This paper argues that grieving a profound loss is a transformative experience, specifically an unchosen transformative experience, understood as an event‐based transformation not chosen by the agent. Grief transforms the self (i) cognitively, by forcing the agent to alter a large set of beliefs and desires, (ii) phenomenologically, by altering their experience in a diffuse or global manner, (iii) normatively, by requiring the agent to revise their practical identity, and (iv) existentially, by confronting the agent with a structuring condition (...) of their life. Grief is a disruption to one's identity that an agent addresses by making sense of the world after the loss, remaking the practical significance of various situations in their lives through their activity. Transformative grief is both an “activity” and a “revelation” (Callard, 2020): Some parts of the grieving process are active (the agent must actively work to become a new kind of person), while others are irreducibly passive (the agent passively undergoes them). (shrink)

Unchosen transformative experiences—transformative experiences that are imposed upon an agent by external circumstances—present a fundamental problem for agency: how does one act intentionally in circumstances that transform oneself as an agent, and that disrupt one’s core projects, cares, or goals? Drawing from William James’s analysis of conversion and Matthew Ratcliffe’s account of grief, I give a phenomenological analysis of transformative experiences as involving the restructuring of systems of practical meaning. On this analysis, an agent’s experience of the world is structured (...) by practically significant possibilities that form organized systems on the basis of the agent’s projects and relationships. Transformative experiences involve shifts to systems of possibility, that is, changes to habitual meanings and to how an agent’s projects are situated in relation to one another. I employ the enactivist notion of sense-making to analyze how an agent rebuilds the meaning structures disrupted by a transformative experience. In an unchosen transformative experience, an agent adjusts to a significant disruption through a process of sense-making in precarious conditions. By establishing new patterns of bodily and social interaction with the world, one alters the practical meanings of one’s surroundings, and thereby reconstitutes oneself as an intentional agent. (shrink)

Unchosen transformative experiences—transformative experiences that are imposed upon an agent by external circumstances—present a fundamental problem for agency: how does one act intentionally in circumstances that transform oneself as an agent, and that disrupt one’s core projects, cares, or goals? Drawing from William James’s analysis of conversion and Matthew Ratcliffe’s account of grief, I give a phenomenological analysis of transformative experiences as involving the restructuring of systems of practical meaning. On this analysis, an agent’s experience of the world is structured (...) by practically significant possibilities that form organized systems on the basis of the agent’s projects and relationships. Transformative experiences involve shifts to systems of possibility, that is, changes to habitual meanings and to how an agent’s projects are situated in relation to one another. I employ the enactivist notion of sense-making to analyze how an agent rebuilds the meaning structures disrupted by a transformative experience. In an unchosen transformative experience, an agent adjusts to a significant disruption through a process of sense-making in precarious conditions. By establishing new patterns of bodily and social interaction with the world, one alters the practical meanings of one’s surroundings, and thereby reconstitutes oneself as an intentional agent. (shrink)

Marxism is often claimed to be incompatible with any kind of ethical theory, because of its assumptions of economic determinism, of the class character of morals, and of the subordination of morality to politics. But the author proposes that these assumptions can be interpreted in such a flexible way as not to rule out the freedom of choice and responsibility, die relative independence of morals from economic conditions and political ends, and concepts of universal human value and a specifically moral (...) ideal. A humanist philosophy, centered in Marx's analysis of alienation, provides a sufficiently rich theoretical basis for the solution of both ethical and meta-ethical' problems. (shrink)

expose some gaps and difficulties in the argument for the causal Markov condition in our essay ‘Independence, Invariance and the Causal Markov Condition’ ([1999]), and we are grateful for the opportunity to reformulate our position. In particular, Cartwright disagrees vigorously with many of the theses we advance about the connection between causation and manipulation. Although we are not persuaded by some of her criticisms, we shall confine ourselves to showing how our central argument can be reconstructed (...) and to casting doubt on Cartwright's claim that the causal Markov condition typically fails when there are indeterministic by-products. Why believe the causal Markov condition? Causation and manipulation The argument Indeterministic by-products and the causal Markov condition The chemical factory counterexample and PM2 Conclusions: causation and manipulability. (shrink)

