A widespread perspective describes emotions as distinct categories bridged by fuzzy boundaries, indicating that emotions are distinct and dimensional at the same time. Theoretical and methodological approaches to this perspective still need further development. We conceptualize emotions as overlapping networks of causal relationships between emotion components—networks representing distinct emotions share components with and relate to each other. To investigate this conceptualization, we introduce network analysis to emotion research and apply it to the reanalysis of a data set (...) on multiple positive emotions. Specifically, we describe the estimation of networks from data, and the detection of overlapping communities of nodes in these networks. The network perspective has implications for the understanding of distinct emotions, their co-occurrence, and their measurement. (shrink)

The network approach to psychopathology posits that mental disorders can be conceptualized and studied as causal systems of mutually reinforcing symptoms. This approach, first posited in 2008, has grown substantially over the past decade and is now a full-fledged area of psychiatric research. In this article, we provide an overview and critical analysis of 363 articles produced in the first decade of this research program, with a focus on key theoretical, methodological, and empirical contributions. In addition, we turn our (...) attention to the next decade of the network approach and propose critical avenues for future research in each of these domains. We argue that this program of research will be best served by working toward two overarching aims: (a) the identification of robust empirical phenomena and (b) the development of formal theories that can explain those phenomena. We recommend specific steps forward within this broad framework and argue that these steps are necessary if the network approach is to develop into a progressive program of research capable of producing a cumulative body of knowledge about how specific mental disorders operate as causal systems. (shrink)

We propose a causal model theory to explain asymmetries in judgments of the intentionality of a foreseen side-effect that is either negative or positive (Knobe, 2003). The theory is implemented as a Bayesian network relating types of mental states, actions, and consequences that integrates previous hypotheses. It appeals to two inferential routes to judgment about the intentionality of someone else's action: bottom-up from action to desire and top-down from character and disposition. Support for the theory comes from three (...) experiments that test the prediction that bottom-up inference should occur only when the actor's primary objective is known. The model fits intentionality judgments reasonably well with no free parameters. (shrink)

The development of causal modelling since the 1950s has been accompanied by a number of controversies, the most striking of which concerns the Markov condition. Reichenbach's conjunctive forks did satisfy the Markov condition, while Salmon's interactive forks did not. Subsequently some experts in the field have argued that adequate causal models should always satisfy the Markov condition, while others have claimed that non-Markovian causal models are needed in some cases. This paper argues for the second position by (...) considering the multi-causal forks, which are widespread in contemporary medicine (Section 2). A non-Markovian causal model for such forks is introduced and shown to be mathematically tractable (Sections 6, 7, and 8). The paper also gives a general discussion of the controversy about the Markov condition (Section 1), and of the related controversy about probabilistic causality (Sections 3, 4, and 5). (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)

Contemporary analytical philosophy has not provided historians with an adequate account of their causal reasoning. Attempts to apply the laws of scientific explanation to history have occasioned an artificial split between historical interpretation and historical explanation. The lawlike generalizations of the natural sciences are both perfectly universal and perfectly delimited, whereas the typical generalizations of the historian are imperfectly universal and imperfectly delimited. In historical analysis, a particular development is hypothetically posited as the ordinary course of events, or as (...) the established "trend," and an intervening process or set of conditions is identified as the cause of some alteration in the expected outcome. It is important to differentiate between historical events and the actions of historical figures. The analysis of historical actions requires attention to a second layer of interpretation, that of intention. In a good narrative, everything "important" for the actual outcome will be clearly displayed and cogently ordered in a network of interacting causal sequences; for that is what the historian means when he proposes to "tell the whole story.". (shrink)

