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David Danks [53]David Joseph Danks [1]
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Profile: David Danks (Carnegie Mellon University)
  1.  42
    A Theory of Causal Learning in Children: Causal Maps and Bayes Nets.Alison Gopnik, Clark Glymour, David M. Sobel, Laura E. Schulz, Tamar Kushnir & David Danks - 2004 - Psychological Review 111 (1):3-32.
    We propose that children employ specialized cognitive systems that allow them to recover an accurate “causal map” of the world: an abstract, coherent, learned representation of the causal relations among events. This kind of knowledge can be perspicuously understood in terms of the formalism of directed graphical causal models, or “Bayes nets”. Children’s causal learning and inference may involve computations similar to those for learning causal Bayes nets and for predicting with them. Experimental results suggest that 2- to 4-year-old children (...)
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  2. In Defense of a Broad Conception of Experimental Philosophy.David Rose & David Danks - 2013 - Metaphilosophy 44 (4):512-532.
    Experimental philosophy is often presented as a new movement that avoids many of the difficulties that face traditional philosophy. This article distinguishes two views of experimental philosophy: a narrow view in which philosophers conduct empirical investigations of intuitions, and a broad view which says that experimental philosophy is just the colocation in the same body of (i) philosophical naturalism and (ii) the actual practice of cognitive science. These two positions are rarely clearly distinguished in the literature about experimental philosophy, both (...)
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  3.  36
    Goal-Dependence in Ontology.David Danks - 2015 - Synthese 192 (11):3601-3616.
    Our best sciences are frequently held to be one way, perhaps the optimal way, to learn about the world’s higher-level ontology and structure. I first argue that which scientific theory is “best” depends in part on our goals or purposes. As a result, it is theoretically possible to have two scientific theories of the same domain, where each theory is best for some goal, but where the two theories posit incompatible ontologies. That is, it is possible for us to have (...)
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  4. Demoralizing Causation.David Danks, David Rose & Edouard Machery - 2013 - Philosophical Studies (2):1-27.
    There have recently been a number of strong claims that normative considerations, broadly construed, influence many philosophically important folk concepts and perhaps are even a constitutive component of various cognitive processes. Many such claims have been made about the influence of such factors on our folk notion of causation. In this paper, we argue that the strong claims found in the recent literature on causal cognition are overstated, as they are based on one narrow type of data about a particular (...)
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  5. The Independence Thesis: When Individual and Social Epistemology Diverge.Conor Mayo-Wilson, Kevin J. S. Zollman & David Danks - 2011 - Philosophy of Science 78 (4):653-677.
    In the latter half of the twentieth century, philosophers of science have argued (implicitly and explicitly) that epistemically rational individuals might compose epistemically irrational groups and that, conversely, epistemically rational groups might be composed of epistemically irrational individuals. We call the conjunction of these two claims the Independence Thesis, as they together imply that methodological prescriptions for scientific communities and those for individual scientists might be logically independent of one another. We develop a formal model of scientific inquiry, define four (...)
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  6. Actual Causation: A Stone Soup Essay.Clark Glymour, David Danks, Bruce Glymour, Frederick Eberhardt, Joseph Ramsey & Richard Scheines - 2010 - Synthese 175 (2):169-192.
    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 (...)
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  7. Reasons as Causes in Bayesian Epistemology.Clark Glymour & David Danks - 2007 - Journal of Philosophy 104 (9):464-474.
    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 (...)
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  8. Causation: Empirical Trends and Future Directions.David Rose & David Danks - 2012 - Philosophy Compass 7 (9):643-653.
    Empirical research has recently emerged as a key method for understanding the nature of causation, and our concept of causation. One thread of research aims to test intuitions about the nature of causation in a variety of classic cases. These experiments have principally been used to try to resolve certain debates within analytic philosophy, most notably that between proponents of transference and dependence views of causation. The other major thread of empirical research on our concept of causation has investigated the (...)
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  9. Wisdom of the Crowds Vs. Groupthink: Learning in Groups and in Isolation.Conor Mayo-Wilson, Kevin Zollman & David Danks - 2013 - International Journal of Game Theory 42 (3):695-723.
    We evaluate the asymptotic performance of boundedly-rational strategies in multi-armed bandit problems, where performance is measured in terms of the tendency (in the limit) to play optimal actions in either (i) isolation or (ii) networks of other learners. We show that, for many strategies commonly employed in economics, psychology, and machine learning, performance in isolation and performance in networks are essentially unrelated. Our results suggest that the appropriateness of various, common boundedly-rational strategies depends crucially upon the social context (if any) (...)
