To a large extent, the evidential base of claims in the philosophy of science has switched from thought experiments to case studies. We argue that abandoning thought experiments was a wrong turn, since they can effectively complement case studies. We make our argument via an analogy with the relationship between experiments and observations within science. Just as experiments and ‘natural’ observations can together evidence claims in science, each mitigating the downsides of the other, so too can thought experiments and case (...) studies be mutually supporting. After presenting the main argument, we look at potential concerns about thought experiments, suggesting that a judiciously applied mixed-methods approach can overcome them. (shrink)

This paper explores the epistemological challenges in using computer simulations for two distinct goals: explanation via hypothesis-testing and prediction. It argues that each goal requires different strategies for justifying inferences drawn from simulation results due to different practical and conceptual constraints. The paper identifies unique and shared strategies researchers employ to increase confidence in their inferences for each goal. For explanation via hypothesis-testing, researchers need to address the underdetermination, interpretability, and attribution challenges. In prediction, the emphasis is on the model's (...) ability to generalize across multiple domains. Shared strategies researchers employ to increase confidence in inferences are empirical corroboration of theoretical assumptions and adequacy of computational operationalizations, and this paper argues that these are necessary for explanation via hypothesis-testing but not for prediction. This paper emphasizes the need for a nuanced approach to the epistemology of computer simulation, given the diverse applications of computer simulation in scientific research. Understanding these differences is crucial for both researchers and philosophers of science, as it helps develop appropriate methodologies and criteria for assessing the trustworthiness of computer simulation. (shrink)

Some experimental results in science are productively surprising or beautiful. Such results are disruptive in their epistemic nature: by violating epistemic expectations they mark the phenomenon at hand as worthy of further investigation. Could it be that there are emotions beyond these two which are also useful for the epistemic evaluation of scientific experiments? Here, I conduct a structured sociological survey to explore affective experiences in scientific experimental research. I identify that learning the results of an experiment is the high (...) emotional point in the experimenting process. Thus, experimental results can be challenging, beautiful, or boring, and they can worry, amuse, make one sad, and so on. They can also drive meta-cognitive evaluations as well as motivate specific research-related actions. From this, I advance two claims: that emotions beyond surprise and disruptive beauty are epistemically useful in empirical research, and that emotions help to overcome specific challenges of reasoning about new experimental results. (shrink)

Historical explanations in evolutionary biology are commonly characterized as narrative explanations. Examples include explanations of the evolution of particular traits and explanations of macroevolutionary transitions. In this paper I present two case studies of explanations in accounts of pathogen evolution and host-pathogen coevolution, respectively, and argue that one of them is captured well by established accounts of time-sequenced narrative explanation. The other one differs from narrative explanations in important respects, even though it shares some characteristics with them as it is (...) also a population-level historical explanation. I thus argue that the second case represents a different kind of explanation that I call historical explanation of type phenomena. The main difference between the two kinds of explanation is the conceptualization of the explanandum phenomena as particulars or type phenomena, respectively. Narrative explanations explain particulars but also deal with generalization, regularities and type phenomena. Historical explanations of type phenomena, on the other hand, explain multiply realizable phenomena but also deal with particulars. The two kinds of explanation complement each other because they explain different aspects of evolution. (shrink)

While discussions of the imagination have been limited in philosophy of science, this is beginning to change. In recent years, a vast literature on imagination in science has emerged. This paper surveys the current field, including the changing attitudes towards the scientific imagination, the fiction view of models, how the imagination can lead to knowledge and understanding, and the value of different types of imagination. It ends with a discussion of the gaps in the current literature, indicating avenues for future (...) research. (shrink)