Let’s consider the following paradox (Fodor [1989], Jackson and Petit [1988] [1992], Drestke [1988], Block [1991], Lepore and Loewer [1987], Lewis [1986], Segal and Sober [1991]): i) The intentional content of a thought (or any other intentional state) is causally relevant to its behavioural (and other) effects. ii) Intentional content is nothing but the meaning of internal representations. But, iii) Internal processors are only sensitive to the syntactic structures of internal representations, not their meanings. Therefore it seems that if we (...) want to defend the idea -absolutely plausible from an intuitive point of view- that mental / intentional states are causally responsible for behavioural outputs and we want to do it on the physicalist basis of any scientific methodology, we will have to give up the conviction that such intentional states qua intentional, i. e. as having a particular meaning, are the ones causally responsible for our behaviour. The path that takes us to mental epiphenomenalism is clear: 1) the causal powers of any event are completely determined by its physical properties; 2) although intentional properties supervene on physical properties, they can’t be identified with them; 3) intentional properties, as intentional, are not causally responsible for behaviour, because they don’t take part in the causal powers of the states to which they belong, i. e., intentional properties are epiphenomenal. Let’s consider now a different yet parallel position to the one just described. There is an important debate in cognitive science about whether the class of mechanisms to which we belong and which the computational modelling project of cognitive processes refers to is best represented by classical or connectionist approaches (McClelland, Rumelhart et. al [1986], Smolensky [1987] [1988], Fodor and Pylyshyn [1988], Pinker and Prince [1988], Clark [1989], Ramsey, Stich and Rumelhart [1991], Clark and Karmiloff-Smith [forthcoming]).In classical, serial processing models, information is encoded in terms of rules that have a linguistic character. In connectionist or parallel distributed processing (PDP) models, the causal relationships among the units that constitute the system determine how the information is processes by the network, although these units don’t have a direct semantic interpretation. But, for both, classical and connectionist models, all the computations can be explained without any reference to the content of the processed information, i.e., in both cases the properties that seem to be responsible for the system’s behaviour are ultimately physical properties nor intentional ones. This situation mirrors thus, within cognitive science, the philosophical discussion concerning the causal efficacy of semantic properties. Now, if in this debate we opt for the classical paradigm, there is a way of finding a solution to the computational version of the epiphenomenalism paradox. This solution is based mainly on the notion of supervenience or, more precisely, on the notion of mereological supervenience (Kim [1984] [1988])1 . The idea of intentional properties supervening on physical properties makes sense within the classical context because there exists an easily isolable supervenience base comprising the syntactic items in the socalled language of thought. But, what happens if we opt for the connectionist paradigm?. The situation here doesn’t seem to favour the use of the same supervenience strategy. For it has been argued (Ramsey, Stich and Garon [1991]) that beliefs, desires, and other mental states are nor, in the connectionist paradigm, individuable as weight or activation states of the system. This is because information is encoded by the network in distributed and superpositional representations, i.e., there are no straightforwardly isolable vehicles at the physical level that can be identified as the articulated supervenience base on which the semantic properties supervene2 . If this is true, then the connectionist not only loses the battle against epiphenomenalism but more drastically, seems to offer a standing invitation to eliminativism, since talk of beliefs and desires, etc. now seems to be floating free of any acceptable scientific underpinning, i.e., she has lost the necessary theoretical apparatus for supporting the intuitive idea that propositional attitudes -beliefs, desires and any mental states with semantic content- are physically realized. This second line of argumentation doesn’t take us to a paradox but to a dilemma: either we accept eliminativism, if connectionist hypothesis are correct or we defend the causal efficacy of mental states with semantic content by showing that, after all, connectionist networks are not plausible cognitive models (Davies [1991]). From my point of view, however, both lines of argumentation need to be revised. The aim of this paper is to find an account of the causal efficacy of content that avoids the aforementioned epiphenomenalist objections and that doesn’t require the discovery of inner symbols in the computational modelling of such contentful mental states. In short, the aim is to find a meeting point where a philosophical story about content and cause and the connectionist computational model can be brought together (Cfr. Clark [1989]). (shrink)

