Complex organisms thwart the simple rectilinear causality paradigm of “necessary and sufficient,” with its experimental strategy of “knock down and overexpress.” This Essay organizes the eccentricities of biology into four categories that call for new mathematical approaches; recaps for the biologist the philosopher's recent refinements to the causation concept and the mathematician's computational tools that handle some but not all of the biological eccentricities; and describes overlooked insights that make causal properties of physical hierarchies such as emergence and downward causation (...) straightforward. Reviewing and extrapolating from similar situations in physics, it is suggested that new mathematical tools for causation analysis incorporating feedback, signal cancellation, nonlinear dependencies, physical hierarchies, and fixed constraints rather than instigative changes will reveal unconventional biological behaviors. These include “eigenisms,” organisms that are limited to quantized states; trajectories that steer a system such as an evolving species toward optimal states; and medical control via distributed “sheets” rather than single control points. (shrink)

A standard view in social science and philosophy is that a lie is a dishonest assertion: to lie is to assert something that you think is false in order to deceive your audience. We report four behavioral experiments designed to evaluate some aspects of this view. Participants read short scenarios and judged several features of interest, including whether an agent lied. We found evidence that ordinary lie attributions can be influenced by aspects of audience uptake, are based on judging that (...) the agent made an assertion (assertion attributions), and, at least in some contexts, are not based on attributions of deceptive intent. The finding on assertion attributions is predicted by the standard view, but the finding on intent attributions is not. These results help to further clarify the ordinary concept of lying and shed light on the psychological processes involved in ordinary lie attributions and related judgments. (shrink)

Ecological economics is an interdisciplinary science that is primarily concerned with developing interventions to achieve sustainable ecological and economic systems. While ecological economists have, over the last few decades, made various empirical, theoretical, and conceptual advancements, there is one concept in particular that remains subject to confusion: critical natural capital. While critical natural capital denotes parts of the environment that are essential for the continued existence of our species, the meaning of terms commonly associated with this concept, such as ‘non-substitutable’ (...) and ‘impossible to substitute,’ require a clearer formulation then they tend to receive. With the help of equations and graphs, this article develops new definite account of critical natural capital that makes explicit what it means for objective environmental conditions to be essential for continued existence. The second main part of this article turns to the question of formally modeling the priority of conserving critical natural capital. While some ecological economists have maintained that, beyond a certain threshold, critical natural capital possesses absolute infinite value, absolute infinite utility models encounter significant problems. This article shows that a relative infinite utility model provides a better way to model the priority of conserving critical natural capital. (shrink)

As the demand for explainable deep learning grows in the evaluation of language technologies, the value of a principled grounding for those explanations grows as well. Here we study the state-of-the-art in explanation for neural models for natural-language processing (NLP) tasks from the viewpoint of philosophy of science. We focus on recent evaluation work that finds brittleness in explanations obtained through attention mechanisms.We harness philosophical accounts of explanation to suggest broader conclusions from these studies. From this analysis, we assert the (...) impossibility of causal explanations from attention layers over text data. We then introduce NLP researchers to contemporary philosophy of science theories that allow robust yet non-causal reasoning in explanation, giving computer scientists a vocabulary for future research. (shrink)

Philosophical theories of evidence have been on offer, but they are mostly evaluated in terms of all-or-none desiderata — if they fail to meet one of the desiderata, they are not a satisfactory theory. In this thesis, I aim to accomplish three missions. Firstly, I construct a new way of evaluating theories of evidence, which I call a tool-based view. Secondly, I analyse the nature of what I will call the various relevance-mediating vehicles that each theory of evidence employs. Thirdly, (...) I articulate the comparative core of evidential reasoning in the historical sciences, one which is overlooked in major theories of evidence. On the first mission, I endorse a meta-thesis of pluralism on theories of evidence, namely a tool-based view. I regard a theory of evidence as a purpose-specific and setting-sensitive tool which has its own strengths, difficulties and limitations. Among the major theories of evidence I have reviewed, I focus on Achinstein’s explanationist theory, Cartwright’s argument theory and Reiss’s inferentialist account, scrutinising and evaluating them against the purposes they set out and the scope of their applications. On the second mission, I note that there is no such thing as intrinsically ‘being evidence’. Rather, I hold that relevance-mediating vehicles configure data, materials or claims in such ways that some of them are labelled evidence. I identify the relevance-mediating vehicles that the theories of evidence employ. On the final mission, I argue that the likelihoodist account is an appropriate tool for explaining the evidential reasoning in poorly specified settings where likelihoods can be only imprecisely compared. Such settings, I believe, are typical in the historical sciences. Using the reconstruction of proto-sounds in historical linguistics as a case study, I formalise the rationale behind it by means of the law of likelihood. (shrink)

