We argue that current discussions of criteria for actual causation are ill-posed in several respects. (1) The methodology of current discussions is by induction from intuitions about an infinitesimal fraction of the possible examples and counterexamples; (2) cases with larger numbers of causes generate novel puzzles; (3) "neuron" and causal Bayes net diagrams are, as deployed in discussions of actual causation, almost always ambiguous; (4) actual causation is (intuitively) relative to an initial system state since state changes are relevant, but (...) most current accounts ignore state changes through time; (5) more generally, there is no reason to think that philosophical judgements about these sorts of cases are normative; but (6) there is a dearth of relevant psychological research that bears on whether various philosophical accounts are descriptive. Our skepticism is not directed towards the possibility of a correct account of actual causation; rather, we argue that standard methods will not lead to such an account. A different approach is required. (shrink)

We argue that current discussions of criteria for actual causation are ill-posed in several respects. (1) The methodology of current discussions is by induction from intuitions about an infinitesimal fraction of the possible examples and counterexamples; (2) cases with larger numbers of causes generate novel puzzles; (3) “neuron” and causal Bayes net diagrams are, as deployed in discussions of actual causation, almost always ambiguous; (4) actual causation is (intuitively) relative to an initial system state since state changes are relevant, but (...) most current accounts ignore state changes through time; (5) more generally, there is no reason to think that philosophical judgements about these sorts of cases are normative; but (6) there is a dearth of relevant psychological research that bears on whether various philosophical accounts are descriptive. Our skepticism is not directed towards the possibility of a correct account of actual causation; rather, we argue that standard methods will not lead to such an account. A different approach is required. Once upon a time a hungry wanderer came into a village. He filled an iron cauldron with water, built a fire under it, and dropped a stone into the water. “I do like a tasty stone soup” he announced. Soon a villager added a cabbage to the pot, another added some salt and others added potatoes, onions, carrots, mushrooms, and so on, until there was a meal for all. (shrink)

It is “well known” that in linear models: (1) testable constraints on the marginal distribution of observed variables distinguish certain cases in which an unobserved cause jointly influences several observed variables; (2) the technique of “instrumental variables” sometimes permits an estimation of the influence of one variable on another even when the association between the variables may be confounded by unobserved common causes; (3) the association (or conditional probability distribution of one variable given another) of two variables connected by a (...) path or pair of paths with a single common vertex (a trek) can be computed directly from the parameter values associated with each edge in the trek; (4) the association of two variables produced by multiple treks can be computed from the parameters associated with each trek; and (5) the independence of two variables conditional on a third implies the corresponding independence of the sums of the variables over all units conditional on the sums over all units of each of the original conditioning variables. (shrink)

Various algorithms have been proposed for learning (partial) genetic regulatory networks through systematic measurements of differential expression in wild type versus strains in which expression of specific genes has been suppressed or enhanced, as well as for determining the most informative next experiment in a sequence. While the behavior of these algorithms has been investigated for toy examples, the full computational complexity of the problem has not received sufficient attention. We show that finding the true regulatory network requires (in the (...) worst-case) exponentially many experiments (in the number of genes). Perhaps more importantly, we provide an algorithm for determining the set of regulatory networks consistent with the observed data. We then show that this algorithm is infeasible for realistic data (specifically, nine genes and ten experiments). This infeasibility is not due to an algorithmic flaw, but rather to the fact that there are far too many networks consistent with the data (10 18 in the provided example). We conclude that gene perturbation experiments are useful in confirming regulatory network models discovered by other techniques, but not a feasible search strategy. (shrink)