Thought experiments (TEs) are important tools in science, used to both undermine and support theories, and communicate and explain complex phenomena. Their interest within philosophy of science has been dominated by a narrow question: How do TEs increase knowledge? My aim is to push beyond this to consider their broader value in scientific practice. I do this through an investigation into the scientific imagination. Part one explores questions regarding TEs as “experiments in the imagination” via a debate concerning the epistemic (...) status of computer simulations in science. I outline how, against Hacking, TEs also have “a life of their own” and I argue against accounts that privilege experiments over simulations (and by extension TEs) in light of their capacity to surprise in a productive way. Part two develops a pluralist account of the nature of the imagination in science. At its core, my view is that when we attend to a number of examples of TEs and consider the context in which they are used, we see that TEs engage a variety of our imaginative capacities. Existing monistic views fail to recognise the richness of the imagination and its potential in science. Part three looks to the “beauty” of TEs which is currently overlooked in the aesthetics of science literature. I put forward a new account that demonstrates the epistemic value of aesthetic features in science by showing how an appropriate fit between form and content enhances the usability of a TE, and its effectiveness as a prompt for our imagination. This also enables a more nuanced take on the proposed similarities between TEs and literary fictions. In the concluding chapter, I outline ways in which the core features of my account can be extended beyond TEs to illuminate the significance of the imagination and aesthetic values in other areas of science. (shrink)

This article starts an engagement on the aesthetics of experiments and offers an account for analysing how aesthetics features in the design, evaluation and reception of experiments. I identify two dimensions of aesthetic evaluation of experiments: design and significance. When it comes to design, a number of qualities, such as simplicity, economy and aptness, are analysed and illustrated with the famous Meselson-Stahl experiment. Beautiful experiments are also regarded to make significant discoveries, but I argue against a narrow construal of experimental (...) aims. By drawing on the plurality of goals experimenters have and diversity of aesthetic responses, I argue that experiments are aesthetically appreciated both when they discover and when they produce disruptive results. (shrink)

This paper argues that an important type of experiment-target inference, extrapolating causal effects, requires models to be successful. Focusing on extrapolation in Evidence-Based Policy, it is ar...

Scientific results are often presented as ‘surprising’ as if that is a good thing. Is it? And if so, why? What is the value of surprise in science? Discussions of surprise in science have been limited, but surprise has been used as a way of defending the epistemic privilege of experiments over simulations. The argument is that while experiments can ‘confound’, simulations can merely surprise (Morgan, 2005). Our aim in this paper is to show that the discussion of surprise can (...) be usefully extended to thought experiments and theoretical derivations. We argue that in focusing on these features of scientific practice, we can see that the surprise-confoundment distinction does not fully capture surprise in science. We set out how thought experiments and theoretical derivations can bring about surprises that can be disruptive in a productive way, and we end by exploring how this links with their future fertility. (shrink)

Astrophysics faces methodological challenges as a result of being a predominantly observation-based science without access to traditional experiments. In light of these challenges, astrophysicists frequently rely on computer simulations. Using collisional ring galaxies as a case study, I argue that computer simulations play three roles in reasoning in astrophysics: (1) hypothesis testing, (2) exploring possibility space, and (3) amplifying observations.

Unlike any other field, the science of morality has drawn attention from an extraordinarily diverse set of disciplines. An interdisciplinary research program has formed in which economists, biologists, neuroscientists, psychologists, and even philosophers have been eager to provide answers to puzzling questions raised by the existence of human morality. Models and simulations, for a variety of reasons, have played various important roles in this endeavor. Their use, however, has sometimes been deemed as useless, trivial and inadequate. The role of models (...) in the science of morality has been vastly underappreciated. This omission shall be remedied here, offering a much more positive picture on the contributions modelers made to our understanding of morality. (shrink)

ABSTRACTExperimentation is traditionally considered a privileged means of confirmation. However, why and how experiments form a better confirmatory source relative to 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 a class of systems, to which a hypothesis refers. 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 utilize specimens, control is diminished. (shrink)

For computer simulation models to usefully inform climate risk management, uncertainties in model projections must be explored and characterized. Because doing so requires running the model many ti...

