Scientific Method

Philosophers of science often suggest that the key feature of scientific research is striving for objectivity and that we should evaluate scientific practice by whether it is objective or not. In this paper, we will analyze several definitions of scientific objectivity to illustrate the complex meaning of this term and examine its role in evaluating scientific practice. First, we will introduce Lorraine Daston and Peter Galison's standpoint concerning the historical connection between the genesis and development of scientific objectivity and the (...) practices of visual representation in the research practice of the 19th and 20th centuries. We will accomplish that by outlining the process of establishing scientific objectivity as an epistemic virtue and a vital feature of the "scientific self ". Subsequently, using Heather Douglas and Mar-ianne Janack's conceptual analysis of scientific objectivity, we will show that scientific objectivity is characterized by an "irreducibility of meaning" and an "endemic instability" caused by the overuse of metaphors in defining this concept. In the final section, in light of contemporary problems such as the crisis of reproducibility, we examine to what extent philosophical definitions help test the objectivity of scientific practice and point to an intriguing attempt to define "objectivity for the research worker" using the model proposed by Noah van Dongen and Michał Sikorski. (shrink)

Ever since its inception, the theory of evolution has been reified into an “-ism”: Darwinism. While biologists today tend to shy away from the term in their research, the term is still actively used in the broader academic and societal contexts. What exactly is Darwinism, and how precisely are its various uses and abuses related to the scientific theory of evolution? Some call for limiting the meaning of the term “Darwinism” to its scientific context; others call for its abolition; yet (...) others claim the term refers to a myth-like story. In this paper we propose a conceptually grounded overview of the term. We show how the scientific dimension of Darwinism feeds into, and is influenced by, guises of Darwinism as a methodology and as an ethically and politically charged “worldview”. The full meaning of Darwinism, as well as how this meaning has changed over time, can only be understood through the complex interaction between these three dimensions. (shrink)

General Morphological Analysis (GMA) is a computer-aided, non-quantified modelling method employing (discrete) category variables for identifying and investigating the total set of possible relationships contained in a given problem complex. The epistemological principle underlying discrete variable morphological modelling is that of decomposing a complex (multivariate) concept into a number of(“simple”) one dimensional concepts (i.e. category variables), the domains of which can then be recombined and recomposed in order to discover all of the other possible (multidimensional) concepts which can be generated (...) combinatorically. (shrink)

Thales is considered to be the first philosopher to pose a question about fundamental principles on which everything else relied. His candidate for ἀρχή was water because he believed that everything comes from water and that the remaining three elements can also be created from water. Alchemists, or the first chemists, relied on the ancient tradition, especially Aristotle and the theory of the four elements. That is how they came to Aristotle’s testimonies about Thales, after which alchemists, like Helmont, accepted (...) Thales’ idea that water is the basis of everything that exists. Alchemists were characterized by a tendency to experiment, not only because they tried to turn other metals into gold but also because they tried to devise medical solutions that would prevent the appearance of various diseases. Due to their rich experimental experience, alchemists have been a convenient target for “new philosophers” since the early seventeenth century. Robert Boyle belonged to that circle because he was a supporter of corpuscular theory. In this paper, I will present Boyle‘s attempt to replicate alchemical experiments, especially Helmont’s experiment with the willow tree and Bacon‘s experiment with water. After that, I will show whether Boyle’s interest in the experimental procedures of alchemists contributed more to the development of modern science than his mechanistic philosophy. (shrink)

Recently, the distinction between nature and technology has been increasingly questioned. We are told that the changes to nature made possible by technology had reached such dimensions that it was no longer possible to clearly differentiate between nature and technology. Against the critical voices, I argue for the possibility and necessity of applying a version of the distinction that I call “classical”. I begin by examining selected historical origins of this distinction and thereby discussing some of its critics debated today (...) – namely Hans Blumenberg, Bruno Latour, Donna Haraway and Philippe Descola. In the next section, I introduce a concept of hybrid of nature and technology, which is suitable for delimiting and differentiating the scope of the classical distinction. I also need the concept of hybrid in order to assess the current tendencies in the development of the nature-technology relation, which are the subject of the last section. Against the background of these tendencies, I conclude by compiling some arguments in favour of maintaining the classical distinction between nature and technology. (shrink)