Complex organisms thwart the simple rectilinear causality paradigm of “necessary and sufficient,” with its experimental strategy of “knock down and overexpress.” This Essay organizes the eccentricities of biology into four categories that call for new mathematical approaches; recaps for the biologist the philosopher's recent refinements to the causation concept and the mathematician's computational tools that handle some but not all of the biological eccentricities; and describes overlooked insights that make causal properties of physical hierarchies such as emergence and downward causation (...) straightforward. Reviewing and extrapolating from similar situations in physics, it is suggested that new mathematical tools for causation analysis incorporating feedback, signal cancellation, nonlinear dependencies, physical hierarchies, and fixed constraints rather than instigative changes will reveal unconventional biological behaviors. These include “eigenisms,” organisms that are limited to quantized states; trajectories that steer a system such as an evolving species toward optimal states; and medical control via distributed “sheets” rather than single control points. (shrink)

How do people judge the degree of causal responsibility that an agent has for the outcomes of her actions? We show that a relatively unexplored factor -- the robustness of the causal chain linking the agent’s action and the outcome -- influences judgments of causal responsibility of the agent. In three experiments, we vary robustness by manipulating the number of background circumstances under which the action causes the effect, and find that causal responsibility judgments increase with robustness. In the first (...) experiment, the robustness manipulation also raises the probability of the effect given the action. Experiments 2 and 3 control for probability-raising, and show that robustness still affects judgments of causal responsibility. In particular, Experiment 3 introduces an Ellsberg type of scenario to manipulate robustness, while keeping the conditional probability and the skill deployed in the action fixed. Experiment 4, replicates the results of Experiment 3, while contrasting between judgments of causal strength and of causal responsibility. The results show that in all cases, the perceived degree of responsibility increases with the robustness of the action-outcome causal chain. (shrink)

The logic of genetic discovery has changed little over time, but the focus of biology is shifting from simple genotype–phenotype relationships to complex metabolic, physiological, developmental, and behavioral traits. In light of this, the traditional reductionist view of individual genes as privileged difference‐making causes of phenotypes is re‐examined. The scope and nature of genetic effects in complex regulatory systems, in which dynamics are driven by regulatory feedback and hierarchical interactions across levels of organization are considered. This review argues that it (...) is appropriate to treat genes as specific actual difference‐makers for the molecular regulation of gene expression. However, they are often neither stable, proportional, nor specific as causes of the overall dynamic behavior of regulatory networks. Dynamical models, properly formulated and validated, provide the tools to probe cause‐and‐effect relationships in complex biological systems, allowing to go beyond the limitations of genetic reductionism to gain an integrative understanding of the causal processes underlying complex phenotypes. (shrink)

How do we make causal judgments? Many studies have demonstrated that people are capable causal reasoners, achieving success on tasks from reasoning to categorization to interventions. However, less is known about the mental processes used to achieve such sophisticated judgments. We propose a new process model—the mutation sampler—that models causal judgments as based on a sample of possible states of the causal system generated using the Metropolis–Hastings sampling algorithm. Across a diverse array of tasks and conditions encompassing over 1,700 participants, (...) we found that our model provided a consistently closer fit to participant judgments than standard causal graphical models. In particular, we found that the biases introduced by mutation sampling accounted for people's consistent, predictable errors that the normative model by definition could not. Moreover, using a novel experimental methodology, we found that those biases appeared in the samples that participants explicitly judged to be representative of a causal system. We conclude by advocating sampling methods as plausible process‐level accounts of the computations specified by the causal graphical model framework and highlight opportunities for future research to identify not just what reasoners compute when drawing causal inferences, but also how they compute it. (shrink)

