Theory and Reality is about the connection between true theories and the world. A mathematical framefork for such connections is given, and it is shown how that framework can be used to infer facts about the structure of reality from facts about the structure of true theories, The book starts with an overview of various approaches to metaphysics. Beginning with Quine's programmatic "On what there is", the first chapter then discusses the perils involved in going from language to metaphysics. It (...) criticises contemporary intuition-driven metaphysics, comments on naturalistic approaches, and then presents the main proposition put forward in the thesis: we should base metaphysics on model theory. In chapters 2 to 5, mathematical treatments are given of concepts that we need: theories, metaphysics, necessitation and semantics. These are used in chapters 6 and 7 to prove that, seen from a certain informative view point, any true theory will give rise to an isomorphism between that theory and the world. This conclusion is similar to Wittgenstein's in the Tractatus, but differs in that it places the structural relationship on the level of whole theories, rather than single propositions. (shrink)

Jaegwon Kim famously challenged non-reductive materialism/physicalism, a popular stance in the philosophy of mind, with the so-called exclusion argument. The argument is alleged to show that if NRM is true, then mental properties cannot be causes. In recent years, there have been many reactions to the exclusion problem based on counterfactual accounts of causation. In particular, List and Menzies gave an interesting response based on a Lewis-style counterfactual theory of causation, with some modifications made to Lewis’s semantics of counterfactuals. In (...) this thesis, I first argue that their central thesis regarding the issue of exclusion can actually be established with Lewis’s original semantics, without modifications. I then clarify the real consequence of List and Menzies’ modification by showing that List and Menzies' modified counterfactual theory, but not Lewis’s original theory, can satisfy Zhong ’s requirement of causal autonomy. My analysis reveals that, despite Zhong ’s employment of an interventionist theory of causation, it is not any peculiarity with the interventionism that is essential for establishing the possibility of causal autonomy. On the contrary, I argue that the existing semantics for interventionist counterfactuals do not seem to serve Zhong’s purpose particularly well. These considerations suggest that once one decides to use a dependence notion of causation to counter the exclusion argument, a Lewis-style theory of causation is good enough. (shrink)

The problem of mental causation is discussed by taking into account some recent developments in the philosophy of science. The problem is viewed from the perspective of the new interventionist theory of causation developed by Woodward. The import of the idea that causal claims involve contrastive classes in mental causation is also discussed. It is argued that mental causation is much less a problem than it has appeared to be.

N. Cartwright's recent results on invariance under intervention and causality (2003) are reconsidered. Procedural approach to causality elicited in this paper and contrasted with Cartwright's apparently philosophical one unravels certain ramifications of her results. The procedural approach seems to license only a constrained notion of intervention and in consequence the "correctness to invariance" part of Cartwright's first theorem fails for a class of cases. The converse "invariance to correctness" part of the theorem relies heavily on modeling assumptions which prove to (...) be difficult to validate in practice and are often buttressed by independently acquired evidence. (shrink)

In this chapter we examine the relationship between biological information, the key biological concept of specificity, and recent philosophical work on causation. We begin by showing how talk of information in the molecular biosciences grew out of efforts to understand the sources of biological specificity. We then introduce the idea of ‘causal specificity’ from recent work on causation in philosophy, and our own, information theoretic measure of causal specificity. Biological specificity, we argue, is simple the causal specificity of certain biological (...) processes. This, we suggest, means that causal relationships in biology are ‘informational’ relationships simply when they are highly specific relationships. Biological information can be identified with the storage, transmission and exercise of biological specificity. It has been argued that causal relationships should not be regarded as informational relationship unless they are ‘arbitrary’. We argue that, whilst arbitrariness is an important feature of many causal relationships in living systems, it should not be used in this way to delimit biological information. Finally, we argue that biological specificity, and hence biological information, is not confined to nucleic acids but distributed among a wide range of entities and processes. (shrink)