Jag tror att det är ett misstag att kräva av humanvetenskaperna (d.v.s. humaniora, samhälls- och beteendevetenskaperna) att de imiterar naturvetenskapernas forskningsmetodik. Humanvetenskaperna studerar meningsfulla fenomen vilkas natur är på ett grundläggande sätt annorlunda än de blott fysiska fenomen som naturvetenskapen studerar. Den största skillnaden är att meningsfulla fenomen inte uppenbarligen är lagbundna på samma sätt som fysiska fenomen och uppvisar därför inte samma regelbundenhet och förutsägbarhet som fysiska fenomen. För att studera meningsfulla fenomen krävs därför andra forskningsmetoder. Trots att humanvetenskaperna (...) studerar fenomen som inte är lagbundna och är på ett visst sätt subjektiva, så vill jag mena att de studerar objektivt verkliga fenomen och kan få giltig kunskap om dem. Att subjektiva fenomen också kan vara objektivt verkliga kan låta märkligt för vissa läsare, varför jag rundar av med en diskussion om vad det egentligen innebär för något att vara objektivt verkligt. Enligt den definition jag föreslår så framstår att meningsfulla fenomen, också de som kallas ”sociala konstruktioner”, kan förstås som objektivt verkliga företeelser trots att de på ett betydelsefullt sätt också är subjektiva. (shrink)

This paper offers a practical argument for metaphysical emergence. The main message is that the growing reliance on so-called irrational scientific methods provides evidence that objects of science are indecomposable and as such, are better described by metaphysical emergence as opposed to the prevalent reductionistic metaphysics. I show that a potential counterargument that science will eventually reduce everything to physics has little weight given where science is heading with its current methodological trend. I substantiate my arguments by detailed examples from (...) biological engineering, but the conclusions are extendable beyond that discipline. (shrink)

Philosophers have recently focused on critical, epistemological challenges that arise from the opacity of deep neural networks. One might conclude from this literature that doing good science with opaque models is exceptionally challenging, if not impossible. Yet, this is hard to square with the recent boom in optimism for AI in science alongside a flood of recent scientific breakthroughs driven by AI methods. In this paper, I argue that the disconnect between philosophical pessimism and scientific optimism is driven by a (...) failure to examine how AI is actually used in science. I show that, in order to understand the epistemic justification for AI-powered breakthroughs, philosophers must examine the role played by deep learning as part of a wider process of discovery. The philosophical distinction between the 'context of discovery' and the 'context of justification' is helpful in this regard. I demonstrate the importance of attending to this distinction with two cases drawn from the scientific literature, and show that epistemic opacity need not diminish AI's capacity to lead scientists to significant and justifiable breakthroughs. (shrink)

This is the Introduction and Chapter 1.1 of the book ‘Scientific Testimony. Its roles in science and society’ (OUP 2022). The introduction contains a brief survey of the book’s chapters and main conclusions, which I hope will be useful to the curious ones.

If one follows the accounts by philosophers of science and the discussions in scientific communities, there can be little doubt that failure is an essential part of scientific practice. It is essential both in the sense of being integral to scientific practice and of being necessary for its overall success. Researchers who create new scientific knowledge face uncertainties about the nature of the problem they are trying to solve, the existence of a solution to that problem, the way in which (...) a solution can be found, and their ability to find such a solution (Gläser, 2007, 247). The existence of these uncertainties means, in turn, that each research process carries the risk of failure. (shrink)

The debate concerning the scientific or unscientific status of the social sciences and the question of the (in) applicability of the methods of research in the natural sciences to social investigations are still unsettled in Philosophy of the Social Sciences. Some of the questions which are often asked concerning these issues include: are the social sciences really scientific? Do they merit the name science? Can we apply the same methods used in the natural research to social research? Are the objects (...) of inquiry in both areas the same? Attempts to answer these questions and numerous related others, have polarized Philosophers of the social sciences into two different ideological camps. Those who answer the questions affirmatively are regarded as the naturalists while those who answer negatively are regarded as the anti-naturalists or humanists. However, using the methods of critical argumentation and conceptual clarification, we intend to argue in this paper for a complementary ground between the two. Consequently, we contend that though the methods of the natural and the social sciences are different due to the fact that their respective objects of investigations are not the same, methods of the two fields are imperative to inquiries that would promote human knowledge and aid their intellectual development. This is necessary in order to foster interdisciplinary research and de-compartmentalisation of disciplines. We also intend to argue that the view that the social sciences are not scientific arose from a narrow conception of the term “science.” Our submission, therefore, is that the social sciences, legitimately, are sciences in their own right. (shrink)