This paper aims to provide two abductive considerations adducing in favor of the thesis of Necessitism in modal ontology. I demonstrate how instances of the Barcan formula can be witnessed, when the modal operators are interpreted 'naturally' -- i.e., as including geometric possibilities -- and the quantifiers in the formula range over a domain of natural, or concrete, entities and their contingently non-concrete analogues. I argue that, because there are considerations within physics and metaphysical inquiry which corroborate modal relationalist claims (...) concerning the possible geometric structures of spacetime, and dispositional properties are actual possible entities, the condition of being grounded in the concrete is consistent with the Barcan formula; and thus -- in the geometric setting -- merits adoption by the Necessitist. (shrink)

The principle of the common cause demands that every pair of causally independent but statistically correlated events should be the effect of a common cause. This demand is often supplemented with the requirement that said cause should screen-off the two events from each other. This paper introduces a new probabilistic model for common causes, which generalises this requirement to include sets of distinct but non-disjoint causes. It is demonstrated that the model hereby proposed satisfies the explanatory function generally attributed to (...) common causes and an existential proof is offered. Finally, the model is compared to other known structures intended to perform a similar function, such as Reichenbachian Common Cause Systems and Bayesian Networks. (shrink)

According to Adams, the acceptability of an indicative conditional goes with the conditional probability of the consequent given the antecedent. However, some conditionals seem to be inappropriate, although their corresponding conditional probability is high. These are cases with a missing link between antecedent and consequent. Other conditionals are appropriate even though the conditional probability is low. Finally, we have the so-called biscuit conditionals. In this paper we will generalize analyses of Douven and others to account for the appropriateness of conditionals (...) in terms of evidential support. Our generalization involves making use of Value, or intensity. We will show how this generalization helps to account for biscuit conditionals and conditional threats and promises. Finally, a link is established between this analysis of conditionals and an analysis of generic sentences. (shrink)

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

Schurz (Synthese 164:201–234, 2008) proposed a justification of creative abduction on the basis of the Reichenbachian principle of the common cause. In this paper we take up the idea of combining creative abduction with causal principles and model instances of successful creative abduction within a Bayes net framework. We identify necessary conditions for such inferences and investigate their unificatory power. We also sketch several interesting applications of modeling creative abduction Bayesian style. In particular, we discuss use-novel predictions, confirmation, and the (...) problem of underdetermination in the context of abductive inferences. (shrink)

An intervention may have an effect on units other than those to which it was administered. This phenomenon is called interference and it usually goes unmodeled. In this paper, we propose to combine Lauritzen-Wermuth-Frydenberg and Andersson-Madigan-Perlman chain graphs to create a new class of causal models that can represent both interference and non-interference relationships for Gaussian distributions. Specifically, we define the new class of models, introduce global and local and pairwise Markov properties for them, and prove their equivalence. We also (...) propose an algorithm for maximum likelihood parameter estimation for the new models, and report experimental results. Finally, we show how to compute the effects of interventions in the new models. (shrink)

We propose a formal framework for interpretable machine learning. Combining elements from statistical learning, causal interventionism, and decision theory, we design an idealised explanation game in which players collaborate to find the best explanation for a given algorithmic prediction. Through an iterative procedure of questions and answers, the players establish a three-dimensional Pareto frontier that describes the optimal trade-offs between explanatory accuracy, simplicity, and relevance. Multiple rounds are played at different levels of abstraction, allowing the players to explore overlapping causal (...) patterns of variable granularity and scope. We characterise the conditions under which such a game is almost surely guaranteed to converge on a optimal explanation surface in polynomial time, and highlight obstacles that will tend to prevent the players from advancing beyond certain explanatory thresholds. The game serves a descriptive and a normative function, establishing a conceptual space in which to analyse and compare existing proposals, as well as design new and improved solutions. (shrink)

This article describes a heuristic argument for understanding certain physical systems in terms of properties that resemble the beliefs and goals of folk psychology. The argument rests on very simple assumptions. The core of the argument is that predictions about certain events can legitimately be based on assumptions about later events, resembling Aristotelian ‘final causation’; however, more nuanced causal entities must be introduced into these types of explanation in order for them to remain consistent with a causally local Universe.

