Weak emergence is the view that a system’s macro properties can be explained by its micro properties but only in an especially complicated way. This paper explains a version of weak emergence based on the notion of explanatory incompressibility and “crawling the causal web.” Then it examines three reasons why weak emergence might be thought to be just in the mind. The first reason is based on contrasting mere epistemological emergence with a form of ontological emergence that involves (...) irreducible downward causation. The second reason is based on the idea that attributions of emergence are always a reflection of our ignorance of non-emergent explanations. The third reason is based on the charge that complex explanations are anthropocentric. Rather than being just in the mind, weak emergence is seen to involve a distinctive kind of complex, macro-pattern in the mind-independent objective micro-causal structure that exists in nature. The paper ends by addressing two further questions. One concerns whether weak emergence applies only or mainly to computer simulations and computational systems. The other concerns the respect in which weak emergence is dynamic rather than static. (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)

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)

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)

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)

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)

The adverse effects of the Covid-19 pandemic have exposed the lack of multi-scalar community resilient strategies that catalyze the development of alternative coping mechanisms for future challenges. To address the immediate needs of vulnerable and marginalized groups, especially in times of crisis, as evidenced by the pandemic, micro-networks within communities have mitigated and reduced harm through self-devised ingenuity based on local ways of life. Socio-spatial micro-networks have the potential to empower communities to self-organize, engage, collaborate, co-design, co-build, and (...) connect with other communities to improve their resilience. This paper focuses on the impact of micro-networks of care, participatory action research, community health, solidarity and mutual aid in Nairobi (Kenya), comparing their resilience strategies, pre- and post Covid-19 pandemic era. Through analytical, empirical, and comparative studies, this paper envisions viable ways of augmenting and enhancing self-devised, self-sufficient, and resilient culture in marginalized communities in Africa and worldwide. -/- . (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)

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)

This article correlates fine-grained semantic variability and change with measures of occurrence frequency to investigate whether a word’s degree of semantic change is sensitive to how often it is used. We show that this sensitivity can be detected within a short time span (i.e., 20 years), basing our analysis on a large corpus of German allowing for a high temporal resolution (i.e., per month). We measure semantic variability and change with the help of local semantic networks, combining elements of deep (...) learning methodology and graph theory. Our micro-scale analysis complements previous macro-scale studies from the field of natural language processing, corroborating the finding that high token frequency has a negative effect on the degree of semantic change in a lexical item. We relate this relationship to the role of exemplars for establishing form–function pairings between words and their habitual usage contexts. (shrink)

The distribution of cultural variants in a population is shaped by both neutral evolutionary dynamics and by selection pressures. The temporal dynamics of social network connectivity, that is, the order in which individuals in a population interact with each other, has been largely unexplored. In this paper, we investigate how, in a fully connected social network, connectivity dynamics, alone and in interaction with different cognitive biases, affect the evolution of cultural variants. Using agent‐based computer simulations, we manipulate population (...) connectivity dynamics (early, mid, and late full‐population connectivity); content bias, or a preference for high‐quality variants; coordination bias, or whether agents tend to use self‐produced variants (egocentric bias), or to switch to variants observed in others (allocentric bias); and memory size, or the number of items that agents can store in their memory. We show that connectivity dynamics affect the time‐course of variant spread, with lower connectivity slowing down convergence of the population onto a single cultural variant. We also show that, compared to a neutral evolutionary model, content bias accelerates convergence and amplifies the effects of connectivity dynamics, while larger memory size and coordination bias, especially egocentric bias, slow down convergence. Furthermore, connectivity dynamics affect the frequency of high‐quality variants (adaptiveness), with late connectivity populations showing bursts of rapid change in adaptiveness followed by periods of relatively slower change, and early connectivity populations following a single‐peak evolutionary dynamic. We evaluate our simulations against existing data collected from previous experiments and show how our model reproduces the empirical patterns of convergence. (shrink)

