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)

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)

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)

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 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)

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)

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)

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 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)

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 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)

Real causal systems are complicated. Despite this, causal learning research has traditionally emphasized how causal relations can be induced on the basis of idealized events, i.e. those that have been mapped to binary variables and abstracted from time. For example, participants may be asked to assess the efficacy of a headache-relief pill on the basis of multiple patients who take the pill (or not) and find their headache relieved (or not). In contrast, the current study examines learning via interactions with (...) continuous dynamic systems, systems that include continuous variables that interact over time (and that can be continuously observed in real time by the learner). To explore such systems, we develop a new framework that represents a causal system as a network of stationary Gauss--Markov (``\OU'') processes and show how such \textit{OU networks} can express complex dynamic phenomena such as feedback loops and oscillations. To assess adult's abilities to learn such systems, we conducted an experiment in which participants were asked to identify the causal relationships of a number of OU networks, potentially carrying out multiple, temporally-extended interventions. We compared their judgments to a normative model for learning OU networks as well as a range of alternative and heuristic learning models from the literature. We found that, although participants exhibited substantial learning of such systems, they committed certain systematic errors. These successes and failures were best accounted for by a model that describes people as focusing on pairs of variables, rather than evaluating the evidence with respect to the full space of possible structural models. We argue that our approach provides both a principled framework for exploring the space of dynamic learning environments as well as new algorithmic insights into how people interact successfully with a continuous causal world. (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)

Causal reasoning is primarily concerned with what would happen to a system under external interventions. In particular, we are often interested in predicting the probability distribution of some random variables that would result if some other variables were forced to take certain values. One prominent approach to tackling this problem is based on causal Bayesian networks, using directed acyclic graphs as causal diagrams to relate post-intervention probabilities to pre-intervention probabilities that are estimable from observational data. However, such causal diagrams are (...) seldom fully testable given observational data. In consequence, many causal discovery algorithms based on data-mining can only output an equivalence class of causal diagrams. This paper is concerned with causal reasoning given an equivalence class of causal diagrams, represented by a ancestral graph. We present two main results. The first result extends Pearl 's celebrated do-calculus to the context of ancestral graphs. In the second result, we focus on a key component of Pearl's calculus---the property of invariance under interventions, and give stronger graphical conditions for this property than those implied by the first result. The second result also improves the earlier, similar results due to Spirtes et al. (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)

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)

This article compares causal and constitutive explanation. While scientific inquiry usually addresses both causal and constitutive questions, making the distinction is crucial for a detailed understanding of scientific questions and their interrelations. These explanations have different kinds of explananda and they track different sorts of dependencies. Constitutive explanations do not address events or behaviors, but causal capacities. While there are some interesting relations between building and causal manipulation, causation and constitution are not to be confused. Constitution is a synchronous and (...) asymmetric relation between relata that cannot be conceived as independent existences. However, despite their metaphysical differences, the same key ideas about explanation largely apply to both. Causal and constitutive explanations face similar challenges (such as the problems of relevance and explanatory regress) and both are in the business of mapping networks of counterfactual dependence—i.e. mechanisms—although the relevant counterfactuals are of a different sort. In the final section the issue of developmental explanation is discussed. It is argued that developmental explanations deserve their own place in taxonomy of explanations, although ultimately developmental dependencies can be analyzed as combinations of causal and constitutive dependencies. Hence, causal and constitutive explanation are distinct, but not always completely separate forms of explanation. (shrink)