In their rich and intricate paper ‘Independence, Invariance, and the Causal Markov Condition’, Daniel Hausman and James Woodward ([1999]) put forward two independent theses, which they label ‘level invariance’ and ‘manipulability’, and they claim that, given a specific set of assumptions, manipulability implies the causal Markov condition. These claims are interesting and important, and this paper is devoted to commenting on them. With respect to level invariance, I argue that Hausman and Woodward's discussion is confusing because, (...) as I point out, they use different senses of ‘intervention’ and ‘invariance’ without saying so. I shall remark on these various uses and point out that the thesis is true in at least two versions. The second thesis, however, is not true. I argue that in their formulation, the manipulability thesis is patently false and that a modified version does not fare better. Furthermore, I think their proof that manipulability implies the causal Markov condition is not conclusive. In the deterministic case it is valid but vacuous, whereas it is invalid in the probabilistic case. 1 Introduction 2 Intervention, invariance and modularity 3 The causal Markov condition: CM1 and CM2 4 From MOD to the causal Markov condition and back 5 A second argument for CM2 6 The proof of the causal Markov condition for probabilistic causes 7 ‘Cartwright's objection’ defended 8 Metaphysical defenses of the causal Markov condition 9 Conclusion. (shrink)

The causal Markov condition (CMC) plays an important role in much recent work on the problem of causal inference from statistical data. It is commonly thought that the CMC is a more problematic assumption for genuinely indeterministic systems than for deterministic ones. In this essay, I critically examine this proposition. I show how the usual motivation for the CMC—that it is true of any acyclic, deterministic causal system in which the exogenous variables are independent—can be extended to the (...) indeterministic case. In light of this result, I consider several arguments for supposing indeterminism a particularly hostile environment for the CMC, but conclude that none are persuasive. Introduction Functional models and directed graphs The causal Markov theorem The causal Markov theorem and genuine indeterminism Are the exogenous variables independent? EPR Conclusion. (shrink)

This article suggests a revision of the theory of causal nets. In section 1 we introduce an axiomatization of TCN based on a realistic understanding. It is shown that the causal Markov condition entails three independent principles. In section 2 we analyze indeterministic decay as the major counterexample to one of these principles: screening off by common causes. We call SCC-violating common causes interactive causes. In section 3 we develop a revised version of TCN, called TCN*, which accounts (...) for interactive causes. It is shown that there are interactive causal models that admit of no faithful noninteractive reconstruction. (shrink)

This paper explores the relationship between a manipulability conception of causation and the causal Markov condition (CM). We argue that violations of CM also violate widely shared expectations—implicit in the manipulability conception—having to do with the absence of spontaneous correlations. They also violate expectations concerning the connection between independence or dependence relationships in the presence and absence of interventions.

This essay explains what the Causal Markov Condition says and defends the condition from the many criticisms that have been launched against it. Although we are skeptical about some of the applications of the Causal Markov Condition, we argue that it is implicit in the view that causes can be used to manipulate their effects and that it cannot be surrendered without surrendering this view of causation.

The aim of the paper is to relate Bell’s notion of local causality to the Causal Markov Condition. To this end, first a framework, called local physical theory, will be introduced integrating spatiotemporal and probabilistic entities and the notions of local causality and Markovity will be defined. Then, illustrated in a simple stochastic model, it will be shown how a discrete local physical theory transforms into a Bayesian network and how the Causal Markov Condition arises as (...) a special case of Bell’s local causality and Markovity. (shrink)

Woodward present an argument for the Causal Markov Condition (CMC) on the basis of a principle they dub ‘modularity’ ([1999, 2004]). I show that the conclusion of their argument is not in fact the CMC but a substantially weaker proposition. In addition, I show that their argument is invalid and trace this invalidity to two features of modularity, namely, that it is stated in terms of pairwise independence and ‘arrow-breaking’ interventions. Hausman & Woodward's argument can be rendered valid (...) through a reformulation of modularity, but it is doubtful that the argument so revised provides any substantially new insight regarding the basis of the CMC. Introduction The CMC versus Hausman & Woodward's conclusion Hausman & Woodward's argument Modularity and independent error terms Conclusion Appendix: D-separation. (shrink)

