In his account of our belief in the Causal Maxim Hume argued, among other things, that it is not absolutely necessary for any event to be caused. Harold Noonan attempts an objection to Hume’s argument: in showing (i) the absolute possibility for any event to exist without its actual cause, Hume would not thereby show (ii) the absolute possibility for any event to exist uncaused. For this objection to succeed, Noonan needs two further assumptions: first, that Hume indeed (...) could not move plausibly from (i) to (ii); second, that Hume needed to move from (i) to (ii) to show (ii). Both assumptions are false. (shrink)

Cet article aborde le problème des « fake news » à partir des interactions qui permettent leur circulation, sans supposer que quiconque adhère aux « fake news » par une « croyance » intellectualisée. À la bêtise, à la crédulité ou à l’ignorance supposée des populations victimes de la malveillance des dirigeants, l’auteur substitue un autre modèle explicatif fondé sur un système complexe de représentations. La difficulté individuelle à maintenir la certitude de sa propre valeur fournit un élément causal (...) décisif. En intégrant à sa réflexion la posture scientifique elle-même, cet article propose un aperçu d’une théorie générale (« interactionnelle ») des échanges discursifs et linguistiques complexes, et une conception de l’intelligence et de la bêtise comme phénomènes émergents. (shrink)

Causal knowledge unquestionably provides useful means to describe, explain and predict both natural and daily phenomena. This article addresses whether causality as such may not be ontologically primary and looks for an alternative fundamental mechanism encapsulating the information load of the causal framework. A probabilistic process view of reality asserting the struggle of natural forces is considered along with lines quoted from Nietzsche’s posthumously published notebooks and published work. Examples from scientific discoveries, in particular neurosciences, echoing his ontology (...) are provided. Furthermore, I propose a basic number game algorithm to illustrate and model the struggle of forces leading to causal inferences for an external observer. Nietzschean dynamic ontology involving the ceaseless struggle of will-to-power units accords with the emergentist approach to causation, which has been principally employed by the scientists working under the fields of thermodynamics, quantum mechanics and complex dynamical systems. (shrink)

Although maxims are central to Kant’s ethical theory, his account of them remains obscure. We can make progress towards understanding Kantian maxims by examining not only their role as the object of moral judgement but also their connection to freedom of the will and causality. This requires understanding maxims as causal laws that explain the actions that we impute to agents. In this way, they are analogous to causal laws of nature, but they are limited in scope to (...) the agents who are responsible for them. Understanding maxims in this way explains our limited epistemic access to them and helps to clarify Kant’s account of virtue and character as well as how they mediate the relationship between practical and theoretical reason. (shrink)

Discovering high-level causal relations from low-level data is an important and challenging problem that comes up frequently in the natural and social sciences. In a series of papers, Chalupka etal. (2015, 2016a, 2016b, 2017) develop a procedure forcausal feature learning (CFL) in an effortto automate this task. We argue that CFL does not recommend coarsening in cases where pragmatic considerations rule in favor of it, and recommends coarsening in cases where pragmatic considerations rule against it. We propose a new (...) technique, pragmatic causal feature learning (PCFL), which extends the original CFL algorithm in useful and intuitive ways. We show that PCFL has the same attractive measure-theoretic properties as the original CFL algorithm. We compare the performance of both methods through theoretical analysis and experiments. (shrink)

Shepherd’s philosophy centers on her rejection of Hume’s arguments against the demonstrability of causal principles. According to Shepherd, the causal maxim—everything that begins to exist must have a cause—is demonstratively true. She begins her first major philosophical work with a proof of this maxim. While scholars have complained that the proof seems blatantly circular, a closer look at Shepherd’s texts and their Lockean background dispels this worry. Shepherd’s premises are motivated not by the causal (...) class='Hi'>maxim or her theory of causation, but by a metaphysics that distinguishes between substances and affections and by an empirical understanding of a ‘beginning of existence.’. (shrink)

There is a widely accepted ancient principle which holds that effects must occur as soon as possible after their causes. This paper takes this principle seriously and shows that if one believes there are causal chains then one is forced to accept the view that the temporal order is discrete or that some causally related events form a dense sequence.