NCG 4.0 is the latest update of the Network of Cancer Genes, a web-based repository of systems-level properties of cancer genes. In its current version, the database collects information on 537 known (i.e. experimentally supported) and 1463 candidate (i.e. inferred using statistical methods) cancer genes. Candidate cancer genes derive from the manual revision of 67 original publications describing the mutational screening of 3460 human exomes and genomes in 23 different cancer types. For all 2000 cancer genes, duplicability, evolutionary origin, expression, (...) functional annotation, interaction network with other human proteins and with microRNAs are reported. In addition to providing a substantial update of cancer-related information, NCG 4.0 also introduces two new features. The first is the annotation of possible false-positive cancer drivers, defined as candidate cancer genes inferred from large-scale screenings whose association with cancer is likely to be spurious. The second is the description of the systems-level properties of 64 human microRNAs that are causally involved in cancer progression (oncomiRs). Owing to the manual revision of all information, NCG 4.0 constitutes a complete and reliable resource on human coding and non-coding genes whose deregulation drives cancer onset and/or progression. NCG 4.0 can also be downloaded as a free application for Android smart phones. (shrink)

Employee social network strategies play a key role in firm strategies and organizational performance. Currently, scholars underestimate the contributions of employee social strategies in firm strategies. Little is known how informal employee social networks, group entitativity and competition could shape and direct firm strategies and organizational performance. The article examines social network theory and strategic management’s content, process and open schools of thought to propose a new interpretation for managing firm strategies. More specifically, the author examines alternate causal (...) paths, underlying processes and structures as mechanisms in employee social network strategies within a theoretical framework. The article proposes 4 theoretically driven propositions and makes two contributions. First, the article contributes to organizational behavior literature by focusing on the literature gap in network dynamics and competitive actions through employee social networks. Second, although there is immense literature on positive and negative employee competition in business, the article makes a contribution to the strategic management literature by moving beyond formalized structures and roles within an organization to focus on the multilevel informal workplace social interactions and processes that impact strategizing activities. Overall, the article extends strategy research in relation to how employee social networks operate through competition and group entitativity in firm strategies. (shrink)

The question of whether “genetic information” is a merely causal factor in development or can be made sense of semantically, in a way analogous to a language or other type of representation, has generated a long debate in the philosophy of biology. It is intimately connected with another intense debate, concerning the limits of genetic determinism. In this paper I argue that widespread attempts to draw analogies between genetic information and information contained in books, blueprints or computer programs, are (...) fundamentally inadequate. In development, gene exons are the central part of an intricate and densely ramified semantic Genetic Informational Network. DNA in the entire genome is in a state of continuous positive and negative feedback with itself and with its ‘environment’, and is ‘read’ and acted upon by the cell in various alternative and complementary ways. The linear combinatorial coding relation between codons and amino acids is but one aspect of semantic genetic information, which is, when considered in its entirety, a far wider and richer concept. (shrink)

Social network sites are a new communication channel to convey CSR information. They are interactive channels that let users participate, spread content and generate positive and negative electronic word-of-mouth about companies that can dramatically affect their reputation and future business. To identify the factors behind this behaviour, we designed a causal model to explain the intention to both comment on and share a negative corporate social responsibility news posted on Facebook. We included the following as explanatory variables: social (...) consciousness, environmental consciousness, information usefulness, corporate image and attitude towards sharing and commenting on CSR news. We surveyed 208 Facebook users who evaluated a fictitious news item about an environmental issue. We observed that social and environmental consciousness influences the usefulness of information and the attitude towards behaviour, which may explain the eWOM intention of the specific news. However, corporate image can cause people to refrain from generating eWOM and spreading the news on their friend social network. (shrink)

Depression is a common and devastating instance of ill-being which deserves an account. Moreover, the ill-being of depression is impacted by digital technology: some uses of digital technology increase such ill-being while other uses of digital technology increase well-being. So a good account of ill-being would explicate the antecedents of depressive symptoms and their relief, digitally and otherwise. This paper borrows a causal network account of well-being and applies it to ill-being, particularly depression. Causal networks are found (...) to provide a principled, coherent, intuitively plausible, and empirically adequate account of cases of depression in every-day and digital contexts. Causal network accounts of ill-being also offer philosophical, scientific, and practical utility. Insofar as other accounts of ill-being cannot offer these advantages, we should prefer causal network accounts of ill-being. (shrink)

