Experimentation in Science

In the first part of this paper, I will sketch the main features of traditional models of evidence, indicating idealizations in such models that I regard as doing more harm than good. I will then proceed to elaborate on an alternative model of evidence that is functionalist, complex, dynamic, and contextual, which I will call DYNAMIC EVIDENTIAL FUNCTIONALISM. I will demonstrate its application to an illuminating example of scientific inquiry, and defend it from some likely objections. In the second part, (...) I will use that alternative to solve a variety of classic and contemporary problems in the literature on scientific evidence having to do with the empirical basis of science and the use of evidence in public policy. (shrink)

I argue that Binkley et al. use causal process tracing in conjunction with a natural trajectory experiment and two natural snapshot experiments in their re-examination of the Kaibab. This shows that Aldo Leopold may have been right about trophic cascade in the Kaibab in the 1920s, i.e., that there are good reasons to think that a loss of predators led to a deer irruption which decreased aspen recruitment. Using the different cause-finding practices in combination can strengthen causal inferences and mitigate (...) the shortcomings that each practice has. (shrink)

It is sometimes said that simulation can serve as epistemic substitute for experimentation. Such a claim might be suggested by the fast-spreading use of computer simulation to investigate phenomena not accessible to experimentation (in astrophysics, ecology, economics, climatology, etc.). But what does that mean? The paper starts with a clarification of the terms of the issue and then focuses on two powerful arguments for the view that simulation and experimentation are ‘epistemically on a par’. One is based on the claim (...) that, in experimentation, no less than in simulation, it is not the system under study that is manipulated but a system that ‘stands-in’ for it. The other one highlights the pervasive use of models in experimentation. It will be argued that these arguments, as compelling as they might seem, are each based on a mistaken interpretation of experimentation and that, far from simulation and experimentation being epistemically on a par, they do not have the same epistemic function, do not produce the same kind of epistemic results. (shrink)

I praise Franklin's full descriptions of important and exemplary experiments, and wish that he had said more about why they are exemplary.

It has been argued that supervenience generates unavoidable confounding problems for interventionist accounts of causation, to the point that we must choose between interventionism and supervenience. According to one solution, the dilemma can be defused by excluding non-causal determinants of an outcome as potential confounders. I argue that this solution undermines the methodological validity of causal tests. Moreover, we don’t have to choose between intervention-ism and supervenience in the first place. Some confounding problems are effectively circumvented by experimental designs routinely (...) employed in science. The remaining confounding issues concern the physical interpretation of variables and cannot be solved by choosing between interventionism and supervenience. (shrink)

Despite the conceptual problems in identifying the placebo effect, an increasing number of multidisciplinary inquiries rest on the assumption that there is a distinct class of effects, placebo effects. In this chapter, I argue against this assumption. I present cases and characterizations of the placebo effect as offered in the literature, and argue that the latter are subject to insurmountable problems. Moreover, I argue that identification of placebo effects as such is not useful for the three main purposes offered in (...) the literature. I then offer suggestions for why it may remain intuitive that some effects are placebo effects and close by noting the potential benefits of ultimately explaining away these intuitions. (shrink)

In 1981, David Hubel and Torsten Wiesel received the Nobel Prize for their research on cortical columns—vertical bands of neurons with similar functional properties. This success led to the view that “cortical column” refers to the basic building block of the mammalian neocortex. Since the 1990s, however, critics questioned this building block picture of “cortical column” and debated whether this concept is useless and should be replaced with successor concepts. This paper inquires which experimental results after 1981 challenged the building (...) block picture and whether these challenges warrant the elimination “cortical column” from neuroscientific discourse. I argue that the proliferation of experimental techniques led to a patchwork of locally adapted uses of the column concept. Each use refers to a different kind of cortical structure, rather than a neocortical building block. Once we acknowledge this diverse-kinds picture of “cortical column”, the elimination of column concept becomes unnecessary. Rather, I suggest that “cortical column” has reached conceptual retirement: although it cannot be used to identify a neocortical building block, column research is still useful as a guide and cautionary tale for ongoing research. At the same time, neuroscientists should search for alternative concepts when studying the functional architecture of the neocortex. keywords: Cortical column, conceptual development, history of neuroscience, patchwork, eliminativism, conceptual retirement. (shrink)

William Gilbert’s 1600 book, De magnete, greatly influenced early modern natural philosophy. The book describes an impressive array of physical experiments, but it also advances a metaphysical view at odds with the soon to emerge mechanical philosophy. That view was animism. I distinguish two kinds of animism – Aristotelian and Platonic – and argue that Gilbert was an Aristotelian animist. Taking Robert Boyle as an example, I then show that early modern arguments against animism were often effective only against Platonic (...) animism. In fact, unacknowledged traces of Aristotelian animism can be found in Boyle’s mechanical account of nature. This was Gilbert’s legacy. (shrink)

