Scientific Method, Miscellaneous

Introduction to Karl Popper's philosophy of science.

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)

Progress in the last few decades in what is widely known as “Chaos Theory” has plainly advanced understanding in the several sciences it has been applied to. But the manner in which such progress has been achieved raises important questions about scientific method and, indeed, about the very objectives and character of science. In this presentation, I hope to engage my audience in a discussion of several of these important new topics.

Analiza de informații are multe asemănări epistemologice importante cu știința (rezolvarea problemelor, descoperirea, utilizarea cu abilitate a instrumentelor, verificarea cererilor de cunoștințe). Metafora puzzle este folosită atât în activitatea de informații cât și în arheologie. Ambele discipline implică colectarea de dovezi pentru a construi o imagine cât mai completă posibil. Firmele private inovatoare adaptează din ce în ce mai mult modelul serviciilor de informații la lumea afacerilor pentru a ajuta la planificarea propriilor strategii. Practica medicală de diagnosticare a identificării, colectării, (...) analizării și diseminării este similară cu cea a activității de informații. DOI: 10.13140/RG.2.2.35820.72324. (shrink)

David Singer afirmă că, în prezent, amenințarea constituie principalul obiectiv al agențiilor de informații. Activitatea de informații poate fi considerată ca fiind procesul prin care anumite tipuri de informații sunt solicitate, colectate, analizate și diseminate, și modul în care sunt concepute și desfășurate anumite tipuri de acțiuni secrete. Ciclul informațional reprezintă un set de procese utilizate pentru a furniza informații utile în luarea deciziilor. Ciclul constă din mai multe procese. Domeniul conex al contrainformațiilor este însărcinat cu împiedicarea eforturilor informative ale (...) altora. DOI: 10.13140/RG.2.2.10807.06563. (shrink)

Les analystes sont dans le domaine des « connaissances ». L'activité de renseignement fait référence à la connaissance et les types de problèmes abordés sont des problèmes de connaissance. Nous avons donc besoin d'un concept de travail basé sur la connaissance. Nous avons besoin d'une compréhension de base de ce que nous savons et de la manière dont nous le savons, de ce que nous ne savons pas et même de ce qui peut être connu et de ce qui ne (...) peut pas être connu. L'analyse du renseignement implique de « transformer des faits disparates en conclusions concentrées. » DOI: 10.13140/RG.2.2.11223.91043. (shrink)

Programele de cercetare permit dezvoltarea unor teorii mai complexe. Termenii pot fi aplicați atât la teorii individuale cât și la programe. În cazul în care se aplică teoriilor din cadrul unui program de cercetare, consider că acestea devin la rândul lor programe de cercetare, pe care le putem numi subprograme de cercetare. Spre deosebire de revoluțiile științifice ale lui Kuhn, Lakatos a presupus că existența simultană a mai multor programe de cercetare este norma. Știința se confruntă în prezent cu o (...) astfel de situație inedită: două teorii incompatibile, dar ambele acceptate de comunitatea științifică descriu aceeași realitate în două moduri diferite. DOI: 10.13140/RG.2.2.11153.28005. (shrink)

The general theory of relativity was developed using as a nucleus a principle of symmetry: the principle of general covariance. Initially, Einstein saw the principle of general covariance as an extension of the principle of relativity in classical mechanics, and in SR. For Einstein, the principle of general covariance was a crucial postulate in the development of GR. The freedom of the GR diffeomorphism (the invariance of the form of the laws under transformations of the coordinates depending on the arbitrary (...) functions of space and time) is a "local" spacetime symmetry, as opposed to the "global" spacetime symmetries of the SR (which depend instead on the constant parameters ). DOI: 10.13140/RG.2.2.30854.32326. (shrink)

Metodologia, în activitatea de informații, constă din metodele folosite pentru a lua decizii despre amenințări, în special în cadrul disciplinei de analiză a informațiilor. Teoria prismatică a lui Robert Flood, denumită de alții drept pluralism metodologic, folosește metafora pentru a descrie gândirea creativă și de transformare, respectiv o prismă care descompune lumina în culorile sale componente prin dubla refracție. Tehnicile analitice structurale sunt folosite pentru a provoca judecata, la identificarea mentalităților, depășirea prejudecăților, stimularea creativității și gestionarea incertitudinii. DOI: 10.13140/RG.2.2.10589.36329.