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  10.  51
    Confirmation in the Cognitive Sciences: The Problematic Case of Bayesian Models. [REVIEW]Frederick Eberhardt & David Danks - 2011 - Minds and Machines 21 (3):389-410.
    Bayesian models of human learning are becoming increasingly popular in cognitive science. We argue that their purported confirmation largely relies on a methodology that depends on premises that are inconsistent with the claim that people are Bayesian about learning and inference. Bayesian models in cognitive science derive their appeal from their normative claim that the modeled inference is in some sense rational. Standard accounts of the rationality of Bayesian inference imply predictions that an agent selects the option that maximizes the (...)
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  11.  12
    The Psychology of Causal Perception and Reasoning.David Danks - 2009 - In Helen Beebee, Christopher Hitchcock & Peter Menzies (eds.), The Oxford Handbook of Causation. Oxford University Press.
  12.  8
    Theory Unification and Graphical Models in Human Categorization.David Danks - 2010 - Causal Learning:173--189.
    Many different, seemingly mutually exclusive, theories of categorization have been proposed in recent years. The most notable theories have been those based on prototypes, exemplars, and causal models. This chapter provides “representation theorems” for each of these theories in the framework of probabilistic graphical models. More specifically, it shows for each of these psychological theories that the categorization judgments predicted and explained by the theory can be wholly captured using probabilistic graphical models. In other words, probabilistic graphical models provide a (...)
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  13.  8
    Equilibria of the Rescorla-Wagner Model.David Danks - unknown
    The Rescorla–Wagner model has been a leading theory of animal causal induction for nearly 30 years, and human causal induction for the past 15 years. Recent theories 367) have provided alternative explanations of how people draw causal conclusions from covariational data. However, theoretical attempts to compare the Rescorla–Wagner model with more recent models have been hampered by the fact that the Rescorla–Wagner model is an algorithmic theory, while the more recent theories are all computational. This paper provides a detailed derivation (...)
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  14. Diversity in Representations; Uniformity in Learning.David Danks & David Rose - 2010 - Behavioral and Brain Sciences 33 (2-3):330-331.
    Henrich et al.'s conclusion that psychologists ought not assume uniformity of psychological phenomena depends on their descriptive claim that there is no pattern to the great diversity in psychological phenomena. We argue that there is a pattern: uniformity of learning processes (broadly construed), and diversity of (some) mental contents (broadly construed).
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  15.  25
    Scientific Coherence and the Fusion of Experimental Results.David Danks - 2005 - British Journal for the Philosophy of Science 56 (4):791-807.
    A pervasive feature of the sciences, particularly the applied sciences, is an experimental focus on a few (often only one) possible causal connections. At the same time, scientists often advance and apply relatively broad models that incorporate many different causal mechanisms. We are naturally led to ask whether there are normative rules for integrating multiple local experimental conclusions into models covering many additional variables. In this paper, we provide a positive answer to this question by developing several inference rules that (...)
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  16.  13
    The Moral Permissibility of Automated Responses During Cyberwarfare.David Danks & Joseph H. Danks - 2013 - Journal of Military Ethics 12 (1):18-33.
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  17.  18
    The Supposed Competition Between Theories of Human Causal Inference.David Danks - 2005 - Philosophical Psychology 18 (2):259 – 272.
    Newsome ((2003). The debate between current versions of covariation and mechanism approaches to causal inference. Philosophical Psychology, 16, 87-107.) recently published a critical review of psychological theories of human causal inference. In that review, he characterized covariation and mechanism theories, the two dominant theory types, as competing, and offered possible ways to integrate them. I argue that Newsome has misunderstood the theoretical landscape, and that covariation and mechanism theories do not directly conflict. Rather, they rely on distinct sets of reliable (...)
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  18.  3
    Rational Analyses, Instrumentalism, and Implementations.David Danks - 2008 - In Nick Chater & Mike Oaksford (eds.), The Probabilistic Mind: Prospects for Bayesian Cognitive Science. Oxford University Press. pp. 59--75.
  19.  21
    Erratum To: Model Change and Methodological Virtues in Scientific Inference.Erich Kummerfeld & David Danks - 2014 - Synthese 191 (14):3469-3472.
    Erratum to: Synthese DOI 10.1007/s11229-014-0408-3Appendix 1: NotationLet \(X\) represent a sequence of data, and let \(X_B^t\) represent an i.i.d. subsequence of length \(t\) of data generated from distribution \(B\).We conjecture that the i.i.d. assumption could be eliminated by defining probability distributions over sequences of arbitrary length, though this complication would not add conceptual clarity. Let \(\mathbf{F}\) be a framework (in this case, a set of probability distributions or densities).Let any \(P(\,)\) functions be either a probability distribution function or probability density (...)