Philosophers of science are divided over the interpretations of scientific normativity. Larry Laudan defends a sort of goal-directed rules for scientific methodology. In contrast, Gerard Doppelt thinks methodological rules are a mixed batch of rules in that some are goal-oriented hypothetical rules and others are goal-independent categorical rules. David Resnik thinks that the debate between them is at a standstill now. He further thinks there are certain rules, such as the rule of consistency which is goal independent. However, he proposes (...) a holistic understanding of the scientific methodology. Taking a thread from Resnik, the present paper also advocates a holistic understanding of the scientific methodology. Given that many scientific practices deal with systems, the focus will be given to the systems by assuming each as a constellation of methodological norms. By taking each system as a set of mutually supportive methodological rules whose instrumental values underwrite the coherence relation among them, the paper aims to provide what could be a viable holistic epistemological account that can explain scientific normativity at work in a scientific system. The paper will lay down specific holistic criteria for understanding the scientific methodology. They will be used to show how a holistic account could satisfactorily account for the success of the Compact Muon Solenoid (CMS) experiment in discovering the Higgs boson and how the holistic account can account for the instrumental error behind the apparent faster-than-light neutrino anomaly of the Oscillation Project with Emulsion-t Racking Apparatus (OPERA) experiments respectively. (shrink)

Likelihoodism is the view that the degree of evidential support should be analysed and measured in terms of likelihoods alone. The paper considers and responds to a popular criticism that a likelihoodist framework is too restrictive to guide belief. First, I show that the most detailed and rigorous version of this criticism, as put forward by Gandenberger (2016), is unsuccessful. Second, I provide a positive argument that a broadly likelihoodist framework can accommodate guidance for comparative belief, even when objectively well-grounded (...) prior probabilities are not available. As I show, the shift from non-relational to comparative probabilities opens up a new space for addressing the belief guidance problem for likelihoodism. (shrink)

What is risk? How do we assess risk? What are the ethical implications of risk? The concept of risk is important – sometimes even crucial – for many philosophical domains, from philosophy of science and technology to ethics and sustainability. Philosophy and Science of Risk is a clear, wide-ranging introduction to this urgent and fast-growing subject. It covers the following key topics: -/- • The philosophical and historical background to understanding and interpreting risk -/- • The meaning of risk and (...) how it differs from closely related concepts, such as uncertainty or dangers -/- • The social construction of risk -/- • Risk perception and risk as an object of scientific study -/- • The measurement of risk, its probability and severity -/- • Risk and scientific modeling -/- • Risk, value judgments, and expertise -/- • Risk management, including cost-benefit analysis and the precautionary approach -/- • Risk communication, including deliberative models -/- • Ethics of risk, including duties toward nonhuman animals and future generations -/- • Risk and sustainability -/- • Decision-making under risk -/- Including helpful additional features such as text boxes, chapter summaries, review, and discussion questions, Philosophy and Science of Risk: An Introduction is an ideal textbook for students of the philosophy of risk. It is also suitable for students studying the conceptual questions surrounding risk in related subjects, such as sociology, psychology, economics, politics, geography, sustainability, and environmental studies. (shrink)

With the approval and dissemination of CHED Memorandum Order Number 15, Series of 2019 in the Philippines, graduate students both in the Master of Science/Master of Arts Academic Track and Doctor of Philosophy Academic Track/Doctor of Philosophy by Research are now compelled to publish or, at least, show evidence of acceptance of research studies in refereed journals, or nationally or internationally indexed journals. Coriat (2019) claims that the value of research to society and its relationship to wealth and competitiveness has (...) long been established. But then, developing countries’ embrace, as in the case of the Philippines, of international publishing standards has received little attention (Vuong, 2019). As a result, publication pollution is growing by the day since developing-country writers including Filipino researchers lack expertise and mentoring (Jawaid, 2016). Thereupon, this policy review brings to the fore various deterrents in the fruition of students’ publications, pertinent questions than can stir waves of reflection among readers, professors, and administrators, to name a few, and mentorship as the saving grace for the emerging problems. Mentorship in this article means that the mentor and the mentee work hand in hand from research ideation to research presentation or publication. (shrink)

Attempts to show that the simplicity of a hypothesis can be measured by attending to how well it answers certain kinds of questions.