Since his death in 1997, Isaiah Berlin's writings have generated continual interest among scholars and educated readers, especially in regard to his ideas about liberalism, value pluralism, and "positive" and "negative" liberty. Most books on Berlin have examined his general political theory, but this volume uses a contemporary perspective to focus specifically on his ideas about freedom and liberty. Isaiah Berlin and the Politics of Freedom brings together an integrated collection of essays by noted and emerging political theorists that commemorate (...) in a critical spirit the recent 50th anniversary of Isaiah Berlin's famous lecture and essay, "Two Concepts of Liberty." The contributors use Berlin's essay as an occasion to rethink the larger politics of freedom from a twenty-first century standpoint, bringing Berlin's ideas into conversation with current political problems and perspectives rooted in postcolonial theory, feminist theory, democratic theory, and critical social theory. The editors begin by surveying the influence of Berlin's essay and the range of debates about freedom that it has inspired. Contributors' chapters then offer various analyses such as competing ways to contextualize Berlin's essay, how to reconsider Berlin's ideas in light of struggles over national self-determination, European colonialism, and racism, and how to view Berlin's controversial distinction between so-called "negative liberty" and "positive liberty." By relating Berlin's thinking about freedom to competing contemporary views of the politics of freedom, this book will be significant for both scholars of Berlin as well as people who are interested in larger debates about the meaning and conditions of freedom. (shrink)

Tianjaou Chu, David Danks, and Clark Glymour. Data Driven Methods for Nonlinear Granger Causality: Climate Teleconnection Mechanisms.

Since the introduction of mathematical population genetics, its machinery has shaped our fundamental understanding of natural selection. Selection is taken to occur when differential fitnesses produce differential rates of reproductive success, where fitnesses are understood as parameters in a population genetics model. To understand selection is to understand what these parameter values measure and how differences in them lead to frequency changes. I argue that this traditional view is mistaken. The descriptions of natural selection rendered by population genetics models are (...) in general neither predictive nor explanatory and introduce avoidable conceptual confusions. I conclude that a correct understanding of natural selection requires explicitly causal models of reproductive success. *Received May 2006; revised December 2006. †To contact the author, please write to: Department of Philosophy, Kansas State University, 201 Dickens Hall, Manhattan, KS 66506; e‐mail: glymour@ksu.edu . (shrink)

I argue that a phenotypic trait can be an adaptation to a particular environmental condition, as against others, only if the environmental condition and the phenotype interactively cause fitness. Models of interactive environmental causes of fitness generally require that environments be individuated by explicit representation rather than by measures of environmental quality, although the latter understanding of ‘environment’ is more prominent in the philosophy of biology. Hence, talk of adaptations to some but not other environmental conditions relies on conceptions of (...) ‘environment’ importantly different from that commonly presupposed in philosophy of biology. (shrink)

I argue that results from foraging theory give us good reason to think some evolutionary phenomena are indeterministic and hence that evolutionary theory must be probabilistic. Foraging theory implies that random search is sometimes selectively advantageous, and experimental work suggests that it is employed by a variety of organisms. There are reasons to think such search will sometimes be genuinely indeterministic. If it is, then individual reproductive success will also be indeterministic, and so too will frequency change in populations of (...) organisms employing such search. (shrink)

In this article I explore some statistical difficulties confronting going conceptions of ‘group’ as understood in accounts of group selection. Most such theories require real groups but define the reality of groups in ways that make it impossible to test for their reality. There are alternatives, but they either require or invite a nominalism about groups that many theorists abjure.

Standard models of statistical explanation face two intractable difficulties. In his 1984 Salmon argues that because statistical explanations are essentially probabilistic we can make sense of statistical explanation only by rejecting the intuition that scientific explanations are contrastive. Further, frequently the point of a statistical explanation is to identify the etiology of its explanandum, but on standard models probabilistic explanations often fail to do so. This paper offers an alternative conception of statistical explanations on which explanations of the frequency of (...) a property consist in the derivation of that frequency from a statistical specification of the mechanism by which instances of the relevant property are produced. Such explanations are contrastive precisely because they identify the determinate causal etiologies of their explananda. (shrink)

argues that correlated interactions are necessary for group selection. His argument turns on a particular procedure for measuring the strength of selection, and employs a restricted conception of correlated interaction. It is here shown that the procedure in question is unreliable, and that while related procedures are reliable in special contexts, they do not require correlated interactions for group selection to occur. It is also shown that none of these procedures, all of which employ partial regression methods, are reliable when (...) correlated interactions of a specific kind arise, and it is argued that such correlated interactions will likely be ubiquitous in natural populations. Introduction Process and Product Fitness, Mean Fitness, and Phenotypic Change Correlated Interactions Causation Implications CiteULike Connotea Del.icio.us What's this? (shrink)

