Bert Leuridan

There are several theses in political science that are usually explicitly called ‘laws’. Other theses are generally thought of as laws, but often without being explicitly labelled as such. Still other claims are well-supported and arguably interesting, while no one would be tempted to call them laws. This situation raises philosophical questions: which theses deserve to be called laws and which not? And how should we decide about this? In this paper we develop and motivate a strategy for thinking about laws in political science which integrates two core concepts: spatio-temporal stability and social mechanisms. The proposed strategy is a set of guidelines that political scientists can use to reflect on and argue about specific cases within their discipline, not a clear-cut demarcation criterion. We defend and motivate this strategy and apply it to two cases (one with respect to state repression, one about parliamentary elections). After we have developed and motivated our strategy for political science, we show that our proposal is relevant for other disciplines in the social sciences. We explain how our views fit into critical realism and embed them in the debate on laws in general philosophy of science and in the philosophy of the social sciences.

The Concussion in Sport Group guidelines have successfully brought the attention of brain injuries to the global medical and sport research communities, and has significantly impacted brain injury-related practices and rules of international sport. Despite being the global repository of state-of-the-art science, diagnostic tools and guides to clinical practice, the ensuing consensus statements remain the object of ethical and sociocultural criticism. The purpose of this paper is to bring to bear a broad range of multidisciplinary challenges to the processes and products of sport-related concussion movement. We identify lacunae in scientific research and clinical guidance in relation to age, disability, gender and race. We also identify, through multidisciplinary and interdisciplinary analysis, a range of ethical problems resulting from conflicts of interest, processes of attributing expertise in sport-related concussion, unjustifiably narrow methodological control and insufficient athlete engagement in research and policy development. We argue that the sport and exercise medicine community need to augment the existing research and practice foci to understand these problems more holistically and, in turn, provide guidance and recommendations that help sport clinicians better care for brain-injured athletes.

This article concerns the way in which philosophers study the epistemology of scientific thought experiments. Starting with a general overview of the main contemporary philosophical accounts, we will first argue that two implicit assumptions are present therein: first, that the epistemology of scientific thought experiments is solely concerned with factual knowledge of the world; and second, that philosophers should account for this in terms of the way in which individuals in general contemplate these thought experiments in thought. Our goal is to evaluate these assumptions and their implications using a particular case study: Albert Einstein's magnet-conductor thought experiment. We will argue that an analysis of this thought experiment based on these assumptions – as John Norton (1991) provides – is, in a sense, both misguided (the thought experiment by itself did not lead Einstein to factual knowledge of the world) and too narrow (to understand the thought experiment's epistemology, its historical context should also be taken into account explicitly). Based on this evaluation we propose an alternative philosophical approach to the epistemology of scientific thought experiments which is more encompassing while preserving what is of value in the dominant view.

In the past 25 years, many philosophers have endorsed the view that the practical value of causal knowledge lies in the fact that manipulation of causes is a good way to bring about a desired change in the effect. This view is intuitively very plausible. For instance, we can predict a storm on the basis of a barometer reading, but we cannot avoid the storm by manipulating the state of the barometer (barometer status and storm are effects of a common cause, viz. atmospheric conditions). In §1 we present textual evidence which shows that this view is very popular. In §2 we show that this standard view is too restrictive: the practical value of causal knowledge is wider. In §3 we introduce the distinction between ‘manipulative policy’ and ‘selective policy’ as a theoretical framework to account for this wider practical value.

Evidence-based management (EBMgt) is a relatively recent approach to management, developed by Denise Rousseau in a series of articles and in a book that she co-authored with Eric Barends (Barends & Rousseau 2018). It is based on the idea that good-quality management decisions require both critical thinking and use of the best available evidence. In this paper we want to contribute to the scholarship on evidence-based management by showing how its central concept – evidence – can and should be defined more strictly. Barends and Rousseau define evidence as a two-place relation between information and a claim that is at stake. Starting from insights from the methodology of the social sciences we argue that evidence is a three-place relation between a method, information and a claim. We offer a guiding principle for adequately characterising what counts as evidence (the inclusion of a procedural component which describes how the information should be collected and reported) and apply it to Barends and Rousseau’s concepts of (i) evidence from practitioners, (ii) evidence from the organization and (iii) evidence from stakeholders. We think that by treating evidence as a three-place relation we can develop an improved account (which we call EBMgt+) of what evidence-based management can and should be.