W. V. Quine calls some general methods of science maxims: general defeasible principles that call on us to approximate, maximize, or minimize a state and that are interpreted and weighed in context-sensitive ways. On my reading, his empiricism asks us to maximize accepting overall theories empirically equivalent to ours but to minimize accepting sentences that both do not affect the empirical content of our overall theory and do not simplify our overall theory. His naturalism asks us to maximize accepting sentences (...) that are solely supported by standards that support our best current scientific theory. Drawing on the Quine archive at Houghton Library at Harvard, I support and apply these interpretations by investigating his rapidly evolving later work on empirical content and empirical equivalence, including some of his views on translation in this later work and his vacillation between what he calls the ecumenical and sectarian attitudes. (shrink)

Reflections on the Practice of Physics has two admirable aims: on one hand, to contribute to a comprehensive historical understanding of Maxwell’s approach to physical inquiry; on the other, to extract philosophical lessons from the story of Maxwell’s astounding scientific breakthroughs. In line with an established tradition in scholarship (for example, Achinstein [1991]; Siegel [1991]; Harman [1998]), the authors defend the view that the method of ‘physical analogy’ that Maxwell first presented in the introductory section of his ‘On Faraday’s Lines (...) of Force’, and continued to invoke throughout his later physical works, was in fact many different methods. The authors deserve praise for bringing new life to the narration of Maxwell’s alleged twists in methodology, merging historical and philosophical perspectives to defend it. In this review, I elaborate on some of the book’s main strengths and weaknesses, in the spirit of pursuing a constructive critical discussion. (shrink)

Scientists who engage in science and the scientific endeavor should seek truth with conviction of morals and commitment to ethics. While the number of publications continues to increase, the number of retractions has increased at a faster rate. Journals publish fraudulent research papers despite claims of peer review and adherence to publishing ethics. Nevertheless, appropriate ethical peer review will remain a gatekeeper when selecting research manuscripts in scholarly publishing and approving research applications for grant funding. However, this peer review must (...) become more open, fair, transparent, equitable, and just with new recommendations and guidelines for reproducible and accountable reviews that support and promote fair citation and citational justice. We should engineer this new peer-review process with modern informatics technology and information science to provide and defend better safeguards for truth and integrity, to clarify and maintain the provenance of information and ideas, and to rebuild and restore trust in scholarly research institutions. Indeed, this new approach will be necessary in the current post-truth era to counter the ease and speed with which mis-information, dis-information, anti-information, caco-information, and mal-information spread through the internet, web, news, and social media. The most important question for application of new peer-review methods to these information wars should be ‘Who does what when?’ in support of reproducible and accountable reviews. Who refers to the authors, reviewers, editors, and publishers as participants in the review process. What refers to disclosure of the participants' identities, the material content of author manuscripts and reviewer commentaries, and other communications between authors and reviewers. When refers to tracking the sequential points in time for which disclosure of whose identity, which content, and which communication at which step of the peer-review process for which audience of readers and reviewers. We believe that quality peer review, and peer review of peer review, must be motivated and maintained by elevating their status and prestige to an art and a science. Both peer review itself and peer review analyses of peer reviews should be incentivised by publishing peer reviews as citable references separately from the research report reviewed while crossreferenced and crosslinked to the report reviewed. (shrink)

Machine learning operates at the intersection of statistics and computer science. This raises the question as to its underlying methodology. While much emphasis has been put on the close link between the process of learning from data and induction, the falsificationist component of machine learning has received minor attention. In this paper, we argue that the idea of falsification is central to the methodology of machine learning. It is commonly thought that machine learning algorithms infer general prediction rules from past (...) observations. This is akin to a statistical procedure by which estimates are obtained from a sample of data. But machine learning algorithms can also be described as choosing one prediction rule from an entire class of functions. In particular, the algorithm that determines the weights of an artificial neural network operates by empirical risk minimization and rejects prediction rules that lack empirical adequacy. It also exhibits a behavior of implicit regularization that pushes hypothesis choice toward simpler prediction rules. We argue that taking both aspects together gives rise to a falsificationist account of artificial neural networks. (shrink)

A commissioned 3-volume (800 pages) in-depth introduction to the philosophy of science, peer-reviewed by 11 specialists. (In Hebrew).