Sober (1984) presents an account of selection motivated by the view that one property can causally explain the occurrence of another only if the first plays a unique role in the causal production of the second. Sober holds that a causal property will play such a unique role if it is a population level cause of its effect, and on this basis argues that there is selection for a trait T only if T is a population level cause of survival (...) and reproductive success. Sterelny and Kitcher (1988) claim against Sober that some traits directly subject to selection will not satisfy the probabilistic condition on population level causation. In this paper I show that Sober has the resources to resist the Sterelny-Kitcher complaint, but I argue that not all traits that satisfy the probabilistic condition play the required unique role in the production of their effects. (shrink)

: Models that fail to satisfy the Markov condition are unstable in the sense that changes in state variable values may cause changes in the values of background variables, and these changes in background lead to predictive error. This sort of error arises exactly from the failure of non-Markovian models to track the set of causal relations upon which the values of response variables depend. The result has implications for discussions of the level of selection: under certain plausible conditions the (...) models of selection presented in such debates will not satisfy the Markov condition when fit to data from real populations. Since this is true both for group and individual level models, models of neither sort correctly represent the causal structure generating, nor correctly explain, the phenomena of interest. (shrink)

Measures of algorithmic bias can be roughly classified into four categories, distinguished by the conditional probabilistic dependencies to which they are sensitive. First, measures of "procedural bias" diagnose bias when the score returned by an algorithm is probabilistically dependent on a sensitive class variable (e.g. race or sex). Second, measures of "outcome bias" capture probabilistic dependence between class variables and the outcome for each subject (e.g. parole granted or loan denied). Third, measures of "behavior-relative error bias" capture probabilistic dependence between (...) class variables and the algorithmic score, conditional on target behaviors (e.g. recidivism or loan default). Fourth, measures of "score-relative error bias" capture probabilistic dependence between class variables and behavior, conditional on score. Several recent discussions have demonstrated a tradeoff between these different measures of algorithmic bias, and at least one recent paper has suggested conditions under which tradeoffs may be minimized. -/- In this paper we use the machinery of causal graphical models to show that, under standard assumptions, the underlying causal relations among variables forces some tradeoffs. We delineate a number of normative considerations that are encoded in different measures of bias, with reference to the philosophical literature on the wrongfulness of disparate treatment and disparate impact. While both kinds of error bias are nominally motivated by concern to avoid disparate impact, we argue that consideration of causal structures shows that these measures are better understood as complicated and unreliable measures of procedural biases (i.e. disparate treatment). Moreover, while procedural bias is indicative of disparate treatment, we show that the measure of procedural bias one ought to adopt is dependent on the account of the wrongfulness of disparate treatment one endorses. Finally, given that neither score-relative nor behavior-relative measures of error bias capture the relevant normative considerations, we suggest that error bias proper is best measured by score-based measures of accuracy, such as the Brier score. (shrink)

We agree with Cramer et al.'s goal of the discovery of causal relationships, but we argue that the authors' characterization of latent variable models (as deployed for such purposes) overlooks a wealth of extant possibilities. We provide a preliminary analysis of their data, using existing algorithms for causal inference and for the specification of latent variable models.

I argue that the orthodox account of probabilistic causation, on which probabilistic causes determine the probability of their effects, is inconsistent with certain ontological assumptions implicit in scientific practice. In particular, scientists recognize the possibility that properties of populations can cause the behavior of members of the populations. Such emergent population‐level causation is metaphysically impossible on the orthodoxy.