A main message from the causal modelling literature in the last several decades is that under some plausible assumptions, there can be statistically consistent procedures for inferring (features of) the causal structure of a set of random variables from observational data. But whether we can control the error probabilities with a finite sample size depends on the kind of consistency the procedures can achieve. It has been shown that in general, under the standard causal Markov and Faithfulness (...) assumptions, the procedures can only be pointwise but not uniformly consistent without substantial background knowledge. This implies the impossibility of choosing a finite sample size to control the worst case error probabilities. In this paper, I consider the simpler task of inferring causal directions when the skeleton of the causal structure is known, and establish a similarly negative result concerning the possibility of controlling error probabilities. Although the result is negative in form, it has an interesting positive implication for causal discovery methods. (shrink)

Knowledge of mechanisms is critical for causal reasoning. We contrasted two possible organizations of causal knowledge—an interconnected causal network, where events are causally connected without any boundaries delineating discrete mechanisms; or a set of disparate mechanisms—causal islands—such that events in different mechanisms are not thought to be related even when they belong to the same causal chain. To distinguish these possibilities, we tested whether people make transitive judgments about causal chains by inferring, given A (...) causes B and B causes C, that A causes C. Specifically, causal chains schematized as one chunk or mechanism in semantic memory led to transitive causal judgments. On the other hand, chains schematized as multiple chunks led to intransitive judgments despite strong intermediate links. Normative accounts of causal intransitivity could not explain these intransitive judgments. (shrink)

This paper has two main parts. In the first part, we motivate a kind of indeterminate, suppositional credences by discussing the prospect for a subjective interpretation of a causal Bayesian network, an important tool for causal reasoning in artificial intelligence. A CBN consists of a causal graph and a collection of interventional probabilities. The subjective interpretation in question would take the causal graph in a CBN to represent the causal structure that is believed by an (...) agent, and interventional probabilities in a CBN to represent suppositional credences. We review a difficulty noted in the literature with such an interpretation, and suggest that a natural way to address the challenge is to go for a generalization of CBN that allows indeterminate credences. In the second part, we develop a decision-theoretic foundation for such indeterminate suppositional credences, by generalizing a theory of coherent choice functions to accommodate some form of act-state dependence. The upshot is a decision-theoretic framework that is not only rich enough to, so to speak, ground the probabilities in a subjectively interpreted causal network, but also interesting in its own right, in that it accommodates both act-state dependence and imprecise probabilities. (shrink)

Recent empirical work has offered strong support for ‘biased pluralism’ and ‘economic elite’ accounts of political power in the United States, according a central role to ‘business interest groups’ as a mechanism through which corporate influence is exerted. Here, we propose an additional channel of influence for corporate interests: the ‘policy-planning network,’ consisting of corporate-dominated foundations, think tanks, and elite policy-discussion groups. To evaluate this assertion, we consider one key policy-discussion group, the Council on Foreign Relations. We first briefly review (...) the origins of this organization and then review earlier findings on its influence. We then code CFR policy preferences on 295 foreign policy issues during the 1981–2002 period. In logistic regression analyses, we find that the preferences of more affluent citizens and the CFR were positive, statistically significant predictors of foreign policy outcomes while business interest group preferences were not. These findings are discussed with a consideration of the patterns of CFR ‘successes’ and ‘failures’ for the issues in our dataset. Although a full demonstration of a causal connection between corporate interests, the policy-planning network and policy outcomes requires further research, we conclude that this has been shown to be a plausible mechanism through which corporate interests are represented and that ‘biased pluralism’ researchers should include it in their future investigations. (shrink)

We study the selection of adjustment sets for estimating the interventional mean under an individualized treatment rule. We assume a non-parametric causal graphical model with, possibly, hidden variables and at least one adjustment set composed of observable variables. Moreover, we assume that observable variables have positive costs associated with them. We define the cost of an observable adjustment set as the sum of the costs of the variables that comprise it. We show that in this setting there exist (...) adjustment sets that are minimum cost optimal, in the sense that they yield non-parametric estimators of the interventional mean with the smallest asymptotic variance among those that control for observable adjustment sets that have minimum cost. Our results are based on the construction of a special flow network associated with the original causal graph. We show that a minimum cost optimal adjustment set can be found by computing a maximum flow on the network, and then finding the set of vertices that are reachable from the source by augmenting paths. The optimaladj Python package implements the algorithms introduced in this article. (shrink)