Externalist theories in natural language semantics have become the orthodoxy since Kripke is widely thought to have refuted descriptive theories involving internal cognitive representation of meaning. This shift may be seen in developments in philosophy of language of the 1970s – the direct reference “revolution against Frege”. I consider Fodor’s heretical thought that something has gone “awfully wrong” in this philosophical consensus, perhaps confirming Chomsky’s view that the whole field of philosophical semantics is “utterly wrongheaded” and “crazy” by virtue of (...) its non-naturalist assumptions and “methodological dualism.” Kripke acknowledges that he was led to his views by his “natural intuition”. Accordingly, I take up the aetiological question Chomsky poses, suggesting that we may go beyond the usual analysis of those doctrines that have been assumed too uncritically “to ask why they seem so compelling”. I suggest that the externalist orthodoxy is a kind of cognitive illusion seen elsewhere in philosophy and cognitive science. (shrink)

The statistical technique of analysis of variance is often used by biologists as a measure of causal factors’ relative strength or importance. I argue that it is a tool ill suited to this purpose, on several grounds. I suggest a superior alternative, and outline some implications. I finish with a diagnosis of the source of error – an unwitting inheritance of bad philosophy that now requires the remedy of better philosophy.

In Nozick’s rendition of the decision situation given in Newcomb’s Paradox dominance and the principle of maximum expected utility recommend different strategies. While evidential decision theory seems to be split over which principle to apply and how to interpret the principles in the first place, causal decision theory seems to go for the solution recommended by dominance. As a reply to the CDT proposal by Wolfgang Spohn, who opts for “one-boxing” by employing reflexive decision graphs, I will draw on the (...) framework of causal knowledge patterns, i.e., Bayes net causal models, augmented by non-causal knowledge, to finally arrive at “one-boxing” – more intuitively and more closely to what actually is in Nozick’s story. This proposal allows the careful re-examination of all relevant concepts in the original story and might cast new light on the following questions: How may causality in general be understood to allow causal inference from hybrid patterns encoding subjective knowledge? How can the notion of prediction be analyzed – philosophically and formally? And what’s the decision-maker’s conceptualization of the situation he will act upon? (shrink)

Spirtes, Glymour and Scheines [Causation, Prediction, and Search Springer] described a pointwise consistent estimator of the Markov equivalence class of any causal structure that can be represented by a directed acyclic graph for any parametric family with a uniformly consistent test of conditional independence, under the Causal Markov and Causal Faithfulness assumptions. Robins et al. [Biometrika 90 491–515], however, proved that there are no uniformly consistent estimators of Markov equivalence classes of causal structures under those assumptions. Subsequently, Kalisch and B¨uhlmann (...) [J. Mach. Learn. Res. 8 613–636] described a uniformly consistent estimator of the Markov equivalence class of a linear Gaussian causal structure under the Causal Markov and Strong Causal Faithfulness assumptions. However, the Strong Faithfulness assumption may be false with high probability in many domains. We describe a uniformly consistent estimator of both the Markov equivalence class of a linear Gaussian causal structure and the identifiable structural coefficients in the Markov equivalence class under the Causal Markov assumption and the considerably weaker k-Triangle-Faithfulness assumption. (shrink)

This paper explores some of the factors that make complex systems complex. We first examine the history of complex systems. It was Aristotle’s insight that how elements are joined together helps determine the properties of the resulting whole. We find (a) that scientific reductionism does not provide a sufficient explanation; (b) that to understand complex systems, one must identify and trace energy flows; and (c) that disproportionate causality, including global tipping points, are all around us. Disproportionate causality results from the (...) wide availability of energy stores. We discuss three categories of emergent phenomena—static, dynamic, and adaptive—and recommend retiring the term emergent, except perhaps as a synonym for creative. Finally, we find that virtually all communication is stigmergic. (shrink)

Actual causes - e.g. Suzy's being exposed to asbestos - often bring about their effects - e.g. Suzy's suffering mesothelioma - probabilistically. I use probabilistic causal models to tackle one of the thornier difficulties for traditional accounts of probabilistic actual causation: namely probabilistic preemption.