Machine learning algorithms may radically improve our ability to diagnose and treat disease. For moral, legal, and scientific reasons, it is essential that doctors and patients be able to understand and explain the predictions of these models. Scalable, customisable, and ethical solutions can be achieved by working together with relevant stakeholders, including patients, data scientists, and policy makers.

The essays in this book discuss the originally proposed Baldwin effect, how it was modified over time, and its possible contribution to contemporary empirical...

We trace the history of the Modern Evolutionary Synthesis, and of genetic Darwinism generally, with a view to showing why, even in its current versions, it can no longer serve as a general framework for evolutionary theory. The main reason is empirical. Genetical Darwinism cannot accommodate the role of development (and of genes in development) in many evolutionary processes. We go on to discuss two conceptual issues: whether natural selection can be the “creative factor” in a new, more general framework (...) for evolutionary theorizing; and whether in such a framework organisms must be conceived as self-organizing systems embedded in self-organizing ecological systems. (shrink)

Glymour (Philos Sci 73:369–389, 2006) claims that classical population genetic models can reliably predict short and medium run population dynamics only given information about future fitnesses those models cannot themselves predict, and that in consequence the causal, ecological models which can predict future fitnesses afford a more foundational description of natural selection than do population genetic models. This paper defends the first claim from objections offered by Gildenhuys (Biol Philos, 2011).

Using the evolution of the stickleback family of subarctic fish as a touchstone, we explore the effect of new discoveries about regulatory genetics, developmental plasticity, and epigenetic inheritance on the conceptual foundations of the Modern Evolutionary Synthesis. Identifying the creativity of natural selection as the hallmark of the Modern Synthesis, we show that since its inception its adherents have pursued a variety of research projects that at first seemed to conflict with its principles, but were accommodated. We situate challenges coming (...) from developmental biology in a dialectic between innovation and tradition, suggesting on the basis of past episodes that even if developmental plasticity and epigenetic inheritance are aligned with its principles the Modern Synthesis will be significantly affected. (shrink)

Lennox and Wilson (1994) critique dispositional accounts of selection on the grounds that such accounts will class evolutionary events as cases of selection whether or not the environment constrains population growth. Lennox and Wilson claim that pure r-selection involves no environmental checks on growth, and that accounts of natural selection ought to distinguish between the two sorts of cases. I argue that Lennox and Wilson are mistaken in claiming that pure r-selection involves no environmental checks, but suggest that two related (...) cases support their substantive complaint, namely that dispositional accounts of selection have resources insufficient for making important distinctions in causal structure. (shrink)

The Darwinian concept of natural selection was conceived within a set of Newtonian background assumptions about systems dynamics. Mendelian genetics at first did not sit well with the gradualist assumptions of the Darwinian theory. Eventually, however, Mendelism and Darwinism were fused by reformulating natural selection in statistical terms. This reflected a shift to a more probabilistic set of background assumptions based upon Boltzmannian systems dynamics. Recent developments in molecular genetics and paleontology have put pressure on Darwinism once again. Current work (...) on self-organizing systems may provide a stimulus not only for increased problem solving within the Darwinian tradition, especially with respect to origins of life, developmental genetics, phylogenetic pattern, and energy-flow ecology, but for deeper understanding of the very phenomenon of natural selection itself. Since self-organizational phenomena depend deeply on stochastic processes, self-organizational systems dynamics advance the probability revolution. In our view, natural selection is an emergent phenomenon of physical and chemical selection. These developments suggest that natural selection may be grounded in physical law more deeply than is allowed by advocates of the autonomy of biology, while still making it possible to deny, with autonomists, that evolutionary explanations can be modeled in terms of a deductive relationship between laws and cases. We explore the relationship between, chance, self-organization, and selection as sources of order in biological systems in order to make these points. (shrink)

