I articulate and defend a new theory of what it is for a physical system to implement an abstract computational model. According to my descriptivist theory, a physical system implements a computational model just in case the model accurately describes the system. Specifically, the system must reliably transit between computational states in accord with mechanical instructions encoded by the model. I contrast my theory with an influential approach to computational implementation espoused by Chalmers, Putnam, and others. I deploy my theory (...) to illuminate the relation between computation and representation. I also rebut arguments, propounded by Putnam and Searle, that computational implementation is trivial. (shrink)

After a brief survey of the literature on ceteris paribus clauses and ceteris paribus laws (1), the problem of exceptions, which creates the need for cp laws, is discussed (2). It emerges that the so-called skeptical view of laws of nature does not apply to laws of any kind whatever. Only some laws of physics are plagued with exceptions, not THE laws (3). Cp clauses promise a remedy, which has to be located among the further reactions to the skeptical view (...) (4). After inspecting various translations of the Latin term 'ceteris paribus' (5), the paper arrives at the conclusion that, on the most reasonable translation, there are no such things as cp laws, for reasons of logical form. Cp clauses have an indexical content, so that they need singular propositions as their habitat, not general ones. Cp clauses and the universal generalizations they are supposed to modify are not fit for each other (6). (shrink)

Przedmowa Problematyka związana z zależnościami przyczynowymi, ich modelowaniem i odkrywa¬niem, po długiej nieobecności w filozofii i metodologii nauk, budzi współcześnie duże zainteresowanie. Wiąże się to przede wszystkim z dynamicznym rozwojem, zwłaszcza od lat 1990., technik obli¬czeniowych. Wypracowane w tym czasie sieci bayesowskie uznaje się za matematyczny język przyczynowości. Pozwalają one na daleko idącą auto¬matyzację wnioskowań, co jest także zachętą do podjęcia prób algorytmiza¬cji odkrywania przyczyn. Na potrzeby badań naukowych, które pozwalają na przeprowadzenie eksperymentu z randomizacją, standardowe metody ustalania zależności przyczynowych (...) opracowano na początku XX wieku. Zupełnie inaczej sprawa przedstawia się w przypadku badań nieeksperymentalnych, gdzie podobne rozwiązania pozostają kwestią przyszłości. Zadaniem tej książki jest podanie warunków, które powinny być spełnione przez te rozwiązania, oraz sformułowanie proceduralnego kryterium zależności przy¬czynowych jako szczegółowej realizacji tych warunków. Pociąga ono waż¬kie konsekwencje dla filozofii i metodologii nauk, które ujawnia – podany w Części II – zarys me-todolo¬gii proceduralnej. W literaturze przedmiotu brakuje w miarę wszechstronnego i systema¬tycznego omówie¬nia najnowszych filozoficznych i metodologicznych dys¬kusji na temat przy¬czynowości, co niech będzie wytłumaczeniem, dlaczego w niektórych punktach obecnej książki szczegółowo referuję trudno dos¬tępne teksty źró¬dłowe. Przymiotnik „proceduralny” używam tu w znaczeniu węższym niż Huw Price (w którego pracach właściwszy byłby termin „kryterialny”) dla podkre¬ślenia – zgodnie z łacińskim źródłosłowem procedo – że dla ustalenia przy¬czyny niezbędne jest podjęcie przez uczonych określonych interakcji z ba¬daną rzeczywistością. Zalążki zamysłu prezentowanego w tej książce przedstawiłem podczas warsztatów filozoficznych „Philosophy and Probability” w roku 2002, zor¬ganizowanych przez Instytut Filozofii Uniwersytetu w Konstancji. Wdzięczny jestem uczestnikom tych warsztatów za uwagi, a przede wszyst¬kim następującym osobom: Luc Bovens, Brandon Fitelson, Alan Hájek, Stephan Hartmann oraz Jon Williamson. Podczas międzynarodowej konferencji „Analytical Pragmatism”, zorgani¬zowanej w Lublinie w roku 2003 przez Wydział Filo¬zofii Katolickiego Uniwersytetu Lubelskiego, odniosłem swoją koncepcję do prac Nancy Cartwright. Szczególnie inspirujący okazał się komentarz Huw Price’a do mojego referatu i przeprowadzona z nim dysku¬sja. Ujęcie koncepcji metodologii proceduralnej na szerszym tle współ-czes¬nego nurtu empirystycznego w filozofii nauki przedstawiłem w roku 2004 podczas konferencji „5th Quadrennial Fellows Conference”, zorgani¬zowanej przez Instytut Filozofii Uniwersytetu Jagiellońskiego oraz Centrum Filozofii Nauki w Pittsburghu. Szczególnie pomocne w dalszych moich pra¬cach były uwagi Jamesa Bogena, Janet Kourany, Jamesa Lennoxa, Johna Nortona, Thomasa Bonka, Jana Woleńskiego i Johna Worralla, za które wyra¬żam swoją wdzięczność. Korpus książki powstał podczas mojego stażu w Centrum Filozofii Nauki w Pittsburghu, który odbyłem jako stypendysta Fundacji na Rzecz Nauki Polskiej w roku akademickim 2004-2005. Uczestniczyłem w tym czasie w życiu naukowym Centrum i w pracach badawczych zespołu z Instytutu Filozofii Uniwersytetu Carnegie-Mellon w Pittsburghu kierowanego przez Clarka Glymoura. Na jego ręce składam podziękowanie za wiele po¬mocnych uwag do moich wy¬stąpień oraz tekstów i za dyskusje przede wszystkim z nim samym i z jego najbliższymi współpracownikami: Peterem Spirtesem oraz Richardem Scheinesem, a także pozostałymi członkami tego zes¬połu, doktorantami i uczestnikami seminarium badawczego „Causality in the Social Sciences”. Za wieloletnie wsparcie, wielopłaszczyznowe inspiracje towarzyszące pi¬saniu tej książki, a także liczne pomocne uwagi do jej wcześniejszych wersji dziękuję przede wszystkim Księdzu Profesorowi Andrzejowi Bronkowi oraz Księdzu Profesorowi Józefowi Herbutowi, współprowadzącemu seminarium doktorskie w Katedrze Metodologii Nauk Katolickiego Uniwersytetu Lubel¬skiego im. Jana Pawła II, jak również pozostałym uczestnikom tego semina¬rium. Dziękuję mojej Żonie, dr Annie Kawalec za wiele wysiłku włożonego w ulepszenie redakcji – językowej i merytorycznej – obecnej książki. Książkę tę można czytać na kilka sposobów. Czytelnikom zainteresowa¬nym przede wszystkim prowadzeniem badań empirycznych polecałbym rozpoczęcie od Rozdziału 2. i kontynuację pozostałych rozdziałów Części I, a następnie Dodatków. Czytelnikom zainteresowanym problemami filozo¬fii i metodologii nauk polecałbym rozpoczęcie lektury książki od Części II i uzupełniającą lekturę Rozdziału 2., a następnie Wprowadzenia i Zakończenia. Czytelnikom mniej zainteresowanym zagadnieniami teoretycznymi pole¬całbym zapoznanie się z fascynującymi dziejami odkrycia przyczyn cholery przez Johna Snowa, które rekonstruuję w Rozdziale 1. W dalszej części nato¬miast polecałbym przejście do Wprowadzenia i Zakończenia, które w mniej specjalistyczny sposób przybliżają proponowane tu rozstrzygnięcia. Tekst książki nie był dotąd publikowany. Wyjątkiem są pewne fragmenty Rozdziału 8. oraz 9., które w zmienionej postaci ukazały się w Rocznikach Filozoficznych (Kawalec 2004). -/- Lublin, luty 2006 r. (shrink)