Explanations in psychiatry often integrate various factors relevant to psychopathology. Identifying genuine causes among them is theoretically and clinically important, but epistemically challenging. Woodward’s interventionism appears to provide a promising tool to achieve this. However, Woodward’s interventionism is too demanding to be applied to psychiatry. I thus introduce difference-making interventionism, which detects relevance in general rather than causation, to make interventionist reasoning viable in clinical practice. DMI mirrors the empirical reality of psychiatry even more closely than interventionism, but it needs (...) to be supplied with additional heuristics to disambiguate between causes and other difference-makers. To achieve this, I suggest employing heuristics based on multiple experiments, temporal order and scientific domain. (shrink)

This dissertation starts with a concise overview of what philosophers of science have written about unification and its role in scientific explanation during the last 50 years to provide the reader with some background knowledge. In order to bring unification back into the picture, I have followed two strategies, resulting respectively in Parts I and II of this dissertation. In Part I the idea of unification is used to refine and enrich the dominant causalmechanist and causal-interventionist accounts of scientific explanation. (...) In this part of the dissertation I bracket the classical ideas about unification: deduction and derivation. I do grant, for the sake of argument, that explanations are causal and argue that unification is important from within this causalist perspective. In Part II I continue my strategy of digging into scientific practice to find cases of ontological unification. But here I distance myself from the dominant literature that all explanations must be causal. I will investigate whether explanatory unification is possible in non-causal explanations. Part III contains some further reflections and conclusions. I will formulate my primary results, and I will elaborate on their implications for thinking about unification and explanation. The different forms of ontological unification were quite diverse. This relates to the method I have used. Throughout this dissertation the types of unification that were discussed emerged from digging into scientific practice. This philosophy-of-science-practice approach steered me towards a pluralistic view on unification and on explanation. In this dissertation I do not try to develop a new model of explanation and compare it to existing models. The aim is to show that there are important types of explanatory practice which cannot be properly analyzed if we neglect unification as a desideratum for explanations. (shrink)

These preprints were automatically compiled into a PDF from the collection of papers deposited in PhilSci-Archive in conjunction with the PSA 2012.

Whether there exist causal relations between guns firing and people dying, between pedals pressed and cars accelerating, or between carbon dioxide emissions and global warming, is typically taken to be a mind-independent, objective, matter of fact. However, recent contributions to the literature on causation, in particular theories of contrastive causation and causal modelling, have undermined this central causal platitude by relativising causal facts to models or to interests. This thesis flies against the prevailing wind by arguing that we must pay (...) greater attention to which elements of our causal talk vary with context and which elements track genuine features of the world around us. I will argue that once these elements are teased apart we will be in a position to better understand some of the most persistent problems in the philosophy of causation: pre-emption cases, absence causation, failures of transitivity and overdetermination. The result is a naturalist account of causation, concordant with the contextual variability we find in our ordinary causal talk, and parsimonious with respect to the theoretical entities posited. (shrink)

It would be hard to find a more fervent advocate of the position that computers are of profound significance to philosophy than Aaron Sloman. Yet, he is not a stereotypical proponent of Artificial Intelligence (AI). Far from it; in his writings, he undermines several popular convictions of functionalists. Through his drafts and polemics, Sloman definitely exerts quite substantial influence on the philosophy of Artificial Intelligence. Sloman's paper “Evolution: The Computer Systems Engineer Designing Minds” presents a bold hypothesis that the evolution (...) of the human mind actually involved the development of a several dozen of virtual machines that support various forms of self-monitoring. This, in turn, helps explain different features of our cognitive functioning. (shrink)

Dyskusja możliwych strategii dla psychologii antynaturalistycznej.

We examine possible causal structures of experiments with entangled quantum objects. Previously, these structures have been obscured by assuming a misleading probabilistic analysis of quantum non locality as 'Outcome Dependence or Parameter Dependence' and by directly associating these correlations with influences. Here we try to overcome these shortcomings: we proceed from a recent stronger Bell argument, which provides an appropriate probabilistic description, and apply the rigorous methods of causal graph theory. Against the standard view that there is only an influence (...) between the measurement outcomes, we show that there must be an influence from one setting to its distant outcome: EPR correlations can only come about if one of the outcomes is a common effect of both settings. Our discussion makes explicit under which assumptions similar conclusions from information theoretic considerations can be interpreted causally. (shrink)

This item has been retired at the request of its author.