It is widely thought that causal cognition underpins technical reasoning. Here we suggest that understanding causal cognition as a thinking system that includes theory of mind (i.e., social cognition) can be a productive theoretical tool for the field of evolutionary cognitive archaeology. With this contribution, we expand on an earlier model that distinguishes seven grades of causal cognition, explicitly presenting it together with a new analysis of the theory of mind involved in the different grades. We then suggest how such (...) thinking may manifest in the archaeological or stone tool record and techno-behaviors of the last three million years or so. Our thesis is threefold: (a) theory of mind is an integral element of causal cognition; (b) generally speaking, the more advanced causal cognition is, the more it is dependent on theory of mind; and (c) the evolution of causal cognition depends more and more on mental representations of hidden variables. Ultimately, the final or seventh grade of causal cognition allows us to reason from a network of hidden variables that, amongst other things, enables the learning, manufacture, and use of complex technological systems. It also facilitates the seamless mapping of knowledge between personal (egocentric), physical, and social networks that allows for newly devised and innovative technical and social outcomes. (shrink)

The thesis explores and suggests a solution to a problem that I identify in John McDowell’s and Lynne Rudder Baker’s approaches to mental and intention-dependent (ID) causation in the physical world. I begin (chapter 1) with a brief discussion of McDowell’s non-reductive and anti-scientistic account of mind and world, which I believe offers, through its vision of the unbounded conceptual and the world as within the space of reasons, to liberate and renew philosophy. However, I find an inconsistency in McDowell’s (...) criticism of Davidson’s anomalous monism (chapter 2), stemming from a tension between McDowell’s naïve common sense view of mental causation and an understanding of ordinary physical causation which I think McDowell ought to embrace, which portrays it as both objective, in the sense of being recognition- independent, and as belonging within the space of reasons. The question of the relation between these two concepts of causation is an aspect of the more general question of the relation between the space of reasons and the realm of law. In chapter 3 I begin examining the possibility that Baker’s account of material and property constitution could form the basis of a bridge between the two spaces, and find it generally promising. However, I find that her defence of her version of non-reductive monism against Kim’s causal arguments also runs into problems, which I attribute to the fact that she holds a view of causation as secondary to causal explanation. 2 3 In chapter 4 I develop an account of what I call manifest physical causation – of objective causal relations in the world of Sellars’s manifest image. Based upon McDowell’s transcendental empiricism, which takes the existence of the ordinary perceived world as a condition of the possibility of our possession of conceptual capacities, I contend that it is this picture of reality, rather than that of fundamental physics, that should be the starting point of our theorizing. Causation in the manifest image, I argue, covers the behaviour of the familiar physical world as well as that of its well-understood extensions into the special sciences and engineering. Manifest physical causation, on my account, is productive, acts through mechanisms which are almost entirely mechanical, electromagnetic, and/ or chemical, and is causally closed. In my view, normative, semantic, contentful property-instances are not part of the manifest physical causal nexus. In my final chapter I suggest a modification of Baker’s constitution account, which I call Constituted Causation, whereby higher-level – mental and other ID – causal relations are constituted, in favourable circumstances, by lower-level ones. ID causal relations belong in their own causal nexus but are connected to the manifest physical world through constitution, a relation of unity without identity. Causation and constitution are, respectively, intra- and inter-level relations, and they are non-overlapping. The constituted network of rational and normative relations bears, I believe, striking parallels with McDowell’s view of reality. According to Baker’s view of constitution, the essential properties of constituted entities subsume those of their constituters; extending this to my account enables us to say that the real cause and explanation of someone’s action is that they consciously performed it 4 rather than that certain manifest causal processes occurred at the lower level. (shrink)

Using Bayesian network causal models, we provide a simple general account of probabilistic causal interaction. We also detail problems in the leading accounts by Ellery Eells, and any others which require valence reversals, contextual unanimity, or average effects.

