regarded as mathematically elegant, but biologically irrelevant, demonstrations of how, in principle, spontaneous pattern formation could occur in an organism, such Turing models have only recently rebounded, thanks to advances in experimental techniques and computational methods. The long-delayed vindication of Turing’s initial model, it is argued, is best explained by recognizing it as an exploratory tool. (shrink)

Data production in experimental sciences depends on localised experimental systems, but the epistemic properties of data transcend the contingencies of the processes that produce them. Philosophers often believe that experimental systems instantiate but do not produce the epistemic properties of data. In this paper, we argue that experimental systems' local functioning entails intrinsic capacities to produce the epistemic properties of data. We develop this idea by applying Derrida's model of arche-writing to study a case of theory-oriented experimental practice. Derrida's model (...) relativises or dissolves the conceptual distinction between the moment of data production and a subsequent moment of data dissemination. It thus has consequences for understanding both data production (despite being intrinsically local, data production a priori generates transferrable and modellable information) and data dissemination (when modelling information, researchers needs to refer this information to the context of its production). We study a case of data production in a non-exploratory experimental system designed to test a pre-existing hypothesis in visual neuroscience. A case of theory-oriented experimental practice should allow us to identify the autonomous functioning of experimental systems in data production more clearly, insofar as it allows us to study the limits of pre-existing theory in the activities of these systems. We suggest that pre-existing concepts, hypotheses and theories condition the relevance but not the production of experimental data. (shrink)

In this article, I focus on the role of computer simulations as exploratory strategies. I begin by establishing the non-theory-driven nature of simulations. This refers to their ability to characterize phenomena without relying on a predefined conceptual framework that is provided by an implemented mathematical model. Drawing on Steinle’s notion of exploratory experimentation and Gelfert’s work on exploratory models, I present three exploratory strategies for computer simulations: (1) starting points and continuation of scientific inquiry, (2) varying the parameters, and (3) (...) scientific prototyping. (shrink)

We argue that microbiome research should be more reflective on the methods that it relies on to build its datasets due to the danger of facing a methodological problem which we call “epistemic misalignment.” An epistemic misalignment occurs when the method used to answer specific scientific questions does not track justified answers, due to the material constraints imposed by the very method. For example, relying on 16S rRNA to answer questions about the function of the microbiome generates epistemic misalignments, due (...) to the different temporal scales that 16S rRNA provides information about and the temporal scales that are required to know about the functionality of some microorganisms. We show how some of these exist in contemporary microbiome science and urge microbiome scientists to take some measures to avoid them, as they may question the credibility of the field as a whole. (shrink)

Discussions of reproducibility are casting doubts on the credibility of experimental outcomes in the life sciences. Although experimental evolution is not typically included in these discussions, this field is also subject to low reproducibility, partly because of the inherent contingencies affecting the evolutionary process. A received view in experimental studies more generally is that standardization (i.e., rigorous homogenization of experimental conditions) is a solution to some issues of significance and internal validity. However, this solution hides several difficulties, including a reduction (...) of external validity and reproducibility. After explaining the meaning of these two notions in the context of experimental evolution, we import from the fields of animal research and ecology and suggests that systematic heterogenization of experimental factors could prove a promising alternative. We also incorporate into our analysis some philosophical reflections on the nature and diversity of research objectives in experimental evolution. (shrink)