Causal decision theory defines a rational action as the one that tends to cause the best outcomes. If we adopt counterfactual or probabilistic theories of causation, then we may face problems in overdetermination cases. Do such problems affect Causal decision theory? The aim of this work is to show that the concept of causation that has been fundamental in all versions of causal decision theory is not the most intuitive one. Since overdetermination poses problems for a counterfactual theory of causation, (...) one can think that a version of causal decision theory based on counterfactual dependence may also be vulnerable to such scenarios. However, only when an intuitive, not analyzed notion of causation is presupposed as a ground for a more plausible version of causal decision theory, overdetermination turns problematic. The first interesting consequence of this is that there are more reasons to dismiss traditional theories of causation (and to accept others). The second is to confirm that traditional causal decision theory is not based on our intuitive concept of the causal relation. (shrink)

It is widely accepted that the violation of Bell inequalities excludes local theories of the quantum realm. In this paper I present a stronger Bell argument which even forbids certain non-local theories. The remaining non-local theories, which can violate Bell inequalities, are characterised by the fact that at least one of the outcomes in some sense probabilistically depends both on its distant as well as on its local parameter. While this is not to say that parameter dependence in the usual (...) sense necessarily holds, it shows that the received analysis of quantum non-locality as “outcome dependence or parameter dependence” is deeply misleading about what the violation of Bell inequalities implies. (shrink)

This thesis studies Judea Pearl’s logic of counterfactuals derived from the causal modeling framework, in comparison to the influential Stanlnaker-Lewis counterfactual logics. My study focuses on a characteristic principle in Pearl’s logic, named reversibility. The principle, as Pearl pointed out, goes beyond Lewis’s logic. Indeed, it also goes beyond the stronger logic of Stanlnaker, which is more analogous to Pearl’s logic. The first result of this thesis is an extension of Stanlnaker’s logic incorporating reversibility. It will be observed that the (...) translation of reversibility from Pearl’s language to the standard language for conditional logic deserves some attention. In particular, a straightforward translation following Pearl’s suggestion would render reversibility incompatible with Stanlnaker’s logic. A new translation of reversibility will be proposed, and an extension of Stanlnaker’s logic with the inclusion of the translated reversibility will be investigated. More importantly, it will be shown that the extended Stanlnaker’s logic is sound and complete with respect to a modified Stanlnaker’s semantics. The extension of Stanlnaker’s logic has an interesting implication. Zhang, Lam, and de Clercq have shown that special case of reversibility, despite its name, actually states an important kind of irreversibility: counterfactual dependence between distinct events is irreversible. In other words, reversibility entails that there is no cycle of counterfactual dependence altogether generalizations of reversibility. However, as shown in Zhang et al., Pearls’ logic does not rule out cycles of counterfactual dependence altogether. It in fact allows cycles that involve three or more distinct events. This is peculiar because the status of cyclic counterfactual dependence seems no more metaphysically secure than that of mutual counterfactual dependence. This consideration leads to an exploration of logics that rule out all cycles of counterfactual dependence. A surprising result is that the extension of Stanlnaker’s logic is precisely a logic of this sort. (shrink)

The aim of this paper is to investigate the ascription of probabilities in a causal model of an episode in the history of science. The aim of such a quantitative approach is to allow the implementation of the causal model in a computer, to run simulations. As an example, we look at the beginning of the science of magnetism, “explaining” — in a probabilistic way, in terms of a single causal model — why the field advanced in China but not (...) in Europe (the difference is due to different prior probabilities of certain cultural manifestations). Given the number of years between the occurrences of two causally connected advances X and Y, one proposes a criterion for stipulating the value pY/X of the conditional probability of an advance Y occurring, given X. Next, one must assume a specific form for the cumulative probability function pY/X(t), which we take to be the time integral of an exponential distribution function, as is done in physics of radioactive decay. Rules for calculating the cumulative functions for more than two events are mentioned, involving composition, disjunction and conjunction of causes. We also consider the problems involved in supposing that the appearance of events in time follows an exponential distribution, which are a consequence of the fact that a composition of causes does not follow an exponential distribution, but a “hypoexponential” one. We suggest that a gamma distribution function might more adequately represent the appearance of advances. (shrink)