Information about the structure of a causal system can come in the form of observational data—random samples of the system's autonomous behavior—or interventional data—samples conditioned on the particular values of one or more variables that have been experimentally manipulated. Here we study people's ability to infer causal structure from both observation and intervention, and to choose informative interventions on the basis of observational data. In three causal inference tasks, participants were to some degree capable of distinguishing between (...) competing causal hypotheses on the basis of purely observational data. Performance improved substantially when participants were allowed to observe the effects of interventions that they performed on the systems. We develop computational models of how people infer causal structure from data and how they plan intervention experiments, based on the representational framework of causal graphical models and the inferential principles of optimal Bayesian decision‐making and maximizing expected information gain. These analyses suggest that people can make rational causal inferences, subject to psychologically reasonable representational assumptions and computationally reasonable processing constraints. (shrink)

This paper sketches a causal account of scientific explanation designed to sustain the judgment that high-level, detail-sparse explanations—particularly those offered in biology—can be at least as explanatorily valuable as lower-level counterparts. The motivating idea is that complete explanations maximize causal economy: they cite those aspects of an event’s causal run-up that offer the biggest-bang-for-your-buck, by costing less (in virtue of being abstract) and delivering more (in virtue making the event stable or robust).

ABSTRACT When Hume, in the Treatise on Human Nature, began his examination of the relation of cause and effect, in particular, of the idea of necessary connection which is its essential constituent, he identified two preliminary questions that should guide his research: For what reason we pronounce it necessary that every thing whose existence has a beginning should also have a cause and Why we conclude that such particular causes must necessarily have such particular effects? Hume observes that our belief (...) in these principles can result neither from an intuitive grasp of their truth nor from a reasoning that could establish them by demonstrative means. In particular, with respect to the first, Hume examines and rejects some arguments with which Locke, Hobbes and Clarke tried to demonstrate it, and suggests, by exclusion, that the belief that we place on it can only come from experience. Somewhat surprisingly, however, Hume does not proceed to show how that derivation of experience could be made, but proposes instead to move directly to an examination of the second principle, saying that, "perhaps, be found in the end, that the same answer will serve for both questions". Hume's answer to the second question is well known, but the first question is never answered in the rest of the Treatise, and it is even doubtful that it could be, which would explain why Hume has simply chosen to remove any mention of it when he recompiled his theses on causation in the Enquiry concerning Human Understanding. Given this situation, an interesting question that naturally arises is to investigate the relations of logical or conceptual implication between these two principles. Hume seems to have thought that an answer to would also be sufficient to provide an answer to. Henry Allison, in his turn, argued that the two questions are logically independent. My proposal here is to try to show that there is indeed a logical dependency between them, but the implication is, rather, from to. If accepted, this result may be particularly interesting for an interpretation of the scope of the so-called "Kant's reply to Hume" in the Second Analogy of Experience, which is structured as a proof of the a priori character of, but whose implications for remain controversial. (shrink)

Causal decision theory produces decision instability in cases such as Death in Damascus where a decision itself provides evidence concerning the utility of options. Several authors have proposed ways of handling this instability. William Harper (1985 and 1986) advances one of the most elegant proposals. He recommends maximizing causal expected utility among the options that are causally ratifiable. Unfortunately, Harper's proposal imposes certain restrictions; for instance, the restriction that mixed strategies are freely available. To obtain a completely general (...) method of handling decision instability, I step outside the confines of pure causal decision theory. I introduce a new kind of backtracking expected utility and propose maximizing it among the options that are causally ratifiable. In other words, I propose a hierarchical maximization of (1) conditional causal expected utility and (2) the new backtracking expected utility. I support this proposal with some intuitive considerations concerning the distinction between optimality and conditional optimality. And I prove that the proposal yields a solution in every finite decision problem. (shrink)

RESUMO Uma das críticas mais contundentes ao argumento de Hume contrário ao status a priori ou logicamente verdadeiro da máxima causal - a de que todo evento deve ter uma causa - é a de Anscombe. Ela critica a passagem de Hume da contingência das associações causais particulares - que este evento deva ter esta causa - para a contingência de qualquer causa - que este evento tenha que ter alguma causa. Meu objetivo é defendê-lo da sua crítica, argumentando (...) que o raciocínio de Hume depende de premissas anteriores do “Tratado da Natureza Humana”, particularmente da sua filosofia da percepção e da sua filosofia do tempo. A apreciação dessas premissas torna o seu argumento mais interessante e menos vulnerável às considerações de Anscombe. ABSTRACT One of the most incisive criticisms of Hume’s argument against the causal maxim - that every event must have a cause - a priori or logically true status comes from Anscombe. She criticizes Hume’s inference from the contingency of particular causal associations - that this event must have this cause - to the contingency of any cause - that this event must have any cause. My aim is to defend him from her criticism, arguing that Hume’s reasoning depends on earlier premises of the “Treatise of Human Nature”, particularly its philosophy of perception and philosophy of time. An appreciation of these premises makes his arguments more interesting and less vulnerable to Anscombe’s considerations. (shrink)