Social movements are using education to generate critical consciousness regarding the social and environmental unsustainability of the current food system, and advocate for agroecological production. In this article, we explore results from a cross-case analysis of six social movements that are using education as a strategy to advance food sovereignty. We conducted participatory research with diverse rural and urban social movements in the United States, Brazil, Cuba, Bolivia, and Mexico, which are each educating for food sovereignty. We synthesize insights from (...) critical food systems education and the political ecology of education in analyzing these cases. We compare the thematic similarities and difference between these movements’ education initiatives in terms of their emergence, initial goals, expansion and institutionalization, relationship to the state, theoretical inspirations, pedagogical approach, educational topics, approach to student research, and outcomes. Among these thematic areas, we find that student-centered research on competing forms of production is an integral way to advance critical consciousness about the food system and the political potential of agroecological alternatives. However, what counts, as success in these programs, is highly case-dependent. For engaged scholars committed to advancing education for food sovereignty, it is essential to reflect upon the lessons learned and challenges faced by these movements. (shrink)

Barresi & Moore provide an impressive account of how the coordination of first and third person information about the self and other could produce an account of intentional relations. They are less explicit as to how the child comes to understand the basic epistemic relation between experience and knowledge, that is, how informational access causes belief. We suggest one route.

This book taps the best American thinkers to answer the essential American question: How do we sustain our experiment in government of, by, and for the people? Authored by an extraordinary and politically diverse roster of public officials, scholars, and educators, these chapters describe our nation's civic education problem, assess its causes, offer an agenda for reform, and explain the high stakes at risk if we fail.

Schools of Faith represents a diversity of essays from scholars in several continents. The contributors, all leading theologians and ethicists, offer reflections on historical and contemporary themes which are significant for wider debates in theological education and church life in today's world. The range of contributor and content provides a fitting tribute to the work of Iain R. Torrance over many years. Amid the numerous subjects discussed, the authors focus on liturgy, textual criticism, public theology, the ethics of war, Christian (...) doctrine, divine action, ecumenism, inter-faith dialogue, spiritual formation, the office of the minister, and the interface between religion and literature. The multi-faceted nature of this collection signifies its importance for historical, systematic and practical theology. (shrink)

In everyday matters, as well as in law, we allow that someone’s reasons can be causes of her actions, and often are. That correct reasoning accords with Bayesian principles is now so widely held in philosophy, psychology, computer science and elsewhere that the contrary is beginning to seem obtuse, or at best quaint. And that rational agents should learn about the world from energies striking sensory inputs nerves in people—seems beyond question. Even rats seem to recognize the difference between correlation (...) and causation,1 and accordingly make different inferences from passive observation than from interventions. A few statisticians aside,” so do most of us. To square these views with the demands of computability, increasing numbers of psychologists and others have embraced a particular formalization, causal Bayes nets, as an account of human reasoning about and to causal connections.111 Such structures can be used by rational agents, including humans in so far as they are rational, to have degrees of belief in various conceptual contents, which they use to reason to expectations, which are realized or defeated by sensory inputs, which cause them to change their degrees of belief in other contents in accord with Bayes Rule, or some generalization of it. How is all of this supposed to be carried out? l. Representing Causal Structures The causal Bayes net framework adopted by a growing number of psychologists goes like this: Our representations of causal relations are captured in a graphical causal. (shrink)

In this essay, Bruce Maxwell, David Waddington, Kevin McDonough, Andrée-Anne Cormier, and Marina Schwimmer compare two competing approaches to social integration policy, Multiculturalism and Interculturalism, from the perspective of the issue of the state funding and regulation of conservative religious schools. After identifying the key differences between Interculturalism and Multiculturalism, as well as their many similarities, the authors present an explanatory analysis of this intractable policy challenge. Conservative religious schooling, they argue, tests a conceptual tension inherent in Multiculturalism (...) between respect for group diversity and autonomy, on the one hand, and the ideal of intercultural citizenship, on the other. Taking as a case study Québec's education system and, in particular, recent curricular innovations aimed at helping young people acquire the capabilities of intercultural citizenship, the authors illustrate how Interculturalism signals a compelling way forward in the effort to overcome the political dilemma of conservative religious schooling. (shrink)