Why do we represent the world around us using causal generalizations, rather than, say, purely statistical generalizations? Do causal representations contain useful additional information, or are they merely more efficient for inferential purposes? This paper considers the second kind of answer: it investigates some ways in which causal cognition might aid us not because of its expressive power, but because of its organizational power. Three styles of explanation are considered. The first, building on the work of Reichenbach in "The Direction (...) of Time", points to causal representation as especially efficient for predictive purposes in a world containing certain pervasive patterns of conditional independence. The second, inspired by work of Woodward and others, finds causal representation to be an excellent vehicle for representing all-important relations of manipulability. The third, based in part on my own work, locates the importance of causal cognition in the special role it reserves for information about underlying mechanisms. All three varieties of explanation show promise, but particular emphasis is placed on the third. (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)

We offer a critical assessment of the “exclusion argument” against free will, which may be summarized by the slogan: “My brain made me do it, therefore I couldn't have been free”. While the exclusion argument has received much attention in debates about mental causation (“could my mental states ever cause my actions?”), it is seldom discussed in relation to free will. However, the argument informally underlies many neuroscientific discussions of free will, especially the claim that advances in neuroscience seriously challenge (...) our belief in free will. We introduce two distinct versions of the argument, discuss several unsuccessful responses to it, and then present our preferred response. This involves showing that a key premise – the “exclusion principle” – is false under what we take to be the most natural account of causation in the context of agency: the difference-making account. We finally revisit the debate about neuroscience and free will. (shrink)

What kinds of fundamental limits are there in how capable artificial intelligence (AI) systems might become? Two questions in particular are of interest: (1) How much more capable could AI become relative to humans, and (2) how easily could superhuman capability be acquired? To answer these questions, we will consider the literature on human expertise and intelligence, discuss its relevance for AI, and consider how AI could improve on humans in two major aspects of thought and expertise, namely simulation and (...) pattern recognition. We find that although there are very real limits to prediction, it seems like AI could still substantially improve on human intelligence. (shrink)

The main goal of this paper is to investigate the relation between the meaning of a sentence and its truth conditions. We report on a comprehension experiment on counterfactual conditionals, based on a context in which a light is controlled by two switches. Our main finding is that the truth-conditionally equivalent clauses (i) "switch A or switch B is down" and (ii) "switch A and switch B are not both up" make different semantic contributions when embedded in a conditional antecedent. (...) Assuming compositionality, this means that (i) and (ii) differ in meaning, which implies that the meaning of a sentential clause cannot be identified with its truth conditions. We show that our data have a clear explanation in inquisitive semantics: in a conditional antecedent, (i) introduces two distinct assumptions, while (ii) introduces only one. Independently of the complications stemming from disjunctive antecedents, our results also challenge analyses of counterfactuals in terms of minimal change from the actual state of affairs: we show that such analyses cannot account for our findings, regardless of what changes are considered minimal. (shrink)

According to the transitive dynamics model, people can construct causal structures by linking together configurations of force. The predictions of the model were tested in two experiments in which participants generated new causal relationships by chaining together two (Experiment 1) or three (Experiment 2) causal relations. The predictions of the transitive dynamics model were compared against those of Goldvarg and Johnson-Laird’s model theory (Goldvarg & Johnson- Laird, 2001). The transitive dynamics model consistently predicted the overall causal relationship drawn by participants (...) for both types of causal chains, and, when compared to the model theory, provided a better fit to the data. The results suggest that certain kinds of causal reasoning may depend on force dynamic—rather than on logical or purely statistical—representations. (shrink)