Ian Hacking’s Representing and Intervening is often credited as being one of the first works to focus on the role of experimentation in philosophy of science, catalyzing a movement which is sometimes called the “philosophy of experiment” or “new experimentalism”. In the 1980s, a number of other movements and scholars also began focusing on the role of experimentation and instruments in science. Philosophical study of experimentation has thus seemed to be an invention of the 1980s whose central figure is Hacking. (...) This article aims to assess this historical claim, made by Hacking himself as well as others. It does so first by highlighting how a broader perspective on the history of philosophy reveals this invention narrative to be incorrect, since experimentation was a topic of interest for earlier philosophers. Secondly, the article evaluates a revision of this historical claim also made by some philosophers of experiment: the rediscovery narrative, which frames Hacking and others as having rediscovered the work of these earlier authors. This second narratives faces problems as well. Therefore we develop a third narrative which we call the contextualist narrative. Rather than considering experimentation in an essentialist manner as a fixed research object that is either present or not in the work of specific authors, experimentation should be addressed through a narrative that asks in what way it becomes a philosophical problem for certain authors and for what purpose. Such contextualization enables a repositioning of Hacking’s philosophy of experiment in relation to the specific debates in which he intervened, such as the realism-antirealism debate, the Science Wars and the debate on incommensurability. (shrink)

Ian Hacking instigated a revolution in 20th century philosophy of science by putting experiments (“interventions”) at the top of a philosophical agenda that historically had focused nearly exclusively on representations (“theories”). In this paper, I focus on a set of conceptual tools Hacking (1992) put forward to understand how laboratory sciences become stable and to explain what such stability meant for the prospects of unity of science and kind discovery in experimental science. I first use Hacking’s tools to understand sources (...) of instability and disunity in rodent behavioral neuroscience. I then use them to understand recent grass-roots collaborative open science initiatives aimed at establishing stability in this research area and tease out some implications for unity of science and kind creation and discovery in cognitive neuroscience. -/- . (shrink)

This paper explores the experiments of the English mathematician Thomas Harriot on specific gravity in the years 1600-1605, as recorded in a series of manuscript notes in British Library Add. MS 6788. It examines the programme of reading undertaken by Harriot before these experiments, and describes a series of experiments conducted by him which compared the weight of a wide variety of substances in air and water. Harriot’s work is compared to that of his contemporary Marino Ghetaldi in Promotus Archimedis, (...) and the work of both mathematicians can be positioned in the context of sixteenth-century mathematical responses to the famous problem posed by Hiero, King of Syracuse to Archimedes. Harriot’s use of proportional mathematics in his experimental work and his application of this technique to problems in the alloying of metal and chymical analysis is examined in detail. (shrink)

The French physicist Jean Baptiste Perrin is widely credited with providing the conclusive argument for atomism. The most well-known part of Perrin’s argument is his description of thirteen different procedures for determining Avogadro’s number (N)–the number of atoms, ions, and molecules contained in a gram-atom, gram-ion, and gram-mole of a substance, respectively. Because of its success in ending the atomism debates Perrin’s argument has been the focus of much philosophical interest. The various philosophers, however, have reached different conclusions, not only (...) about the argument’s general rationale but also the role that the multiple determination of N played in it. This paper emphasizes the historical development of Perrin’s experimental work in order to understand the role that the multiple determination of molecular magnitudes played in his argument for molecular reality. It claims that Perrin used the multiple determination strategy to put forward an exceptionally strong no-coincidence argument to argue for both the correctness of the values for the molecular magnitudes determined and the validity of the auxiliary assumptions upon which the different determinations were based. The historicist approach also allows the identification of the elements responsible for the epistemic strength of Perrin’s no-coincidence argument. (shrink)

Multiple determination is the epistemic strategy of establishing the same result by means of multiple, independent procedures. It is an important strategy praised by both philosophers of science and practicing scientists. Despite the heavy appeal to multiple determination, little analysis has been provided regarding the specific grounds upon which its epistemic virtues rest. This article distinguishes between the various dimensions of multiple determination and shows how they can be used to evaluate the epistemic force of the strategy in particular cases. (...) Distinguishing between the various dimensions helps to resolve the disagreements regarding the relevance and epistemic import of multiple determination for scientific research. It also suggests a fruitful mode of interaction between the philosophy of science and accounts of scientific practice. (shrink)