In this thesis, I give a metascientific account of causality in medicine. I begin with two historical cases of causal discovery. These are the discovery of the causation of Burkitt’s lymphoma by the Epstein-Barr virus, and of the various viral causes suggested for cervical cancer. These historical cases then support a philosophical discussion of causality in medicine. This begins with an introduction to the Russo- Williamson thesis (RWT), and discussion of a range of counter-arguments against it. Despite these, I argue (...) that the RWT is historically workable, given a small number of modifications. I then expand Russo and Williamson’s account. I first develop their suggestion that causal relationships in medicine require some kind of evidence of mechanism. I begin with a number of accounts of mechanisms and produce a range of consensus features of them. I then develop this consensus position by reference to the two historical case studies with an eye to their operational competence. In particular, I suggest that it is mechanistic models and their representations which we are concerned with in medicine, rather than the mechanism as it exists in the world. -/- I then employ these mechanistic models to give an account of the sorts of evidence used in formulating and evaluating causal claims. Again, I use the two human viral oncogenesis cases to give this account. I characterise and distinguish evidence of mechanism from evidence of difference-making, and relate this to mechanistic models. I then suggest the relationship between types of evidence presents us with a means of tackling the reference-class problem. This sets the scene for the final chapter. Here, I suggest the manner in which these two different classes of evidence become integrated is also reflected in the way that developing research programmes change as their associated causal claims develop. (shrink)

This paper examines methodological issues raised by revealed preference theory in economics: particularly contemporary revealed preference theory. The paper has three goals. First, to make the case that revealed preference theory is a broad research program in choice theory—not a single theory—and understanding this diversity is essential to any methodological analysis of the program. Second, to explore some of the existing criticisms of revealed preference theory in a way that emphasizes how the effectiveness of the critique depends on the particular (...) version of revealed preference under consideration. Finally, three additional criticisms are presented that are aimed specifically at contemporary revealed preference theory. (shrink)