Daniel Hausman and James Woodward claim to prove that the causal Markov condition, so important to Bayes-nets methods for causal inference, is the ‘flip side’ of an important metaphysical fact about causation—that causes can be used to manipulate their effects. This paper disagrees. First, the premise of their proof does not demand that causes can be used to manipulate their effects but rather that if a relation passes a certain specific kind of test, it is causal. Second, the (...) proof is invalid. Third, the kind of testability they require can easily be had without the causal Markov condition. Introduction Earlier views: manipulability v testability Increasingly weaker theses The proof is invalid MOD* is implausible Two alternative claims and their defects A true claim and a valid argument Indeterminism Overall conclusion. (shrink)

It is still a matter of controversy whether the Principle of the Common Cause can be used as a basis for sound causal inference. It is thus to be expected that its application to quantum mechanics should be a correspondingly controversial issue. Indeed the early 90’s saw a flurry of papers addressing just this issue in connection with the EPR correlations. Yet, that debate does not seem to have caught up with the most recent literature on causal inference generally, which (...) has moved on to consider the virtues of a generalised PCC inspired condition, the so called Causal Markov Condition. In this paper we argue that the CMC is an appropriate benchmark for debating possible causal explanations of the EPR correlations. But we go on to take issue with some pronouncements on EPR by defenders of the CMC. (shrink)

The present text comments on Steel 2005 , in which the author claims to extend from the deterministic to the general case, the result according to which the causal Markov condition is satisfied by systems with jointly independent exogenous variables. I show that Steel’s claim cannot be accepted unless one is prepared to abandon standard causal modeling terminology. Correlatively, I argue that the most fruitful aspect of Steel 2005 consists in a realist conception of error terms, and I (...) show how this conception sheds new light on the relationship between determinism and the causal Markov condition. †To contact the author, please write to: Institut Supérieur de Philosophie, Université Catholique de Louvain, Place du Cardinal Mercier 14, 1348 Louvain la Neuve, Belgium; e‐mail: [email protected]. (shrink)

Daniel Hausman and James Woodward claim to prove that the causal Markov condition, so important to Bayes-nets methods for causal inference, is the ‘flip side’ of an important metaphysical fact about causation—that causes can be used to manipulate their effects. This paper disagrees. First, the premise of their proof does not demand that causes can be used to manipulate their effects but rather that if a relation passes a certain specific kind of test, it is causal. Second, the (...) proof is invalid. Third, the kind of testability they require can easily be had without the causal Markov condition. (shrink)

Nancy Cartwright believes that we live in a Dappled World– a world in which theories, principles, and methods applicable in one domain may be inapplicable in others; in which there are no universal principles. One of the targets of Cartwright’s arguments for this conclusion is the Causal Markov condition, a condition which has been proposed as a universal condition on causal structures.1 The Causal Markov condition, Cartwright argues, is applicable only in a limited domain (...) of special cases, and thus cannot be used as a universal principle in causal discovery. I have no dispute with any of these claims here. Rather, I wish to argue for a very limited thesis: that the Causal Markov condition is applicable in the specific domain of microscopic quantum mechanical systems; further, that the condition can fruitfully be applied to the much discussed EPR setup. This is perhaps a surprising conclusion, for it is precisely in this domain that Cartwright’s arguments against the Causal Markov condition have been considered to be the most successful. (shrink)

It is still a matter of controversy whether the Principle of the Common Cause (PCC) can be used as a basis for sound causal inference. It is thus to be expected that its application to quantum mechanics should be a correspondingly controversial issue. Indeed the early 90’s saw a flurry of papers addressing just this issue in connection with the EPR correlations. Yet, that debate does not seem to have caught up with the most recent literature on causal inference generally, (...) which has moved on to consider the virtues of a generalised PCC-inspired condition, the so-called Causal Markov Condition (CMC). In this paper we argue that the CMC is an appropriate benchmark for debating possible causal explanations of the EPR correlations. But we go on to take issue with some pronouncements on EPR by defenders of the CMC. (shrink)

Bruineberg et al. underestimate the ontological weight of Markov blankets as actual boundaries of systems and lean toward an instrumentalist understanding thereof. Yet Markov blankets need not be deemed mere tools. Determining their reality depends on the fundamental problem of distinguishing between system and environment in physics, which, in turn, demands a metaphysical bedrock backed by a realist stance on science.