Behavior genetics is a controversial science. For decades, scholars have sought to understand the role of heredity in human behavior and life-course outcomes. Recently, technological advances and the rapid expansion of genomic databases have facilitated the discovery of genes associated with human phenotypes such as educational attainment and substance use disorders. To maximize the potential of this flourishing science, and to minimize potential harms, careful analysis of what it would mean for genes to be causes of human behavior is needed. (...) In this paper, we advance a framework for identifying instances of genetic causes, interpreting those causal relationships, and applying them to advance causal knowledge more generally in the social sciences. Central to thinking about genes as causes is counterfactual reasoning, the cornerstone of causal thinking in statistics, medicine, and philosophy. We argue that within-family genetic effects represent the product of a counterfactual comparison in the same way as average treatment effects (ATEs) from randomized controlled trials (RCTs). Both ATEs from RCTs and within-family genetic effects are shallow causes: They operate within intricate causal systems (non-unitary), produce heterogeneous effects across individuals (non-uniform), and are not mechanistically informative (non-explanatory). Despite these limitations, shallow causal knowledge can be used to improve understanding of the etiology of human behavior and to explore sources of heterogeneity and fade-out in treatment effects. (shrink)

In social evolution theory, biological individuals are often represented on the model of rational agents, that is, as if they were ‘seeking’ to maximize their own (expected) reproductive success. In the 1990s, important criticisms of this mode of thinking were made by Brian Skyrms ([1994], [1996]) and Elliott Sober ([1998]), who both argued that ‘rational agent’ models can lead to incorrect predictions when there are positive correlations between individuals’ phenotypes. In this article, I argue that one model of rational choice—namely, (...) Savage’s model ([1954])—can actually be vindicated in evolutionary biology, provided that the pay-offs are computed in inclusive fitness terms. I also show that the use of this model is better avoided when pay-offs are non-additive, or when certain causal influences (due to manipulative behaviours) affect the outcome of natural selection. The result is a partial rehabilitation of this mode of thinking, conditional on both the additivity of the pay-off structure and the absence of any form of manipulation or coercion. 1 Introduction2 When Natural Selection and Rational Deliberation Part Ways3 A Simple Solution: Redefining the Pay-offs in Inclusive Fitness Terms4 Sober on Inclusive Fitness Maximization5 Inclusive Fitness with Non-additive Pay-offs6 Causal Influences and the Savage– Hamilton Model6.1 Reciprocity and partner choice6.2 Coercion and manipulation7 ConclusionAppendix. (shrink)

Andy Egan has recently produced a set of alleged counterexamples to causal decision theory in which agents are forced to decide among causally unratifiable options, thereby making choices they know they will regret. I show that, far from being counterexamples, CDT gets Egan's cases exactly right. Egan thinks otherwise because he has misapplied CDT by requiring agents to make binding choices before they have processed all available information about the causal consequences of their acts. I elucidate CDT in (...) a way that makes it clear where Egan goes wrong, and which explains why his examples pose no threat to the theory. My approach has similarities to a modification of CDT proposed by Frank Arntzenius, but it differs in the significance that it assigns to potential regrets. I maintain, contrary to Arntzenius, that an agent facing Egan's decisions can rationally choose actions that she knows she will later regret. All rationality demands of agents it that they maximize unconditional causal expected utility from an epistemic perspective that accurately reflects all the available evidence about what their acts are likely to cause. This yields correct answers even in outlandish cases in which one is sure to regret whatever one does. (shrink)

The principle of insufficient reason assigns equal probabilities to each alternative of a random experiment whenever there is no reason to prefer one over the other. The maximum entropy principle generalizes PIR to the case where statistical information like expectations are given. It is known that both principles result in paradoxical probability updates for joint distributions of cause and effect. This is because constraints on the conditional P P\left result in changes of P P\left that assign higher probability to those (...) values of the cause that offer more options for the effect, suggesting “intentional behavior.” Earlier work therefore suggested sequentially maximizing entropy according to the causal order, but without further justification apart from plausibility on toy examples. We justify causal modifications of PIR and MaxEnt by separating constraints into restrictions for the cause and restrictions for the mechanism that generates the effect from the cause. We further sketch why causal PIR also entails “Information Geometric Causal Inference.” We briefly discuss problems of generalizing the causal version of MaxEnt to arbitrary causal DAGs. (shrink)

Russian translation of the XVIII and XIX sections of the third volume of Ingarden R. Spór o istnienie Świata . Translated by Maxim Lebedev and Yanina Kubka from Polish, the translation is compared with German translation by Alexander Nesterow.