Science is epistemically special, or so I will assume: it is better able to produce knowledge about the workings of the world than other knowledge-directed pursuits. Further, its superior epistemic powers are due to its being in some sense especially empirical: in particular, science puts great weight on a form of inductive reasoning that I call empirical con rmation. My aim in this paper is to investigate the nature of science’s “empiricism”, and to provide a preliminary explanation of the connection (...) between empirical confirmation and epistemic efficacy. I will try to convince you that the place to find an account of empirical confirmation is the dusty, long-neglected instantialist account of scientific inference offered by mid-century logical empiricists. Some revision of instantialism will be required. As for what is advantageous in empirical confirmation, I propose that it is an unusual degree of independence from background belief. (shrink)

The role of judgments of beauty in scientific theory evaluation is the subject of significant debate in contemporary philosophy of science. This book advances that debate by broadening its scope. In Judgments of Beauty in Theory Evaluation, the author argues that judgments of beauty are a justified part of theory evaluation of all sorts: not only scientific theory evaluation, but also philosophical theory evaluation. The author argues for this thesis by providing an account of beauty—inherited from Kant and Mothersill—on which (...) the distinctive nature of judgments of beauty is that they are unprincipled, yet possible. The author then analyzes two important methods of theory evaluation—reflective equilibrium and simplicity—and argues that these methods require making judgments of beauty so understood. Finally, the author argues that these methods of theory evaluation are not anomalies, but that they point to a deeper lesson about the nature of theorizing and the necessity of using judgments of beauty to evaluate systems, like theories. This conclusion has implications for the debate in philosophy of science over judgments of beauty, but also prompts a reckoning in philosophy itself over the use of judgments of beauty in philosophical theory evaluation. (shrink)

What is science, and what is it not? Is falsifiability the key to drawing this line? How and why does science work? Should we worry whether science is talking about a "real" world? And should we stop thinking there is a single thing we can call "the scientific method"? With Deborah Mayo, Robert Rynasiewicz, and Drew Arrowood.

The history of the philosophy of thought experiments has touched on the work of Kuhn, Popper, Duhem, Mach, Lakatos, and other big names of the 20th century, but so far, almost nothing has been written about Paul Feyerabend. His most influential work was Against Method, 8 chapters of which concern a case study of Galileo with a specific focus on Galileo’s thought experiments. In addition, the later Feyerabend was very interested in what might be called the epistemology of drama, including (...) stories and myths. This paper brings these different aspects of Feyerabend’s work together in an attempt to present what might have been his considered views on scientific thought experiments. I conclude by contrasting Feyerabend’s ideas with two modern currents in the debate surrounding thought experiments: 1) the claim that the epistemology of thought experiments is just the epistemology of deductive or inductive arguments, and 2) the claim that the specifically narrative quality of thought experiments must be taken into account if we want a complete epistemology of thought experiments. (shrink)

"Correlation is not causation" is one of the mantras of the sciences—a cautionary warning especially to fields like epidemiology and pharmacology where the seduction of compelling correlations naturally leads to causal hypotheses. The standard view from the epistemology of causation is that to tell whether one correlated variable is causing the other, one needs to intervene on the system—the best sort of intervention being a trial that is both randomized and controlled. In this paper, we argue that some purely correlational (...) data contains information that allows us to draw causal inferences: statistical noise. Methods for extracting causal knowledge from noise provide us with an alternative to randomized controlled trials that allows us to reach causal conclusions from purely correlational data. (shrink)

This article explains Kant’s claim that sciences must take, at least as their ideal, the form of a ‘system’. I argue that Kant’s notion of systematicity can be understood against the background of de Jong & Betti’s Classical Model of Science (2010) and the writings of Georg Friedrich Meier and Johann Heinrich Lambert. According to my interpretation, Meier, Lambert, and Kant accepted an axiomatic idea of science, articulated by the Classical Model, which elucidates their conceptions of systematicity. I show that (...) Kant’s critique of the mathematical method is compatible with his adherence to this axiomatic conception of science. I further show that systematicity furthers traditionally accepted logical ideals of scientific knowledge, which explains why Meier and Kant think that sciences must be ‘systematic’. (shrink)

Neil Levy defends no-platforming people who espouse dangerous or unacceptable views. I reject his notion of higher-order evidence as authoritarian and dogmatic. I argue that no-platforming frustrates the growth of knowledge.