In their theoretical and experimental reflections on the capacities and behaviours of living systems, neuroscientists often formulate generalizations about the behaviour of neural circuits. These generalizations are highly idealized, as they omit reference to the myriads of conditions that could perturb the behaviour of the modelled system in real-world settings. This article analyses an experimental investigation of the behaviour of place cells in the rat hippocampus, in which highly idealized generalizations were tested by comparing predictions flowing from them with real-world (...) experimental results. The aim of the article is to identify under what conditions even single prediction failures regarding the behaviour of single cells sufficed to reject highly idealized generalizations, and under what conditions prima facie counter-examples were deemed to be irrelevant to the testing of highly idealized generalizations. The results of this analysis may contribute to understanding how idealized models are tested experimentally in neuroscience and used to make reliable predictions concerning living systems in real-world settings. (shrink)

ABSTRACT: Whereas relevance in scientific explanations is usually discussed as if it was a single problem, several criteria of relevance will be distinguished in this paper. Emphasis is laid upon the notion of intra-scientific relevance, which is illustrated using explanation of the law of areas as an example. Traditional accounts of explanation, such as the causal and unificationist accounts, are analyzed against these criteria of relevance. Particularly, it will be shown that these accounts fail to indicate which explanations fulfill the (...) condition of intra-scientific relevance. Finally, the significance of this latter criterion is emphasized, and the epistemic benefits of explanations that fulfill it are highlighted. (shrink)

This paper will examine the nature of mechanisms and the distinction between the relevant and irrelevant parts involved in a mechanism’s operation. I first consider Craver’s account of this distinction in his book on the nature of mechanisms, and explain some problems. I then offer a novel account of the distinction that appeals to some resources from Mackie’s theory of causation. I end by explaining how this account enables us to better understand what mechanisms are and their various features.

The brain is often taken to be a paradigmatic example of a signaling system with semantic and representational properties, in which neurons are senders and receivers of information carried in action potentials. A closer look at this picture shows that it is not as appealing as it might initially seem in explaining the function of the brain. Working from several sender-receiver models within the teleosemantic framework, I will first argue that two requirements must be met for a system to support (...) genuine semantic information: 1. The receiver must be competent —that is, it must be able to extract rewards from its environment on the basis of the signals that it receives. 2. The receiver must have some flexibility of response relative to the signal received. In the second part of the paper, this initial framework will be applied to neural processes, pointing to the surprising conclusion that signaling at the single-neuron level is only weakly semantic at best. Contrary to received views, neurons will have little or no access to semantic information (though their patterns of activity may carry plenty of quantitative, correlational information) about the world outside the organism. Genuine representation of the world requires an organism - level receiver of semantic information, to which any particular set of neurons makes only a small contribution. (shrink)

Laws of nature take center stage in philosophy of science. Laws are usually believed to stand in a tight conceptual relation to many important key concepts such as causation, explanation, confirmation, determinism, counterfactuals etc. Traditionally, philosophers of science have focused on physical laws, which were taken to be at least true, universal statements that support counterfactual claims. But, although this claim about laws might be true with respect to physics, laws in the special sciences (such as biology, psychology, economics etc.) (...) appear to have—maybe not surprisingly—different features than the laws of physics. Special science laws—for instance, the economic law “Under the condition of perfect competition, an increase of demand of a commodity leads to an increase of price, given that the quantity of the supplied commodity remains constant” and, in biology, Mendel's Laws—are usually taken to “have exceptions”, to be “non-universal” or “to be ceteris paribus laws”. How and whether the laws of physics and the laws of the special sciences differ is one of the crucial questions motivating the debate on ceteris paribus laws. Another major, controversial question concerns the determination of the precise meaning of “ceteris paribus”. Philosophers have attempted to explicate the meaning of ceteris paribus clauses in different ways. The question of meaning is connected to the problem of empirical content, i.e., the question whether ceteris paribus laws have non-trivial and empirically testable content. Since many philosophers have argued that ceteris paribus laws lack empirically testable content, this problem constitutes a major challenge to a theory of ceteris paribus laws. (shrink)

Employing a well-known local regularity from macroeconomics, the Phillips curve, I examine Woodward’s ([2000], [2003]) account of the explanatory power of such historically restricted generalizations and the mathematical models with which they are sometimes associated. The article seeks to show that, pace Woodward, to be explanatory such generalizations need to be underwritten by more fundamental ones, and that rational choice theory would not avail in this case to provide the required underwriting. Examining how such explanatory restricted regularities are underwritten in (...) biology—by unrestricted Darwinian regularities—provides the basis for an argument that Darwinian regularities serve the same function in human affairs. The general argument for this claim requires, inter alia , that we accept some version or other of a theory of memes. The article concludes by clearing the field of some prominent objections to the existence of memes, and extracting some policy implications from the persistence and acceleration of arms races in human affairs. (shrink)