Biased research occurs frequently in the sciences. In this paper, I will focus on one particular kind of biased research: research that is subject to sponsorship bias. I will address the following epistemological question: what precisely is epistemically wrong with biased research of this kind? I will defend the evidential account of epistemic wrongness: that is, research affected by sponsorship bias is epistemically wrong if and only if the researchers in question make false claims about the evidential support of some (...) hypothesis H by data E. I will argue that the evidential account captures the epistemic wrongness of three paradigmatic types of sponsorship bias. (shrink)

What would it be for a process to happen backwards in time? Would such a process involve different causal relations? It is common to understand the time-reversal invariance of a physical theory in causal terms, such that whatever can happen forwards in time can also happen backwards in time. This has led many to hold that time-reversal symmetry is incompatible with the asymmetry of cause and effect. This article critiques the causal reading of time reversal. First, I argue that the (...) causal reading requires time-reversal-related models to be understood as representing distinct possible worlds and, on such a reading, causal relations are compatible with time-reversal symmetry. Second, I argue that the former approach does, however, raise serious sceptical problems regarding the causal relations of paradigm causal processes and as a consequence there are overwhelming reasons to prefer a non-causal reading of time reversal, whereby time reversal leaves causal relations invariant. On the non-causal reading, time-reversal symmetry poses no significant conceptual nor epistemological problems for causation. _1_ Introduction _1.1_ The directionality argument _1.2_ Time reversal _2_ What Does Time Reversal Reverse? _2.1_ The B- and C-theory of time _2.2_ Time reversal on the C-theory _2.3_ Answers _3_ Does Time Reversal Reverse Causal Relations? _3.1_ Causation, billiards, and snooker _3.2_ The epistemology of causal direction _3.3_ Answers _4_ Is Time-Reversal Symmetry Compatible with Causation? _4.1_ Incompatibilism _4.2_ Compatibilism _4.3_ Answers _5_ Outlook. (shrink)

Richard Bradley’s landmark book Decision Theory with a Human Face makes seminal contributions to nearly every major area of decision theory, as well as most areas of formal epistemology and many areas of semantics. In addition to sketching Bradley’s distinctive semantics for conditional beliefs and desires, I will explain his theory of conditional desire, focusing particularly on his claim that we should not desire events, either positively or negatively, under the supposition that they will occur. I shall argue, to the (...) contrary, that permitting non-trivial desirabilities for events whose occurrence is known or assumed is both more intuitively plausible and more theoretically fruitful than Bradley’s approach. In the course of the discussion I will contrast Bradley’s broadly evidentialist picture of decision theory with my own more orthodox causal approach. (shrink)

Suppose that A explains B. Do A and B need to be true? Provided that we have metaphysical explanation in mind, orthodoxy answers “yes:” metaphysical explanation is factive. This article introduces and defends a non-factive notion of metaphysical explanation. I argue that we need a non-factive notion of explanation in order to make sense of explanationist arguments where we motivate a view by claiming that it offers better explanations than its competitors. After presenting and rejecting some initially plausible rivals, I (...) account for non-factive metaphysical explanation by drawing on existing applications of structural equation models to metaphysical grounding. (shrink)

Philosophical works on actual causation make wide use of thought experiments. The principal aim of this paper is to show how thought experiments are used in the contemporary debate over actual causation and to discuss their role in relation to formal approaches in terms of causal models. I claim that a recourse to thought experiments is not something old fashioned or superseded by abstract models, but it is useful to interpret abstract models themselves and to use our intuitions to judge (...) the results of the model. Recent research on actual causation has stressed the importance of integrating formal models with some notion of normality; I suggest that thought experiments can be useful in eliciting intuitions where normality is not intended in a statistical sense. The first expository part gives a short presentation of the notion of actual causation, summarising some typical problems of counterfactual approaches and how they are treated in causal and structural models. The second part focuses on the problems of model isomorphism and criticises some radical ideas opposing the role of thought experiments, claiming that they may also be of use in evaluating formal models. (shrink)