In this article, we explore the micro-foundations of elite politics by focusing on changes in network structures that emerge from informal conversations. Empirically, we offer a novel “situational conflict” explanation to account for the puzzle of why reformist leaders were periodically ousted during China’s reform era (1977–1992), emphasizing the unexpected power collision that catalyzed the violent crackdown on the Tiananmen movement in 1989. To do so, we employ network analysis and narrative to utilize an original dataset of (...) elite conversations and primary sources that have only recently been made available. We find that ideological cleavage and manipulative brokerage produced each conflict to varying degrees but were contingent on the relational structure arising from elite conversational interactions. Furthermore, the actual unfolding of those conflicts often resulted from key actors’ discrepant understandings of the changing relationships via ongoing interactions at vital moments, such as during the Tiananmen movement. Integrating micro-sociological theories and network analysis, our work has methodological and theoretical implications for unpacking the black box of elite politics and its role in macro-historical change. (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)

The business world is facing a very complicated situation due to the COVID-19 pandemic. Small- and medium-sized companies —both in Spain and at the global level—are seeing their survival jeopardized by a fall in revenues. This scenario is aggravated in the case of micro-SMEs headed by female entrepreneurs. Accordingly, micro-SMEs, particularly those led by female entrepreneurs, need to reinvent themselves to overcome the current adversities that could lead to the destruction of their businesses and hence their jobs. One (...) of the ways to do this is to take advantage of digital transformation. Therefore, the aim of this paper is to analyze which variables influence the financial results of female-led Spanish micro-SMEs when they carry out social marketing actions. For that purpose, an online survey was designed and analyzed using the “PROCESS” macro. Results show that social media marketing actions have significant effects on financial performance. (shrink)

SummaryThe traditional account of micro‐reductive explanations, in terms of bridge‐law derivations and attribute‐identities, is subjected to critical analysis. Formal expositions of this approach especially those of R. L. Causey, are shown to have oversimplified certain relations between micro‐parts and wholes, and between identities and explanations, and to have neglected a key difference between homogeneous and heterogeneous micro‐explanatory contexts. An alternative treatment of part‐explanation adequacy is outlined and illustrated.

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)

This article argues that empirical social scientists can be freed from having to account for “micro-to-macro transitions.” The article shows, in opposition to the (still) dominant perspective based on Coleman’s macro-micro-macro model, that no micro-macro transitions or mechanisms connect the individual level to the macro level in empirical social science. Rather, when considering that social macro entities and properties are micro manifest rather than macro manifest, it becomes clear that the micro-macro move in empirical social (...) science is purely conceptual or analytical. (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)

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)

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)

Micro-expressions can reflect an individual’s subjective emotions and true mental state, and they are widely used in the fields of mental health, justice, law enforcement, intelligence, and security. However, one of the major challenges of working with MEs is that their neural mechanism is not entirely understood. To the best of our knowledge, the present study is the first to use electroencephalography to investigate the reorganizations of functional brain networks involved in MEs. We aimed to reveal the underlying neural (...) mechanisms that can provide electrophysiological indicators for ME recognition. A real-time supervision and emotional expression suppression experimental paradigm was designed to collect video and EEG data of MEs and no expressions of 70 participants expressing positive emotions. Based on the graph theory, we analyzed the efficiency of functional brain network at the scalp level on both macro and micro scales. The results revealed that in the presence of MEs compared with NEs, the participants exhibited higher global efficiency and nodal efficiency in the frontal, occipital, and temporal regions. Additionally, using the random forest algorithm to select a subset of functional connectivity features as input, the support vector machine classifier achieved a classification accuracy for MEs and NEs of 0.81, with an area under the curve of 0.85. This finding demonstrates the possibility of using EEG to recognize MEs, with a wide range of application scenarios, such as persons wearing face masks or patients with expression disorders. (shrink)