How are scientific explanations possible in ecology, given that there do not appear to be many—if any—ecological laws? To answer this question, I present and defend an account of scientific causal explanation in which ecological generalizations are explanatory if they are invariant rather than lawlike. An invariant generalization continues to hold or be valid under a special change—called an intervention—that changes the value of its variables. According to this account, causes are difference-makers that can be intervened upon to manipulate or (...) control their effects. I apply the account to ecological generalizations to show that invariance under interventions as a criterion of explanatory relevance provides interesting interpretations for the explanatory status of many ecological generalizations. Thus, I argue that there could be causal explanations in ecology by generalizations that are not, in a strict sense, laws. I also address the issue of mechanistic explanations in ecology by arguing that invariance and modularity constitute such explanations. (shrink)

I argue that recent advances in developmental biology demonstrate the inadequacy of suborganismal mechanism. The category of the organism, construed as a ’natural purpose’ should play an ineliminable role in explaining ontogenetic development and adaptive evolution. According to Kant the natural purposiveness of organisms cannot be demonstrated to be an objective principle in nature, nor can purposiveness figure in genuine explain. I attempt to argue, by appeal to recent work on self-organization, that the purposiveness of organisms is a natural phenomenon (...) and, by appeal to the apparatus of invariance explanation, that biological purposiveness provides genuine, ineliminable biological explanations. (edited). (shrink)

The Representational Theory of Measurement conceives measurement as establishing homomorphisms from empirical relational structures into numerical relation structures, called models. There are two different approaches to deal with the justification of a model: an axiomatic and an empirical approach. The axiomatic approach verifies whether a given relational structure satisfies certain axioms to secure homomorphic mapping. The empirical approach conceives models to function as measuring instruments by transferring observations of a phenomenon under investigation into quantitative facts about that phenomenon. These facts (...) are evaluated by their accuracy and precision. Precision is generally achieved by least squares methods and accuracy by calibration. For calibration standards are needed. Then two polar strategies can be distinguished: white-box modeling and black-box modeling. The first strategy of modeling aims at estimating the invariant equations of the phenomenon, thereby fulfilling Hertz’s correctness requirement. The latter strategy of modeling is to use known stable facts about the phenomenon to adjust the model parameters, thereby fulfilling Hertz’s appropriateness requirement. For this latter strategy, the requirement of models as homomorphic mappings has been dropped. Where one will find the axiomatic approach more often used for measurement in the laboratory, the empirical approach is more appropriate for measurement outside the laboratory. The reason for this is that for measurement of phenomena outside the laboratory, one also needs to take account of the environment to achieve accurate results. Environments are generally too relation-rich for an axiomatic approach, which are only applicable for relation-poor systems. The white-box modeling strategy, reflecting the complexity of the environment due to its correctness requirement, will, however, lead to immensely large models. To avoid this problem, modular design is an appropriate strategy to reduce this complexity. Modular design is a grey-box modeling strategy. Grey-box models are assemblies of modules; these are black boxes with standard interface. It should be noted that the structure of the assemblage need not be homomorphic to the relations describing the interaction between phenomenon and environment. These three modeling strategies map out the possible designs for computer simulations as measuring instruments. Whether a simulation is based on a white-box, grey-box or black-box model is only determined by the relationship between the phenomenon and its environment and not by e.g. its materiality or physicality. (shrink)

Philosophers of science have often favoured reductive approaches to how-possibly explanation. This article identifies three alternative conceptions making how-possibly explanation an interesting phenomenon in its own right. The first variety approaches “how possibly X?” by showing that X is not epistemically impossible. This can sometimes be achieved by removing misunderstandings concerning the implications of one’s current belief system but involves characteristically a modification of this belief system so that acceptance of X does not result in contradiction. The second variety offers (...) a potential how-explanation of X. It is usually followed by a range of further potential how-explanations of the same phenomenon. In recent literature the factual claims implied by the second variety have been downplayed whereas the heuristic role of mapping the space of conceptual possibilities has been emphasized. I will focus especially on this truth-bracketing sense of potentiality when looking closer at the second variety in the paper. The third variety has attracted less interest. It presents a partial how-explanation of X. Typically it aims to establish the existence of a mechanism by which X could be and was generated. The third conception stands out as the natural alternative for the advocate of ontic how-possibly explanations. This article transfers Salmon’s view that explanation-concepts can be broadly divided into epistemic, modal, and ontic to the context of how-possibly explanations. Moreover, it is argued that each of the three above-mentioned varieties of how-possibly explanation occurs in science. To recognize this may be especially relevant for philosophers. We are often misled by the promises of various why-explanation accounts, and seem to have forgotten nearly everything about the diversity of how-possibly explanations. (shrink)

This article examines methodological individualism in terms of the theory that invariance under intervention is the signal feature of generalizations that serve as a basis for causal explanation. This theory supports the holist contention that macro-level generalizations can explain, but it also suggests a defense of methodological individualism on the grounds that greater range of invariance under intervention entails deeper explanation. Although this individualist position is not threatened by multiple-realizability, an argument for it based on rational choice theory is called (...) into question by experimental results concerning preference reversals. Key Words: methodological individualism • mechanisms • explanation • invariance • preference reversal. (shrink)

This paper presents a counterfactual account of what a mechanism is. Mechanisms consist of parts, the behavior of which conforms to generalizations that are invariant under interventions, and which are modular in the sense that it is possible in principle to change the behavior of one part independently of the others. Each of these features can be captured by the truth of certain counterfactuals.