The hypothesis of extended cognition (HEC), or the claim that cognitive processes are not entirely organism-bound and can extend into the world, has received a barrage of criticism. Likewise, defenders of HEC have responded and even retreated into more moderate positions. In this paper, I trace the debate, rehearsing what I take to be the three strongest cases against HEC: nonderived content, causally natural kinds, and informational integration. I then argue that so far, the replies have been unsatisfactory, mainly because (...) they rest on tacit assumptions about the nature of ?human organisms,? which are the very subjects in question. One such reply, Clark's (2008) recent hypothesis of organism-centered cognition (HOC), is shown to be a particularly hasty retreat, specifically in terms of tackling the issue of informational integration. Namely, my position is that Clark concedes too quickly that the organism is the ?chief executor? of this cognitive function and I claim this on the grounds that (1) not all human organisms integrate information in the way Weiskopf (2008) argues they do, and (2) it is not impossible for external tools to be an informationally integrative component of a ?systemic whole.? (shrink)

Science and philosophy study well-being with different but complementary methods. Marry these methods and a new picture emerges: To have well-being is to be "stuck" in a positive cycle of emotions, attitudes, traits and success. This book unites the scientific and philosophical worldviews into a powerful new theory of well-being.

Sober develops an account of adaptations on which they must have been positive causal factors for reproductive success. Glymour defends an account of a proper subset of adaptations—adaptations to particular environmental conditions—on which traits must interact in a special way with adapting conditions to cause reproductive success. These theories render conflicting judgments about which traits count as adaptations in some interesting cases. In this article I explore one such case and argue that we ought to replace the notion (...) of adaptation qua positive causal factor with a novel notion of adaptation. (shrink)

In this paper I offer a unified causal account of natural kinds. Using as a starting point the widely held view that natural kind terms or predicates are projectible, I argue that the ontological bases of their projectibility are the causal properties and relations associated with the natural kinds themselves. Natural kinds are not just concatenations of properties but ordered hierarchies of properties, whose instances are related to one another as causes and effects in recurrent causal processes. (...) The resulting account of natural kinds as clusters of core causal properties that give rise to clusters of derivative properties enables us to distinguish genuine natural kinds from non-natural kinds. For instance, it enables us to say why some of the purely conventional categories derived from the social domain do not correspond to natural kinds, though other social categories may. (shrink)

In this paper, a positive organizational ethics -based framework is informed by the microfinance and socially responsible investing movements to capture the process of sustainable financial innovations. Both of these movements are uniquely characterized by the formation of positive ethical networks to develop sustainability innovations in response to external crises. The crisis–PEN–innovation framework proposed makes four contributions to the POE literature: positions corporate sustainability through a POE lens; formalizes the PEN construction through POE theory; proposes PENs are (...) mobilized to respond to external crises; and demonstrates how PENs facilitate sustainability innovations. The theoretical framework is tested using theory-guided process tracing in the sustainable banking sector to understand how sustainability innovations were realized. The findings are consistent with the crisis–PEN–innovation framework proposed. (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)

How do human infants learn the causal dependencies between events? Evidence suggests that this remarkable feat can be achieved by observation of only a handful of examples. Many computational models have been produced to explain how infants perform causal inference without explicit teaching about statistics or the scientific method. Here, we propose a spiking neuronal network implementation that can be entrained to form a dynamical model of the temporal and causal relationships between events that it observes. The (...) network uses spike-time dependent plasticity, long-term depression, and heterosynaptic competition rules to implement Rescorla–Wagner-like learning. Transmission delays between neurons allow the network to learn a forward model of the temporal relationships between events. Within this framework, biologically realistic synaptic plasticity rules account for well-known behavioral data regarding cognitive causal assumptions such as backwards blocking and screening-off. These models can then be run as emulators for state inference. Furthermore, this mechanism is capable of copying synaptic connectivity patterns between neuronal networks by observing the spontaneous spike activity from the neuronal circuit that is to be copied, and it thereby provides a powerful method for transmission of circuit functionality between brain regions. (shrink)