Independence of Conditionals (IC) has recently been proposed as a basic rule for causal structure learning. If a Bayesian network represents the causal structure, its Conditional Probability Distributions (CPDs) should be algorithmically independent. In this paper we compare IC with causal faithfulness (FF), stating that only those conditional independences that are implied by the causal Markov condition hold true. The latter is a basic postulate in common approaches to causal structure learning. The common spirit of FF and IC is (...) to reject causal graphs for which the joint distribution looks ‘non-generic’. The difference lies in the notion of genericity: FF sometimes rejects models just because one of the CPDs is simple, for instance if the CPD describes a deterministic relation. IC does not behave in this undesirable way. It only rejects a model when there is a non-generic relation between different CPDs although each CPD looks generic when considered separately. Moreover, it detects relations between CPDs that cannot be captured by conditional independences. IC therefore helps in distinguishing causal graphs that induce the same conditional independences (i.e., they belong to the same Markov equivalence class). The usual justification for FF implicitly assumes a prior that is a probability density on the parameter space. IC can be justified by Solomonoff’s universal prior, assigning non-zero probability to those points in parameter space that have a finite description. In this way, it favours simple CPDs, and therefore respects Occam’s razor. Since Kolmogorov complexity is uncomputable, IC is not directly applicable in practice. We argue that it is nevertheless helpful, since it has already served as inspiration and justification for novel causal inference algorithms. (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)

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)

Social network analysis (SNA) is an increasingly popular approach that provides researchers with highly developed tools to map and analyze complexes of social relations. Although a number of network scholars have explicated the assumptions that underpin SNA, the approach has yet to be discussed in relation to established philosophies of science. This article argues that there is a tension between applied and methods-oriented SNA studies, on the one hand, and those addressing the social-theoretical nature and implications of networks, (...) on the other. The former, in many cases, exhibits positivist tendencies, whereas the latter incorporate a number of assumptions that are directly compatible with core critical realist views on the nature of social reality and knowledge. This article suggests that SNA may be detached from positivist social science and come to constitute a valuable instrument in the critical realist toolbox. (shrink)

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

Artificial Neural Networks have reached “grandmaster” and even “super-human” performance across a variety of games, from those involving perfect information, such as Go, to those involving imperfect information, such as “Starcraft”. Such technological developments from artificial intelligence (AI) labs have ushered concomitant applications across the world of business, where an “AI” brand-tag is quickly becoming ubiquitous. A corollary of such widespread commercial deployment is that when AI gets things wrong—an autonomous vehicle crashes, a chatbot exhibits “racist” behavior, automated credit-scoring processes (...) “discriminate” on gender, etc.—there are often significant financial, legal, and brand consequences, and the incident becomes major news. As Judea Pearl sees it, the underlying reason for such mistakes is that “... all the impressive achievements of deep learning amount to just curve fitting.” The key, as Pearl suggests, is to replace “reasoning by association” with “causal reasoning” —the ability to infer causes from observed phenomena. It is a point that was echoed by Gary Marcus and Ernest Davis in a recent piece for theNew York Times: “we need to stop building computer systems that merely get better and better at detecting statistical patterns in data sets—often using an approach known as ‘Deep Learning’—and start building computer systems that from the moment of their assembly innately grasp three basic concepts: time, space, and causality.” In this paper, foregrounding what in 1949 Gilbert Ryle termed “a category mistake”, I will offer an alternative explanation for AI errors; it is not so much that AI machinery cannot “grasp” causality, but that AI machinery (qua computation) cannot understand anything at all. (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)

With the advent of evolutionary developmental research, or EvoDevo, there is hope of discovering the roles that the genetic bases of development play in morphological evolution. Studies in EvoDevo span several levels of organismal organization. Low-level studies identify the ultimate genetic changes responsible for morphological variation and diversity. High-level studies of development focus on how genetic differences affect the dynamics of gene networks and epigenetic interactions to modify morphology. Whereas an increasing number of studies link independent acquisition of homoplastic or (...) convergent morphologies to similar changes in the genomes, homoplasies are not always found to have identical low-level genetic underpinnings. This suggests that a combination of low- and high-level approaches may be useful in understanding the relationship between genetic and morphological variation. Therefore, as an empirical and conceptual framework, we propose the causality horizon to signify the lowest level that allows linking homoplastic morphologies to similar changes in the development. A change in a system below the causality horizon cannot be generalized. In more concrete terms, homoplastic morphologies cannot be reduced to the same change in gene regulation when that change occurs below the causality horizon; rather, a higher-level mechanism should be identified. (shrink)