An important obstacle to lawhood in the special sciences is the worry that such laws would require metaphysically extravagant conspiracies among fundamental particles. How, short of conspiracy, is this possible? In this paper we'll review a number of strategies that allow for the projectibility of special science generalizations without positing outlandish conspiracies: non-Humean pluralism, classical MRL theories of laws, and Albert and Loewer's theory. After arguing that none of the above fully succeed, we consider the conspiracy problem through the lens (...) of our preferred view of laws, an elaboration of the MRL view that we call the Better Best System (BBS) theory. BBS offers a picture on which, although all events supervene on a fundamental level, there is no one unique locus of projectibility; rather there are a large number of loci corresponding to the different areas (ecology, economics, solid-state chemistry, etc.) in which there are simple and strong generalizations to be made. While we expect that some amount of conspiracy-fear-inducing special science projectibility is inevitable given BBS, we'll argue that this is unobjectionable. It follows from BBS that the laws of any particular special or fundamental science amount to a proper subset of the laws. From this vantage point, the existence of projectible special science generalizations not guaranteed by the fundamental laws is not an occasion for conspiracy fantasies, but a predictable fact of life in a complex world. (shrink)

The issue of downward causation (and mental causation in particular), and the exclusion problem is discussed by taking into account some recent advances in the philosophy of science. The problem is viewed from the perspective of the new interventionist theory of causation developed by Woodward. It is argued that from this viewpoint, a higher-level (e.g., mental) state can sometimes truly be causally relevant, and moreover, that the underlying physical state which realizes it may fail to be such.

Concepts employed in folk descriptions of the world often turn out to be more perspectival than they seem at first sight, involving previously unrecognised sensitivity to the viewpoint or 'situation' of the user of the concept in question. Often, it is progress in science that reveals such perspectivity, and the deciding factor is that we realise that other creatures would apply the same concepts with different extension, in virtue of differences between their circumstances and ours. In this paper I argue (...) that causal concepts are perspectival in this way, and describe the 'situation' on which they depend in terms of an abstract characterisation of the viewpoint of a deliberating agent. I argue that this approach makes better sense than rivals of the apparent asymmetry and temporal orientation of the causal relation. (shrink)

Most philosophical accounts of causation take causal relations to obtain between individuals and events in virtue of nomological relations between properties of these individuals and events. Such views fail to take into account the consequences of the fact that in general the properties of individuals and events will depend upon mechanisms that realize those properties. In this paper I attempt to rectify this failure, and in so doing to provide an account of the causal relevance of higher-level properties. I do (...) this by critiquing one prominent model of higher-level properties—Kim’s functional model of reduction—and contrasting it with a mechanistic approach to higher-level properties and causation. (shrink)

In this paper I criticize the commonly accepted idea that the generalizations of the special sciences should be construed as ceteris paribus laws. This idea rests on mistaken assumptions about the role of laws in explanation and their relation to causal claims. Moreover, the major proposals in the literature for the analysis of ceteris paribus laws are, on their own terms, complete failures. I sketch a more adequate alternative account of the content of causal generalizations in the special sciences which (...) I argue should replace the ceteris paribus conception. (shrink)

This paper consists of four parts. Part 1 is an introduction. Part 2 evaluates arguments for the claim that there are no strict empirical laws in biology. I argue that there are two types of arguments for this claim and they are as follows: (1) Biological properties are multiply realized and they require complex processes. For this reason, it is almost impossible to formulate strict empirical laws in biology. (2) Generalizations in biology hold contingently but laws go beyond describing contingencies, (...) so there cannot be strict laws in biology. I argue that both types of arguments fail. Part 3 considers some examples of biological laws in recent biological research and argues that they exemplify strict laws in biology. Part 4 considers the objection that the examples in part 3 may be strict laws but they are not distinctively biological laws. I argue that given a plausible account of what distinctively biological means, such laws are distinctively biological. (shrink)

We currently have on offer a variety of different theories of causation. Many are strikingly good, providing detailed and plausible treatments of exemplary cases; and all suffer from clear counterexamples. I argue that, contra Hume and Kant, this is because causation is not a single, monolithic concept. There are different kinds of causal relations imbedded in different kinds of systems, readily described using thick causal concepts. Our causal theories pick out important and useful structures that fit some familiar cases—cases we (...) discover and ones we devise to fit. (shrink)