The theory of causal Bayes nets [15, 19] is, from an empirical point of view, currently one of the most promising approaches to causation on the market. There are, however, counterexamples to its core axiom, the causal Markov condition. Probably the most serious of these counterexamples are EPR/B experiments in quantum mechanics (cf. [13, 23]). However, these are also the only counterexamples yet known from the quantum realm. One might therefore wonder whether they are the only phenomena in (...) the quantum realm that create problems for causal Bayes nets. The aim of this paper is to demonstrate that not only the phenomenon of quantum correlations in EPR/B experiments create problems for causal Bayes nets, but also the temporal evolution of quantum systems, which is described as dualistic by quantum mechanics. For this purpose, it is shown that single photon experiments in a Mach-Zehnder interferometer (MZI) violate the causal Markov condition as well. It is then argued, however, that the Markov violation does not occur under the de Broglie-Bohm interpretation of Bohmian mechanics. (shrink)

Cartwright (Synthese 121(1/2):3–27, 1999a; The dappled world, Cambridge University Press, Cambridge, 1999b) attacked the view that causal relations conform to the Markov condition by providing a counterexample in which a common cause does not screen off its effects: the prominent chemical factory. In this paper we suggest a new way to handle counterexamples to Markov causation such as the chemical factory. We argue that Cartwright’s as well as similar scenarios feature a certain kind of non-causal dependence that (...) kicks in once the common cause occurs. We then develop a representation of this specific kind of non-causal dependence that allows for modeling the problematic scenarios in such a way that the Markov condition is not violated anymore. (shrink)

Bruineberg et al. argue that one ought not confuse the map (model) for the territory (reality) and delineate a distinction between innocuous Pearl blankets and metaphysically laden Friston blankets. I argue that all we have are models, all knowledge is conditional, and that if there is a Pearl/Friston distinction, it is a matter of the domain of application: latents or observations. This suggests that, if anything, Friston blankets may inherit philosophical significance previously assigned to observations.

It is well known that there may be many causal explanations that are consistent with a given set of data. Recent work has been done to represent the common aspects of these explanations into one representation. In this paper, we address what is less well known: how do the relationships common to every causal explanation among the observed variables of some DAG process change in the presence of latent variables? Ancestral graphs provide a class of graphs that can encode conditional (...) independence relations that arise in DAG models with latent and selection variables. In this paper we present a set of orientation rules that construct the Markov equivalence class representative for ancestral graphs, given a member of the equivalence class. These rules are sound and complete. We also show that when the equivalence class includes a DAG, the equivalence class representative is the essential graph for the said DAG. (shrink)

This paper introduces a class of graphical independence models that is closed under marginalization and conditioning but that contains all DAG independence models. This class of graphs, called maximal ancestral graphs, has two attractive features: there is at most one edge between each pair of vertices; every missing edge corresponds to an independence relation. These features lead to a simple parameterization of the corresponding set of distributions in the Gaussian case.

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)

Different directed acyclic graphs may be Markov equivalent in the sense that they entail the same conditional independence relations among the observed variables. Chickering provided a transformational characterization of Markov equivalence for DAGs, which is useful in deriving properties shared by Markov equivalent DAGs, and, with certain generalization, is needed to prove the asymptotic correctness of a search procedure over Markov equivalence classes, known as the GES algorithm. For DAG models with latent variables, maximal ancestral graphs (...) provide a neat representation that facilitates model search. However, no transformational characterization -- analogous to Chickering's -- of Markov equivalent MAGs is yet available. This paper establishes such a characterization for directed MAGs, which we expect will have similar uses as it does for DAGs. (shrink)

Some conditions are given to ensure that for a jump homogeneous Markov process $\{X(t),t\ge 0\}$ the law of the integral functional of the process $T^{-1/2} \int^T_0\varphi(X(t))dt$ converges to the normal law $N(0,\sigma^2)$ as $T\to \infty$, where $\varphi$ is a mapping from the state space $E$ into $\bbfR$.