In a very short time, it is likely that we will identify many of the genetic variants underlying individual differences in intelligence. We should be prepared for the possibility that these variants are not distributed identically among all geographic populations, and that this explains some of the phenotypic differences in measured intelligence among groups. However, some philosophers and scientists believe that we should refrain from conducting research that might demonstrate the (partly) genetic origin of group differences in IQ. Many scholars (...) view academic interest in this topic as inherently morally suspect or even racist. The majority of philosophers and social scientists take it for granted that all population differences in intelligence are due to environmental factors. The present paper argues that the widespread practice of ignoring or rejecting research on intelligence differences can have unintended negative consequences. Social policies predicated on environmentalist theories of group differences may fail to achieve their aims. Large swaths of academic work in both the humanities and social sciences assume the truth of environmentalism and are vulnerable to being undermined. We have failed to work through the moral implications of group differences to prepare for the possibility that they will be shown to exist. (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)

“Explanation” refers to a wide range of activities, with a family resemblance between them. Most satisfactory explanations in a discipline for a domain fail to satisfy some general desiderata, while fulfilling others. This can happen in various ways. Why? An idealizing response would be to say that in real science explanations fall short along some dimensions, so that for any explanatory failure there is a conceivable improvement that addresses its shortcomings. The improvement may be more accurate causally or possess more (...) unifying power, or deliver deeper understanding. We formulate a drastically less idealizing response. We argue that there are typically trade-offs in explanation, so that in strengthening one explanatory virtue one will usually weaken another. Scientific explanations, if this is correct, are constrained by such trade-offs. Particular trade-offs are appropriate for particular explanatory vehicles. There are the overarching equations of theoretical physics, which produce unification at the expense of causal detail; there are theoretical models of phenomena that occupy a middle ground between generality and the detailed workings of particular cases and get closer to explaining the workings of specific systems at the expense of unification. Sometimes experiments aim at general causal patterns at the expense of particular detail; and sometimes they are designed to give us information of particular detail at the expense of generality. There are further trade-offs associated with other vehicles of explanation. We provide examples from physics and biology. (shrink)

Philosophical works on actual causation make wide use of thought experiments. The principal aim of this paper is to show how thought experiments are used in the contemporary debate over actual causation and to discuss their role in relation to formal approaches in terms of causal models. I claim that a recourse to thought experiments is not something old fashioned or superseded by abstract models, but it is useful to interpret abstract models themselves and to use our intuitions to judge (...) the results of the model. Recent research on actual causation has stressed the importance of integrating formal models with some notion of normality; I suggest that thought experiments can be useful in eliciting intuitions where normality is not intended in a statistical sense. The first expository part gives a short presentation of the notion of actual causation, summarising some typical problems of counterfactual approaches and how they are treated in causal and structural models. The second part focuses on the problems of model isomorphism and criticises some radical ideas opposing the role of thought experiments, claiming that they may also be of use in evaluating formal models. (shrink)

The paper discusses from a metaphysical standpoint the nature of the dependence relation underpinning the talk of mutual action between material and spatiotemporal structures in general relativity. It is shown that the standard analyses of dependence in terms of causation or grounding are ill-suited for the general relativistic context. Instead, a non-standard analytical framework in terms of structural equation modeling is exploited, which leads to the conclusion that the kind of dependence encoded in the Einstein field equations is a novel (...) one. (shrink)

El objetivo del presente artículo es proponer una nueva interpretación del concepto de información en contextos comunicacionales: una interpretación físicocausal. Apelando a las teorías manipulabilistas de la causación, principalmente en su versión intervencionista, buscaremos mostrar que la información comunicacional es una propiedad física que podemos manipular para generar situaciones comunicacionales. Este enfoque nos permite entender la naturaleza de la comunicación como estructura causal, puesta de manifiesto mediante criterios manipulabilistas. Confiamos en que el enfoque propuesto logra superar ciertas dificultades de las (...) interpretaciones de la información actualmente vigentes en la literatura. (shrink)

The epistemic probability of A given B is the degree to which B evidentially supports A, or makes A plausible. This paper is a first step in answering the question of what determines the values of epistemic probabilities. I break this question into two parts: the structural question and the substantive question. Just as an object’s weight is determined by its mass and gravitational acceleration, some probabilities are determined by other, more basic ones. The structural question asks what probabilities are (...) not determined in this way—these are the basic probabilities which determine values for all other probabilities. The substantive question asks how the values of these basic probabilities are determined. I defend an answer to the structural question on which basic probabilities are the probabilities of atomic propositions conditional on potential direct explanations. I defend this against the view, implicit in orthodox mathematical treatments of probability, that basic probabilities are the unconditional probabilities of complete worlds. I then apply my answer to the structural question to clear up common confusions in expositions of Bayesianism and shed light on the “problem of the priors.”. (shrink)

