(Canadian Journal of Philosophy 37 (2007), pp. 111-127) A popular view about why death is bad for the one who dies is that death deprives its subject of the good things in life. This is the “deprivation account” of the evil of death. There is another view about death that seems incompatible with the deprivation account: the view that a person’s death is less bad if she has lived a good life. I give some arguments against this view and defend (...) the deprivation account. Penultimate draft posted with kind permission of the Canadian Journal of Philosophy; please use published version for citations. (shrink)
How ought we learn causal relationships? While Popper advocated a hypothetico-deductive logic of causal discovery, inductive accounts are currently in vogue. Many inductive approaches depend on the causal Markov condition as a fundamental assumption. This condition, I maintain, is not universally valid, though it is justiﬁable as a default assumption. In which case the results of the inductive causal learning procedure must be tested before they can be accepted. This yields a synthesis of the hypothetico-deductive and inductive accounts, which forms (...) the focus of this paper. I discuss the justiﬁcation of this synthesis and draw an analogy between objective Bayesianism and the account of causal learning presented here. (shrink)
Inductive logic admits a variety of semantics (Haenni et al., 2011, Part 1). This paper develops semantics based on the norms of Bayesian epistemology (Williamson, 2010, Chapter 7). §1 introduces the semantics and then, in §2, the paper explores methods for drawing inferences in the resulting logic and compares the methods of this paper with the methods of Barnett and Paris (2008). §3 then evaluates this Bayesian inductive logic in the light of four traditional critiques of inductive logic, arguing (i) (...) that it is language independent in a key sense, (ii) that it admits connections with the Principle of Indiﬀerence but these connections do not lead to paradox, (iii) that it can capture the phenomenon of learning from experience, and (iv) that while the logic advocates scepticism with regard to some universal hypotheses, such scepticism is not problematic from the point of view of scientiﬁc theorising. (shrink)
Machamer, Darden and Craver: ‘Mechanisms are entities and activities organized such that they are productive of regular changes from start or set-up to finish or termination conditions.’ (Machamer, Darden and Craver 2000 p3.) Glennan: ‘A mechanism for a behavior is a complex system that produces that behavior by the interaction of a number of parts, where the interactions between parts can be characterized by direct, invariant, change-relating generalizations.’ (Glennan 2002b pS344.) Bechtel and Abrahamsen: ‘A mechanism is a structure performing a (...) function in virtue of its component parts, component operations, and their organization. The orchestrated functioning of the mechanism is responsible for one or more phenomena.’ (Bechtel and Abrahamsen 2005 p423.). (shrink)
Practical reasoning requires decision—making in the face of uncertainty. Xenelda has just left to go to work when she hears a burglar alarm. She doesn’t know whether it is hers but remembers that she left a window slightly open. Should she be worried? Her house may not be being burgled, since the wind or a power cut may have set the burglar alarm off, and even if it isn’t her alarm sounding she might conceivably be being burgled. Thus Xenelda can (...) not be certain that her house is being burgled, and the decision that she takes must be based on her degree of certainty, together with the possible outcomes of that decision. (shrink)
this paper we argue that the formalism can also be applied to modelling the hierarchical structure of physical mechanisms. The resulting network contains quantitative information about probabilities, as well as qualitative information about mechanistic structure and causal relations. Since information about probabilities, mechanisms and causal relations are vital for prediction, explanation and control respectively, a recursive Bayesian net can be applied to all these tasks. We show how a Recursive Bayesian Net can be used to model mechanisms in cancer science. (...) The highest level of the proposed model will contain variables at the clinical level, while a middle level will map the structure of the DNA damage response mechanism and the lowest level will contain information about gene expression. (shrink)
In this chapter I discuss connections between machine learning and the philosophy of science. First I consider the relationship between the two disciplines. There is a clear analogy between hypothesis choice in science and model selection in machine learning. While this analogy has been invoked to argue that the two disciplines are essentially doing the same thing and should merge, I maintain that the disciplines are distinct but related and that there is a dynamic interaction operating between the two: a (...) series of mutually beneficial interactions that changes over time. I will introduce some particularly fruitful interactions, in particular the consequences of automated scientific discovery for the debate on inductivism versus falsificationism in the philosophy of science, and the importance of philosophical work on Bayesian epistemology and causality for contemporary machine learning. I will close by suggesting the locus of a possible future interaction: evidence integration. (shrink)
This introduction to the volume begins with a manifesto that puts forward two theses: ﬁrst, that the sciences are the best place to turn in order to understand causality; second, that scientiﬁcally-informed philosophical investigation can bring something to the sciences too. Next, the chapter goes through the various parts of the volume, drawing out relevant background and themes of the chapters in those parts. Finally, the chapter discusses the progeny of the papers and identiﬁes some next steps for research into (...) causality in the sciences. (shrink)
Bayesian theory now incorporates a vast body of mathematical, statistical and computational techniques that are widely applied in a panoply of disciplines, from artificial intelligence to zoology. Yet Bayesians rarely agree on the basics, even on the question of what Bayesianism actually is. This book is about the basics e about the opportunities, questions and problems that face Bayesianism today.