The dominant neuroscientific theory of spatial memory is, like many theories in neuroscience, a multilevel description of a mechanism. The theory links the activities of molecules, cells, brain regions, and whole organisms into an integrated sketch of an explanation for the ability of organisms to navigate novel environments. Here I develop a taxonomy of interlevel experimental strategies for integrating the levels in such multilevel mechanisms. These experimental strategies include activation strategies, interference strategies, and additive strategies. These strategies are mutually reinforcing, (...) providing a kind of interlevel and intratheoretic robustness that has not previously been recognized. (shrink)

Many philosophers claim that we have duty to know our motives. However, prominent theories of the mind suggest that we can’t. Such scepticism about knowledge of one’s motives is based on psychological evidence. I show that this evidence only mandates scepticism about knowledge of one’s motives if we rely on a mistaken assumption which I call ‘the myth of the one true motive’. If we reject this myth, we see that there is space to plausibly interpret the empirical data such (...) that knowledge of one’s motives is difficult, but not impossible. (shrink)

The integrated information theory (IIT) is an ambitious theory of consciousness that aims to provide both a neuroscientific and a metaphysical account of consciousness by identifying consciousness with integrated information. In the philosophical literature, IIT is often associated with a panpsychist worldview. In this paper, I show that IIT can be considered, instead, as a form of emergentism that is incompatible with panpsychism. First, I show that the panpsychist interpretation of IIT is based on two properties of integrated information: intrinsicality (...) and fundamentality. I show that the way IIT deals with these two properties, however, aligns better with emergentism than panpsychism. Then, after plugging some anti-panpsychist assumptions into IIT’s structure, I analyse different philosophical options for interpreting the formal result of causal emergence of integrated information in terms of dependence on and autonomy from its physical substrate. The upshot is that integrated information can be seen as dependent upon the fusion of the cause-effect powers of a physical substrate, and as autonomous in virtue of global-to-local determination. According to this interpretation, consciousness is the constraining power of the system as a whole upon itself, when this power emerges from the fusion on the cause-effect powers of the system’s components. (shrink)

We ask how to use machine learning to expand observability, which presently depends on human learning that informs conceivability. The issue is engaged by considering the question of correspondence between conceived observability counterfactuals and observable, yet so far unobserved or unconceived, states of affairs. A possible answer lies in importing out of reference frame content which could provide means for conceiving further observability counterfactuals. They allow us to define high-fidelity observability, increasing the level of correspondence in question. To achieve high-fidelity (...) observability, we propose to use generative machine learning models as the providers of the out of reference frame content. From an applied point of view, such a role of generative machine learning models shows an emerging dimension of human-machine cooperation. (shrink)

I begin by retracing an argument from Aristotle for final causes in science. Then, I advance this ancient thought, and defend an argument for a stronger conclusion: that no scientific explanation can succeed, if Naturalism is true. The argument goes like this: (1) Any scientific explanation can be successful only if it crucially involves a natural regularity. Next, I argue that (2) any explanation can be successful only if it crucially involves no element that calls out for explanation but lacks (...) one. From (1) and (2) it follows that (3) a scientific explanation can be successful only if it crucially involves a natural regularity, and this regularity does not call out for explanation while lacking one. I then argue that (4) if Naturalism is true, then all natural regularities call out for explanation but lack them. From (3) and (4) it follows that (5) if Naturalism is true, then no scientific explanation can be successful. If you believe that scientific explanation can be (indeed, often has been) successful, as I do, then this is a reason to reject Naturalism. (shrink)

A minimal essentialism (‘intrinsic biological essentialism’) about natural kinds is required to explain the projectability of human science terms. Human classifications that yield robust and ampliative projectable inferences refer to biological kinds. I articulate this argument with reference to an intrinsic essentialist account of HPC kinds. This account implies that human sciences (e.g., medicine, psychiatry) that aim to formulate predictive kind categories should classify biological kinds. Issues concerning psychiatric classification and pluralism are examined.

This article examines the role of historical science in clarifying the causal structure of complex natural processes. I reject the pervasive view that historical science does not uncover natural regularities. To show why, I consider an important methodological distinction in geology between uniformitarianism and actualism; methodological actualism, the preferred method of geologists, often relies on historical reconstructions to test the stability of currently observed processes. I provide several case studies that illustrate this, including one that highlights how historical narratives can (...) improve predictive models. (shrink)