This article considers the extent to which Bayesian networks with imprecise probabilities, which are used in statistics and computer science for predictive purposes, can be used to represent causal structure. It is argued that the adequacy conditions for causal representation in the precise context—the Causal Markov Condition and Minimality—do not readily translate into the imprecise context. Crucial to this argument is the fact that the independence relation between random variables can be understood in several different ways when (...) the joint probability distribution over those variables is imprecise, none of which provides a compelling basis for the causal interpretation of imprecise Bayes nets. I conclude that there are serious limits to the use of imprecise Bayesian networks to represent causal structure. (shrink)

In the following we study the evolution of support networks among egoists who differ widely in their degree of neediness, are free to choose their partners, and do so in opportunistic ways. No central authority is involved. The question we address is to what degree and under what aspect it shapes the structure of emerging solidarity networks whether we model egoistic actors as rational actors in a game theoretical sense or as adaptive actors, i.e. learning beings following a simple learning (...) mechanism which is driven by success and failure.1 The central modelling ideas to address that question are: Support relationships are modelled by a 2-person support game. The social space in which a network can emerge is a 2-dimensional cellular grid. Actors can play the support game simultaneously with all their neighbours. Not all cells are occupied. From time to time players have the opportunities to migrate. In one model we make our actors rational beings, in a second one we make them adaptive agents. Obviously we use a cellular automata framework of modelling . Especially because of its simplicity that framework has often proved fruitful for analysing micro/macro relations. (shrink)

Causal models provide a framework for precisely representing complex causal structures, where specific models can be used to efficiently predict, infer, and explain the world. At the same time, we often do not know the full causal structure a priori and so must learn it from data using a causal model search algorithm. This chapter provides a general overview of causal models and their uses, with a particular focus on causal graphical models (the most (...) commonly used causal modeling framework) and methods for learning such models from observational and experimental data. The chapter concludes with two examples of productive causal search and modeling in experimental philosophy. (shrink)

While many periods of history are popularly known by their 'great men',the Enlightenment stands out for the prominence of its 'great groups'. This volume assemblesleading scholars using data-driven scholarship to study the networks that madethe Enlightenment possible, and contributed to creating a new sense of Europeanidentity. From Voltaire's correspondence with Catherine the Great, to AdamSmith's travels on the European continent, mediated and unmediatedcommunication networks were the lifeline of the Enlightenment. What is particularly notable about theEnlightenment is how these different networks (...) were central to theirparticipants' identity. One could nottake part in the Enlightenment on one's own. Although some older historical studies highlightthe importance of social networks in the Enlightenment, data-driven approachesallow for a more comprehensive and granular understanding of the many differenttypes of networks that formed the intellectual and cultural infrastructure ofthe Enlightenment throughout Europe. The recent influx of metadata from thecorrespondences of major Enlightenment figures now allows scholars to studythese networks at both the micro and macro levels, and to explore the worlds ofthe philosophes and the "nodes" intheir networks in rich detail. It is at this intersection of Enlightenmenthistoriography, data capture, and social network analysis that the essayscollected in this volume all fall, taking advantage of new data sources,configurations, and modes of analysis to deepen our understanding of howEnlightenment sociability worked, who it included, and what it meant forparticipants. (shrink)

The rational choice approach focuses on explaining macrosocial phenomena or relationships by applying a theory about the behavior of individual actors. This paper addresses James S. Coleman’s account of micro-macro transitions involved in rational choice explanations. The starting point of this account is a macro-relationship. Its independent variable has a causal effect on the independent variable of a micro-relationship. The dependent variable of this relationship in turn influences the dependent variable of the macro-relationship (see Figure 1 of (...) this paper). The paper extends this account by delineating other types of micro-macro explanations and discusses some of its problems. (shrink)

Introduction: The law of causality and its methodology -- Recent causal realism in quantum physics -- The law of causality : Hume, Quine and quantum physics -- Ontological and probabilistic causalisms -- Laplacean causalism in quantum physics -- Ontological commitment in quantum physics -- Causality in some quantum experiments -- Interpretations of important results in quantum physics -- Causality in the EPR paradox: Part 1. The physics -- Causality in the EPR paradox: Part 2. The physical ontology -- Causality (...) in a new double slit experiment and in EPR -- Causality in the special theory of relativity -- Micro-physical and cosmic causal continuity -- Conclusion: Causal ubiquity in the micro-world. (shrink)