According to a widely held view, scientific modelling consists in entertaining a set of model descriptions that specify a model. Rather than studying the phenomenon of interest directly, scientists investigate the phenomenon indirectly via a model in the hope of learning about some of the phenomenon’s features. I call this view the description-driven modelling (DDM) account. I argue that although an accurate description of much of scientific research, the DDM account is found wanting as regards the mechanistic modelling found in (...) many branches of biology. By analysing research practices in cancer immunology concerning the development of mechanistic models of the process of cancer metastasis, this paper presents and argues for a complementary account of scientific modelling, herein called the experimentation-driven modelling (EDM) account. In EDM, scientists investigate a set of experimental systems and then integrate the results obtained from experiments into a mechanistic model. While EDM shares some key features with DDM, the two are epistemically very different approaches to research. (shrink)

Critical realism, championed by its proponents as the most promising post-positivist social science paradigm, has gained significant influence in the last few decades. This paper provides a critical evaluation of the critical realism movement in the hope of facilitating more fruitful dialogues between its proponents and rivalling schools of sociologists. Two concerns are raised about contemporary critical realism. First, critical realism is not the only philosophical school against positivism and not necessarily the best. Second, critical realists exaggerate the importance of (...) critical realism to social science and conflate philosophy of science with sociological theories. (shrink)

Recent case studies have revealed that purely formal analogies have been successfully used as a heuristic in physics. This is at odds with most general philosophical accounts of analogies, which require analogies to be physical in order to be justifiably used. The main goal of this paper is to supply a philosophical account that justifies the use of purely formal analogies in physics. Using Bartha’s (2010) articulation model as a starting point, I offer precise definitions of formal and physical analogies (...) and propose a new submodel of analogical reasoning that accounts for the successful use of purely formal analogies in the development of renormalization group methods for use in particle physics in the early 1970’s (Fraser 2020). Two distinctive features of this new applied mathematics submodel for analogical reasoning are that the conclusion of the argument from analogy includes both an entire model (and not only a hypothesis or a prediction) and the construction procedure for this model. A third important difference from arguments from physical analogy is that only the prima facie plausibility of the conclusion is established, and not stronger types of plausibility associated with confirmation. The use of purely formal analogies is justified because they are suited to supporting conclusions of this sort. Formulating a general philosophical account of analogies that covers purely formal analogies also serves two additional purposes: (1) to highlight the features of this case that are novel in the context of examples of analogies traditionally considered by philosophers and (2) to establish that physicists should not automatically dismiss purely formal analogies when evaluating heuristics for the development of new models. (shrink)

Frank Cabrera argues that informational explanatory virtues—specifically, mechanism, precision, and explanatory scope—cannot be confirmational virtues, since hypotheses that possess them must have a lower probability than less virtuous, entailed hypotheses. We argue against Cabrera’s characterization of confirmational virtue and for an alternative on which the informational virtues clearly are confirmational virtues. Our illustration of their confirmational virtuousness appeals to aspects of causal inference, suggesting that causal inference has a role for the explanatory virtues. We briefly explore this possibility, delineating a (...) path from Mill’s method of agreement to Inference to the Best Explanation (IBE). (shrink)

The practice of science rests on the assumption of dependable regularity in the behavior of the physical world. It presumes that the world has an investigable causal structure and that scientific experimentation, observation, and theorizing provide a reliable pathway to its discernment. This much is not in dispute. What is in dispute is what warrants the metaphysical and methodological assumption—essential to the heuristic utility of science—that nature is uniform in such a way that the present can serve as a key (...) to both the past and the future. This article focuses on the metaphysical foundation and justification for uniformitarian assumptions about nature and argues that they are inconsistent with both metaphysical and methodological naturalism. (shrink)

The present paper focuses on the work of Adolphe Quetelet (1796-1874), the Belgian author of the Social Physics who worked in the tradition of the French mathématique sociale, and of Otto Neurath (1882-1945), the Vienna Circle’s member who supported a “sociology within physicalism”. They shared some important philosophical and methodological positions: an empiricist approach to the social sciences, a unitary conception of the natural and the social sciences, and the appreciation of statistics as a tool for investigating and also reforming (...) society. My paper analyses these elements of continuity between the two authors, in order to highlight how an empirical-quantitative approach to society was (at least partially) inherited by Austrian sociology from an earlier French tradition. On the contrary, most German statisticians refused such an approach at the time in which social sciences were developing in Germany, and they strongly believed in an in-principle difference between natural and social sciences. From a systematic point of view, my paper devotes a special attention to the relationship between the application of statistics (praxis) and a unitary conception of the sciences (theory). Both in Quetelet’s and in Neurath’s work these two aspects support each other. Nevertheless Neurath can be said to have turned Quetelet’s reasoning upside-down: While Quetelet appealed to statistics to claim that the social sciences can reach the same objectivity and determinacy as the natural ones, Neurath referred to statistics to argue that the natural sciences – exactly like the social ones – are characterized by a certain degree of indeterminacy and underdetermination. (shrink)