Our understanding of folk and scientific psychology often informs the law’s conclusions regarding questions about the voluntariness of a defendant’s action. The field of psychology plays a direct role in the law’s conclusions about a defendant’s guilt, innocence, and term of incarceration. However, physical sciences such as neuroscience increasingly deny the intuitions behind psychology. This paper examines contemporary biases against the autonomy of psychology and responds with considerations that cast doubt upon the legitimacy of those biases. The upshot is that (...) if reasonable doubt is established regarding whether psychology’s role in the law should be displaced, then there is room for future work to be done with respect to the truth of psychology’s conclusions about criminal responsibility. (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)

The idea of science being the best – or the only – way to reach the truth about our cosmos has been a major belief of modern civilization. Yet, science has grown tall on fragile legs of clay. Every scientific theory uses axioms and assumptions that by definition cannot be proved. This poses a serious limitation to the use of science as a tool to find the truth. The only way to search for the latter is to redefine the former (...) to its original glory. In the days well before Galileo and Newton, science and religion were not separated. They worked together to discover the truth and while the latter had God as its final destination, the former had God as its starting point. Science is based on the irrational (unproven) belief that the world is intelligible along many other assumptions. This poses a serious limitation to science that can only be overcome if we accept the irrationality of the cosmos. The motto “Credo quia absurdum” holds more truth than one can ever realize at first glance. There is nothing logical in logic, whereas there is deep wisdom in the irrational. For while the former tries to build castles on moving sand, the latter digs deep inside the depths of existence itself in order to build on the most concrete foundations that there can be: the cosmos itself. The only way forward is backwards. Backwards to a time when religion led the quest for knowledge by accepting what we cannot know, rather than trying to comprehend what we do not. Science was anyway based on that in the first place. (shrink)

For centuries, science was considered as something radically different from religion. Yet, the foundations of true science are deeply religious in nature. This paper seeks to show how religion is the only foundation needed for the formulation of scientific theories, since it provides the core principles on which the building of exact sciences is based upon. Our need to understand the cosmos and our faith in us being able to do so, are the main prerequisites for conducting science; prerequisites that (...) are derived from our belief in us being the sons of God and, thus, being able to read His mind. From its birth on 7 March 1277 up to today, science seems to be the only logical attitude of religious people towards the unknown cosmos. (shrink)

Fukuyama's 'The End of History' has referred to Kojeve's 'homogenous state' as some sort of conceptual container for the evolving idea of liberal democracy. This paper critically re-assess the homogeneity of state as final stage of liberal idea and defends civil society in terms of democratic governance. It also invites to discuss the role of scholars as public intellectuals and repels the ideological abuse of the scientific method.

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)

It is generally accepted among philosophers of science that hypothesis testing is a key methodological feature of science. As far as philosophical theories of confirmation are con...

Has the rise of data-intensive science, or ‘big data’, revolutionized our ability to predict? Does it imply a new priority for prediction over causal understanding, and a diminished role for theory and human experts? I examine four important cases where prediction is desirable: political elections, the weather, GDP, and the results of interventions suggested by economic experiments. These cases suggest caution. Although big data methods are indeed very useful sometimes, in this paper’s cases they improve predictions either limitedly or not (...) at all, and their prospects of doing so in the future are limited too. (shrink)

In the late 19th century, the questionnaire was one means of taking the case study into the multitudes. This article engages with Forrester’s idea of thinking in cases as a means of interrogating questionnaire-based research in early American psychology. Questionnaire research was explicitly framed by psychologists as a practice involving both natural historical and statistical forms of scientific reasoning. At the same time, questionnaire projects failed to successfully enact the latter aspiration in terms of synthesizing masses of collected data into (...) a coherent whole. Difficulties in managing the scores of descriptive information questionnaires generated ensured the continuing presence of individuals in the results of this research, as the individual case was excerpted and discussed alongside a cast of others. As a consequence, questionnaire research embodied an amalgam of case, natural historical, and statistical thinking. Ultimately, large-scale data collection undertaken with questionnaires failed in its aim to construct composite exemplars or ‘types’ of particular kinds of individuals; to produce the singular from the multitudes. (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)