This study focuses on the static output feedback control of nonlinear Markov jump singularly perturbed systems within the framework of Takagi–Sugeno fuzzy approximation. From a practical point of view, the phenomenon of asynchronous switching between the plant and the controller is considered and characterized by a finite piecewise-homogenous Markov process. Particularly, for facilitating the controller synthesis, the closed-loop system is transformed into a fuzzy Markov jump singularly perturbed descriptor system by adopting descriptor representation. In order to fully (...) accommodate the system features, an appropriate stochastic Lyapunov function is constructed. Afterwards, by combining Finsler’s lemma, the mean square exponential admissibility of the system is analyzed. The conditions ensuring the existence of the predesigned controller are given and further solved by designing a brief search algorithm. Finally, a typical circuit system is used to demonstrate the application potential of the developed control technology and the effectiveness of the control strategy. (shrink)

We clarify the status of the so-called causal minimality condition in the theory of causal Bayesian networks, which has received much attention in the recent literature on the epistemology of causation. In doing so, we argue that the condition is well motivated in the interventionist (or manipulability) account of causation, assuming the causal Markov condition which is essential to the semantics of causal Bayesian networks. Our argument has two parts. First, we show that the causal minimality (...) condition, rather than an add-on methodological assumption of simplicity, necessarily follows from the substantive interventionist theses, provided that the actual probability distribution is strictly positive. Second, we demonstrate that the causal minimality condition can fail when the actual probability distribution is not positive, as is the case in the presence of deterministic relationships. But we argue that the interventionist account still entails a pragmatic justification of the causal minimality condition. Our argument in the second part exemplifies a general perspective that we think commendable: when evaluating methods for inferring causal structures and their underlying assumptions, it is relevant to consider how the inferred causal structure will be subsequently used for counterfactual reasoning. (shrink)

Different directed acyclic graphs may be Markov equivalent in the sense that they entail the same conditional indepen- dence relations among the observed variables. Meek characterizes Markov equiva- lence classes for DAGs by presenting a set of orientation rules that can correctly identify all arrow orienta- tions shared by all DAGs in a Markov equiv- alence class, given a member of that class. For DAG models with latent variables, maxi- mal ancestral graphs provide a neat representation that (...) facilitates model search. Earlier work has identified a set of orientation rules sufficient to con- struct all arrowheads common to a Markov equivalence class of MAGs. In this paper, we provide extra rules sufficient to construct all common tails as well. We end up with a set of orientation rules sound and complete for identifying commonalities across a Markov equivalence class of MAGs, which is partic- ularly useful for causal inference. (shrink)

Markov models of evolution describe changes in the probability distribution of the trait values a population might exhibit. In consequence, they also describe how entropy and conditional entropy values evolve, and how the mutual information that characterizes the relation between an earlier and a later moment in a lineage’s history depends on how much time separates them. These models therefore provide an interesting perspective on questions that usually are considered in the foundations of physics—when and why does entropy increase (...) and at what rates do changes in entropy take place? They also throw light on an important epistemological question: are there limits on what your observations of the present can tell you about the evolutionary past? (shrink)

With small to modest sample sizes and complex models, maximum likelihood estimation of confirmatory factor analysis models can show serious estimation problems such as non-convergence or parameter estimates outside the admissible parameter space. In this article, we distinguish different Bayesian estimators that can be used to stabilize the parameter estimates of a CFA: the mode of the joint posterior distribution that is obtained from penalized maximum likelihood estimation, and the mean, median, or mode of the marginal posterior distribution that are (...) calculated by using Markov Chain Monte Carlo methods. In two simulation studies, we evaluated the performance of the Bayesian estimators from a frequentist point of view. The results show that the EAP produced more accurate estimates of the latent correlation in many conditions and outperformed the other Bayesian estimators in terms of root mean squared error. We also argue that it is often advantageous to choose a parameterization in which the main parameters of interest are bounded, and we suggest the four-parameter beta distribution as a prior distribution for loadings and correlations. Using simulated data, we show that selecting weakly informative four-parameter beta priors can further stabilize parameter estimates, even in cases when the priors were mildly misspecified. Finally, we derive recommendations and propose directions for further research. (shrink)