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

I identify a controversial hypothesis in evolutionary biology called the plasticity-first hypothesis. I argue that the plasticity-first hypothesis is underdetermined and that the most popular means of studying the plasticity-first hypothesis are insufficient to confirm or disconfirm it. I offer a strategy for overcoming this problem. Researchers need to develop a richer middle range theory of plasticity-first evolution that allows them to identify distinctive empirical traces of the hypothesis. They can then use those traces to discriminate between rival explanations of (...) evolutionary episodes. The best tools for developing such a middle range theory are experimental evolution and formal modelling. (shrink)

This paper examines a constellation of ethical and editorial issues that have arisen since philosophers started to conduct, submit and publish empirical research. These issues encompass concerns over responsible authorship, fair treatment of human subjects, ethicality of experimental procedures, availability of data, unselective reporting and publishability of research findings. This study aims to assess whether the philosophical community has as yet successfully addressed such issues. To do so, the instructions for authors, submission process and published research papers of 29 main (...) journals in philosophy have been considered and analyzed. In light of the evidence reported here, it is argued that the philosophical community has as yet failed to properly tackle such issues. The paper also delivers some recommendations for authors, reviewers and editors in the field. (shrink)

Simulation and Similarity: Using Models to Understand the World is an account of modeling in contemporary science. Modeling is a form of surrogate reasoning where target systems in the natural world are studied using models, which are similar to these targets. My book develops an account of the nature of models, the practice of modeling, and the similarity relation that holds between models and their targets. I also analyze the conceptual tools that allow theorists to identify the trustworthy aspects of (...) models. Taken as a whole, I try to account for the ways that modeling is actually practiced by theorists, while abstracting sufficiently to understand the similarities and differences among examples of concrete, mathematical, and computational modeling.I am grateful to Wendy Parker, Jay Odenbaugh, and Bill Wimsatt for their careful and interesting reading of my book, as well as their constructive criticisms. Although I naturally disagree with some of their critiques, I have learned much .. (shrink)

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

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)

In silico medicine is still forging a road for itself in the current biomedical landscape. Discursively and rhetorically, it is using a three-way positioning, first, deploying discourses of personalised medicine, second, extending the 3Rs from animal to clinical research, and third, aligning its methods with experimental methods. The discursive and rhetorical positioning in promotions and statements of the programme gives us insight into the sociability of the scientific labour of advancing the programme. Its progress depends on complex social, institutional and (...) technological conditions which are not external to its epistemology, but intricately interwoven with it. This article sets out to show that this is the case through an analysis of the process of computational modelling that is at the core of its epistemology. In this paper I show that the very notion of ‘model’ needs to be re-thought for in silico medicine, and propose a replacement, in the form of the ‘Model-Simulation-Experiment-System’ or MSE-system, which is simultaneously an epistemological, social and technological system. I argue that the MSE-system is radically mediated by social relations, technologies and symbolic systems. We need now to understand how such mediations operate effectively in the construction of robust MSE-systems. (shrink)

If well-designed, the results of a Randomised Clinical Trial can justify a causal claim between treatment and effect in the study population; however, additional information might be needed to carry over this result to another population. RCTs have been criticized exactly on grounds of failing to provide this sort of information Evidence, inference and enquiry. Oxford University Press, New York, 2011), as well as to black-box important details regarding the mechanisms underpinning the causal law instantiated by the RCT result. On (...) the other side, so-called In Silico Clinical Trials face the same criticisms addressed against standard modelling and simulation techniques, and cannot be equated to experiments Philosophy of molecular medicine: foundational issues in research and practice, Routledge, New York, 2017; Parker in Synthese 169:483–496, 2009; Parke in Philos Sci 81:516–536, 2014; Diez Roux in Am J Epidemiol 181:100–102, 2015 and related discussions in Frigg and Reiss in Synthese 169:593–613, 2009; Winsberg in Synthese 169:575–592, 2009; Beisbart and Norton in Int Stud Philos Sci 26:403–422, 2012). We undertake a formal analysis of both methods in order to identify their distinct contribution to causal inference in the clinical setting. Britton et al.’s study :E2098–E2105, 2013) on the impact of ion current variability on cardiac electrophysiology is used for illustrative purposes. We deduce that, by predicting variability through interpolation, ISCTs aid with problems regarding extrapolation of RCTs results, and therefore in assessing their external validity. Furthermore, ISCTs can be said to encode “thick” causal knowledge —as opposed to “thin” difference-making information inferred from RCTs. Hence, ISCTs and RCTs cannot replace one another but rather, they are complementary in that the former provide information about the determinants of variability of causal effects, while the latter can, under certain conditions, establish causality in the first place. (shrink)

Since the discovery of dark matter in the 1980s, multiple experiments have been set up to detect dark matter particles through some other mode than gravity. Particle physicists provide detailed jus...