The paper revisits the old controversy over causality and determinism and argues, in the first place, that non˗deterministic theories of modern science are largely irrelevant to the philosophical issue of the causality principle. As it seems to be the ‘moral’ of the uncertainty principle, the reason why a deterministic theory cannot be applied to the description of certain physical systems is that it is impossible to capture such properties of the system, which are required by a desired theory. These properties (...) constitute what is called ‘the state’ of a system. However, the notion of a state of a system is relative: it depends on a particular theory which one would like to use to describe given kinds of phenomena. This implies that, even in the case where the desired state of a system is fundamentally impossible to be captured, neither ontological nor epistemological determinism may be excluded. Some following critical considerations are also offered with regard to the claim that uncertainty is “rooted in the things themselves”. The cradle of modern discussions about causality and determinism is, of course, quantum mechanics. Because, as it appears, a judgment on deterministic or non˗deterministic character of a theory can be made only after some interpretation of this theory has been given, the paper briefly reminds some chosen interpretations of quantum mechanics (Schrödinger's, probabilistic, statistical, Copenhagen, and the interpretation of quantum ensembles). Many of such interpretations, offered in the past, have now been rejected, and some gained more credibility than the others. Nonetheless, even the claim that indeterminism is irremovable from the description of the micro-world doesn't imply the rejection of the most general formula of the philosophical causality principle. There is no direct implication between theses of the epistemology of scientific knowledge and those of the ontology of the real world. (shrink)

Decision-making typically requires judgments about causal relations: we need to know the causal effects of our actions and the causal relevance of various environmental factors. We investigate how several individuals' causal judgments can be aggregated into collective causal judgments. First, we consider the aggregation of causal judgments via the aggregation of probabilistic judgments, and identify the limitations of this approach. We then explore the possibility of aggregating causal judgments independently of probabilistic ones. Formally, (...) we introduce the problem of causal-network aggregation. Finally, we revisit the aggregation of probabilistic judgments when this is constrained by prior aggregation of qualitative causal judgments. (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)

An algebraic interpretation of multigraph networks is introduced in relation to conscious experience, brain and body. These multigraphs have the ability to merge by an associative binary operator \(\odot \), accounting for biological composition. We also study a mathematical formulation of splitting layers, resulting in a formal analysis of the transition from conscious to non-conscious activity. From this construction, we recover core structures for conscious experience, dynamical content and causal constraints that conscious interactions may impose. An important result is (...) the prediction of structural topological changes after conscious interactions. These results may inspire further use of formal mathematics to describe and predict new features of conscious experience while aligning well with formal tries to mathematize phenomenology, phenomenological tradition and applications to artificial consciousness. (shrink)

Micro-interaction dynamics of affective sanctioning have been widely acknowledged but rarely related to the emergence of social phenomena. This paper aims to highlight the constitutive force of interaction activity by critically analysing two sociological models, Bourdieu's theory of practice and Barnes's Performative Theory of Social Institutions (PTSI). Such a comparison allows me to reveal two differing models of social phenomena currently operating in sociological debates: an extrinsic structuralist model which tacitly conveys macro-structural phenomena as prior and determinant of individuals (...) and their micro-interactions, and an intrinsic structuralism model which prioritizes individuals’ interactions and conceives them as constituting both the individual and the structural. I argue that the latter's emphasis on the dynamics of mutual susceptibility to affective sanctioning as underpinning consensus among inherently heterogeneous individuals provides a platform to further support the tenets of Interactionism and helps to expose Bourdieu's over-deterministic methodological individualism prevalent in most sociological theory. I conclude that by conceiving emotions as causal, rather than the effect of social forces, sociological theory can provide an explanation of both individual practices and systemic phenomena which resolves macro-structural tensions. In doing so, I suggest an ontological understanding of the “social” which supports the Interactionist central tenet that the local takes priority over wider structural phenomena. (shrink)