Conducting research on animals is supposed to be valuable because it provides information on how human mechanisms work. But for the use of animal models to be ethically justified, it must be epistemically justified. The inference from an observation about an animal model to a conclusion about humans must be warranted for the use of animals to be moral. When researchers infer from animals to humans, it’s an extrapolation. Often non-human primates are used as animal models in laboratory behavioral research. (...) The target populations are humans and other non-human primates. I argue that the epistemology of extrapolation renders the use of non-human primates in laboratory behavioral research unreliable. If the model is relevantly similar to the target, then the experimental conditions introduce confounding variables. If the model is not relevantly similar to the target, then the observations of the model cannot be extrapolated to the target. Since using non-human primates in as animal models in laboratory behavioral research is not epistemically justified, using them as animal models in laboratory behavioral research is not ethically justified. (shrink)

The Historia densi et rari, published posthumously in 1658, is probably Francis Bacon’s most complex natural and experimental history. It contains observations and experimental reports, quantitative estimates and tables, and theoretical and methodological considerations, in a structure which has never been fully investigated. I provide here a fresh reading of this text from the perspective of scientific practices. I claim that Historia densi et rari represents a quantitative and instrumental investigation assembled with the help of Bacon’s philosophy of experiment as (...) developed in the Novum organum. I first discuss the role played in the Historia densi et rari by the various instances of special power in delineating the object of research. I then analyze the ways in which a special class of instances of special power, the “mathematical instances,” are used in the Historia densi et rari to make the inquiry both more precise, and more abstract. Bacon used mathematical instances to transform traditional recipes of pneumatics into quantitative, more general investigations into universal motions and processes. Finally, I discuss two examples of scientific practice at work: the attempt to use instruments to gradually define and clarify “proper” notions of “rarefaction” and “condensation,” and the redefinition of a metaphysical concept in instrumental and operational terms. (shrink)

This discussion centers on two desiderata: the role of measurement in information-gathering and physical interaction in scientific practice. By taking inspiration from van Fraassen’s view, we present a methodological account of perspectival measurement that addresses empirical practice where there is complex intervention, disagreeing results, and limited theory. The specific aim of our account is to provide a methodological prescription for developing measurement processes in the context of limited theory. The account should be useful to philosophers of science, who are interested (...) in the intersection between representation and intervention; scientists, who are interested in methodological suggestions for theory-development and reliability; and interdisciplinary researchers, who are interested in the intersections between the pandemic, built environments, and social processes. We apply the process-view of measurement to COVID-19, specifically, measuring replication in the SARS-CoV-2 virus. The aim is to show that our account tracks key elements—manipulation/intervention, independence, invariance, and theory-development—by organizing unfolding measurement processes. Additionally, we use our account to make prescriptive suggestions for measurement practice in the COVID-19 context by discussing the need to broaden measurement perspective on interaction, manipulation, and production. (shrink)

Experimental design is one aspect of a scientific method. A well-designed, properly conducted experiment aims to control variables in order to isolate and manipulate causal effects and thereby maximize internal validity, support causal inferences, and guarantee reliable results. Traditionally employed in the natural sciences, experimental design has become an important part of research in the social and behavioral sciences. Experimental methods are also endorsed as the most reliable guides to policy effectiveness. Through a discussion of some of the central concepts (...) associated with experimental design, including controlled variation and randomization, this chapter will provide a summary of key ethical issues that tend to arise in experimental contexts. In addition, by exploring assumptions about the nature of causation and by analyzing features of causal relationships, systems, and inferences in social contexts, this chapter will summarize the ways in which experimental design can undermine the integrity of not only social and behavioral research but policies implemented on the basis of such research. (shrink)

This paper explores how a set of observations on the weight of lead were interpreted and assessed between the 1540s and the 1630s across three different interconnecting disciplines: medicine, mineralogy and chemistry. The epistemic import of these discussions will be demonstrated by showing: 1) the changing role and articulation of experience and quantification in the investigation of metals; and 2) the notions associated with weight in different disciplinary frameworks. In medicine and mineralogy, weight was not considered as a specific subject (...) of inquiry in itself, but as a “sign” indicating other relevant properties of metals. In contrast, the chemistry tradition was increasingly concerned with the specific investigation of weight as a property of matter, as seen in the debates that took place in the “chemical revolution.” In addition, this study will reveal the versatility, polysemy, and parallel purposes of the recourse to experiential knowledge in different contexts, where the same “facts” operate within different disciplines. (shrink)

Numerous early modern experimentalists, including Galileo Galilei, Francis Bacon and Thomas Harriot, viewed one seemingly humble principle – that at a given volume, different substances can be identified by their particular weight, or specific gravity – as a fundamental key to the understanding of nature in general. Johannes Kepler’s Messekunst Archimedis of 1616 contains a striking summary of the experimental research on specific gravities in the long sixteenth-century. Counting himself amongst an extensive list of authors interested in this problem, Kepler (...) mentions not only natural philosophers or mathematicians interested in Archimedes. His account surprisingly includes humanists, instrument makers, antiquarians and assayers. Received histories of specific gravities often focus on antecedents of modern disciplinary concepts and methodologies, where instead, Kepler’s account suggests the existence of a heterogeneous group of early modern experts involved in experiments on the quantification of matter, at the intersection between the history of science, practical mathematics and the humanities. (shrink)