It is often thought that the vindication of experimental work lies in its capacity to be revelatory of natural systems. I challenge this idea by examining laboratory experiments in ecology. A central task of community ecology involves combining mathematical models and observational data to identify trophic interactions in natural systems. But many ecologists are also lab scientists: constructing microcosm or ‘bottle’ experiments, physically realizing the idealized circumstances described in mathematical models. What vindicates such ecological experiments? I argue that ‘extrapolationism’, the (...) view that ecological lab work is valuable because it generates truths about natural systems, does not exhaust the epistemic value of such practices. Instead, bottle experiments also generate ‘understanding’ of both ecological dynamics and empirical tools. Some lab work, then, aids theoretical understanding, as well as targeting hypotheses about nature. 1Introduction2Trophic Interactions and Observational Techniques3Cryptic Dynamics in Bottle Experiments4Extrapolationism 4.1Ecological possibility and actuality4.2Ecological heterogeneity5Understanding 5.1The epistemic good of understanding5.2Bottle experiments as understanding-generators5.3How understanding travels6Conclusion. (shrink)

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

I develop an account of productive surprise as an epistemic virtue of scientific investigations which does not turn on psychology alone. On my account, a scientific investigation is potentially productively surprising when results can conflict with epistemic expectations, those expectations pertain to a wide set of subjects. I argue that there are two sources of such surprise in science. One source, often identified with experiments, involves bringing our theoretical ideas in contact with new empirical observations. Another, often identified with simulations, (...) involves articulating and bringing together different parts of our knowledge. Both experiments and simulations, then, can surprise. (shrink)

Even though the philosophy of simulation is intended as a comprehensive reflection about the practice of computer simulation in contemporary science, its output has been disproportionately shaped by research on equation-based simulation in the physical and climate sciences. Hence, the particularities of alternative practices of computer simulation in other scientific domains are not sufficiently accounted for in the current philosophy of simulation literature. This article centres on agent-based social simulation, a relatively established type of simulation in the social sciences, to (...) exemplify this claim. The analysis advanced has a twofold goal. First, it shows that the philosophy of agent-based social simulation, mostly developed by practitioners themselves, is, on one hand, heavily influenced by the methodological features of agent-based modelling and by the loose and fragmented character of social theory and, on the other hand, distinctively shaped by contrasting views of what it implies to do social research with virtual or artificial societies. Second, it suggests ways in which cross-fertilisation could enrich the philosophical understanding of computer simulation both in agent-based social simulation and in the philosophy of simulation. (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)

We identify a species of experiment—Kon-Tiki experiments—used to demonstrate the competence of a cause to produce a certain effect, and we examine their role in the historical sciences. We argue that Kon-Tiki experiments are used to test middle-range theory, to test assumptions within historical narratives, and to open new avenues of inquiry. We show how the results of Kon-Tiki experiments are involved in projective inferences, and we argue that reliance on projective inferences does not provide historical scientists with any special (...) protection against the problem of unconceived alternatives. (shrink)