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  20.  21
    Model Change and Reliability in Scientific Inference.Erich Kummerfeld & David Danks - 2014 - Synthese 191 (12):2673-2693.
    One persistent challenge in scientific practice is that the structure of the world can be unstable: changes in the broader context can alter which model of a phenomenon is preferred, all without any overt signal. Scientific discovery becomes much harder when we have a moving target, and the resulting incorrect understandings of relationships in the world can have significant real-world and practical consequences. In this paper, we argue that it is common (in certain sciences) to have changes of context that (...)
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  21.  1
    Demoralizing Causation.David Danks, David Rose & Edouard Machery - 2014 - Philosophical Studies 171 (2):251-277.
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  22.  8
    Beyond Machines: Humans in Cyber Operations, Espionage, and Conflict.David Danks & Joseph H. Danks - unknown
    It is the height of banality to observe that people, not bullets, fight kinetic wars. The machinery of kinetic warfare is obviously relevant to the conduct of each particular act of warfare, but the reasons for, and meanings of, those acts depend critically on the fact that they are done by humans. Any attempt to understand warfare—its causes, strategies, legitimacy, dynamics, and resolutions—must incorporate humans as an intrinsic part, both descriptively and normatively. Humans from general staff to “boots on the (...)
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  23.  26
    Functions and Cognitive Bases for the Concept of Actual Causation.David Danks - 2013 - Erkenntnis 78 (1):111-128.
    Our concept of actual causation plays a deep, ever-present role in our experiences. I first argue that traditional philosophical methods for understanding this concept are unlikely to be successful. I contend that we should instead use functional analyses and an understanding of the cognitive bases of causal cognition to gain insight into the concept of actual causation. I additionally provide initial, programmatic steps towards carrying out such analyses. The characterization of the concept of actual causation that results is quite different (...)
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  24.  15
    Not Different Kinds, Just Special Cases.David Danks - 2010 - Behavioral and Brain Sciences 33 (2-3):208-209.
    Machery's Heterogeneity Hypothesis depends on his argument that no theory of concepts can account for all the extant reliable categorization data. I argue that a single theoretical framework based on graphical models can explain all of the behavioral data to which this argument refers. These different theories of concepts thus (arguably) correspond to different special cases, not different kinds.
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  25.  7
    Singular Causation.David Danks - unknown
    In many people, caffeine causes slight muscle tremors, particularly in their hands. In general, the Caffeine → Muscle Tremors causal connection is a noisy one: someone can drink coffee and experience no hand shaking, and there are many other factors that can lead to muscle tremors. Now suppose that Jane drinks several cups of coffee and then notices that her hands are trembling; an obvious question is: did this instance of coffee drinking cause this instance of hand-trembling? Structurally similar questions (...)
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  26. Online Causal Structure Learning.David Danks - unknown
    Causal structure learning algorithms have focused on learning in ”batch-mode”: i.e., when a full dataset is presented. In many domains, however, it is important to learn in an online fashion from sequential or ordered data, whether because of memory storage constraints or because of potential changes in the underlying causal structure over the course of learning. In this paper, we present TDSL, a novel causal structure learning algorithm that processes data sequentially. This algorithm can track changes in the generating causal (...)
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  27.  11
    Adaptively Rational Learning.Sarah Wellen & David Danks - 2016 - Minds and Machines 26 (1-2):87-102.
    Research on adaptive rationality has focused principally on inference, judgment, and decision-making that lead to behaviors and actions. These processes typically require cognitive representations as input, and these representations must presumably be acquired via learning. Nonetheless, there has been little work on the nature of, and justification for, adaptively rational learning processes. In this paper, we argue that there are strong reasons to believe that some learning is adaptively rational in the same way as judgment and decision-making. Indeed, overall adaptive (...)
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  28.  41
    Explaining Norms and Norms Explained.David Danks & Frederick Eberhardt - 2009 - Behavioral and Brain Sciences 32 (1):86-87.
    Oaksford & Chater (O&C) aim to provide teleological explanations of behavior by giving an appropriate normative standard: Bayesian inference. We argue that there is no uncontroversial independent justification for the normativity of Bayesian inference, and that O&C fail to satisfy a necessary condition for teleological explanations: demonstration that the normative prescription played a causal role in the behavior's existence.