The multiple detections of gravitational waves by LIGO (the Laser Interferometer Gravitational-Wave Observatory), operated by Caltech and MIT, have been acclaimed as confirming Einstein's prediction, a century ago, that gravitational waves propagating as ripples in spacetime would be detected. Yunes and Pretorius (2009) investigate whether LIGO's template-based searches encode fundamental assumptions, especially the assumption that the background theory of general relativity is an accurate description of the phenomena detected in the search. They construct the parametrized post-Einsteinian (ppE) framework in response, (...) which broadens those assumptions and allows for wider testing under more flexible assumptions. Their methods are consistent with work on confirmation and testing found in Carnap (1936), Hempel (1969), and Stein (1992, 1994), with the following principles in common: that confirmation is distinct from testing, and that, counterintuitively, revising a theory's formal basis can make it more broadly empirically testable. These views encourage a method according to which theories can be made abstract, to define families of general structures for the purpose of testing. With the development of the ppE framework and related approaches, multi-messenger astronomy is a catalyst for deep reasoning about the limits and potential of the theoretical framework of general relativity. (shrink)

The enduring replication crisis in many scientific disciplines casts doubt on the ability of science to estimate effect sizes accurately, and in a wider sense, to self-correct its findings and to produce reliable knowledge. We investigate the merits of a particular countermeasure—replacing null hypothesis significance testing with Bayesian inference—in the context of the meta-analytic aggregation of effect sizes. In particular, we elaborate on the advantages of this Bayesian reform proposal under conditions of publication bias and other methodological imperfections that are (...) typical of experimental research in the behavioral sciences. Moving to Bayesian statistics would not solve the replication crisis single-handedly. However, the move would eliminate important sources of effect size overestimation for the conditions we study. (shrink)

Examining tensions between the past and present uses of scientific concepts can help clarify their contributions as tools in experimental practices. This point can be illustrated by considering the concepts of mental imagery and hallucinations: despite debates over their respective referential reliabilities remaining unresolved within their interdependent histories, both are used as independently stable concepts in neuroimaging experiments. Building on an account of how these concepts function as tools structured for pursuit of diverging goals in experiments, this paper explores this (...) tension by re-examining the continued reliance of each concept on inverse characterisations inherited from the nominally-discarded ‘mediator-view’ of sensory-like mental phenomena (SLMP). In doing so, I seek to demonstrate how examining unresolved tensions can help highlight that entrenched associations can remain both integral to, and obscured by, the uses of concepts as goal-directed tools within experimental practices. (shrink)

Neutral Theory is controversial in ecology. Ecologists and philosophers have diagnosed the source of the controversy as: its false assumption that individuals in different species within the same trophic level are ecologically equivalent, its conflict with Competition Theory and the adaptation of species, its role as a null hypothesis, and as a Lakatosian research programme. In this paper, I show why we should instead understand the conflict at the level of research programs which involve more than theory. The Neutralist and (...) Competitionist research programs borrow and construct theories, models, and experiments for various aims and given their home ecological systems. I present a holistic and pragmatic view of the controversy that foregrounds the interrelation between many kinds of practices and decisions in ecological research. (shrink)

Many questions about the fundamentals of some area take the form “What is …?” It does not come as a surprise then that, at the dawn of Western philosophy, Socrates asked the questions of what piety, courage, and justice are. Nor is it a wonder that the philosophical preoccupation with computer simulations centered, among other things, about the question of what computer simulations are. Very often, this question has been answered by stating that computer simulation is a species of a (...) well-known method, e.g., experimentation. Other answers claim at least a close relationship between computer simulation and another method. In any case, correct answers to the question of what a computer simulation is should help us to better understand what validation of simulations is. The aim of this chapter is to discuss the most important proposals to understand computer simulation in terms of another method and to trace consequences for validation. Although it has sometimes been claimed that computer simulations are experiments, there are strong reasons to reject this view. A more appropriate proposal is to say that computer simulations often model experiments. This implies that the simulation scientists should to some extent imitate the validation of an experiment. But the validation of computer simulations turns out to be more comprehensive. Computer simulations have also been conceptualized as thought experimentsThought experiment or close cousins of the latter. This seems true, but not very telling since thought experiments are not a standardStandard method and since it is controversial how they contribute to our acquisition of knowledge. I thus consider a specific view on thought experiments to make some progress on understanding simulations and their validation. There is finally a close connection between computer simulation and modeling, and it can be shown that the validation of a computer simulation is the validation of a specific model, which may either be thought to be mathematical or fictional. (shrink)