The origins of human social cooperation confound simple evolutionary explanation. But from Darwin and Durkheim onward, theorists (anthropologists and sociologists especially) have posited a potential link with another curious and distinctively human social trait that cries out for explanation: religion. This dissertation explores one contemporary theory of the co-evolution of religion and human social cooperation: the signalling theory of religion, or religious signalling theory (RST). According to the signalling theory, participation in social religion (and its associated rituals and sanctions) acts (...) as an honest signal of one’s commitment to a religiously demarcated community and its way of doing things. This signal would allow prosocial individuals to positively assort with one another for mutual advantage, to the exclusion of more exploitative individuals. In effect, the theory offers a way that religion and cooperation might explain one another, but which that stays within an individualist adaptive paradigm. My approach is not to assess the empirical adequacy of the religious signalling explanation or contrast it with other explanations, but rather to deal with the theory in its own terms – isolating and fleshing out its core commitments, explanatory potential, and limitations. The key to this is acknowledging the internal complexities of signalling theory, with respect to the available models of honest signalling and the extent of their fit (or otherwise) with religion as a target system. The method is to take seriously the findings of formal modelling in animal signalling and other disciplines, and to apply these (and methods from the philosophy of biology more generally) to progressively build up a comprehensive picture of the theory, its inherent strengths and weaknesses. The first two chapters outline the dual explanatory problems that cooperation and religion present for evolutionary human science, and surveys contemporary approaches toward explaining them. Chapter three articulates an evolutionary conception of the signalling theory, and chapters four to six make the case for a series of requirements, limitations, and principles of application. Chapters seven and eight argue for the value of formal modelling to further flesh out the theory’s commitments and potential and describe some simple simulation results which make progress in this regard. Though the inquiry often problematizes the signalling theory, it also shows that it should not be dismissed outright, and that it makes predictions which are apt for empirical testing. (shrink)

This paper attempts to revive the epistemological discussion of scientific articles. What are their epistemic aims, and how are they achieved? We argue that scientific experimental articles are best understood as a particular kind of narrative: i.e., modernist narratives (think: Woolf, Joyce), at least in the sense that they employ many of the same techniques, including colligation and the juxtaposition of multiple perspectives. We suggest that this way of writing is necessary given the nature of modern science, but it also (...) has specific epistemic benefits: it provides readers with an effective way to grasp the content of scientific articles which increases their understanding. On the other hand, modernist writing is vulnerable to certain kinds of epistemic abuses, which can be found instantiated in modern scientific writing as well. (shrink)

Philosophers have yet to provide a systematic analysis of the relationship between historical science and model-based science. In this paper I argue that prototypical model-based sciences exhibit features understood to be central to historical science. Philosophers of science have argued that historical scientists are distinctly concerned with inference to the best explanation, that explanations in historical science tend to increase in complexity over time, and that the explanations take the form of narratives. Using general circulation models in climate science as (...) a reference, I illustrate how simulation models in model-based science share these features exhibited by historical science. That model based sciences share these features raises important philosophical questions about how we should understand prototypical types of scientific enquiry, including the relationship between experimental science, historical science, and model-based science. I conclude by exploring several options for how to accommodate the noted similarities within a more general taxonomy of the sciences. (shrink)

Microbial model systems have a long history of fruitful use in fields that include evolution and ecology. In order to develop further insight into modelling practice, we examine how the competitive exclusion and coexistence of competing species have been modelled mathematically and materially over the course of a long research history. In particular, we investigate how microbial models of these dynamics interact with mathematical or computational models of the same phenomena. Our cases illuminate the ways in which microbial systems and (...) equations work as models, and what happens when they generate inconsistent findings about shared targets. We reveal an iterative strategy of comparative modelling in different media, and suggest reasons why microbial models have a special degree of epistemic tractability in multimodel inquiry. (shrink)

Verification and validation of computer codes and models used in simulations are two aspects of the scientific practice of high importance that recently have been discussed widely by philosophers of science. While verification is predominantly associated with the correctness of the way a model is represented by a computer code or algorithm, validation more often refers to the model’s relation to the real world and its intended use. Because complex simulations are generally opaque to a practitioner, the Duhem problem can (...) arise with verification and validation due to their entanglement; such an entanglement makes it impossible to distinguish whether a coding error or the model’s general inadequacy to its target should be blamed in the case of a failure. I argue that a clear distinction between computer modeling and simulation has to be made to disentangle verification and validation. Drawing on that distinction, I suggest to associate modeling with verification and simulation, which shares common epistemic strategies with experimentation, with validation. To explain the reasons for their entanglement in practice, I propose a Weberian ideal–typical model of modeling and simulation as roles in practice. I examine an approach to mitigate the Duhem problem for verification and validation that is generally applicable in practice and is based on differences in epistemic strategies and scopes. Based on this analysis, I suggest two strategies to increase the reliability of simulation results, namely, avoiding alterations of verified models at the validation stage as well as performing simulations of the same target system using two or more different models. In response to Winsberg’s claim that verification and validation are entangled I argue that deploying the methodology proposed in this work it is possible to mitigate inseparability of V&V in many if not all domains where modeling and simulation are used. (shrink)