The EARN Theory of Well-Being includes a practical model for ascertaining and analyzing the well-being of individuals or groups; in its most recent iteration, EARN Theory includes insights from Lang's Network Theory of Well-Being, Revamped, which states that well-being is a matter of instantiating a holistically authentic positive causal network. Both EARN Theory and the Network Theory of Well-Being, Revamped are informed by the science of well-being and guided by a sense of justice. Presented as an inference to (...) the best explanation, the argument for EARN Theory is naturalistic while also inclusive of existential and moral considerations. EARN Theory gives us something to strive for where our well-being is concerned, and it helps us to articulate personal and societal reasons that our well-being gets derailed or diminished at times. EARN Theory has numerous practical applications, including philosophy-based consulting, self-help endeavors, and public policy reform that promotes a renewed notion of justice. (shrink)

Applied Evolutionary Epistemology is a scientific-philosophical theory that defines evolution as the set of phenomena whereby units evolve at levels of ontological hierarchies by mechanisms and processes. This theory also provides a methodology to study evolution, namely, studying evolution involves identifying the units that evolve, the levels at which they evolve, and the mechanisms and processes whereby they evolve. Identifying units and levels of evolution in turn requires the development of ontological hierarchy theories, and examining mechanisms and processes necessitates theorizing (...) about causality. Together, hierarchy and causality theories explain how biorealities form and diversify with time. This paper analyzes how Applied EE redefines both hierarchy and causality theories in the light of the recent explosion of network approaches to causal reasoning associated with studies on reticulate and macroevolution. Causality theories have often been framed from within a rigid, ladder-like hierarchy theory where the rungs of the ladder represent the different levels, and the elements on the rungs represent the evolving units. Causality then is either defined reductionistically as an upward movement along the strands of a singular hierarchy, or holistically as a downward movement along that same hierarchy. Upward causation theories thereby analyze causal processes in time, i.e. over the course of natural history or phylogenetically, as Darwin and the founders of the Modern Synthesis intended. Downward causation theories analyze causal processes in space, ontogenetically or ecologically, as the current eco-evo-devo schools are evidencing. This work demonstrates how macroevolution and reticulate evolution theories add to the complexity by examining reticulate causal processes in space–time, and the interactional hierarchies that such studies bring forth introduce a new form of causation that is here called reticulate causation. Reticulate causation occurs between units and levels belonging to different as well as to the same ontological hierarchies. This article concludes that beyond recognizing the existence of multiple units, levels, and mechanisms or processes of evolution, also the existence of multiple kinds of evolutionary causation as well as the existence of multiple evolutionary hierarchies needs to be acknowledged. This furthermore implies that evolution is a pluralistic process divisible into different kinds. (shrink)

Chronic myeloid leukemia (CML) is a cancer of the hematopoietic system initiated by a single genetic mutation which results in the oncogenic fusion protein Bcr-Abl. Untreated, patients pass through different phases of the disease beginning with the rather asymptomatic chronic phase and ultimately culminating into blast crisis, an acute leukemia resembling phase with a very high mortality. Although many processes underlying the chronic phase are well understood, the exact mechanisms of disease progression to blast crisis are not yet revealed. In (...) this paper we develop a mathematical model of CML based on causal Bayesian networks in order to study possible disease progression mechanisms. Our results indicate that an increase of Bcr-Abl levels alone is not sufficient to explain the phenotype of blast crisis and that secondary changes such as additional mutations are necessary to explain disease progression and the poor therapy response of patients in blast crisis. (shrink)

This paper investigates the relations between causality and propensity. Aparticular version of the propensity theory of probability is introduced, and it is argued that propensities in this sense are not causes. Some conclusions regarding propensities can, however, be inferred from causal statements, but these hold only under restrictive conditions which prevent cause being defined in terms of propensity. The notion of a Bayesian propensity network is introduced, and the relations between such networks and causal networks is (...) investigated. It is argued that causal networks cannot be identified with Bayesian propensity networks, but that causal networks can be a valuable heuristic guide for the construction of Bayesian propensity networks. (shrink)