A neuroscience-based approach has recently been proposed for the relation between the mind and the brain. The proposal is that events at the sub-neuronal, neuronal, and neuronal network levels take place simultaneously to perform a computation that can be described at a high level as a mental state, with content about the world. It is argued that as the processes at the different levels of explanation take place at the same time, they are linked by a non-causal supervenient relationship: (...) causality can best be described in brains as operating within but not between levels. This mind-brain theory allows mental events to be different in kind from the mechanistic events that underlie them; but does not lead one to argue that mental events cause brain events, or vice versa: they are different levels of explanation of the operation of the computational system. Here, some implications are developed. It is proposed that causality, at least as it applies to the brain, should satisfy three conditions. First, interventionist tests for causality must be satisfied. Second, the causally related events should be at the same level of explanation. Third, a temporal order condition must be satisfied, with a suitable time scale in the order of 10 ms (to exclude application to quantum physics; and a cause cannot follow an effect). Next, although it may be useful for different purposes to describe causality involving the mind and brain at the mental level, or at the brain level, it is argued that the brain level may sometimes be more accurate, for sometimes causal accounts at the mental level may arise from confabulation by the mentalee, whereas understanding exactly what computations have occurred in the brain that result in a choice or action will provide the correct causal account for why a choice or action was made. Next, it is argued that possible cases of “downward causation” can be accounted for by a within-levels-of-explanation account of causality. This computational neuroscience approach provides an opportunity to proceed beyond Cartesian dualism and physical reductionism in considering the relations between the mind and the brain. (shrink)

I argue for an inferentialist account of the meaning of causal claims, which draws on the writings of Sellars and Brandom. The account is meant to be widely applicable. In this work, it is motivated and defended with reference to complex systems sciences, i.e., sciences that study the behaviour of systems with many components interacting at various levels of organisation (e.g. cells, brain, social groups). Here are three, seemingly-uncontroversial platitudes about causality. (1) Causal relations are objective, mind-independent relations and, (...) as such, analysable in objective, mind-independent terms. (2) There is a tight connection between our practice of predicting, explaining and controlling phenomena, and the use of causal notions. (3) The second platitude should be explained in terms of the first. Contrary to this widely-held stance, I suggest that we reverse the order of analysis, by taking our activities of agents as the raw material in terms of which to account for the obtaining of causal relations. To this end, I propose and defend an inferentialist account of causality. Causality is a ‘category’ that the knowing subject employs to ‘mediate’ between himself and the world. In inferentialist terms, this mediation is the result of the concept of cause figuring in a network of inferences, used in our practice of gathering evidence and using it to explain, predict and intervene. Complexity only makes the mediation more difficult, thereby rendering the meaning of causality more evident. (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 this paper, through research and analysis of the communication network of the physical activity monitoring system, we combine wearable technology and identification technology and design a physical health monitoring bracelet that integrates multifaceted physical data collection and effective identity matching function. We match the identity through the chip and collect the physical fitness data generated in the process of exercise and centralized test by the sensor in real-time. Finally, the data transmission is realized through the WIFI communication function (...) to achieve the purpose of monitoring physical exercise and improving physical quality. To ensure the continuity and stability of information transmission, the joint transmission method of direct transmission and indirect transmission is essential. Besides, considering the energy causality limitation of sensor nodes and relay nodes, a collaborative transmission model of wireless body area network based on wireless cognitive network is constructed. And, a power allocation algorithm based on maximum ratio merging and wireless cognitive network is proposed, which puts forward a new idea for the future research of wireless body area network resource allocation. (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)