Compared to constraint-based causal discovery, causal discovery based on functional causal models is able to identify the whole causal model under appropriate assumptions [Shimizu et al. 2006; Hoyer et al. 2009; Zhang and Hyvärinen 2009b]. Functional causal models represent the effect as a function of the direct causes together with an independent noise term. Examples include the linear non-Gaussian acyclic model, nonlinear additive noise model, and post-nonlinear model. Currently, there are two ways to estimate the (...) parameters in the models: dependence minimization and maximum likelihood. In this article, we show that for any acyclic functional causal model, minimizing the mutual information between the hypothetical cause and the noise term is equivalent to maximizing the data likelihood with a flexible model for the distribution of the noise term. We then focus on estimation of the PNL causal model and propose to estimate it with the warped Gaussian process with the noise modeled by the mixture of Gaussians. As a Bayesian nonparametric approach, it outperforms the previous one based on mutual information minimization with nonlinear functions represented by multilayer perceptrons; we also show that unlike the ordinary regression, estimation results of the PNL causal model are sensitive to the assumption on the noise distribution. Experimental results on both synthetic and real data support our theoretical claims. (shrink)

ABSTRACT In social evolution theory, biological individuals are often represented on the model of rational agents, that is, as if they were ‘seeking’ to maximize their own reproductive success. In the 1990s, important criticisms of this mode of thinking were made by Brian Skyrms and Elliott Sober, who both argued that ‘rational agent’ models can lead to incorrect predictions when there are positive correlations between individuals’ phenotypes. In this article, I argue that one model of rational choice—namely, Savage’s model —can (...) actually be vindicated in evolutionary biology, provided that the pay-offs are computed in inclusive fitness terms. I also show that the use of this model is better avoided when pay-offs are non-additive, or when certain causal influences affect the outcome of natural selection. The result is a partial rehabilitation of this mode of thinking, conditional on both the additivity of the pay-off structure and the absence of any form of manipulation or coercion. _1_ Introduction _2_ When Natural Selection and Rational Deliberation Part Ways _3_ A Simple Solution: Redefining the Pay-offs in Inclusive Fitness Terms _4_ Sober on Inclusive Fitness Maximization _5_ Inclusive Fitness with Non-additive Pay-offs _6_ Causal Influences and the Savage– Hamilton Model _6.1_ Reciprocity and partner choice _6.2_ Coercion and manipulation _7_ Conclusion Appendix. (shrink)

Pearl opened the door to formally defining actual causation using causal models. His approach rests on two strategies: first, capturing the widespread intuition that X = x causes Y = y iff X = x is a Necessary Element of a Sufficient Set for Y = y, and second, showing that his definition gives intuitive answers on a wide set of problem cases. This inspired dozens of variations of his definition of actual causation, the most prominent of which are (...) due to Halpern & Pearl. Yet all of them ignore Pearl’s first strategy, and the second strategy taken by itself is unable to deliver a consensus. This paper offers a way out by going back to the first strategy: it offers six formal definitions of causal sufficiency and two interpretations of necessity. Combining the two gives twelve new definitions of actual causation. Several interesting results about these definitions and their relation to the various Halpern & Pearl definitions are presented. Afterwards the second strategy is evaluated as well. In order to maximize neutrality, the paper relies mostly on the examples and intuitions of Halpern & Pearl. One definition comes out as being superior to all others, and is therefore suggested as a new definition of actual causation. (shrink)

Suppose that Billy and Suzy each throw a rock at window, and either rock is sufficient to shatter the window. While some consider this a paradigmatic case of causal overdetermination, in which multiple cases are sufficient for an outcome, others consider it a case of joint causation, in which multiple causes are necessary to bring about an effect. Some hold that every case of overdetermination is a case of joint causation underdescribed: at a maximal level of description, every cause (...) is necessary to bring about the outcome in precisely the way that it occurs. -/- This paper shows the latter principle to be false. I introduce a novel class of events that are insensitive to the additive force of multiple causes. They are to be contrasted with sensitive events, which physically and counterfactually vary according to the number and sorts of causes they have. I argue that sensitive effects are symptoms of joint causation; insensitive effects are symptoms of overdetermination. Insensitive effects resulting from multiple causes cannot be classified as "joint causation underdescribed," but only as overdetermination. -/- I suggest that cases of "trumping preemption" should be understood as cases of overdetermination with insensitive effects. Consequently, Lewis' influence account of causation cannot handle these cases. (shrink)

Young children, in general, appear to have a strong drive to explore the environment in ways that reveal its underlying causal structure. But are they really attuned specifically to casual information in this quest for understanding, or do they show equal interest in other types of non-obvious information about the world? To answer this question, we introduced 20 three-year-old children to two puppets who were anxious to tell the child about a set of novel artifacts and animals. One puppet (...) consistently described causal properties of the items while the other puppet consistently described carefully matched non-causal properties of the same items. After a familiarization period in which children learned which type of information to expect from each informant, children were given the opportunity to choose which they wanted to hear describe each of eight pictured test items. On average, children chose to hear from the informant that provided causal descriptions on 72% of the trials. This preference for causal information has important implications for explaining the role of conceptual information in supporting early learning and may suggest means for maximizing interest and motivation in young children. (shrink)