The two major modern accounts of explanation are the causal and unification accounts. My aim in this paper is to provide a kind of unification of the causal and the unification accounts, by using the central technical apparatus of the unification account to solve a central problem faced by the causal account, namely, the problem of determining which parts of a causal network are explanatorily relevant to the occurrence of an explanandum. The end product of (...) my investigation is a causal account of explanation that has many of the advantages of the unification account. (shrink)

In a series of joint papers, Teppo Felin and Nicolai J. Foss recently launched a microfoundations project in the field of strategic management. Felin and Foss observe that extant explanations in strategic management are predominantly collectivist or macro. Routines and organizational capabilities, which are supposed to be properties of firms, loom large in the field of strategic management. Routines figure as explanantia in explanations of firm behavior and firm performance, for example. Felin and Foss plead for a replacement of such (...) macro-explanations by micro-explanations. Such a replacement is needed, Felin and Foss argue, because macro-explanations are necessarily incomplete: they miss out on crucial links in the causal chain that connect macro phenomena with each other. I argue that this argument is flawed. It is based on a doubtful if not outright incorrect understanding and use of Coleman’s diagram. In a sense to be explained below, only if Coleman’s diagram is squared it can accurately account for the relations between individual action and interaction, routines and firm behavior and firm performance. Once Coleman’s diagram is squared, one can see why and how macro-explanations need not miss out on any link in the causal chains that connect macro phenomena. Micro-analyses are still needed, not to highlight and specify causal links that macro-explanations miss out on, but to check whether the many properties that are ascribed to routines in macro-explanations of firm behavior are warranted. (shrink)

This article discusses the role of moment-to-moment affect dynamics in mental disorder and aims to integrate recent literature on this topic in the context of complex dynamical system theory. First, we will review the relevance of temporal and contextual aspects of affect dynamics in relation to psychopathology. Related to this, we will discuss recent insights resulting from a network view on affect dynamics in psychopathology. Next, we explore how we can reconcile literature findings from a perspective of complex dynamical (...) system theory. Finally, future directions with regard to personalized assessment of risk and diagnostic use are discussed. (shrink)

The rise of causality and the attendant graph-theoretic modeling tools in the study of counterfactual reasoning has had resounding effects in many areas of cognitive science, but it has thus far not permeated the mainstream in linguistic theory to a comparable degree. In this study I show that a version of the predominant framework for the formal semantic analysis of conditionals, Kratzer-style premise semantics, allows for a straightforward implementation of the crucial ideas and insights of Pearl-style causal networks. I (...) spell out the details of such an implementation, focusing especially on the notions of intervention on a network and backtracking interpretations of counterfactuals. (shrink)

It has been argued that most truths about macroscopic states of affairs are entailed by a (hypothetical) complete descriptionPof the world in microscopic terms. In principle, micro-reductive explanations of non-microphysical truths could be constructeda priori.Against this claim, I show that reductive explanation requires knowledge about the phenomena to be reduced which cannot bea prioriextracted from microphysical information alone. Such reductions proceed in two steps: a “reductionR0” (“role-occupant”) establishes that a macroproperty M plays a certain causal role (specified in (...) macro-terms), while a “reductionMM” (“micro-macro”) establishes that some microproperty gives rise toM. (shrink)

Hall has recently argued that there are two concepts of causality, picking out two different kinds of causal relation. McGrath, and Hitchcock and Knobe, have recently argued that the facts about causality depend on what counts as a “default” or “normal” state, or even on the moral facts. In the light of these claims you might be tempted to agree with Skyrms that causal relations constitute, metaphysically speaking, an “amiable jumble”, or with Cartwright that ‘causation’, though a single (...) word, encompasses many different kinds of things. This paper argues, drawing on the author’s recent work on explanation, that the evidence adduced in support of causal pluralism can be accommodated easily by a unified theory of causality—a theory according to which all singular causal claims concern the same fundamental causal network. (shrink)