There has been a growing trend to include non-causal models in accounts of scientific explanation. A worry addressed in this paper is that without a higher threshold for explanation there are no tools for distinguishing between models that provide genuine explanations and those that provide merely potential explanations. To remedy this, a condition is introduced that extends a veridicality requirement to models that are empirically underdetermined, highly-idealised, or otherwise non-causal. This condition is applied to models of electroweak symmetry breaking beyond (...) the Standard Model. (shrink)

ABSTRACT Our goal in this paper is to extend counterfactual accounts of scientific explanation to mathematics. Our focus, in particular, is on intra-mathematical explanations: explanations of one mathematical fact in terms of another. We offer a basic counterfactual theory of intra-mathematical explanations, before modelling the explanatory structure of a test case using counterfactual machinery. We finish by considering the application of counterpossibles to mathematical explanation, and explore a second test case along these lines.

What kinds of fundamental limits are there in how capable artificial intelligence (AI) systems might become? Two questions in particular are of interest: (1) How much more capable could AI become relative to humans, and (2) how easily could superhuman capability be acquired? To answer these questions, we will consider the literature on human expertise and intelligence, discuss its relevance for AI, and consider how AI could improve on humans in two major aspects of thought and expertise, namely simulation and (...) pattern recognition. We find that although there are very real limits to prediction, it seems like AI could still substantially improve on human intelligence. (shrink)

The informal logic movement began as an attempt to develop – and teach – an alternative logic which can account for the real life arguing that surrounds us in our daily lives – in newspapers and the popular media, political and social commentary, advertising, and interpersonal exchange. The movement was rooted in research and discussion in Canada and especially at the University of Windsor, and has become a branch of argumentation theory which intersects with related traditions and approaches (notably formal (...) logic, rhetoric and dialectics in the form of pragma-dialectics). In this volume, some of the best known contributors to the movement discuss their views and the reasoning and argument which is informal logic’s subject matter. Many themes and issues are explored in a way that will fuel the continued evolution of the field. Federico Puppo adds an insightful essay which considers the origins and development of informal logic and whether informal logicians are properly described as a “school” of thought. In considering that proposition, Puppo introduces readers to a diverse range of essays, some of them previously published, others written specifically for this volume. (shrink)

Causal judgments are widely known to be sensitive to violations of both prescriptive norms (e.g., immoral events) and statistical norms (e.g., improbable events). There is ongoing discussion as to whether both effects are best explained in a unified way through changes in the relevance of counterfactual possibilities, or whether these two effects arise from unrelated cognitive mechanisms. Recent work has shown that moral norm violations affect causal judgments of agents, but not inanimate artifacts used by those agents. These results have (...) been interpreted as showing that prescriptive norm violations only affect causal reasoning about intentional agents, but not the use of inanimate artifacts, thereby providing evidence that the effect of prescriptive norm violations arises from mechanisms specific to reasoning about intentional agents, and thus casting doubt on a unified counterfactual analysis of causal reasoning. Four experiments explore this recent finding and provide clear support for a unified counterfactual analysis. Experiment 1 demonstrates that these newly observed patterns in causal judgments are closely mirrored by judgments of counterfactual relevance. Experiment 2 shows that the relationship between causal and counterfactual judgments is moderated by causal structure, as uniquely predicted by counterfactual accounts. Experiment 3 directly manipulates the relevance of counterfactual alternatives and finds that causal judgments of intentional agents and inanimate artifacts are similarly affected. Finally, Experiment 4 shows that prescriptive norm violations (in which artifacts malfunction) affect causal judgments of inanimate artifacts in much the same way that prescriptive norm violations (in which agents act immorally) affect causal judgments of intentional agents. (shrink)