Mechanist philosophers have examined several strategies scientists use for discovering causal mechanisms in neuroscience. Findings about the anatomical organization of the brain play a central role in several such strategies. Little attention has been paid, however, to the use of network analysis and causal modeling techniques for mechanism discovery. In particular, mechanist philosophers have not explored whether and how these strategies incorporate information about the anatomical organization of the brain. This paper clarifies these issues in the light of (...) the distinction between structural, functional and effective connectivity. Specifically, we examine two quantitative strategies currently used for causal discovery from functional neuroimaging data: dynamic causal modeling and probabilistic graphical modeling. We show that dynamic causal modeling uses findings about the brain’s anatomical organization to improve the statistical estimation of parameters in an already specified causal model of the target brain mechanism. Probabilistic graphical modeling, in contrast, makes no appeal to the brain’s anatomical organization, but lays bare the conditions under which correlational data suffice to license reliable inferences about the causal organization of a target brain mechanism. The question of whether findings about the anatomical organization of the brain can and should constrain the inference of causal networks remains open, but we show how the tools supplied by graphical modeling methods help to address it. (shrink)

Proponents of various causal exclusion arguments claim that for any given event, there is often a unique level of granularity at which that event is caused. Against these causal exclusion arguments, causal ecumenists argue that the same event or phenomenon can be caused at multiple levels of granularity. This paper argues that the Bayesian network approach to representing the causal structure of target systems is consistent with causal ecumenism. Given the ubiquity of Bayesian networks (...) as a tool for representing causal structure in both philosophy of science and science itself, this result speaks in favor of the ecumenical view, and against rival exclusionary accounts. Gebharter’s (Philos Phenomenol Res 95(2):353–375, 2017) argument that the Bayes nets formalism is consistent with causal exclusion is considered and rebutted. (shrink)

Artificial Intelligence (AI) and Philosophy of Science share a fundamental problem—that of understanding causality. Bayesian network techniques have recently been used by Judea Pearl in a new approach to understanding causality and causal processes (Pearl, 2000). Pearl’s approach has great promise, but needs to be supplemented with an explicit account of causal interaction. Thus far, despite considerable interest, philosophy has provided no useful account of causal interaction. Here we provide one, employing the concepts of Bayesian networks. (...) With it we demonstrate the failure of one of philosophy’s more sophisticated attempts to deal with the concept of causal interaction, that of Ellery Eells’ Probabilistic Causality (1991). (shrink)

Artificial Intelligence (AI) and Philosophy of Science share a fundamental problem—understanding causality. Bayesian networks have recently been used by Judea Pearl in a new approach to understanding causality (Pearl, 2000). Part of understanding causality is understanding causal interaction. Bayes nets can represent any degree of causal interaction, and researchers normally try to limit interactions, usually by replacing the full CPT with a noisy-OR function. But we show that noisy-OR and another common model are merely special cases of (...) the general linear systems definition of noninteraction. However, they apply in different situations, and we can measure the degree of causal interaction relative to any such model. (shrink)

This paper makes use of a new development in social psychology, Positioning Theory, the study of the way rights and duties are ascribed, attributed and justified to and by individuals in local social groups. It links this theory with a generally Vygotsky inspired approach to understanding the means by which people are brought into terrorist networks. Focusing on the use of the Internet as a device to bring mentor and novice together, the unique role of chat rooms and personal (...) conversations made possible by the Internet in this psychological process is revealed. Examining Vygotsky's ideas about the influence of a collective identity on the development of individual identities by psychological symbiosis in the Zone of Proximal Development sheds new light on the positioning of terrorist recruits. Incorporating Wittgenstein's concept of hinges into understanding the groundings of individual positions explains how the jihadist form of life turns on the unexamined grounding of beliefs about the non-believers. (shrink)

In this paper we compare causal models with reason models in the construction of Bayesian networks for legal evidence. In causal models, arrows in the network are drawn from causes to effects. In a reason model, the arrows are instead drawn towards the evidence, from factum probandum to factum probans. We explore the differences between causal models and reason models and observe several distinct advantages with reason models. Reason models are better aligned with the philosophy of (...) Bayesian inference, as they model reasons for up-dating beliefs. Reason models are better suited for measuring the combined support of the evidence, and a prior probability of guilt that reflects the number of possible perpetrators is accommodated more easily with reason models. (shrink)