David Lewis has formulated a well-known challenge to his Best System account of lawhood: the content of any system whatever can be formulated very simply if one allows for perverse choices of primitive vocabulary. We show that the challenge is not that dangerous, and that to account for it one need not invoke natural properties or relativized versions of the Best System account. This way, we help to move towards an even better Best System account. We discuss extensions of our strategy to the discussions about the indexicality of the notion of laws of nature, and to another trivialization argument.

Gregor Mendel, Thomas Hunt Morgan and experiments in classical genetics In the middle of the 19th century, Gregor Mendel performed a series of crosses with pea plants to investigate how hybrids are formed. Decades later, Thomas Hunt Morgan finalized the theory of classical genetics. An important aspect of Mendel’s and Morgan’s scientific approach is that they worked in a systematic, experimental fashion. But how did these experiments proceed? What is the relation between these experiments and Mendel’s and Morgan’s explanatory theories? What was their evidential value? Using present-day insights in the nature of experimentation I will show that the answer to these questions is fascinating but not obvious. Crossings in classical genetics lacked a crucial feature of traditional experiments for causal discovery: manipulation of the purported causes. Hence they were not traditional, ‘manipulative’ experiments, but ‘selective experiments’.

Mechanistic approaches are very common in the causal interpretation of biological and neuroscientific experimental work in today’s philosophy of science. In the mechanistic literature a strict distinction is often made between causal relations and constitutive relations, where the latter cannot be causal. One of the typical reasons for this strict distinction is that constitutive relations are supposedly synchronic whereas most if not all causal relations are diachronic. This strict distinction gives rise to a number of problems, however. Our end goal in this paper is to argue that it should be given up, at least in the context of the biological and the psychological sciences. To that effect, we argue that constitutive relations in this context are diachronic, thus undermining the aforementioned reason. We offer two cases from scientific practice in which constitutive relations are regarded as both diachronic and causally efficacious, review three existing ways of dealing with the apparent diachronic nature of interlevel relations in mechanisms and propose a new account of diachronic, causal constitutive relevance.

Mechanistic philosophy of science views a large part of scientific activity as engaged in modelling mechanisms. While science textbooks tend to offer qualitative models of mechanisms, there is increasing demand for models from which one can draw quantitative predictions and explanations. Casini et al. (Theoria 26(1):5–33, 2011) put forward the Recursive Bayesian Networks (RBN) formalism as well suited to this end. The RBN formalism is an extension of the standard Bayesian net formalism, an extension that allows for modelling the hierarchical nature of mechanisms. Like the standard Bayesian net formalism, it models causal relationships using directed acyclic graphs. Given this appeal to acyclicity, causal cycles pose a prima facie problem for the RBN approach. This paper argues that the problem is a significant one given the ubiquity of causal cycles in mechanisms, but that the problem can be solved by combining two sorts of solution strategy in a judicious way

We present a logic, $$\mathbf {ELI^r}$$ ELI r, for the discovery of deterministic causal regularities starting from empirical data. Our approach is inspired by Mackie’s theory of causes as INUS-conditions, and implements a more recent adjustment to Mackie’s theory according to which the left-hand side of causal regularities is required to be a minimal disjunction of minimal conjunctions. To derive such regularities from a given set of data, we make use of the adaptive logics framework. Our knowledge of deterministic causal regularities is, as Mackie noted, most often gappy or elliptical. The adaptive logics framework is well-suited to explicate both the internal and the external dynamics of the discovery of such gappy regularities. After presenting $$\mathbf {ELI^r}$$ ELI r, we first discuss these forms of dynamics in more detail. Next, we consider some criticisms of the INUS-account and show how our approach avoids them, and we compare $$\mathbf {ELI^r}$$ ELI r with the CNA algorithm that was recently proposed by Michael Baumgartner.

Today, mechanisms and mechanistic explanation are very popular in philosophy of science and are deemed a welcome alternative to laws of nature and deductive‐nomological explanation. Starting from Mitchell's pragmatic notion of laws, I cast doubt on their status as a genuine alternative. I argue that (1) all complex‐systems mechanisms ontologically must rely on stable regularities, while (2) the reverse need not hold. Analogously, (3) models of mechanisms must incorporate pragmatic laws, while (4) such laws themselves need not always refer to underlying mechanisms. Finally, I show that Mitchell's account is more encompassing than the mechanistic account *Received August 2008; revised January 2010. †To contact the author, please write to: Centre for Logic and Philosophy of Science, Ghent University, Blandijnberg 2, B‐9000 Belgium; e‐mail: [email protected].