Ian Hacking uses the looping effect to describe how classificatory practices in the human sciences interact with the classified people. While arguably this interaction renders the affected human kinds unstable and hence different from natural kinds, realists argue that also some prototypical natural kinds are interactive and human kinds in general are stable enough to support explanations and predictions. I defend a more fine-grained realist interpretation of interactive human kinds by arguing for an explanatory domain account of the looping effect. (...) First, I argue that knowledge of the feedback mechanisms that mediate the looping effect can supplement, and help to identify, the applicability domain over which a kind and its property variations are stably explainable. Second, by applying this account to cross-cultural case studies of psychiatric disorders, I distinguish between congruent feedback mechanisms that explain matches between classifications and kinds, and incongruent feedback mechanisms that explain mismatches. For example, congruent mechanisms maintain Western auditory experiences in schizophrenia, whereas exporting diagnostic labels inflicts incongruence by influencing local experiences. Knowledge of the mechanisms can strengthen explanatory domains, and thereby facilitate classificatory adjustments and possible interventions on psychiatric disorders. (shrink)

Winner of the 2020 Essay Competition of the International Social Ontology Society. -/- Ian Hacking uses the looping effect to describe how classificatory practices in the human sciences interact with the classified people. While arguably this interaction renders the affected human kinds unstable and hence different from natural kinds, realists argue that also some prototypical natural kinds are interactive and human kinds in general are stable enough to support explanations and predictions. I defend a more fine-grained realist interpretation of interactive (...) human kinds by arguing for an explanatory domain account of the looping effect. First, I argue that knowledge of the feedback mechanisms that mediate the looping effect can supplement, and help to identify, the applicability domain over which a kind and its property variations are stably explainable. Second, by applying this account to cross-cultural case studies of psychiatric disorders, I distinguish between congruent feedback mechanisms that explain matches between classifications and kinds, and incongruent feedback mecha- nisms that explain mismatches. For example, congruent mechanisms maintain Western auditory experiences in schizophrenia, whereas exporting diagnostic labels inflicts incongruence by influencing local experiences. Knowledge of the mechanisms can strengthen explanatory domains, and thereby facilitate classificatory adjustments and possible interventions on psychiatric disorders. (shrink)

As the demand for explainable deep learning grows in the evaluation of language technologies, the value of a principled grounding for those explanations grows as well. Here we study the state-of-the-art in explanation for neural models for natural-language processing (NLP) tasks from the viewpoint of philosophy of science. We focus on recent evaluation work that finds brittleness in explanations obtained through attention mechanisms.We harness philosophical accounts of explanation to suggest broader conclusions from these studies. From this analysis, we assert the (...) impossibility of causal explanations from attention layers over text data. We then introduce NLP researchers to contemporary philosophy of science theories that allow robust yet non-causal reasoning in explanation, giving computer scientists a vocabulary for future research. (shrink)

According to a standard assumption in epistemology, if one only partially believes that p , then one cannot thereby have knowledge that p. For example, if one only partially believes that that it is raining outside, one cannot know that it is raining outside; and if one only partially believes that it is likely that it will rain outside, one cannot know that it is likely that it will rain outside. Many epistemologists will agree that epistemic agents are capable of (...) partial beliefs in addition to full beliefs and that partial beliefs can be epistemically assessed along some dimensions. However, it has been generally assumed that such doxastic attitudes cannot possibly amount to knowledge. In Probabilistic Knowledge, Moss challenges this standard assumption and provides a formidable defense of the claim that probabilistic beliefs—a class of doxastic attitudes including credences and degrees of beliefs—can amount to knowledge too. Call this the probabilistic knowledge claim . Throughout the book, Moss goes to great lengths to show that probabilistic knowledge can be fruitfully applied to a variety of debates in epistemology and beyond. My goal in this essay is to explore a further application for probabilistic knowledge. I want to look at the role of probabilistic knowledge within a “knowledge-centered” psychology—a kind of psychology that assigns knowledge a central stage in explanations of intentional behavior. My suggestion is that Moss’s notion of probabilistic knowledge considerably helps further both a knowledge-centered psychology and a broadly intellectualist picture of action and know-how that naturally goes along with it. At the same time, though, it raises some interesting issues about the notion of explanation afforded by the resulting psychology. (shrink)

Jaegwon Kim’s views on mental causation and the exclusion argument are evaluated systematically. Particular attention is paid to different theories of causation. It is argued that the exclusion argument and its premises do not cohere well with any systematic view of causation.

Die Einleitung unseres Kapitels bietet eine grundsäzliche Charakterisierung der Soziologie und zeichnet einige wichtige historische Entwicklungslinien der Philosophie der Soziologie (PdS) nach. Im Hauptteil werden zentrale ontologische sowie ausgewählte explanatorische Themen der PdS vorgestellt. Im Schlussteil sollen einige aktuelle Diskussionen umrissen werden.