Certain characteristics make machine learning a powerful tool for processing large amounts of data, and also particularly unsuitable for explanatory purposes. There are worries that its increasing use in science may sideline the explanatory goals of research. We analyze the key characteristics of ML that might have implications for the future directions in scientific research: epistemic opacity and the ‘theory-agnostic’ modeling. These characteristics are further analyzed in a comparison of ML with the traditional statistical methods, in order to demonstrate what (...) it is specifically that makes ML methodology substantially unsuitable for reaching explanations. The analysis is given broader philosophical context by connecting it with the views on the role of prediction and explanation in science, their relationship, and the value of explanation. We proceed to show, first, that ML disrupts the relationship between prediction and explanation commonly understood as a functional relationship. Then we show that the value of explanation is not exhausted in purely predictive functions, but rather has a ubiquitously recognized value for both science and everyday life. We then invoke two hypothetical scenarios with different degrees of automatization of science, which help test our intuitions on the role of explanation in science. The main question we address is whether ML will reorient or otherwise impact our standard explanatory practice. We conclude with a prognosis that ML would diversify science into purely predictively oriented research based on ML-like techniques and, on the other hand, remaining faithful to anthropocentric research focused on the search for explanation. (shrink)

I argue that if simplicity is a theoretical virtue and some theoretical virtues are the constituents of the aims of theorizing in physics—i.e., theory choice and theory development in physics—and scientific rationality is instrumental rationality, then simplicity cannot be a mere means to achieve the aims. I do this by showing that considering simplicity as a mere means brings about counterintuitive ramifications concerning scientific rationality. These counterintuitive ramifications can be avoided if we consider simplicity a constituent of the aims of (...) theorizing in physics. (shrink)

According to falsificationism, a theory is scientific if it can be incompatible with some empirically testable statements. This epistemological approach has been criticized because, in practice, it is impossible to decide when a particular fact should be considered incompatible with a theory. These criticisms, however, neglect the fact that the Popperian sense of falsification is a “logical sense.” Thus, the Popperian criterion of falsifiability only requires that, assuming certain auxiliary hypotheses, the theory in question be logically incompatible with some empirically (...) testable statements. This paper presents a reassessment of these criticisms, justifying that the criterion of falsifiability is unrenounceable insofar as our best theories should aspire to satisfy it. (shrink)

Human microbiome research makes causal connections between entire microbial communities and a wide array of traits that range from physiological diseases to psychological states. To evaluate these causal claims, we first examine a well-known single-microbe causal explanation: of Helicobacter pylori causing ulcers. This apparently straightforward causal explanation is not so simple, however. It does not achieve a key explanatory standard in microbiology, of Koch’s postulates, which rely on manipulations of single-microorganism cultures to infer causal relationships to disease. When Koch’s postulates (...) are framed by an interventionist causal framework, it is clearer what the H. pylori explanation achieves and where its explanatory strengths lie. After assessing this ‘simple’, single-microbe case, we apply the interventionist framework to two key areas of microbiome research, in which obesity and mental health states are purportedly explained by microbiomes. Despite the experimental data available, interventionist criteria for explanation show that many of the causal claims generated by microbiome research are weak or misleading. We focus on the stability, specificity and proportionality of proposed microbiome causal explanations, and evaluate how effectively these dimensions of causal explanation are achieved in some promising avenues of research. We suggest some conceptual and explanatory strategies to improve how causal claims about microbiomes are made. (shrink)

Human microbiome research makes causal connections between entire microbial communities and a wide array of traits that range from physiological diseases to psychological states. To evaluate these causal claims, we first examine a well-known single-microbe causal explanation: of Helicobacter pylori causing ulcers. This apparently straightforward causal explanation is not so simple, however. It does not achieve a key explanatory standard in microbiology, of Koch’s postulates, which rely on manipulations of single-microorganism cultures to infer causal relationships to disease. When Koch’s postulates (...) are framed by an interventionist causal framework, it is clearer what the H. pylori explanation achieves and where its explanatory strengths lie. After assessing this ‘simple’, single-microbe case, we apply the interventionist framework to two key areas of microbiome research, in which obesity and mental health states are purportedly explained by microbiomes. Despite the experimental data available, interventionist criteria for explanation show that many of the causal claims generated by microbiome research are weak or misleading. We focus on the stability, specificity and proportionality of proposed microbiome causal explanations, and evaluate how effectively these dimensions of causal explanation are achieved in some promising avenues of research. We suggest some conceptual and explanatory strategies to improve how causal claims about microbiomes are made. (shrink)