For decades, much literature on causality has focused on causal processes and causal reasoning in the natural sciences. According to a relatively new trend however, such research on causality remains insufficient because of its refusal to accept a certain degree of pluralism within the concept, a pluralism that is evident in how we use ideas of cause and effect in everyday life. I will build on work in this latter trend, following philosophers like G. E. M. Anscombe and (...) N. Cartwright. I explore the limits of the concept of causality by determining the extent to which our ideas can remain consistent as we stretch this concept along two dimensions, one concerning the maximizing of the effect and the other the maximizing of explanatory depth. Dealing with the cause of the universe, such an investigation touches upon some issues in current empirical cosmology and revisits some classic arguments regarding philosophy of religion and the Platonic notion of participation. The results indicate that the use of current conceptual, logical and analytic tools can deliver new insights that are useful especially for those interested in how causality in the natural sciences links with causality in everyday life. (shrink)

The conditional independence relations present in a data set usually admit multiple causal explanations — typically represented by directed graphs — which are Markov equivalent in that they entail the same conditional independence relations among the observed variables. Markov equivalence between directed acyclic graphs (DAGs) has been characterized in various ways, each of which has been found useful for certain purposes. In particular, Chickering’s transformational characterization is useful in deriving properties shared by Markov equivalent DAGs, and, with certain generalization, (...) is needed to justify a search procedure over Markov equivalence classes, known as the GES algorithm. Markov equivalence between DAGs with latent variables has also been characterized, in the spirit of Verma and Pearl (1990), via maximal ancestral graphs (MAGs). The latter can represent the observable conditional independence relations as well as some causal features of DAG models with latent variables. However, no characterization of Markov equivalent MAGs is yet available that is analogous to the transformational characterization for Markov equivalent DAGs. The main contribution of the current paper is to establish such a characterization for directed MAGs, which we expect will have similar uses as Chickering’s characterization does for DAGs. (shrink)

Much recent work on Kant's argument that the Categorical Imperative is the fundamental principle of morality has focused on the gap in that argument between the conclusion that rational agents conform to laws that apply to every rational agent, and the requirement contained in the Universal Law of Nature formula.1 While it seems plausible – even trivial– that a rational agent, insofar as she is a rational agent, conforms to whatever laws there are that are valid for all rational agents, (...) there does not appear to be any obvious route from this seemingly trivial claim to the controversial and substantive principle of acting only on maxims that one can at the same time rationally will every rational agent to act on.2 In what follows, I argue that the connection between rational agency and mere conformity to universally valid laws is not as trivial as it may seem. Many readers, I suspect, assume that Kant makes the trivial claim about rational agency because rational agency is rational, and being rational requires conforming to those laws that apply to all rational beings. For instance, this assumption is implicit in Onora O'Neill claim that "the interest of a Kantian universality test is that it aims to ground an ethical theory on notions of consistency and rationality rather than upon considerations of desire and preference."3 But, as I contend in what follows, the requirement to conform to laws valid for all rational agents is based on the fact that rational agency is, not rational, but agency. Indeed, the arguments leading to the first formulation of the Categorical Imperative rely on the idea of rational willing as a kind of causation in order to show this. And, as it turns out, the claim that the concept of causation contains the idea of conformity to universally valid laws is not a trivial claim. (shrink)

This book makes a significant contribution to the standard decision theory, that is, the theory of choice built around the principle of maximizing expected utility, both to its causal version and to the more traditional noncausal approach. The author’s success in clarifying the foundations of the standard decision theory in general, and causal decision theory in particular, also makes the book uniquely suitable for a person whose research in philosophy has led her to want to learn about contemporary (...) decision theory. The book presupposes some mathematical sophistication, and it contains more philosophical and mathematical argumentation per page than may be suitable for an undergraduate level course; but it stands out as an ideal textbook for a graduate-level course focused on the foundations of the standard decision theory. (shrink)

This paper was written for a workshop, Chance and Contingency in Indian Philosophy, that was held at Yale University in May 2017. It examines the role that chance plays by focusing on the popular maxim of the crow and the palm tree. It argues that while representatives of different schools of thought were aware of the possibility of purely random occurrences, they dealt with it very differently. For some like the Vedāntins chance provided proof of their positions, while for (...) others, Naiyāyikas and Buddhists, chance was a challenge, particularly to their theories of inference. (shrink)