The Linguistic Turn is the title of an influential anthology edited by Richard Rorty, published in 1967. In his introduction, Rorty explained: The purpose of the present volume is to provide materials for reflection on the most recent philosophical revolution, that of linguistic philosophy. I shall mean by “linguistic philosophy” the view that philosophical problems are problems which may be solved (or dissolved) either by reforming language, or by understanding more about the language we presently use. (1967: 3) ‘The linguistic (...) turn’ has subsequently become a standard vague phrase for a diffuse event — some regard it as the event — in twentieth century philosophy, one not confined to signed-up linguistic philosophers in Rorty’s sense. For those who took the turn, language was somehow the central theme of philosophy. There is an increasingly widespread sense that the linguistic turn is past. In this essay I ask how far the turn has been, or should be, reversed. (shrink)
1. As John Hawthorne and Maria Lasonen-Aarnio appreciate, some of the central issues raised in their ‘Knowledge and Objective Chance’ arise for all but the most extreme theories of knowledge. In a wide range of cases, according to very plausible everyday judgments, we know something about the future, even though, according to quantum mechanics, our belief has a small but nonzero chance (objective probability) of being untrue. In easily constructed examples, we are in that position simultaneously with respect to many (...) different propositions about the future that are equiprobable and probabilistically independent of each other, at least to a reasonable approximation. (shrink)
Some systems of modal logic, such as S5, which are often used as epistemic logics with the ‘necessity’ operator read as ‘the agent knows that’, are problematic as general epistemic logics for agents whose computational capacity does not exceed that of a Turing machine because they impose unwarranted constraints on the agent’s theory of non-epistemic aspects of the world, for example by requiring the theory to be decidable rather than merely recursively axiomatizable. To generalize this idea, two constraints on an (...) epistemic logic are formulated: r.e. conservativeness, that any recursively enumerable theory R in the sublanguage without the epistemic operator is conservatively extended by some recursively enumerable theory in the language with the epistemic operator which is permitted by the logic to be the agent’s overall theory; the weaker requirement of r.e. quasi-conservativeness is similar except for applying only when R is consistent. The logic S5 is not even r.e. quasiconservative; this result is generalized to many other modal logics. However, it is also proved that the modal logics S4, Grz and KDE are r.e. quasi-conservative and that K4, KE and the provability logic GLS are r.e. conservative. Finally, r.e. conservativeness and r.e. quasiconservativeness are compared with related non-computational constraints. (shrink)
The theory of belief revision and merging has recently been applied to judgement aggregation. In this paper I argue that judgements are best aggregated by merging the evidence on which they are based, rather than by directly merging the judgements themselves. This leads to a threestep strategy for judgement aggregation. First, merge the evidence bases of the various agents using some method of belief merging. Second, determine which degrees of belief one should adopt on the basis of this merged evidence (...) base, by applying objective Bayesian theory. Third, determine which judgements are appropriate given these degrees of belief by applying a decision-theoretic account of rational judgement formation. (shrink)
ϕ1, . . . , ϕn |≈ ψ? Here ϕ1, . . . , ϕn, ψ are premisses of some formal language, such as a propositional language or a predicate language. |≈ is an entailment relation: the entailment holds if all models of the premisses also satisfy the conclusion, where the logic provides some suitable notion of ‘model’ and ‘satisfy’. Proof theory is normally invoked to answer a question of this form: one tries to prove the conclusion from the premisses (...) in a finite sequence of steps, where at each step one invokes an axiom or applies a rule of inference. (shrink)
How should we reason with causal relationships? Much recent work on this question has been devoted to the theses (i) that Bayesian nets provide a calculus for causal reasoning and (ii) that we can learn causal relationships by the automated learning of Bayesian nets from observational data. The aim of this book is to..