Gravitational interactions allowed astronomers to conclude that dark matter rings all luminous galaxies in gigantic halos, but this only accounts for a fraction of the total mass of dark matter believed to exist. Where is the rest? We hypothesize that some of it resides in dark galaxies, pure dark matter halos that either never possessed or have totally lost their baryonic matter. This article explores methodological challenges that arise because of the nature of observation in astrophysics and examines how the (...) blend of observation, simulation, and theory we call the Observing the Invisible approach might make detecting such dark objects possible. (shrink)

This paper provides the first systematic epistemological account of simulated data in empirical science. We focus on the epistemic issues modelers face when they generate simulated data to solve problems with empirical datasets, research tools, or experiments. We argue that for simulated data to count as epistemically reliable, a simulation model does not have to mimic its target. Instead, some models take empirical data as a target, and simulated data may successfully mimic such a target even if the model does (...) not. We show how to distinguish between simulated and empirical data, and we also offer a definition of simulation that can accommodate Monte Carlo models. We shed light on the epistemology of simulated data by providing a taxonomy of four different mimicking relations that differ concerning the nature of the relation or relata. We illustrate mimicking relations with examples from different sciences. Our main claim is that the epistemic evaluation of simulated data should start with recognizing the diversity of mimicking relations rather than presuming that only one relation existed. (shrink)

We analyze the usage of computer simulation at the LHC and derive seven jointly necessary requirements for a simulation to be considered ’trustworthy’, such that it can be used as proxy for experiments. We show that these requirements can also be applied to systems without direct experimental access and discuss their validity for properties that have not yet been probed. While being necessary, these requirements are not sufficient. Such trustworthy simulations will be analyzed for the relative epistemic statuses of simulation (...) and material measurements, from which we argue that claims of their parity are unfounded. Instead, using credibility as a measure for epistemic status, and in view of the temporal and epistemic dependence of simulation on material measurements, we argue that the latter have a higher epistemic status than the former. We further argue that suggestions to qualify the epistemic status by ’defocussing’ on the material connection to the target system of either human or natural experiments are misleading. (shrink)

This article explores the aesthetic dimensions of scientific experimentation, addressing specifically how aesthetic features enter the construction, evaluation and reception of an experiment. I highlight the relationship between experiments and artistic acts in the early years of the Royal Society where experiments do not serve only epistemic aims but also aim to generate feelings of awe and pleasure. I turn to analysing which aspects of experiments are appreciated aesthetically, identifying several contenders, from the ability of an experiment to uncover nature’s (...) beauty, to encapsulating original designs and human creativity. Following this analysis, I focus on the notion of beauty: what makes an experiment beautiful? Several common qualities are explored, from the simplicity and economy of the experiment, to the significance of the experimental results. (shrink)

Calibrating a simulation is a crucial step for certain kinds of simulation modeling, and it results in a simulation that is epistemically different from its pre- or uncalibrated counterpart. This article discusses how simulation model builders mitigate uncertainty about model parameters that are necessary for modeling through calibration and argues that the simulation outcomes after calibration are physically meaningful and relevant. When evaluating the epistemic status of computer simulations, comparisons between computer simulations and traditional experiments need to consider this important (...) methodological step. (shrink)

This paper examines a constellation of ethical and editorial issues that have arisen since philosophers started to conduct, submit and publish empirical research. These issues encompass concerns over responsible authorship, fair treatment of human subjects, ethicality of experimental procedures, availability of data, unselective reporting and publishability of research findings. This study aims to assess whether the philosophical community has as yet successfully addressed such issues. To do so, the instructions for authors, submission process and published research papers of 29 main (...) journals in philosophy have been considered and analyzed. In light of the evidence reported here, it is argued that the philosophical community has as yet failed to properly tackle such issues. The paper also delivers some recommendations for authors, reviewers and editors in the field. (shrink)

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