Equilibrium explanations use an equilibrium to represent and explain a system’s dynamic behavior. They provide a system with the property of global stability: a system will converge towards and remain in equilibrium regardless of its initial conditions and dynamic process. Thus, equilibrium explanations are generally treated as non-causal explanations. There are two claims subsumed under that comprehensive thesis. The first claim is that equilibrium explanations do not identify any causes because a system with global stability resists manipulation. The second claim (...) is that even if equilibrium explanations do identify causes by manipulation, those causes are embedded in a system’s deeper, underlying structural relationships, and those causes are irrelevant to explaining a system’s behavior. Only the system’s structural relationships are relevant. But equilibrium explanations are not monolithic. I compare dynamic systems with multiple, competing equilibria to systems with a globally stable equilibrium. Equilibrium explanations of the former use intervention on a system’s initial conditions and dynamic process to manipulate equilibria, which identifies causes. Furthermore, a system’s initial conditions and dynamic process are relevant to explaining why one equilibrium is selected instead of another. I then apply these lessons to systems with a globally stable equilibrium and discuss when their corresponding equilibrium explanations have a proper role in the parent sciences. (shrink)

It has often been noted that there is some tension between engaging in decision-making and believing that one’s choices might be predetermined. The possibility that our choices are predetermined forces us to consider, in our decisions, act-state pairs which are inconsistent, and hence to which we cannot assign sensible utilities. But the reasoning which justifies two-boxing in Newcomb’s problem also justifies associating a non-zero causal probability with these inconsistent act-state pairs. Put together these undefined utilities and non-zero probabilities entail that (...) expected utilities are undefined whenever it is a possibility that our choices are predetermined. There are three ways to solve the problem, but all of them suffer serious costs: always assume that, contrary to our evidence, the outcome of our present decision-making is not predetermined; give up the reasoning that justifies unconditional two-boxing in Newcomb’s problem; or allow epistemically impossible outcomes to contribute to expected utility, leading to the wrong results in a series of cases introduced by Ahmed :665–685, 2014a, Evidence, decision and causality, Cambridge University Press, Cambridge, 2014b). However they choose to respond, causal decision theorists cannot remain silent: the intuitive tension between decision-making and the possibility of predetermination can be made precise, and resolving it will require giving up something. Causal decision theorists have a predetermination problem. (shrink)

There is a tension between, on the one hand, the view that natural selection refers to individual-level causes, and on the other hand, the view that it refers to a population-level cause. In this article, I make the case for the individual-level cause view. I respond to recent claims made by McLoone that the individual-level cause view is inconsistent. I show that if one were to follow his arguments, any causal claim in any context would have to be regarded as (...) vindicating a form of population-level cause view. I show why this is implausible and how a consistent individual-level cause position can be held within the interventionist account of causation. Finally, I argue that there is one sense in which natural selection might be said to refer to population-level causes of evolutionary change. The upshot is that, as noted by others, natural selection can be regarded as referring to a population-level cause in the context of frequency-dependent selection and other situations of fitness-altering interactions between the individuals of a population. But whether this statement is true will depend on the empirical case investigated, not some a priori conceptual distinction. Thus, even though situations of frequency dependence might be ubiquitous, it is orthogonal to the conceptual question of whether frequency-independent natural selection—McLoone’s target—refers to individual- or population-level causes. (shrink)

This is one of a pair of discussion notes comparing some features of the account of causation in Wolfgang Spohn’s Laws of Belief with the “interventionist” account in James Woodward’s Making Things Happen. This note locates the core difference of the accounts in the fact that Woodward’s account follows an epistemological order, while Spohn’s follows a conceptual order. This unfolds in five further differences: type- versus token-level causation, reference to time, actual/counterfactual intervention versus epistemic/suppositional wiggling, a circular versus a circle-free (...) conception of the circumstances of a direct causal relation, and absolute versus model-relative causation. (shrink)

Two long-standing arguments in cognitive science invoke the assumption that holistic inference is computationally infeasible. The first is Fodor’s skeptical argument toward computational modeling of ordinary inductive reasoning. The second advocates modular computational mechanisms of the kind posited by Cosmides, Tooby and Sperber. Based on advances in machine learning related to Bayes nets, as well as investigations into the structure of scientific and ordinary information, I maintain neither argument establishes its architectural conclusion. Similar considerations also undermine Fodor’s decades-long diagnosis of (...) artificial intelligence research as confounded by an inability to circumscribe the amount of information relevant to inferential processes. This diagnosis is particularly inapposite with respect to Bayes nets, since one of their strengths as machine learning systems has been their capacity to reason probabilistically about large data sets whose size overwhelms the capacities of individual human reasoners. A general moral follows from these criticisms: Insights into artificial and human cognitive systems are likely to be cultivated by focusing greater attention on the structure and density of connections among items of information that are available to them. (shrink)