Traditionally, in the literature on robustness analysis objects are classified as genuine phenomena (natural objects, events, and processes) or artifacts (results produced in error). But much of biological measurement requires the manipulation of local experimental conditions in order to produce new effects. These types of intervention-based regularities are neither natural objects nor artifacts; characterizing them as either fails adequately to address key ontological properties as well as their role in scientific practice. It is argued that a new classification, based on (...) methodological considerations, can be useful in order to characterize experimental productions. Experimentally-useful context-sensitive objects are referred to as ‘artefacts’. To show how the new classification works and why it is instructive for scientific practice, two case studies are discussed. First, the puzzle of arsenic-consuming living organisms is analyzed, where under a set of specific experimental conditions a given organism was found to replace phosphorous with arsenic in its DNA. Second, ecological epigenetic measurement is discussed to show the complexity of variant effects in the context of lab, field, and computer measurements. It is argued that the line between artifacts and artefacts is fluid because theoretical, experimental, and practical considerations vary. (shrink)

In a well-cited book chapter, ecologist Jared Diamond characterizes three main types of experiment performed in community ecology: laboratory experiment, field experiment, and natural experiment. Diamond argues that each form of experiment has strengths and weaknesses, with respect to, for example, realism or the ability to follow a causal trajectory. But does Diamond’s typology exhaust the available kinds of cause-finding practices? Some social scientists have characterized something they call “causal process tracing.” Is this a fourth type of experiment or something (...) else? I examine Diamond’s typology and causal process tracing in the context of a case study concerning the dynamics of wolf and deer populations on the Kaibab Plateau in the 1920s, a case that has been used as a canonical example of a trophic cascade by ecologists but which has also been subject to controversy. I argue that ecologists have profitably deployed causal process tracing together with other types of experiment to help settle questions of causality in this case. It remains to be seen how widespread the use of causal process tracing outside of the social sciences is (or could be), but there are some potentially promising applications, particularly with respect to questions about specific causal sequences. (shrink)

Replicability is widely taken to ground the epistemic authority of science. However, in recent years, important published findings in the social, behavioral, and biomedical sciences have failed to replicate, suggesting that these fields are facing a “replicability crisis.” For philosophers, the crisis should not be taken as bad news but as an opportunity to do work on several fronts, including conceptual analysis, history and philosophy of science, research ethics, and social epistemology. This article introduces philosophers to these discussions. First, I (...) discuss precedents and evidence for the crisis. Second, I discuss methodological, statistical, and social-structural factors that have contributed to the crisis. Third, I focus on the philosophical issues raised by the crisis. Finally, I discuss proposed solutions and highlight the gaps that philosophers could focus on. (shrink)

Scientific reasoning represents complex argumentation patterns that eventually lead to scientific discoveries. Social epistemology of science provides a perspective on the scientific community as a whole and on its collective knowledge acquisition. Different techniques have been employed with the goal of maximization of scientific knowledge on the group level. These techniques include formal models and computer simulations of scientific reasoning and interaction. Still, these models have tested mainly abstract hypothetical scenarios. The present thesis instead presents data-driven approaches in social epistemology (...) of science. A data-driven approach requires data collection and curation for its further usage, which can include creating empirically calibrated models and simulations of scientific inquiry, performing statistical analyses, or employing data- mining techniques and other procedures. -/- We present and analyze in detail three co-authored research projects on which the thesis’ author was engaged during her PhD. The first project sought to identify optimal team composition in high energy physics laboratories using data-mining techniques. The results of this project are published in (Perović et al. 2016), and indicate that projects with smaller numbers of teams and team members outperform bigger ones. In the second project, we attempted to determine whether there is an epistemic saturation point in experimentation in high energy physics. The initial results from this project are published in (Sikimić et al. 2018). In the thesis, we expand on this topic by using computer simulations to test for biases that could induce scientists to invest in projects beyond their epistemic saturation point. Finally, in previous examples of data-driven analyses, citations are used as a measure of epistemic efficiency of projects in high energy physics. In order to additionally justify and analyze the usage of this parameter in their data-driven research, in the third project Perović & Sikimić (2019) analyzed and compared inductive patterns in experimental physics and biology with the reliability of citation records in these fields. They conclude that while citations are a relatively reliable measure of efficiency in high energy physics research, the same does not hold for the majority of research in experimental biology. -/- Additionally, contributions of the author that are for the first time published in this theses are: (a) an empirically calibrated model of scientific interaction of research groups in biology, (b) a case study of irregular argumentation patterns in some pathogen discoveries, and (c) an introductory discussion of the benefits and limitations of data- driven approaches to the social epistemology of science. Using computer simulations of an empirically calibrated model, we demonstrate that having several levels of hierarchy and division into smaller research sub-teams is epistemically beneficial for researchers in experimental biology. We also show that argumentation analysis in biology represents a good starting point for further data-driven analyses in the field. Finally, we conclude that a data-driven approach is informative and useful for science policy, but requires careful considerations about data collection, curation, and interpretation. (shrink)