A good surgeon knows how to perform a surgery; a good architect knows how to design a house. We value their know-how. We ordinarily look for it. What makes it so valuable? A natural response is that know-how is valuable because it explains success. A surgeon’s know-how explains their success at performing a surgery. And an architect’s know-how explains their success at designing houses that stand up. We value know-how because of its special explanatory link to success. But in virtue (...) of what is know-how explanatorily linked to success? This essay provides a novel argument for the thesis that know-how’s special link to success is to be explained at least in part in terms of its being, or involving, a doxastic attitude that is epistemically alike propositional knowledge. It is argued that the role played by know-how in explaining intentional success shows that the epistemic differences between know-how and knowledge, if any, are less than usually thought; and that "revisionary intellectualism", the view that know-how is true belief that might well fall short of knowledge, is not really a stable position. If its explanatory link to success is what makes know-how valuable, an upshot of my argument is that the value of know-how is due, to a considerable extent, to its being, or involving, a kind of propositional knowledge. (shrink)

This essay develops an inferential account of model explanation, based on Mauricio Suárez’s inferential conception of scientific representation and Alisa Bokulich’s counterfactual account of model explanation. It is suggested that the fact that a scientific model can explain is essentially linked to how a modeler uses an established model to make various inferences about the target system on the basis of results derived from the model. The inference practice is understood as a two-step activity, with the first step involving making (...) counterfactual statements about the model itself and the second step involving making hypothetical statements transferring over claims derived from the model onto the target. To illustrate how this two-step activity proceeds, an agent-based simulation model is discussed. (shrink)

Philosophers sometimes claim that economics, and the idealizing strategies it employs, is ultimately unable to provide genuine laws of nature. Therefore, unlike physics, it does not qualify as an actual science. Careful consideration of thermodynamics, a well-developed physical theory, reveals substantial parallels with economic methodology. The corrective account of scientific understanding I offer appreciates these parallels: understanding in terms of efficient performance.

"Explanation and Understanding" (1971) by Georg Henrik von Wright is a modern classic in analytic hermeneutics, and in the philosophy of the social sciences and humanities in general. In this work, von Wright argues against naturalism, or methodological monism, i.e. the idea that both the natural sciences and the social sciences follow broadly the same general scientific approach and aim to achieve causal explanations. Against this view, von Wright contends that the social sciences are qualitatively different from the natural sciences: (...) according to his view, the natural sciences aim at causal explanations, whereas the purpose of the social sciences is to understand their subjects. In support of this conviction, von Wright also puts forward a version of the so-called logical connection argument. -/- Von Wright views scientific explanation along the lines of the traditional covering law model. He suggests that the social sciences, in contrast, utilize what he calls “practical syllogism” in understanding human actions. In addition, von Wright presents in this work an original picture on causation: a version of the manipulability theory of causation. -/- In the four decades following von Wright’s classic work, the overall picture in in the philosophy of science has changed significantly, and much progress has been made in various fronts. The aim of the contribution is to revisit the central ideas of "Explanation and Understanding" and evaluate them from this perspective. The covering law model of explanation and the regularity theory of causation behind it have since then fallen into disfavor, and virtually no one believes that causal explanations even in the natural sciences comply with the covering law model. No wonder then that covering law explanations are not found in the social sciences either. Ironically, the most popular theory of causal explanation in the philosophy of science nowadays is the interventionist theory, which is a descendant of the manipulability theory of von Wright and others. However, this theory can be applied with no special difficulties in both the natural sciences and the social sciences. -/- Von Wright’s logical connection argument and his ideas concerning practical syllogisms are also critically assessed. It is argued that in closer scrutiny, they do not pose serious problems for the view that the social sciences too provide causal explanations. In sum, von Wright’s arguments against naturalism do not appear, in today’s perspective, particularly convincing. (shrink)

In the late summer of 1998, the authors, a cognitive scientist and a logician, started talking about the relevance of modern mathematical logic to the study of human reasoning, and we have been talking ever since. This book is an interim report of that conversation. It argues that results such as those on the Wason selection task, purportedly showing the irrelevance of formal logic to actual human reasoning, have been widely misinterpreted, mainly because the picture of logic current in psychology (...) and cognitive science is completely mistaken. We aim to give the reader a more accurate picture of mathematical logic and, in doing so, hope to show that logic, properly conceived, is still a very helpful tool in cognitive science. The main thrust of the book is therefore constructive. We give a number of examples in which logical theorizing helps in understanding and modeling observed behavior in reasoning tasks, deviations of that behavior in a psychiatric disorder (autism), and even the roots of that behavior in the evolution of the brain. (shrink)

This paper argues for several related theses. First, the epistemological position that knowledge requires safe belief can be motivated by views in the philosophy of science, according to which good explanations show that their explananda are robust. This motivation goes via the idea—recently defended on both conceptual and empirical grounds—that knowledge attributions play a crucial role in explaining successful action. Second, motivating the safety requirement in this way creates a choice point—depending on how we understand robustness, we'll end up with (...) different conceptions of safety in epistemology. Lastly, and most controversially, there's an attractive choice at this point that will not vindicate some of the most influential applications of the safety-theoretic framework in epistemology, e.g., Williamson's arguments against the KK principle, and luminosity. (shrink)