This article discusses fundamental problems in "rational choice theory," as outlined by Jon Elster. Elster's discussion of why institutions may not be said to act shows his fundamental presupposition that only "monolithic," unitary entities are capable of action. This is, for him, a reason why only individual human beings may be said to act. Furthermore, human beings may be said to act only insofar as they "maximize" (their "utility") on the basis of a unitary, complete, consistent "preference structure." All action (...) that is not maximization in this sense is for Elster not really human action, but rather instances of "pure causality." Elster distinguishes between the "real," intentional person, who "maximizes," and "purely causal forces" within the person. This article tries to show that this radical, sharp dichotomy between "intentionality," in this narrow sense, and "pure causality" is inadequate as a basis for understanding human action. This radical dichotomy is central to important arguments made by Elster more generally. (shrink)

This monograph is a new interpretation of Kant’s àtemporal conception of the causality of the freedom of the will. The interpretation is based on an analysis of Kant’s primary conception of an action, viz., as a causal consequence of the will. The analysis in turn is based on H. P. Grice’s causal theory of perception and on P. F. Strawson’s modification of the theory. The monograph rejects the customary assumption that Kant’s maxim of an action is a (...) causal determination of the action. It assumes instead that the maxim is definitive of the action, and since its main thesis is that an action for Kant is to be primarily understood as an effect of the will, it concludes that the maxim of an action can only be its logical determination. Kant’s àtemporal conception of the causality of free will is confronted not only by contemporary philosophical conceptions of causality, but by Kant’s own complementary theory of causality, in the Second Analogy of Experience. According to this latter conception, causality is a natural relation among physical and psychological objects, and is therefore a temporal relation among them. Faced with this conflict, Kant scholars like Allen W. Wood either reject Kant’s àtemporal conception of causality or like Henry E. Allison accept it, but only in an anodyne form. Both camps, however, make the aforementioned assumption that Kant’s maxim of an action is a causal determination of the action. The monograph, rejecting the assumption, belongs to neither camp. (shrink)

In this paper we study the emergence of Minkowski space–time from a discrete causal network representing a classical information flow. Differently from previous approaches, we require the network to be topologically homogeneous, so that the metric is derived from pure event-counting. Emergence from events has an operational motivation in requiring that every physical quantity—including space–time—be defined through precise measurement procedures. Topological homogeneity is a requirement for having space–time metric emergent from the pure topology of causal connections, whereas physically (...) homogeneity corresponds to the universality of the physical law. We analyze in detail the case of 1+1 dimensions. If we consider the causal connections as an exchange of classical information, we can establish coordinate systems via an Einsteinian protocol, and this leads to a digital version of the Lorentz transformations. In a computational analogy, the foliation construction can be regarded as the synchronization with a global clock of the calls to independent subroutines in a parallel distributed computation. Thus the Lorentz time-dilation emerges as an increased density of leaves within a single tic-tac of a clock, whereas space-contraction results from the corresponding decrease of density of events per leaf. The operational procedure of building up the coordinate system introduces an in-principle indistinguishability between neighboring events, resulting in a network that is coarse-grained, the thickness of the event being a function of the observer's clock. The illustrated simple classical construction can be extended to space dimension greater than one, with the price of anisotropy of the maximal speed, due to the Weyl-tiling problem. This issue is cured if the causal network is quantum, as e.g. in a quantum cellular automaton, and isotropy is recovered by quantum coherence via superposition of causal paths. We thus argue that in a causal network description of space–time, the quantum nature of the network is crucial. (shrink)

I argue that classical counterfactual semantics in the style of Stalnaker, Lewis, and Kratzer validates an inference pattern that is disconfirmed in natural language. The solution is to alter the algorithm we use to handle inconsistency in premise sets: rather than checking all maximally consistent fragments of a premise sets, as in Krazter’s semantics, we selectively remove some of the premises. The proposed implementation starts from standard premise semantics and involves a new ‘filtering’ operation that achieves just this removal. The (...) resulting semantics is interestingly related to the semantics for counterfactuals emerging from Judea Pearl’s causal models framework in computer science: in particular, filtering is a possible worlds semantics counterpart of Pearl’s interventions. (shrink)

Kant’s formula of universal law (FUL) is standardly understood as a test of the moral permissibility of an agent’s maxim: maxims which pass the test are morally neutral, and so permissible, while those which do not are morally impermissible. In contrast, I argue that the FUL tests whether a maxim is the cause or determining ground of an action at all. According to Kant’s general account of causality, nothing can be a cause of some effect unless there is (...) a law-like relation between the putative cause and effect. Applied to the case of action, no maxim can be the cause of an agent’s action unless there is a law-like relation between maxims of that kind and actions of that kind. The special capacity to act according to maxims as law-like causes is what Kant calls a will; the basic constitutive principle of the will is a non-normative principle I call the categorical declarative. While the actions of a perfectly good will would be described by the categorical declarative alone, human action is determined not only by the causality of the will, but also by competing causes, namely those stemming from inclination. There is thus need for a causal test for putative maxims. The test contained in the FUL is meant to determine whether an action could be grounded solely on the agent’s maxim, or whether it requires a cause external to the will. This account permits one to build eventual distinctions concerning the morality of actions on prior and independent distinctions concerning their causality. (shrink)