The Greenough stereomicroscope, or “Stemi” as it is colloquially known among microscopists, is a stereoscopic binocular instrument yielding three-dimensional depth perception when working with larger microscopic specimens. It has become ubiquitous in laboratory practice since its introduction by the unknown scientist Horatio Saltonstall Greenough in 1892. However, because it enabled new experimental practices rather than new knowledge, it has largely eluded historical and epistemological investigation, even though its design, production, and reception in the scientific community was inextricably connected to the (...) new epistemological ideals of the life sciences caught between natural history and modern science. The development of the microscope will be contextualized within the scientific and technological landscape, showing how Greenough navigated his way through this terrain, and what led him to sow the seeds for the stereoscopic microscope. The historical controversy over the optical mechanism, through which the instrument would generate the desired depth perception, and how this quality was embedded into laboratory practice, will be examined. Subsequently, it will become evident that the specific image of nature produced by the stereoscopic microscope corresponded to the new ideals of the life sciences and their representation. (shrink)

Examining the historical development of scientific concepts is important for understanding the structured routines within which these concepts are currently used as goal-directed tools in experiments. To illustrate this claim, I will outline how the concepts of mental imagery and hallucinations each draw on an older interdependent set of associations that, although nominally-discarded, continues to structure their current independent uses for pursuing discrete experimental goals. In doing so, I will highlight how three strands of literature offer mutually instructive insights for (...) understanding how the uses of our current scientific concepts contribute to experimental practices. The first strand of literature includes recent scholarship examining how the uses of scientific concepts can enable scientific practices (e.g., Boon 2012; Brigandt 2012; Feest 2010; Steinle 2012). In this context, scientific concepts have been described as tools that function in ways that contribute to empirical knowledge; contributions that extend beyond their traditionally recognized roles in mental and linguistic representation. The second strand of literature can be glimpsed within the technoscientific studies focus on non-human agency; a focus that supports an account of the disciplined routines within which concepts are used as acting in analogous ways to the routinized participation of machines in experiments (Pickering 1995). The third strand of the literature draws attention to how the functions of concepts are grounded by the set of historically-contingent experimental practices (e.g., Canguilhem 2008; Tiles 1984). Although maintaining valuable differences, the focus of these three strands of literature are mutually instructive. Together, they offer a view of the uses of concepts as taken-for-granted tools that function within networks of subterranean associations; associations that are grounded by historically contingent experimental practices that structure how they act (in concert with human and material participants) within the temporally dynamic processes of scientific practice. Through examples drawn from a comparison of the concepts of mental imagery and hallucinations, I aim to demonstrate the value that the converging insights of these three strands of literature offer for studying the role of scientific concepts in experimental practices. (shrink)

Computer simulations and experiments share many important features. One way of explaining the similarities is to say that computer simulations just are experiments. This claim is quite popular in the literature. The aim of this paper is to argue against the claim and to develop an alternative explanation of why computer simulations resemble experiments. To this purpose, experiment is characterized in terms of an intervention on a system and of the observation of the reaction. Thus, if computer simulations are experiments, (...) either the computer hardware or the target system must be intervened on and observed. I argue against the first option using the non-observation argument, among others. The second option is excluded by e.g. the over-control argument, which stresses epistemological differences between experiments and simulations. To account for the similarities between experiments and computer simulations, I propose to say that computer simulations can model possible experiments and do in fact often do so. (shrink)

Traditionally, experimentation is considered a privileged means of confirmation. However, how experiments are a better confirmatory source than other strategies is unclear, and recent discussions have identified experiments with various modeling strategies on the one hand, and with ‘natural’ experiments on the other hand. We argue that experiments aiming to test theories are best understood as controlled investigations of specimens. ‘Control’ involves repeated, fine-grained causal manipulation of focal properties. This capacity generates rich knowledge of the object investigated. ‘Specimenhood’ involves possessing (...) relevant properties given the investigative target and the hypothesis in question. Specimens are thus representative members of the class which a hypothesis refers to. It is in virtue of both control and specimenhood that experiments provide powerful confirmatory evidence. This explains the distinctive power of experiments: although modelers exert extensive control, they do not exert this control over specimens; although natural experiments are of specimens, control is diminished. (shrink)

I recently argued that critical realists ought to adopt transcendental idealism in favour of Bhaskar’s transcendental realism. In response, Dustin McWherter presents two arguments against transcendental idealism: it is inferior to transcendental realism because it cannot account for the epistemic significance of experimentation, and it is internally inconsistent because it affirms the existence of things-in-themselves. This brief reply defends transcendental idealism against both objections.