The counterfactual tradition to defining actual causation has come a long way since Lewis started it off. However there are still important open problems that need to be solved. One of them is the (in)transitivity of causation. Endorsing transitivity was a major source of trouble for the approach taken by Lewis, which is why currently most approaches reject it. But transitivity has never lost its appeal, and there is a large literature devoted to understanding why this is so. Starting from (...) a survey of this work, we will develop a formal analysis of transitivity and the problems it poses for causation. This analysis provides us with a sufficient condition for causation to be transitive, a sufficient condition for dependence to be necessary for causation, and several characterisations of the transitivity of dependence. Finally, we show how this analysis leads naturally to several conditions a definition of causation should satisfy, and use those to suggest a new definition of causation. (shrink)

The interventionist account of causation offers a criterion to distinguish causes from non-causes. It also aims at defining various desirable properties of causal relationships, such as specificity, proportionality and stability. Here we apply an information-theoretic approach to these properties. We show that the interventionist criterion of causation is formally equivalent to non-zero specificity, and that there are natural, information-theoretic ways to explicate the distinction between potential and actual causal influence. We explicate the idea that the description of causes should be (...) proportional to that of their effects. Then we draw a distinction between two ideas in the existing literature, the range of invariance of a causal relationship and its stability. The range of invariance is related to specificity and range of causal values. Stability concerns the effect of additional variables on the relationship between some focal pair of cause and effect variables. We show how to distinguish and measure the direct influence of background variables on the effect variable, and their influence on the relationship between the focal cause and the effect variable. Finally, we discuss the limitations of the information-theoretic approach, and offer prospects for complementary approaches. (shrink)

This paper discusses von Wright's theory of causation from Explanation and Understanding and Causality and Determinism in contemporary context. I argue that there are two important common points that von Wright's view shares with the version of manipulability currently supported by Woodward: the analysis of causal relations in a system modelled on controlled experiments, and the explanation of manipulability through counterfactuals - with focus on the counterfactual account of unmanipulable causes. These points also mark von Wright's departure from previous action-based (...) theories of causation. Owing to these two features, I argue that, upon classifying different versions of manipulability theories, von Wright's view should be placed closer to the interventionist approach than to the agency theory, where it currently stands. Furthermore, given its relevance in contemporary context, which this paper aims to establish, I claim that von Wright's theory can be employed to solve present problems connected to manipulability approaches to causation. (shrink)

Conscious experience is the direct observation of conscious events. Human conscious experience is four-dimensional. Conscious events are linked (associated) by spacetime intervals to produce a coherent conscious experience. This explains why conscious experience appears to us the way it does. Conscious experience is an orientation in space and time, an understanding of the position of the observer in space and time. Causality, past-future relations, learning, memory, cognitive processing, and goal-directed actions all evolve from four-dimensional conscious experience. A neural correlate for (...) four-dimensional conscious experience can be found in the human brain and is modelled by Einstein's special theory of relativity. The relativistic concept of spacetime interval is central for understanding conscious experience and cognition. (shrink)

Pearl and Woodward are both well-known advocates of interventionist causation. What is less well-known is the interesting relationship between their respective accounts. In this paper we discuss the different perspectives of causation these two accounts present and show that they are two sides of the same coin. Pearl’s focus is on leveraging global network constraints to correctly identify local causal relations. The rules by which global causal structures are composed from distinct causal relations are precisely defined by the global constraints. (...) Woodward’s focus, however, is on the use of local manipulation to identify single causal relations that then compose into global causal structures. The rules by which this composition takes place emerge as a result of local interventionist constraints. We contend that the complete picture of causality to be found between these two perspectives from the interventionist tradition must recognise both the global constraints of the sort identified by Pearl and the local constraints of the sort identified by Woodward, and the interplay between them: Pearl requires the possibility of local interventions and Woodward requires a global statistical framework within which to build composite causal structures. (shrink)

UCLA Cognitive Systems Laboratory, Technical Report (R-321), June 2005. In Proceedings of International Joint Conference on Artificial Intelligen ce, Edinburgh, Scotland, August 2005.