ABSTRACT. Probability and statistics play an important role in contemporary -philosophy of causality. They are viewed as glasses through which we can see or detect causal relations. However, they may sometimes act as blinding glasses, as I will argue in this paper. In the 19th century, Francis Galton tried to statistically analyze hereditary phenomena. Although he was a far better statistician than Gregor Mendel, his biological theory turned out to be less fruitful. This was no sheer accident. His knowledge of statistics generated two explananda (unknown to Mendel) which in turn generated constraints for any theory of heredity. These constraints misguided Galtons search for the causal mechanism of inheritance. This is not just. an interesting case for philosophers and historians of ·science. Notwithstanding the progress made by statitics, Galtons problem is still relevant today. In the final section, I briefly explore the implications for statistical techniques such as structural equation modelling.

What are scientific theories and how should they be represented? In this article, I propose a causal–structural account, according to which scientific theories are to be represented as sets of interrelated causal and credal nets. In contrast with other accounts of scientific theories (such as Sneedian structuralism, Kitcher’s unificationist view, and Darden’s theory of theoretical components), this leaves room for causality to play a substantial role. As a result, an interesting account of explanation is provided, which sheds light on explanatory unification within a causalist framework. The theory of classical genetics is used as a case study. 1 Introduction2 The Theory of Classical Genetics3 Three Philosophical Accounts of the Theory of Classical Genetics3.1 The structuralist account3.2 Kitcher’s unificationism3.3 Darden and theory change in science4 A Common Lacuna: Where is Causality?5 Woodward’s Interventionist Account of Causation6 Causal Bayes Nets and Their Interrelations6.1 Causal Bayes nets6.2 Relations among causal nets6.3 Credal nets and their interrelations7 The Theory of the Gene and its Causal Graph8 A First Exemplar: Stem Length in Pea Plants8.1 Three crosses on stem length in pea plants8.2 The causal graph for stem length in pea plants8.3 Morgan’s explanatory principles and the credal net for stem length in pea plants9 Explaining Mendel’s Crosses: A Causal–Structural Account10 Monohybrid Crosses with Complete Dominance11 Exemplars,Explanatory Patterns, Generic Credal Nets, and Mechanism Schemas12 Incomplete Dominance13 Anomalies14 Multi-hybrid Crosses with Independent Assortment15 Multi-hybrid Crosses with Linkage and Crossing-Over16 Double Crossing-Over and the Linear Order of the Gene17 Causal–Structural Explanation18 Explanatory Unification19 Concluding Remarks

Sober has reconstructed the biological design argument in the framework of likelihoodism, purporting to demonstrate that it is defective for intrinsic reasons. We argue that Sober’s restriction on the introduction of auxiliary hypotheses is too restrictive, as it commits him to rejecting types of everyday reasoning that are clearly valid. Our account shows that the design argument fails, not because it is intrinsically untestable but because it clashes with the empirical evidence and fails to satisfy certain theoretical desiderata (in particular, unification). Likewise, Sober’s critique of the arguments from imperfections and from evil against design is off the mark.

The International Agency for Research on Cancer (IARC) is an organization which seeks to identify the causes of human cancer. Per agent, such as betel quid or Human Papillomaviruses, they review the available evidence deriving from epidemiological studies, animal experiments and information about mechanisms (and other data). The evidence of the different groups is combined such that an overall assessment of the carcinogenicity of the agent in question is obtained. In this paper, we critically review the IARC’s carcinogenicity evaluations. First we show that serious objections can be raised against their criteria and procedures – more specifically regarding the role of mechanistic knowledge in establishing causal claims. Our arguments are based on the problem of confounders, of the assessment of the temporal stability of carcinogenic relations, and of the extrapolation from animal experiments. Then we address a very important question, viz. how we should treat the carcinogenicity evaluations that were based on the current procedures. After showing that this question is important, we argue that an overall dismissal of the current evaluations would be too radical. Instead, we argue in favour of a stepwise re-evaluation of the current findings.

The use of general and universal laws in historiography has been the subject of debate ever since the end of the nineteenth century. Since the 1970s there has been a growing consensus that general laws such as those in the natural sciences are not applicable in the scientific writing of history. We will argue against this consensus view, not by claiming that the underlying conception of what historiography is—or should be—is wrong, but by contending that it is based on a misconception of what general laws such as those of the natural sciences are. We will show that a revised notion of law, one inspired by the work of Sandra D. Mitchell, in tandem with Jim Woodward’s notion of “invariance,” is indeed applicable to historiography, much in the same way as it is to most other scientific disciplines. Having developed a more adequate account of general laws, we then show, by means of three examples, that what are called “pragmatic laws” and “invariance” do in fact play a role in history in several interesting ways. These examples—from cultural history, economic history, and the history of religion—have been selected on the basis of their diversity in order to illustrate the widespread use of pragmatic laws in history.