In A Realist Theory of Science, Roy Bhaskar provides several transcendental arguments for critical realism – a position Bhaskar himself characterized as transcendental realism. Bhaskar provides an argument from perception and from the intelligibility of scientific experimentation, maintaining that transcendental realism is necessary for both. I argue that neither argument succeeds, and that transcendental idealism can better vindicate scientific practice than Bhaskar’s realism. Bhaskar’s arguments against the Kantian view fail, for they misrepresent the transcendental idealist position. I conclude that, if (...) they wish to retain their theoretical commitments, critical realists ought to be transcendental idealists. (shrink)

Observations, experiments and inquiries into the world of plants figure prominently in Francis Bacon’s Sylva Sylvarum. My purpose in this article is to offer a survey of this very rich and relatively under-investigated natural historical material, with the purpose of showing two things. First, I show that these inquiries unveil a sophisticated instrumental approach. Bacon treats plants as chemical laboratories in which one can investigate the fundamental processes of nature and the continuous ‘pneumatisation’ of matter. A detailed examination of this (...) instrumental approach can shed important light upon Bacon’s reductionist strategies of explanation. Second, I show that some of the experiments with plants recorded in the Sylva Sylvarum uncover several layers of theoretical vocabulary. In this experimental context, key concepts of Bacon’s natural philosophy are further refined and acquire new meanings. (shrink)

A heated debate surrounds the significance of reproducibility as an indicator for research quality and reliability, with many commentators linking a "crisis of reproducibility" to the rise of fraudulent, careless and unreliable practices of knowledge production. Through the analysis of discourse and practices across research fields, I point out that reproducibility is not only interpreted in different ways, but also serves a variety of epistemic functions depending on the research at hand. Given such variation, I argue that the uncritical pursuit (...) of reproducibility as an overarching epistemic value is misleading and potentially damaging to scientific advancement. Requirements for reproducibility, however they are interpreted, are one of many available means to secure reliable research outcomes. Furthermore, there are cases where the focus on enhancing reproducibility turns out not to foster high-quality research. Scientific communities and Open Science advocates should learn from inferential reasoning from irreproducible data, and promote incentives for all researchers to explicitly and publicly discuss their methodological commitments, the ways in which they learn from mistakes and problems in everyday practice, and the strategies they use to choose which research component of any project needs to be preserved in the long term, and how. (shrink)

There is widespread recognition at universities that a proper understanding of science is needed for all undergraduates. Good jobs are increasingly found in fields related to Science, Technology, Engineering, and Medicine, and science now enters almost all aspects of our daily lives. For these reasons, scientific literacy and an understanding of scientific methodology are a foundational part of any undergraduate education. Recipes for Science provides an accessible introduction to the main concepts and methods of scientific reasoning. With the help of (...) an array of contemporary and historical examples, definitions, visual aids, and exercises for active learning, the textbook helps to increase students’ scientific literacy. The first part of the book covers the definitive features of science: naturalism, experimentation, modeling, and the merits and shortcomings of both activities. The second part covers the main forms of inference in science: deductive, inductive, abductive, probabilistic, statistical, and causal. The book concludes with a discussion of explanation, theorizing and theory-change, and the relationship between science and society. The textbook is designed to be adaptable to a wide variety of different kinds of courses. In any of these different uses, the book helps students better navigate our scientific, 21st-century world, and it lays the foundation for more advanced undergraduate coursework in a wide variety of liberal arts and science courses. Selling Points Helps students develop scientific literacy—an essential aspect of _any_ undergraduate education in the 21 st century, including a broad understanding of scientific reasoning, methods, and concepts Written for all beginning college students: preparing science majors for more focused work in particular science; introducing the humanities’ investigations of science; and helping non-science majors become more sophisticated consumers of scientific information Provides an abundance of both contemporary and historical examples Covers reasoning strategies and norms applicable in all fields of physical, life, and social sciences, _as well as_ strategies and norms distinctive of specific sciences Includes visual aids to clarify and illustrate ideas Provides text boxes with related topics and helpful definitions of key terms, and includes a final Glossary with all key terms Includes Exercises for Active Learning at the end of each chapter, which will ensure full student engagement and mastery of the information include earlier in the chapter Provides annotated ‘For Further Reading’ sections at the end of each chapter, guiding students to the best primary and secondary sources available Offers a Companion Website, with: For Students: direct links to many of the primary sources discussed in the text, student self-check assessments, a bank of exam questions, and ideas for extended out-of-class projects For Instructors: a password-protected Teacher’s Manual, which provides student exam questions with answers, extensive lecture notes, classroom-ready Power Point presentations, and sample syllabi Extensive Curricular Development materials, helping any instructor who needs to create a Scientific Reasoning Course, ex nihilo. (shrink)