J. Howard Sobel has long been recognized as an important figure in philosophical discussions of rational decision. He has done much to help formulate the concept of causal decision theory. In this volume of essays Sobel explores the Bayesian idea that rational actions maximize expected values, where an action's expected value is a weighted average of its agent's values for its possible total outcomes. Newcomb's Problem and The Prisoner's Dilemma are discussed, and Allais-type puzzles are viewed from the perspective (...) of causal world Bayesianism. The author establishes principles for distinguishing options in decision problems, and studies ways in which perfectly rational causal maximizers can be capable of resolute choices. Sobel also views critically Gauthier's revisionist ideas about maximizing rationality. This collection will be a desideratum for anyone working in the field of rational choice theory, whether in philosophy, economics, political science, psychology or statistics. Howard Sobel's work in decision theory is certainly among the most important, interesting and challenging that is being done by philosophers. (shrink)

It is plausible that Newcomb problems in which causal maximizers and evidential maximizers would do different things would not be possible for ideal maximizers who are attentive to metatickles. An objection to Eells’s first argument for this makes welcome a second. Against it I argue that even ideal evidential and causal maximizers would do different things in some non-dominance Newcomb problems; and that they would hope for different things in some third-person and non-action problems, which is relevant if (...) a good theory of rational choices of acts should fit smoothly into a good theory of rational desires for facts. (shrink)

Hume claims to prove in Treatise 1.3.3.3 that the causal maxim is neither intuitively nor demonstratively certain. The aim of this paper is to elucidate some puzzling features of his argument and thereby show that objections raised by James Beattie, Barry Stroud, and Harold Noonan can be answered. The conclusion is that Hume's argument goes through given convictions Hume expects his readers to share long before they reach this point of the Treatise. These convictions are that all ideas (...) are imagistic entities, that all images must be fully determinate, and that there is no empirical evidence against the claim that nothing we can conceive or imagine in detail implies a contradiction. (shrink)

In general, intuition and demonstration are explained in terms of the class of relations of ideas that remain the same as long as the ideas remain the same. The main negative point is that our concept of a deductively valid argument, even one with necessarily true premises, has little to do with Hume's conception of demonstration. Following Descartes and Locke, the emphasis is on content and certainty, not necessity and formal validity. Two ideas are intuitively related if the relation between (...) them is immediately conceived. Two ideas are demonstratively related if the relation between them is conceived, not immediately, but via other intermediate ideas. The link between each pair of adjacent ideas in the resulting chain must be intuitive. With this background, some of Hume's more infamous claims are explained: we know in advance that two ideas cannot be demonstratively related if we can conceive one without the other; the causal maxim is neither intuitively nor demonstratively known; the only relation capable of sustaining demonstrations is that of quantity and number. (shrink)

It is plausible that Newcomb problems in which causal maximizers and evidential maximizers would do different things would not be possible for ideal maximizers who are attentive to metatickles. An objection to Eells's first argument for this makes welcome a second. Against it I argue that even ideal evidential and causal maximizers would do different things in some non-dominance Newcomb problems; and that they would hope for different things in some third-person and non-action problems, which is relevant if (...) a good theory of rational choices of acts should fit smoothly into a good theory of rational desires for facts. (shrink)

This article examines the relationship in Kant between transcendental laws and empirical laws (focusing on causal laws), and then brings a particular interpretation of that issue to bear on familiar puzzles concerning the status of the regulative maxims of reason and reflective judgment. It is argued that the 'indeterminate objective validity' possessed by the regulative maxims derives ultimately from strictly constitutive demands of understanding.