This chapter addresses two questions: what are causal relationships? how can one discover causal relationships? I provide a survey of the principal answers given to these questions, followed by an introduction to my own view, epistemic causality, and then a comparison of epistemic causality with accounts provided by Judea Pearl and Huw Price.
How is probability related to logic? Should probability and logic be combined? If so, how? Bayesianism tells us we ought to reason probabilistically. In that sense, probability theory is logic. How then does probability theory relate to classical logic and the various non-classical logics that also stake a claim on normative reasoning? Is probability theory to be preferred over other logics or vice versa? Is probability theory to be used in some situations, and the other logics in other situations? Or (...) should probability be combined with other logics? (shrink)
Evidence can be complex in various ways: e.g., it may exhibit structural complexity, containing information about causal, hierarchical or logical structure as well as empirical data, or it may exhibit combinatorial complexity, containing a complex combination of kinds of information. This paper examines evidential complexity from the point of view of Bayesian epistemology, asking: how should complex evidence impact on an agent’s degrees of belief? The paper presents a high-level overview of an objective Bayesian answer: it presents the objective Bayesian (...) norms concerning the relation between evidence and degrees of belief, and goes on to show how evidence of causal, hierarchical and logical structure lead to natural constraints on degrees of belief. The objective Bayesian network formalism is presented, and it is shown how this formalism can be used to handle both kinds of evidential complexity—structural complexity and combinatorial complexity. (shrink)
This paper is a comparison of how first-order Kyburgian Evidential Probability (EP), second-order EP, and objective Bayesian epistemology compare as to the KLM system-P rules for consequence relations and the monotonic / non-monotonic divide.
Bayesian networks are normally given one of two types of foundations: they are either treated purely formally as an abstract way of representing probability functions, or they are interpreted, with some causal interpretation given to the graph in a network and some standard interpretation of probability given to the probabilities specified in the network. In this chapter I argue that current foundations are problematic, and put forward new foundations which involve aspects of both the interpreted and the formal approaches.
How should probabilities be interpreted in causal models in the social and health sciences? In this paper we take a step towards answering this question by investigating the case of cancer in epidemiology and arguing that the objective Bayesian interpretation is most appropriate in this domain.
We consider the use of intervention data for eliminating the underdetermination in statistical modelling, and for guiding extensions of the statistical models. The leading example is factor analysis, a major statistical tool in the social sciences. We first relate indeterminacy in factor analysis to the problem of underdetermination. Then we draw a parallel between factor analysis models and Bayesian networks with hidden nodes, which allows us to clarify the use of intervention data for dealing with indeterminacy. It will be shown (...) that in some cases, the indeterminacy can be resolved by an intervention. In the other cases, the intervention data suggest specific extensions of the model. The upshot is that intervention data can replace theoretical criteria that are typically employed in resolving underdetermination and theory change. (shrink)
How ought we learn causal relationships? While Popper advocated a hypothetico-deductive logic of causal discovery, inductive accounts are currently in vogue. Many inductive approaches depend on the causal Markov condition as a fundamental assumption. This condition, I maintain, is not universally valid, though it is justifiable as a default assumption. In which case the results of the inductive causal learning procedure must be tested before they can be accepted. This yields a synthesis of the hypothetico-deductive and inductive accounts, which forms (...) the focus of this paper. I discuss the justification of this synthesis and draw an analogy between objective Bayesianism and the account of causal learning presented here. (shrink)
Kyburg goes half-way towards objective Bayesianism. He accepts that frequencies constrain rational belief to an interval but stops short of isolating an optimal degree of belief within this interval. I examine the case for going the whole hog.