We argue that moral judgment studies currently conducted utilizing virtual reality (VR) devices must confront a dilemma due to how virtual environments are designed and how those environments are experienced. We begin by first describing the contexts present in paradigmatic cases of naturalistic moral judgments. We then compare these contexts to current traditional (vignette-based) and VR-based moral judgment research. We show that, contra to paradigmatic cases, vignette-based and VR-based moral judgment research often fails to accurately model the situational features of (...) paradigmatic moral judgments. In particular, we compare and contrast six recent VR studies to support our view that only simulations high in context-realism and perspectival-fidelity can produce ‘virtually real experiences.’ After analyzing the constituents of a virtually-real experience, we go on to propose guidelines for the creation of VR studies. These guidelines serve two purposes. First, we aim to increase the ecological validity of such studies in order to advance our understanding of moral judgments. Second, we believe that such guidelines should inform how Institutional Review Boards assess VR research. We show that our guidelines are urgently needed given the current lax review standards in place. (shrink)

. Scientists, for the most part, want to get it right. However, the social structures that govern their work undermine that aim, and this leads to nonreplicable findings in many fields. Because the social structure of science is a decentralized system, it is difficult to intervene. In this article, I discuss how we might do so, focusing on self-corrective-labor schemes. First, I argue that we need to implement a scheme that makes replication work outcome independent, systematic, and sustainable. Second, I (...) use these three criteria to evaluate extant proposals, which place the responsibility for replication on original researchers, consumers of their research, students, or many labs. Third, on the basis of a philosophical analysis of the reward system of science and the benefits of the division of cognitive labor, I propose a scheme that satisfies the criteria better: the professional scheme. This scheme has two main components. First, the scientific community is organized into two groups: discovery researchers, who produce new findings, and confirmation researchers, whose primary function is to do confirmation work. Second, a distinct reward system is established for confirmation researchers so that their career advancement is separated from whether they obtain positive experimental results. (shrink)

This paper defends the naïve thesis that the method of experiment has per se an epistemic superiority over the method of computer simulation, a view that has been rejected by some philosophers writing about simulation, and whose grounds have been hard to pin down by its defenders. I further argue that this superiority does not come from the experiment’s object being materially similar to the target in the world that the investigator is trying to learn about, as both sides of (...) dispute over the epistemic superiority thesis have assumed. The superiority depends on features of the question and on a property of natural kinds that has been mistaken for material similarity. Seeing this requires holding other things equal in the comparison of the two methods, thereby exposing that, under the conditions that will be specified, the simulation is necessarily epistemically one step behind the corresponding experiment. Practical constraints like feasibility and morality mean that scientists do not often face an other-things- equal comparison when they choose between experiment and simulation. Nevertheless, I argue, awareness of this superiority and of the general distinction between experiment and simulation is important for maintaining motivation to seek answers to new questions. (shrink)

This paper defends the naïve thesis that the method of experiment has per se an epistemic superiority over the method of computer simulation, a view that has been rejected by some philosophers writing about simulation, and whose grounds have been hard to pin down by its defenders. I further argue that this superiority does not come from the experiment’s object being materially similar to the target in the world that the investigator is trying to learn about, as both sides of (...) dispute over the epistemic superiority thesis have assumed. The superiority depends on features of the question and on a property of natural kinds that has been mistaken for material similarity. Seeing this requires holding other things equal in the comparison of the two methods, thereby exposing that, under the conditions that will be specified, the simulation is necessarily epistemically one step behind the corresponding experiment. Practical constraints like feasibility and morality mean that scientists do not often face an other-things- equal comparison when they choose between experiment and simulation. Nevertheless, I argue, awareness of this superiority and of the general distinction between experiment and simulation is important for maintaining motivation to seek answers to new questions. (shrink)

Revue de l'ouvrage "l'observation scientifique" de Vincent Israël-Jost. -/- Review of the book "l'observation scientifique" of Vincent Israël-Jost.

The question of when to stop an unsuccessful experiment can be difficult to answer from an individual perspective. To help to guide these decisions, we turn to the social epistemology of science and investigate knowledge inquisition within a group. We focused on the expensive and lengthy experiments in high energy physics, which were suitable for citation-based analysis because of the relatively quick and reliable consensus about the importance of results in the field. In particular, we tested whether the time spent (...) on a scientific project correlates with the project output. Our results are based on data from the high energy physics laboratory Fermilab. They point out that there is an epistemic saturation point in experimenting, after which the likelihood of obtaining major results drops. With time the number of less significant publications does increase, but highly cited ones do not get published. Since many projects continue to run after the epistemic saturation point, it becomes clearer that decisions made about continuing them are not always rational. (shrink)