Human microbiome research makes causal connections between entire microbial communities and a wide array of traits that range from physiological diseases to psychological states. To evaluate these causal claims, we first examine a well-known single-microbe causal explanation: of Helicobacter pylori causing ulcers. This apparently straightforward causal explanation is not so simple, however. It does not achieve a key explanatory standard in microbiology, of Koch’s postulates, which rely on manipulations of single-microorganism cultures to infer causal relationships to disease. When Koch’s postulates (...) are framed by an interventionist causal framework, it is clearer what the H. pylori explanation achieves and where its explanatory strengths lie. After assessing this ‘simple’, single-microbe case, we apply the interventionist framework to two key areas of microbiome research, in which obesity and mental health states are purportedly explained by microbiomes. Despite the experimental data available, interventionist criteria for explanation show that many of the causal claims generated by microbiome research are weak or misleading. We focus on the stability, specificity and proportionality of proposed microbiome causal explanations, and evaluate how effectively these dimensions of causal explanation are achieved in some promising avenues of research. We suggest some conceptual and explanatory strategies to improve how causal claims about microbiomes are made. (shrink)

When explaining the causes of human behavior, genes are often given a special status. They are thought to relate to an intrinsic human 'essence', and essentialist biases have been shown to skew the way in which causation is assessed. Causal reasoning in general is subject to other pre-existing biases, including beliefs about normativity and morality. In this synthesis we show how factors which influence causal reasoning can be mapped to a framework of genetic essentialism, which reveals both the shared and (...) unique factors underpinning biases in causal reasoning and genetic essentialism. This comparison identifies overlooked areas of research which could provide fruitful investigation, such as whether normative assessments of behaviors influence the way that genetic causes are ascribed or endorsed. We also outline the importance of distinguishing reasoning processes regarding genetic causal influences on one's self versus others, as different cognitive processes and biases are likely to be at play. (shrink)

Much microbiota-gut-brain research focuses on the causal role of microbiomes as a whole, rather than their component parts: microbes. Hooks et al. find these whole-community explanations inadequate; however, they do not provide suggestions for better explanations. By appealing to proportionality – a criterion that can be used to develop more appropriate causal explanations – more accurate causal claims can be made.

A high heritability estimate usually corresponds to a situation in which trait variation is largely caused by genetic variation. However, in some cases of gene-environment covariance, causal intuitions about the sources of trait difference can vary, leading experts to disagree as to how the heritability estimate should be interpreted. We argue that the source of contention for these cases is an inconsistency in the interpretation of the concepts ‘genotype’, ‘phenotype’, and ‘environment’. We propose an interpretation of these terms under which (...) trait variance initially caused by genetic variance is subsumed into a heritability for all cases of gene-environment covariance. (shrink)

In this paper I shall venture into an area with which I am not very familiar and in which I feel far from confident; namely into phenomenology. My main motive is not to get away from standard, boring, methodological questions like those of induction and demarcation; but the conviction that a phenomenological account of the empirical basis forms a necessary complement to Popper's falsificationism. According to the latter, a scientific theory is a synthetic and universal, hence unverifiable proposition. In fact, (...) in order to be technologically useful, a scientific hypothesis must refer to future states-of-affairs; it ought therefore to remain unverified. But in order to be empirical, a theory must bear some kind of relation to factual statements. According to Popper, such a relation can only be one of potential conflict. Thus a theory T will be termed scientific if and only if T is logically incompatible with a so-called basic statement b, where b is both empirically verifiable and empirically falsifiable. In other words: T is scientific if it entails ¬b; where b, hence also ¬b, is an empirically decidable proposition. (shrink)

The process of scientific investigation is reconstructed as a process of empirical approximation to the truth. This last concept is explicated as a combination of “degree of simmilarity between theory A and the strongest accepted empirical law at moment t” and the “degree of depth of this empirical law”. A number of methodological theorems are proved, and avision of science closer to sophisticated falsificationism is mathematically deduced from our definitions.

The paper analyses not well known beginning of Cracow philosophy of nature originating from the 19th century. A selected number of issues formed by two founders of Cracow philosophical centre – W. Heinrich and M. Straszewski are presented. Finally, the author tries to refl ect on modern nature philosophy in the context of its relations with tradition lasting over the centuries.