We argue that there is no general theory of explanation that spans the sciences, mathematics, and ethics, etc. More specifically, there is no good reason to believe that substantive and domain-invariant constraints on explanatory information exist. Using Nickel (Noûs 44(2):305–328, 2010 ) as an exemplar of the contrary, generalist position, we first show that Nickel’s arguments rest on several ambiguities, and then show that even when these ambiguities are charitably corrected, Nickel’s defense of general theories of explanation is inadequate along several different dimensions. Specifically, we argue that Nickel’s argument has three fatal flaws. First, he has not provided any compelling illustrations of domain-invariant constraints on explanation. Second, in order to fend off the most vehement skeptics of domain-invariant theories of explanation, Nickel must beg all of the important questions. Third, Nickel’s examples of explanations from different domains with common explanatory structure rely on incorrect formulations of the explanations under consideration, circular justifications, and/or a mischaracterization of the position Nickel intends to critique. Given that the best and most elaborate defense of the generalist position fails in so many ways, we conclude that the standard practice in philosophy (and in philosophy of science in particular), which is to develop theories of explanation that are tailored to specific domains, still is justified. For those who want to buy into a more ambitious project: beware of the costs!

In this article, I present two conceptual problems for Craver's mutual manipulability account of constitutive relevance in mechanisms. First, constitutive relevance threatens to imply causal relevance despite Craver (and Bechtel)'s claim that they are strictly distinct. Second, if (as is intuitively appealing) parthood is defined in terms of spatio-temporal inclusion, then the mutual manipulability account is prone to counterexamples, as I show by a case of endosymbiosis. I also present a methodological problem (a case of experimental underdetermination) and formulate two partial, but fallible solutions based on the notions of parthood and synchronicity

Supervenience is mostly conceived of as a purely philosophical concept. Nevertheless, I will argue, it played an important and very fruitful inferential role in classical genetics. Gregor Mendel assumed that phenotypic traits supervene on underlying factors, and this assumption allowed him to successfully predict and explain the phenotypical regularities he had experimentally discovered. Therefore it is interesting to explicate how we reason about supervenience relations. I will tackle the following two questions. Firstly, can a reliable method (a logic) be found for infenfog supervenience claims from data? Secondly, can a reliable method (a logic) be found to empirically test supervenience claims? I will answer these questions within the framework of the adaptive logics programme.

In this paper, I want to substantiate three related claims regarding causal discovery from non-experimental data. Firstly, in scientific practice, the problem of ignorance is ubiquitous, persistent, and far-reaching. Intuitively, the problem of ignorance bears upon the following situation. A set of random variables V is studied but only partly tested for (conditional) independencies; i.e. for some variables A and B it is not known whether they are (conditionally) independent. Secondly, Judea Pearl’s most meritorious and influential algorithm for causal discovery (the IC algorithm) cannot be applied in cases of ignorance. It presupposes that a full list of (conditional) independence relations is on hand and it would lead to unsatisfactory results when applied to partial lists. Finally, the problem of ignorance is successfully treated by means of ALIC, the adaptive logic for causal discovery presented in this paper.

In the past 25 years, many philosophers have endorsed the view that the practical value of causal knowledge lies in the fact that manipulation of causes is a good way to bring about a desired change in the effect. This view is intuitively very plausible. For instance, we can predict a storm on the basis of a barometer reading, but we cannot avoid the storm by manipulating the state of the barometer (barometer status and storm are effects of a common cause, viz. atmospheric conditions). In Section 1 we present textual evidence which shows that this view is very popular. In Section 2 we show that this standard view is too restrictive: the practical value of causal knowledge is wider. In Section 3 we introduce the distinction between ‘manipulative policy’ and ‘selective policy’ as a theoretical framework to account for this wider practical value.

We present a logic, \, for the discovery of deterministic causal regularities starting from empirical data. Our approach is inspired by Mackie’s theory of causes as INUS-conditions, and implements a more recent adjustment to Mackie’s theory according to which the left-hand side of causal regularities is required to be a minimal disjunction of minimal conjunctions. To derive such regularities from a given set of data, we make use of the adaptive logics framework. Our knowledge of deterministic causal regularities is, as Mackie noted, most often gappy or elliptical. The adaptive logics framework is well-suited to explicate both the internal and the external dynamics of the discovery of such gappy regularities. After presenting \, we first discuss these forms of dynamics in more detail. Next, we consider some criticisms of the INUS-account and show how our approach avoids them, and we compare \ with the CNA algorithm that was recently proposed by Michael Baumgartner.