After introducing a range of mechanistic theories of causality and some of the problems they face, I argue that while there is a decisive case against a purely mechanistic analysis, a viable theory of causality must incorporate mechanisms as an ingredient. I describe one way of providing an analysis of causality which reaps the rewards of the mechanistic approach without succumbing to its pitfalls.
I present a formalism that combines two methodologies: objective Bayesianism and Bayesian nets. According to objective Bayesianism, an agent’s degrees of belief (i) ought to satisfy the axioms of probability, (ii) ought to satisfy constraints imposed by background knowledge, and (iii) should otherwise be as non-committal as possible (i.e. have maximum entropy). Bayesian nets offer an efficient way of representing and updating probability functions. An objective Bayesian net is a Bayesian net representation of the maximum entropy probability function.
Cancer treatment decisions should be based on all available evidence. But this evidence is complex and varied: it includes not only the patient’s symptoms and expert knowledge of the relevant causal processes, but also clinical databases relating to past patients, databases of observations made at the molecular level, and evidence encapsulated in scientific papers and medical informatics systems. Objective Bayesian nets offer a principled path to knowledge integration, and we show in this chapter how they can be applied to integrate (...) various kinds of evidence in the cancer domain. This is important from the systems biology perspective, which needs to integrate data that concern different levels of analysis, and is also important from the point of view of medical informatics. (shrink)
This paper develops connections between objective Bayesian epistemology—which holds that the strengths of an agent’s beliefs should be representable by probabilities, should be calibrated with evidence of empirical probability, and should otherwise be equivocal—and probabilistic logic. After introducing objective Bayesian epistemology over propositional languages, the formalism is extended to handle predicate languages. A rather general probabilistic logic is formulated and then given a natural semantics in terms of objective Bayesian epistemology. The machinery of objective Bayesian nets and objective credal nets (...) is introduced and this machinery is applied to provide a calculus for probabilistic logic that meshes with the objective Bayesian semantics. (shrink)
This chapter presents an overview of the major interpretations of probability followed by an outline of the objective Bayesian interpretation and a discussion of the key challenges it faces. I discuss the ramifications of interpretations of probability and objective Bayesianism for the philosophy of mathematics in general.
This chapter provides an overview of a range of probabilistic theories of causality, including those of Reichenbach, Good and Suppes, and the contemporary causal net approach. It discusses two key problems for probabilistic accounts: counterexamples to these theories and their failure to account for the relationship between causality and mechanisms. It is argued that to overcome the problems, an epistemic theory of causality is required.