After rejecting traditional accounts in terms of reason, Hume presents his own explanation of how we are led from a present impression directly to an idea of something unobserved by the association of ideas set up by past experience. It is this that explains our most basic probable inferences. Hume also has to explain why and how the results of such inferences are believed. What distinguishes belief from mere conception is the very same thing as that which distinguishes impressions from (...) ideas, and ideas of memory from ideas of the imagination: force and vivacity. Once he has an account of these basic inferences and beliefs, he is able to explain how we manage to engage in more complex, reflective reasonings. Such reasonings make use of the uniformity principle and the causal maxim, and Hume is even able to explain how we get the idea of necessary connection. At the end of the story, we have an account of how we manage to perform really difficult and complex feats of reasoning. But this account requires his explanation of the simplest cases of probable reasoning as merely associative, and his account of belief as belonging more to the sensitive than the cogitative part of our natures. (shrink)

Hume's examination of the causal maxim in 1.3.3 of A Treatise of Human Nature can be considered, at least in part, a thinly veiled critique of the cosmological argument, attacking as it does the privileged status of the principle upon which that proof rests. As well, Hume's remarks on the impossibility of demonstrating matters of fact a priori in Part 3 of Section 12 of An Enquiry concerning Human Understanding clearly strike at the heart of the ontological argument, (...) even if not explicitly. Unfortunately, it is only in the very brief Part 9 of his Dialogues Concerning Natural Religion that Hume directly discusses, at any length, the attempt to demonstrate a priori the existence of a deity. The argument, put forward by Demea, and Cleanthes's criticism of that argument take up so little space that for ease of reference I will reproduce them before we proceed any further. Part 9 consists of eleven paragraphs, and in accordance with a now fairly common convention, I will refer to the paragraphs by number. I reproduce only those paragraphs that will be the focus of this paper. (shrink)

This paper describes the development of theories of scientific explanation since Hempel's earliest models in the 1940ies. It focuses on deductive and probabilistic whyexplanations and their main problems: lawlikeness, explanation-prediction asymmetries, causality, deductive and probabilistic relevance, maximal specifity and homogenity, the height of the probability value. For all of these topic the paper explains the most important approaches as well as their criticism, including the author's own accounts. Three main theses of this paper are: (1) Both deductive and probabilistic explanations (...) are important in science, not reducible to each other. (2) One must distinguish between (cause giving) explanations and (reason giving) justifications and predictions. (3) The adequacy of deductive as well as probabilistic explanations is relative to a pragmatically given background knowledge-which does not exclude, however, the possibility of purely semantic models. (shrink)

J. Howard Sobel has long been recognized as an important figure in philosophical discussions of rational decision. He has done much to help formulate the concept of causal decision theory. In this volume of essays Sobel explores the Bayesian idea that rational actions maximize expected values, where an action's expected value is a weighted average of its agent's values for its possible total outcomes. Newcomb's Problem and The Prisoner's Dilemma are discussed, and Allais-type puzzles are viewed from the perspective (...) of causal world Bayesianism. The author establishes principles for distinguishing options in decision problems, and studies ways in which perfectly rational causal maximizers can be capable of resolute choices. Sobel also views critically Gauthier's revisionist ideas about maximizing rationality. This collection will be a desideratum for anyone working in the field of rational choice theory, whether in philosophy, economics, political science, psychology or statistics. Howard Sobel's work in decision theory is certainly among the most important, interesting and challenging that is being done by philosophers. (shrink)

Hume appeals to different kinds of certainties and necessities in the Treatise. He contrasts the certainty that arises from intuition and demonstrative reasoning with the certainty that arises from causal reasoning. He denies that the causal maxim is absolutely or metaphysically necessary, but he nonetheless takes the causal maxim and ‘proofs’ to be necessary. The focus of this paper is the certainty and necessity involved in Hume’s concept of knowledge. I defend the view that intuitive (...) certainty, in particular, is certainty of the invariability or necessity of relations between ideas. Against David Owen and Helen Beebee, I argue that the certainty involved in intuition depends on the activity of the mind. I argue, further, that understanding this activity helps us understand more clearly one of Hume’s most important theses, namely that experience is the source of a distinct kind of certainty and of necessity. (shrink)

The current research investigates how prior preferences affect causal learning. Participants were tasked with repeatedly choosing policies (e.g., increase vs. decrease border security funding) in order to maximize the economic output of an imaginary country and inferred the influence of the policies on the economy. The task was challenging and ambiguous, allowing participants to interpret the relations between the policies and the economy in multiple ways. In three studies, we found evidence of motivated reasoning despite financial incentives for accuracy. (...) For example, participants who believed that border security funding should be increased were more likely to conclude that increasing border security funding actually caused a better economy in the task. In Study 2, we hypothesized that having neutral preferences (e.g., preferring neither increased nor decreased spending on border security) would lead to more accurate assessments overall, compared to having a strong initial preference; however, we did not find evidence for such an effect. In Study 3, we tested whether providing participants with possible functional forms of the policies (e.g., the policy takes some time to work or initially has a negative influence but eventually a positive influence) would lead to a smaller influence of motivated reasoning but found little evidence for this effect. This research advances the field of causal learning by studying the role of prior preferences, and in doing so, integrates the fields of causal learning and motivated reasoning using a novel explore‐exploit task. (shrink)