In this paper I defend the propriety of explaining the behavior of distributed connectionist networks by appeal to selected data stored therein. In particular, I argue that if there is a problem with such explanations, it is a consequence of the fact that information storage in networks is superpositional, and not because it is distributed. I then develop a ``proto-account'''' of causation for networks, based on an account of Andy Clark''s, that shows even superpositionality does not undermine (...) information-based explanation. Finally, I argue that the resulting explanations are genuinely informative and not vacuous. (shrink)

Two lines of evidence indicate that there exists a reciprocal inhibitory relationship between opposed brain networks. First, most attention-demanding cognitive tasks activate a stereotypical set of brain areas, known as the task-positive network and simultaneously deactivate a different set of brain regions, commonly referred to as the task negative or defaultmode network. Second, functional connectivity analyses show that these same opposed networks are anti-correlated in the resting state. Wehypothesize that these reciprocally inhibitory effects reflect two incompatible cognitive (...) modes, each of which may be directed towards understanding the external world. Thus, engaging onemode activates one set of regions and suppresses activity in the other.Wetest this hypothesis by identifying two types of problem-solving task which, on the basis of prior work, have been consistently associated with the task positive and task negative regions: tasks requiring social cognition, i.e., reasoning about the mental states of other persons, and tasks requiring physical cognition, i.e., reasoning about the causal/mechanical properties of inanimate objects. Social and mechanical reasoning tasks were presented to neurologically normal participants during fMRI. Each task type was presented using both text and video clips. Regardless of presentation modality, we observed clear evidence of reciprocal suppression: social tasks deactivated regions associated with mechanical reasoning and mechanical tasks deactivated regions associated with social reasoning. These findings are not explained by self-referential processes, task engagement, mental simulation,mental time travel or external vs. internal attention, all factors previously hypothesized to explain default mode network activity. Analyses of resting state data revealed a close match between the regions our tasks identified as reciprocally inhibitory and regions of maximal anti-correlation in the resting state. These results indicate the reciprocal inhibition is not attributable to constraints inherent in the tasks, but is neural in origin. Hence, there is a physiological constraint on our ability to simultaneously engage two distinct cognitive modes. Furtherwork is needed tomore precisely characterize these opposing cognitive domains. (shrink)

Much research in the philosophy of psychiatry has been devoted to the characterization of the normal and the pathological. In this article, we identify and deconstruct two postulates that have held sway in the philosophy of psychiatry. The first postulate concerns the belief that clinicians would benefit from conceiving of psychiatric disorders as stable entities with clear boundaries. By relying on a symptom-based approach, we support a conception of psychiatric disorders whose symptoms are the products of many activated mechanisms in (...) mutual interactions. The second postulate concerns the way in which the philosophy of psychiatry has approached the question of harm. We posit that clinicians perceive harms primarily through networks of clinical manifestations. The identification and deconstruction of these two postulates leads us to propose a practical definition of a psychiatric disorder that is useful for the clinician, while adopting a principle of prudential conservatism that does not exclude other theoretical definitions of psychiatric disorder that are useful as an epistemic hub. Thus, we propose the following definition of a psychiatric disorder that is relevant to clinical practice: “a set of clinical manifestations belonging to the prototypical central core of psychiatry, organized in a causal network, involving at least one harm, and whose set of harms exceeds the traditional threshold of significance for a given psychiatric disorder, directly related to an activated mechanism. Such a network composed of clinical manifestations is frozen in a state that is unable to allow the deactivation of these manifestations spontaneously without a therapeutic intervention”. (shrink)

Eleventh-century Europe was dominated by a single political and economic elite with position based on control of the means of coercion; by the end of the fifteenth. century there were various elites with power based on control of some form of production. Theories based on trade, population, and the class struggle have been advanced to account for this change but are inadequate because they posit causal relationships running from some single independent factor. A different form of explanation emphasizes the (...) network of relationships among economic and political units. Here economic power is crucial. The development of new technology shifted the economic leverage of the nobility to the mer chants, then to town craftsmen, with detailed effects on all other economic relationships. The argument is primarily theoretical, not a reconstruction of events, and a mathematical model is provided. (shrink)