The The sustainability of social life is based on social values. The manifestation of these values also takes place within the society. On the other hand, the realization of individuals is possible in society because a non-social human being is not a "human" in the philosophical sense. With human-oriented conditions that manifest themselves in the network of social relations, the criteria (moral values) related to the purpose of creation crystallize and become known so that the construction of a social (...) paradigm becomes possible. However, the society in which individuals live is also surrounded by an external world (material world). For this reason, the social paradigm also indirectly relies on a perception of the outside world. Thus, no matter what period of history, people's endeavor for an ideal society is accompanied by an interpretation of the external world, and with this "cumulative" knowledge, a "reducing" simulation of being is derived (cosmology). In fact, causality, which is the subject of physics, becomes the subject of philosophy in this mediation. As it can be understood, a significant problem here is the correlation between the fixed values (unconditional imperatives) that form the core of the social paradigm and the variable external world knowledge that is culturally effective in the manifestation of these values. Consequently, while determining the rules in the social order, one should act on the basis of values, not as if they have absolute knowledge of the material world. Whether we believe that the universe consists of "seven heavens" and is geocentric or heliocentric; Existential values such as justice, equality, and freedom are essential regardless of time and space. However, as historical facts show, those who have problems with this value system, which is accompanied by an interpretation of the external world, have raised objections through ever-developing knowledge of nature instead of universal values. For this reason, the geocentric perception of the universe, which forms the basis of the political paradigm in the class-based society of medieval Europe, was strongly desired to be maintained by the ruling classes. Ultimately, although this model of the universe collapsed, moral pursuits of human life continued to be sabotaged for the same political reasons, and disinformation began. Thus, scientific studies and the search for the truth (philosophy) were accompanied by endless polemics disguised as "philosophy." In fact, this situation reveals two opposing attitudes that repeat themselves in history, either in the East or the West: paradigmatic attitude (paradigmism) and enigmatic attitude (enigmatism). As a reflection of these two attitudes, the dominant discourse, which categorically accuses even the interest in understanding Western modernization in the Islamic world as heresy, did not occasionally hesitate to refer to some Western references by confirming with a vulgar pragmatism. For instance, Newtonian physics has been ignored. On the other hand, an attempt has been made to jump directly from Aristotelian physics to quantum physics by assuming an overlapping in classical texts. However, facing the new knowledge of nature has more actual consequences than polemics. To face these results and to show the legitimacy of Islamic values in the modern era, which are in conformity with the creation of human beings, the developments in the current knowledge of the universe (modern cosmology) cannot be ignored. This endeavor can be possible only when the philosophical and historical foundations of the subject are examined throughout the Western experience. (shrink)

Network neuroscience represents the brain as a collection of regions and inter-regional connections. Given its ability to formalize systems-level models, network neuroscience has generated unique explanations of neural function and behavior. The mechanistic status of these explanations and how they can contribute to and fit within the field of neuroscience as a whole has received careful treatment from philosophers. However, these philosophical contributions have not yet reached many neuroscientists. Here we complement formal philosophical efforts by providing an applied (...) perspective from and for neuroscientists. We discuss the mechanistic status of the explanations offered by network neuroscience and how they contribute to, enhance, and interdigitate with other types of explanations in neuroscience. In doing so, we rely on philosophical work concerning the role of causality, scale, and mechanisms in scientific explanations. In particular, we make the distinction between an explanation and the evidence supporting that explanation, and we argue for a scale-free nature of mechanistic explanations. In the course of these discussions, we hope to provide a useful applied framework in which network neuroscience explanations can be exercised across scales and combined with other fields of neuroscience to gain deeper insights into the brain and behavior. (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)

This article is organized in four sections. The first section introduces sensing governance in terms of the governance of effects rather than causation, focusing on the work of Bruno Latour in establishing the problematic of contingent interaction, rather than causal depth, as key to emergent effects, which can be unexpected and catastrophic. The second section considers in more depth how sensing governance enables politics by other means through putting greater emphasis on relations of interaction, rather than on ontologies of being, (...) and links the methodological approach of sensing governance closely to actor network assumptions that disavow structures of causation. The final two sections analyze how correlation works to reveal new agencies and processes of emergence and how new technologies have been deployed in this area, providing some examples of how the shift from causal relations to sensing effects has begun to alter governmental approaches. (shrink)