In this dissertation, I develop and defend a model of causation that allows for dualist mental causation in worlds where the physical domain is physically complete. In Part I, I present the dualist ontology that will be assumed throughout the thesis and identify two challenges for models of mental causation within such an ontology: the exclusion worry and the common cause worry. I also argue that a proper response to these challenges requires a thoroughly lightweight account of causation, i.e. an (...) account that allows for causes to be metaphysically distinct from the phenomena that produce or physically necessitate their effects. In Part II, I critically evaluate contemporary responses to these challenges from the philosophical literature. In particular, I discuss List and Stoljar’s criticism of exclusion worries, Kroedel’s alternative dualist ontology, concerns about the notion of causal sufficiency, and Lowe’s models of dualist mental causation. I argue that none of these proposals provide independent motivation for a thoroughly lightweight account of causation and therefore leave room for improvement. In the first four chapters of Part III, I develop a thoroughly lightweight model of causation, which builds on interventionist approaches to causation. First, I explain how so-called ‘holding fixed’-requirements in standard interventionist accounts stand in the way of dualist mental causation. I then argue that interventionist accounts should impose a robustness condition on causal correlations and that, with this condition in place, the ‘holding fixed’-requirements can be weakened such that they do allow for dualist mental causation. I dub the interventionist model with such weakened ‘holding fixed’-requirements ‘insensitive interventionism’, argue that it can counter the exclusion worry as well as the common cause worry, and explain under which circumstances it would predict there to be dualist mental causation. Importantly, these circumstances might, for all we know, hold in the actual world. In the final three chapters of Part III, I defend insensitive interventionism against some objections. I consider the objection that causation must be productive, the objection that causes must physically necessitate their effects, and the objection that insensitive interventionism is too permissive. I respond by drawing from the literature on causation by absences and on the relation between causation and fundamental physics. Overall, insensitive interventionism performs as well as standard interventionist accounts. I conclude that insensitive interventionism is a credible model of causation. (shrink)

Part of Allen et al.’s criticism of Hamilton’s rule makes sense only if we are interested in social adaptation rather than merely social selection. Under the assumption that we are interested in casually modeling social adaptation, I illustrate how graphical causal models of social adaptation can be useful for predicting evolution by adaptation. I then argue for two consequences of this approach given some of the recent philosophical literature. I argue Birch’s claim that the proper way to understand Hamilton’s rule (...) is as providing an organizational framework for causal models is incorrect. I provide an account of a causally adequate decomposition of evolutionary change due to social adaptation and show that my account is superior to Okasha’s. (shrink)

The problem of understanding how statistical inference is, and can be, applied in empirical sciences is important for the methodology of science. It is the objective of this paper to gain a better understanding of the role of statistical methods in scientific modeling. The important question of whether the applicability reduces to the representational properties of statistical models is discussed. It will be shown that while the answer to this question is positive, representation in statistical models is not purely structural. (...) In spite of the fact that representation in statistical models is based on the structural similarities between the statistical model and the empirical systems under study, these relationships are shown to be appropriate for representing relations in the target system by agent function, too. A second aspect of the paper involves the claim that agent-based components of statistical modeling are: a) interpretation of random variables, b) selection of the goal of statistical research, and c) selection of estimator properties. To justify these claims, a preliminary discussion will be presented on the role of statistics in modeling, as in regression and other structural models. This role will be explored and realized using a structural viewpoint. Also the role of statistical estimation in statistical modeling is discussed to explain the representational role of models and the inferential role of the agent in modeling. (shrink)

In this paper we develop an analysis of dispositions by means of causal Bayes nets. In particular, we analyze dispositions as cause-effect structures that increase the probability of the manifestation when the stimulus is brought about by intervention in certain circumstances. We then highlight several advantages of our analysis and how it can handle problems arising for classical analyses of dispositions such as masks, mimickers, and finks.