This chapter clarifies the concept of validation of computer simulations by comparing various definitions that have been proposed for the notion. While the definitions agree in taking validation to be an evaluationEvaluation, they differ on the following questions: What exactly is evaluated—results from a computer simulation, a model, a computer codeCode? What are the standardsStandard of evaluationEvaluation––truthTruth, accuracyAccuracy, and credibilityCredibility or also something else? What type of verdict does validation lead to––that the simulation is such and such good, or that (...) it passes a testTest defined by a certain threshold? How strong needs the case to be for the verdict? Does validation necessarily proceed by comparing simulation outputsOutput with measured dataData? Along with these questions, the chapter explains notions that figure prominently in them, e.g., the concepts of accuracy and credibility. It further discusses natural answers to the questions as well as arguments that speak in favor and against these answers. The aim is to obtain a better understandingUnderstanding of the options we have for defining validation and how they are related to each other. (shrink)

Verification and validation are methods with which computer simulations are tested. While many practitioners draw a clear line between verification and validation and demand that the former precedes the latter, some philosophers have suggested that the distinction has been over-exaggerated. This chapter clarifies the relationship between verification and validation. Regarding the latter, validation of the conceptual and of the computational modelComputational model are distinguished. I argue that, as a method, verification is clearly different from validation of either of the models. (...) However, the methods are related to each other as follows: If we allow that the validation of the computational model need not include the comparisonComparison between simulation output and measured data, then the computational model may be validated by validating the conceptual model independently and by verifying the simulation with respect to it. This is often not realistic, however, because, in most cases, the conceptual modelConceptual model cannot be validated independently from the simulation. In such cases, the computational modelComputational model is verified with the aim to use it as an appropriate substitute for the conceptual model. Then simulation output is compared to measured data to validate both the computational and the conceptual model. I analyze the underlying inferences and argue that they require some prior confidence in the conceptual model and in verification. This suggests that verification precede validation that proceeds via a comparisonComparison between simulation outputOutput and measured data. Recent arguments to the effect that the distinction between verification and validation is not clear-cut do not refute these results, or so I argue against philosopher E. Winsberg. (shrink)

Bayesian epistemologyEpistemology offers a powerful framework for characterizing scientific inference. Its basic idea is that rational belief comes in degrees that can be measured in terms of probabilities. The axioms of the probability calculus and a rule for updatingUpdating emerge as constraints on the formation of rational belief. Bayesian epistemologyEpistemology has led to useful explications of notions such asConfirmation confirmation. It thus is natural to ask whether Bayesian epistemologyEpistemology offers a useful framework for thinking about the inferences implicit in the (...) validation of computer simulations. The aim of this chapter is to answer this question. Bayesian epistemologyEpistemology is briefly summarized and then applied to validation. UpdatingUpdating is shown to form a viable method for data-driven validation. Bayesians can also express how a simulation obtains prior credibilityCredibility because the underlying conceptual modelConceptual model is credible. But the impact of this prior credibilityCredibility is indirect since simulations at best provide partial and approximate solutions to theConceptual model conceptual model. Fortunately, this gap between the simulations and the conceptual model can be addressed using what we call Bayesian verification. The final part of the chapter systematically evaluates the use of Bayesian epistemologyEpistemology in validation, e.g., by comparing it to a falsificationist approach. It is argued that Bayesian epistemologyEpistemology goes beyond mere calibrationCalibration and that it can provide the foundations for a sound evaluationEvaluation of computer simulations. (shrink)

This book on Philosophy of Science includes a comprehensive discussion of ontology, epistemology, and axiology of science in the constellation of various other knowledge, as well as the development of scientific knowledge holistically contained in each module in this course. These three things are branches of philosophy which are very useful for students of Teacher Training and Education in mediating the learning and learning process so that the essence of Philosophy of Science can then be implemented within the scope of (...) professionalism in particular and society in general and the challenges of the industrial era 4.0 in the 20th century 21 The discussion on ontology is focused on elements of empirical reality (empericism) such as facts, data, and information without releasing it from rational reality (rationalism), and its position in scientific activities. The epistemology of science is focused on the scientific method and its operationalization in research methodology. The axiology of science discusses the values ​​associated with scientific activities and their uses, both internally, externally, and socially. (shrink)