This paper separates conditional parameter estima- tion, which consistently raises test set accuracy on statistical NLP tasks, from conditional model struc- tures, such as the conditional Markov model used for maximum-entropy tagging, which tend to lower accuracy. Error analysis on part-of-speech tagging shows that the actual tagging errors made by the conditionally structured model derive not only from label bias, but also from other ways in which the independence assumptions of the conditional model structure are unsuited to linguistic sequences. (...) The paper presents new word-sense disambiguation and POS tagging experiments, and integrates apparently conflicting reports from other recent work. (shrink)

The free-energy principle (FEP) builds on an assumption that sensor–motor loops exhibit Markov blankets in stationary state. We argue that there is rarely reason to assume a system's internal and external states are conditionally independent given the sensorimotor states, and often reason to assume otherwise. However, under mild assumptions internal and external states are conditionally independent given the sensorimotor history.

This work is devoted to studying the stochastic stabilization of a class of neutral-type complex-valued neural networks with partly unknown Markov jump. Firstly, in order to reduce the conservation of our stability conditions, two integral inequalities are generalized to the complex-valued domain. Secondly, a state-feedback controller is designed to investigate the stability of the neutral-type CVNNs with H ∞ performance, making the stability problem a further extension, and then, the stabilization of the CVNNs with H ∞ performance is investigated (...) through a sampling-based event-triggered control for the first time that the transmission event is not triggered except when it violates the event-triggered condition. Finally, two examples are given to illustrate the validity and correctness of our obtained theorems. (shrink)

Recursive linear structural equation models can be represented by directed acyclic graphs. When represented in this way, they satisfy the Markov Condition. Hence it is possible to use the graphical d-separation to determine what conditional independence relations are entailed by a given linear structural equation model. I prove in this paper that it is also possible to use the graphical d-separation applied to a cyclic graph to determine what conditional independence relations are entailed to hold by a given (...) non-recursive linear structural equation model. I also give a causal intepretation to the linear coefficients in a non- recursive structural equation models, and explore the relationships between cyclic graphs and undirected graphs, directed acyclic graphs with latent variables, and chain independence graphs. (shrink)

We propose the quantum field formalism as a new type of stochastic Markov process determined by local configuration. Our proposed Markov process is different with the classical one, in which the transition probability is determined by the state labels related to the character of state. In the new quantum Markov process, the transition probability is determined not only by the state character, but also by the occupation of the state. Due to the probability occupation of the state, (...) the classical relation between the probability and condition probability at different times is no more valid. We have formulated the chain relation of successive transition for transition probability of the quantum Markov process instead of classical Chapman–Kolmogorov equation. The master equation is valid only in a classical Markov process. We have derived Euler-Lagrange equation in operator form as a characteristic equation of motion for the evolution of particle states as a Markov process. (shrink)

Humans excel in categorization. Yet from a computational standpoint, learning a novel probabilistic classification task involves severe computational challenges. The present paper investigates one way to address these challenges: assuming class-conditional independence of features. This feature independence assumption simplifies the inference problem, allows for informed inferences about novel feature combinations, and performs robustly across different statistical environments. We designed a new Bayesian classification learning model that incorporates varying degrees of prior belief in class-conditional independence, learns whether or not independence holds, (...) and adapts its behavior accordingly. Theoretical results from two simulation studies demonstrate that classification behavior can appear to start simple, yet adapt effectively to unexpected task structures. Two experiments—designed using optimal experimental design principles—were conducted with human learners. Classification decisions of the majority of participants were best accounted for by a version of the model with very high initial prior belief in class-conditional independence, before adapting to the true environmental structure. Class-conditional independence may be a strong and useful default assumption in category learning tasks. (shrink)