This volume arose out of an international, interdisciplinary academic network on Probabilistic Logic and Probabilistic Networks involving four of us (Haenni, Romeijn, Wheeler and Williamson), called Progicnet and funded by the Leverhulme Trust from 2006–8. Many of the papers in this volume were presented at an associated conference, the Third Workshop on Combining Probability and Logic (Progic 2007), held at the University of Kent on 5–7 September 2007. The papers in this volume concern either the special focus on the connection (...) between probabilistic logic and probabilistic networks or the more general question of the links between probability and logic. Here we introduce probabilistic logic, probabilistic networks, current and future directions of research and also the themes of the papers that follow. (shrink)
Mechanisms have become much-discussed, yet there is still no consensus on how to characterise them. In this paper, we start with something everyone is agreed on – that mechanisms explain – and investigate what constraints this imposes on our metaphysics of mechanisms. We examine two widely shared premises about how to understand mechanistic explanation: (1) that mechanistic explanation offers a welcome alternative to traditional laws-based explanation and (2) that there are two senses of mechanistic explanation that we call ‘epistemic explanation’ (...) and ‘physical explanation’. We argue that mechanistic explanation requires that mechanisms are both real and local. We then go on to argue that real, local mechanisms require a broadly active metaphysics for mechanisms, such as a capacities metaphysics. (shrink)
Xenotransplantation - the transfer of living tissue between species - has long been heralded as a potential solution to the severe organ shortage crisis experienced by the United Kingdom and other 'developed' nations. However, the significant risks which accompany this biotechnology led the United Kingdom to adopt a cautious approach to its regulation, with the establishment of a non-departmental public body - UKXIRA - to oversee the development of this technology on a national basis. In December 2006 UKXIRA was quietly (...) disbanded and replaced with revised guidance, which entrusts the regulation of xenotransplantation largely to research ethics committees. In this article we seek to problematize this new regulatory framework, arguing that specialist expertise and national oversight are necessary components of an adequate regulatory framework for a biotechnology which poses new orders of risk, challenges the adequacy of traditional understandings of autonomy and consent, and raises significant animal welfare concerns. We argue for a more considered and holistic approach, based on adequate consultation, to regulating biotechnological developments in the United Kingdom. (shrink)
Audi explains what he means by ‘normative’ in the case of belief: cognitive (epistemic) normativity is a matter of what ought to be believed, where the force of the “ought” is in part to attribute liability to criticism and negative (disapproving) attitudes toward the person(s) in question.
The article explores the impact of civil regulation on the environmental behaviour of SMEs. It shows that although civil regulatory pressures are generally subdued, and that conventional regulation continues to be an important driver of behaviour, there are circumstances where civil pressures nevertheless produce a ‘regulatory’ stimulus. Where they do, it appears that civil regulatory pressures tend to derive from stakeholders pursuing relatively narrow self-interest (rather than public interest) mandates; and they normally target particular issues rather than ‘social responsibility’ in (...) any broad sense. SME responses typically take the form of compliance-reinforcing (rather than beyond compliance) measures. For SMEs, it is suggested that, in some circumstances, civil regulation provides a bespoke regulatory mechanism which is more likely to bring about changes in basic practices on narrow issues. It can also be seen as producing a particular type of consensual micro-social contract and public interest service. (shrink)
The mechanistic and causal accounts of explanation are often conflated to yield a ‘causal-mechanical’ account. This paper prizes them apart and asks: if the mechanistic account is correct, how can causal explanations be explanatory? The answer to this question varies according to how causality itself is understood. It is argued that difference-making, mechanistic, dualist and inferentialist accounts of causality all struggle to yield explanatory causal explanations, but that an epistemic account of causality is more promising in this regard.
Expert probability forecasts can be useful for decision making (Sect. 1). But levels of uncertainty escalate: however the forecaster expresses the uncertainty that attaches to a forecast, there are good reasons for her to express a further level of uncertainty, in the shape of either imprecision or higher order uncertainty (Sect. 2). Bayesian epistemology provides the means to halt this escalator, by tying expressions of uncertainty to the propositions expressible in an agent’s language (Sect. 3). But Bayesian epistemology comes in (...) three main varieties. Strictly subjective Bayesianism and empirically-based subjective Bayesianism have difficulty in justifying the use of a forecaster’s probabilities for decision making (Sect. 4). On the other hand, objective Bayesianism can justify the use of these probabilities, at least when the probabilities are consistent with the agent’s evidence (Sect. 5). Hence objective Bayesianism offers the most promise overall for explaining how testimony of uncertainty can be useful for decision making. Interestingly, the objective Bayesian analysis provided in Sect. 5 can also be used to justify a version of the Principle of Reflection (Sect. 6). (shrink)
Second-order logic and modal logic are both, separately, major topics of philosophical discussion. Although both have been criticized by Quine and others, increasingly many philosophers find their strictures uncompelling, and regard both branches of logic as valuable resources for the articulation and investigation of significant issues in logical metaphysics and elsewhere. One might therefore expect some combination of the two sorts of logic to constitute a natural and more comprehensive background logic for metaphysics. So it is somewhat surprising to find (...) that philosophical discussion of secondorder modal logic is almost totally absent, despite the pioneering contribution of Barcan.. (shrink)