Throughout the biological and biomedical sciences there is a growing need for, prescriptive ‘minimum information’ (MI) checklists specifying the key information to include when reporting experimental results are beginning to find favor with experimentalists, analysts, publishers and funders alike. Such checklists aim to ensure that methods, data, analyses and results are described to a level sufficient to support the unambiguous interpretation, sophisticated search, reanalysis and experimental corroboration and reuse of data sets, facilitating the extraction of maximum value from data sets (...) them. However, such ‘minimum information’ MI checklists are usually developed independently by groups working within representatives of particular biologically- or technologically-delineated domains. Consequently, an overview of the full range of checklists can be difficult to establish without intensive searching, and even tracking thetheir individual evolution of single checklists may be a non-trivial exercise. Checklists are also inevitably partially redundant when measured one against another, and where they overlap is far from straightforward. Furthermore, conflicts in scope and arbitrary decisions on wording and sub-structuring make integration difficult. This presents inhibit their use in combination. Overall, these issues present significant difficulties for the users of checklists, especially those in areas such as systems biology, who routinely combine information from multiple biological domains and technology platforms. To address all of the above, we present MIBBI (Minimum Information for Biological and Biomedical Investigations); a web-based communal resource for such checklists, designed to act as a ‘one-stop shop’ for those exploring the range of extant checklist projects, and to foster collaborative, integrative development and ultimately promote gradual integration of checklists. (shrink)

The Ontology for Biomedical Investigations (OBI) is an ontology that provides terms with precisely defined meanings to describe all aspects of how investigations in the biological and medical domains are conducted. OBI re-uses ontologies that provide a representation of biomedical knowledge from the Open Biological and Biomedical Ontologies (OBO) project and adds the ability to describe how this knowledge was derived. We here describe the state of OBI and several applications that are using it, such as adding semantic expressivity to (...) existing databases, building data entry forms, and enabling interoperability between knowledge resources. OBI covers all phases of the investigation process, such as planning, execution and reporting. It represents information and material entities that participate in these processes, as well as roles and functions. Prior to OBI, it was not possible to use a single internally consistent resource that could be applied to multiple types of experiments for these applications. OBI has made this possible by creating terms for entities involved in biological and medical investigations and by importing parts of other biomedical ontologies such as GO, Chemical Entities of Biological Interest (ChEBI) and Phenotype Attribute and Trait Ontology (PATO) without altering their meaning. OBI is being used in a wide range of projects covering genomics, multi-omics, immunology, and catalogs of services. OBI has also spawned other ontologies (Information Artifact Ontology) and methods for importing parts of ontologies (Minimum information to reference an external ontology term (MIREOT)). The OBI project is an open cross-disciplinary collaborative effort, encompassing multiple research communities from around the globe. To date, OBI has created 2366 classes and 40 relations along with textual and formal definitions. The OBI Consortium maintains a web resource providing details on the people, policies, and issues being addressed in association with OBI. (shrink)

The familiar theories of physics have the feature that the application of the theory to make predictions in specific circumstances can be done by means of an algorithm. We propose a more precise formulation of this feature—one based on the issue of whether or not the physically measurable numbers predicted by the theory are computable in the mathematical sense. Applying this formulation to one approach to a quantum theory of gravity, there are found indications that there may exist no such (...) algorithms in this case. Finally, we discuss the issue of whether the existence of an algorithm to implement a theory should be adopted as a criterion for acceptable physical theories.“Can it then be that there is... something of use for unraveling the universe to be learned from the philosophy of computer design?” —J. A. Wheeler(1). (shrink)

The world's leading authorities in the sciences and humanities—dozens of top scholars, including three Nobel laureates—join a cultural and intellectual battle that leaves no human life untouched. Is the universe self-existent, self-sufficient, and self-organizing, or is it grounded instead in a reality that transcends space, time, matter, and energy?

: Errors in science range along a spectrum from those relatively local to the phenomenon (usually easily remedied in the laboratory) to those more conceptually derived (involving theory or cultural factors, sometimes quite long-term). One may classify error types broadly as material, observational, conceptual or discoursive. This framework bridges philosophical and sociological perspectives, offering a basis for interfield discourse. A repertoire of error types also supports error analytics, a program for deepening reliability through strategies for regulating and probing error.

The process of scientific investigation is reconstructed as a process of empirical approximation to the truth. This last concept is explicated as a combination of “degree of simmilarity between theory A and the strongest accepted empirical law at moment t” and the “degree of depth of this empirical law”. A number of methodological theorems are proved, and avision of science closer to sophisticated falsificationism is mathematically deduced from our definitions.