Recent studies of scientific interaction based on agent-based models suggest that a crucial factor conducive to efficient inquiry is what Zollman has dubbed ‘transient diversity’. It signifies a process in which a community engages in parallel exploration of rivaling theories lasting sufficiently long for the community to identify the best theory and to converge on it. But what exactly generates transient diversity? And is transient diversity a decisive factor when it comes to the efficiency of inquiry? In this paper we (...) examine the impact of different conditions on the efficiency of inquiry, as well as the relation between diversity and efficiency. This includes certain diversity-generating mechanisms previously proposed in the literature, as well as some factors that have so far been neglected. This study is obtained via an argumentation-based ABM. Our results suggest that cautious decision-making does not always have a significant impact on the efficiency of inquiry while different evaluations underlying theory-choice and different social networks do. Moreover, we find a correlation between diversity and a successful performance of agents only under specific conditions, which indicates that transient diversity is sometimes not the primary factor responsible for efficiency. Altogether, when comparing our results to those obtained by structurally different ABMs based on Zollman’s work, the impact of specific factors on efficiency of inquiry, as well as the role of transient diversity in achieving efficiency, appear to be highly dependent on the underlying model. (shrink)

Recent studies of scientific interaction based on agent-based models suggest that a crucial factor conducive to efficient inquiry is what Zollman has dubbed ‘transient diversity’. It signifies a process in which a community engages in parallel exploration of rivaling theories lasting sufficiently long for the community to identify the best theory and to converge on it. But what exactly generates transient diversity? And is transient diversity a decisive factor when it comes to the efficiency of inquiry? In this paper we (...) examine the impact of different conditions on the efficiency of inquiry, as well as the relation between diversity and efficiency. This includes certain diversity-generating mechanisms previously proposed in the literature, as well as some factors that have so far been neglected. This study is obtained via an argumentation-based ABM. Our results suggest that cautious decision-making does not always have a significant impact on the efficiency of inquiry while different evaluations underlying theory-choice and different social networks do. Moreover, we find a correlation between diversity and a successful performance of agents only under specific conditions, which indicates that transient diversity is sometimes not the primary factor responsible for efficiency. Altogether, when comparing our results to those obtained by structurally different ABMs based on Zollman’s work, the impact of specific factors on efficiency of inquiry, as well as the role of transient diversity in achieving efficiency, appear to be highly dependent on the underlying model. (shrink)

The overflow of scientific literature stimulates poor reading habits which can aggravate science's reproducibility crisis. Thus, solving the reproducibility crisis demands not only methodological changes, but also changes in our relationship with the scientific literature, especially our reading habits. Importantly, this does not mean reading more, it means reading better.

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 response to Anderson and Arzyutov’s paper, I argue that ambiguities in the Russian social-scientific concept of “etnos” reveal its place in what I call a “field style” for thinking and doing science. Tolerance for ambiguity is, I suggest, a methodological strength of the field sciences. I support these reflections by also addressing the etnos concept’s origins in the complex history of Ukrainian nationalism.

I present a recent historical case from cosmology—the story of inflationary cosmology—and on its basis argue that solving explanatory problems is a reliable method for making progress in science. In particular, I claim that the success of inflationary theory at solving its predecessor’s explanatory problems justified the theory epistemically, even in advance of the development of novel predictions from the theory and the later confirmation of those predictions.

For many old and new mechanists, Mechanism is both a metaphysical position and a thesis about scientific methodology. In this paper we discuss the relation between the metaphysics of mechanisms and the role of mechanical explanation in the practice of science, by presenting and comparing the key tenets of Old and New Mechanism. First, by focusing on the case of gravity, we show how the metaphysics of Old Mechanism constrained scientific explanation, and discuss Newton’s critique of Old Mechanism. Second, we (...) examine the current mechanistic metaphysics, arguing that it is not warranted by the use of the concept of mechanism in scientific practice, and motivate a thin conception of mechanism (the truly minimal view), according to which mechanisms are causal pathways for a certain effect or phenomenon. Finally, we draw analogies between Newton’s critique of Old Mechanism and our thesis that the metaphysical commitments of New Mechanism are not necessary in order to illuminate scientific practice. (shrink)

Francesco Guala once wrote that ‘The problem of extrapolation is a minor scandal in the philosophy of science’. This paper agrees with the statement, but for reasons different from Guala’s. The scandal is not, or not any longer, that the problem has been ignored in the philosophy of science. The scandal is that framing the problem as one of external validity encourages poor evidential reasoning. The aim of this paper is to propose an alternative—an alternative which constitutes much better evidential (...) reasoning about target systems of interest, and which makes do without consideration of external validity. (shrink)

Design plays an integral role in the functions of modern society. Yet the abstract process by which designers carry out their work is not obvious. The study of design thinking has grown in recent years into a major area of academic research, yet it presently lacks a clear theoretical basis; and as a discipline, its methodologies are disparate. Here, we outline and clarify the framework of the scholarly study of design thinking, introducing the major ideas and concepts upon which the (...) field is based. We then discuss in detail the various methodological issues of the field, and argue that, in its current state, the field of design thinking cannot sustain itself as an independent area of academic research. We suggest that design thinking may best be studied from a sociological or science, technology, and society (STS) studies perspective. (shrink)

Scientific models are used to predict and understand the target phenomena in the reality. The kind of epistemic relationship between the model and the reality is always regarded by most of the philosophers as a representational one. I argue that, complementary to this representational role, some of the scientific models have a constructive role to play in altering and reconstructing the reality in a physical way. I hold that the idealized model assumptions and elements bestow the constructive force of a (...) model on the reality. By recognizing the physical constructive force of some scientific models, the merit of these models could be judged by how successful they are in the reality construction, rather than by the traditional criterion of model-world representation. (shrink)

When philosophers discuss the possibility of machines making scientific discoveries, they typically focus on discoveries in physics, biology, chemistry and mathematics. Observing the rapid increase of computer-use in science, however, it becomes natural to ask whether there are any scientific domains out of reach for machine discovery. For example, could machines also make discoveries in qualitative social science? Is there something about humans that makes us uniquely suited to studying humans? Is there something about machines that would bar them from (...) such activity? A close look at the methodology of interpretive social science reveals several abilities necessary to make a social scientific discovery, and one capacity necessary to possess any of them is imagination. For machines to make discoveries in social science, therefore, they must possess imagination algorithms. (shrink)

A map is not its territory. Taking a map too seriously may lead to pernicious reification: map and world are conflated. As one family of cases of such reification, I focus on maps exuding the omphalos syndrome, whereby a centred location on the map is taken to be the world navel of, for instance, an empire. I build on themes from my book _When Maps Become the World_, in which I analogize scientific theories to maps, and develop the tools of (...) assumption archaeology and integration platforms. Here I argue that excavating assumptions helps fill cartographic silences, showing the limitations of perspectives often at war. Furthermore, integrating perspectives permits resisting imperial central or master images. A worthwhile future project would be a repository of world-navel maps, critically annotated with cultural context and imperial information. Mutual understanding may result from such an integration platform, perhaps implemented online or in a museum. (shrink)

In recent years, several authors have debated about the justifiability of so-called scientific imperialism. To date, however, widespread disagreements remain regarding both the identification and the normative evaluation of scientific imperialism. In this paper, I aim to remedy this situation by making some conceptual distinctions concerning scientific imperialism and by providing a detailed assessment of the most prominent objections to it. I shall argue that these objections provide a valuable basis for opposing some instances of scientific imperialism, but do not (...) yield cogent reasons to think that scientific imperialism in general is objectionable or unjustified. I then highlight three wide-ranging implications of this result for the ongoing philosophical debate about the justifiability of scientific imperialism. (shrink)

The increasing application of network models to interpret biological systems raises a number of important methodological and epistemological questions. What novel insights can network analysis provide in biology? Are network approaches an extension of or in conflict with mechanistic research strategies? When and how can network and mechanistic approaches interact in productive ways? In this paper we address these questions by focusing on how biological networks are represented and analyzed in a diverse class of case studies. Our examples span from (...) the investigation of organizational properties of biological networks using tools from graph theory to the application of dynamical systems theory to understand the behavior of complex biological systems. We show how network approaches support and extend traditional mechanistic strategies but also offer novel strategies for dealing with biological complexity. (shrink)

Modern science began as natural philosophy, an admixture of philosophy and science. It was then killed off by Newton, as a result of his claim to have derived his law of gravitation from the phenomena by induction. But this post-Newtonian conception of science, which holds that theories are accepted on the basis of evidence, is untenable, as the long-standing insolubility of the problem of induction indicates. Persistent acceptance of unified theories only in physics, when endless equally empirically successful disunified rivals (...) are available, means that physics makes a persistent, problematic metaphysical assumption about the universe: that all disunified theories are false. This assumption, precisely because it is problematic, needs to be explicitly articulated within physics, so that it can be critically assessed and, we may hope, improved. The outcome is a new conception of science—aim-oriented empiricism—that puts science and philosophy together again, and amounts to a modern version of natural philosophy. Furthermore, aim-oriented empiricism leads to the solution to the problem of induction. Natural philosophy pursued within the methodological framework of aim-oriented empiricism is shown to meet standards of intellectual rigour that science without metaphysics cannot meet. (shrink)