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  29.  37
    Biological Codes and Topological Causation.Benjamin Jantzen & David Danks - 2008 - Philosophy of Science 75 (3):259-277.
    Various causal details of the genetic process of translation have been singled out to account for its privileged status as a ‘code'. We explicate the biological uses of coding talk by characterizing a class of special causal processes in which topological properties are the causally relevant ones. This class contains both the process of translation and communication theoretic coding processes as special cases. We propose a formalism in terms of graphs for expressing our theory of biological codes and discuss its (...)
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  30.  25
    Teaching the Normative Theory of Causal Reasoning.Richard Scheines, Matt Easterday & David Danks - 2007 - In Alison Gopnik & Laura Schulz (eds.), Causal Learning: Psychology, Philosophy, and Computation. Oxford University Press. pp. 119--38.
    There is now substantial agreement about the representational component of a normative theory of causal reasoning: Causal Bayes Nets. There is less agreement about a normative theory of causal discovery from data, either computationally or cognitively, and almost no work investigating how teaching the Causal Bayes Nets representational apparatus might help individuals faced with a causal learning task. Psychologists working to describe how naïve participants represent and learn causal structure from data have focused primarily on learning from single trials under (...)
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  31.  28
    Linearity Properties of Bayes Nets with Binary Variables.David Danks & Clark Glymour - unknown
    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 (...)
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  32.  23
    The Computational and Experimental Complexity of Gene Perturbations for Regulatory Network Search.David Danks, Clark Glymour & Peter Spirtes - 2003 - In W. H. Hsu, R. Joehanes & C. D. Page (eds.), Proceedings of IJCAI-2003 workshop on learning graphical models for computational genomics.
    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 (...)
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  33.  7
    The Mathematics of Causal Capacities.David Danks - unknown
    Models based on causal capacities, or independent causal influences/mechanisms, are widespread in the sciences. This paper develops a natural mathematical framework for representing such capacities by extending and generalizing previous results in cognitive psychology and machine learning, based on observations and arguments from prior philosophical debates. In addition to its substantial generality, the resulting framework provides a theoretical unification of the widely-used noisy-OR/AND and linear models, thereby showing how they are complementary rather than competing. This unification helps to explain many (...)
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  34.  14
    Data Driven Methods for Nonlinear Granger Causality: Climate Teleconnection Mechanisms.Tianjiao Chu, David Danks & Clark Glymour - unknown
    Tianjaou Chu, David Danks, and Clark Glymour. Data Driven Methods for Nonlinear Granger Causality: Climate Teleconnection Mechanisms.
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  35.  4
    Chaos, Causation, and Describing Dynamics.David Danks & Maralee Harrell - unknown
    A standard platitude about the function of causal knowledge or theories is that they are valuable because they support prediction, explanation, and control. Knowledge of predator-prey relations enables us to predict future animal populations, as well as design policies or interventions that help influence those populations. If we understand the underlying biochemical mechanisms of some disease, then we can predict who is at risk for it, explain why it produces particular symptoms, and develop interventions to try to reduce its prevalence (...)
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  36.  14
    Dynamical Causal Learning.David Danks, Thomas L. Griffiths & Joshua B. Tenenbaum - unknown
    Current psychological theories of human causal learning and judgment focus primarily on long-run predictions: two by estimating parameters of a causal Bayes nets, and a third through structural learning. This paper focuses on people’s short-run behavior by examining dynamical versions of these three theories, and comparing their predictions to a real-world dataset.
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  37.  14
    Problems for Structure Learning: Aggregation and Computational Complexity.Frank Wimberly, David Danks, Clark Glymour & Tianjiao Chu - unknown
  38.  3
    Moving From Levels & Reduction to Dimensions & Constraints.David Danks - unknown
    Arguments, claims, and discussions about the “level of description” of a theory are ubiquitous in cognitive science. Such talk is typically expressed more precisely in terms of the granularity of the theory, or in terms of Marr’s three levels. I argue that these ways of understanding levels of description are insufficient to capture the range of different types of theoretical commitments that one can have in cognitive science. When we understand these commitments as points in a multi-dimensional space, we find (...)
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  39.  6
    Keeping Bayesian Models Rational: The Need for an Account of Algorithmic Rationality.David Danks & Frederick Eberhardt - 2011 - Behavioral and Brain Sciences 34 (4):197-197.
    We argue that the authors’ call to integrate Bayesian models more strongly with algorithmic- and implementational-level models must go hand in hand with a call for a fully developed account of algorithmic rationality. Without such an account, the integration of levels would come at the expense of the explanatory benefit that rational models provide.
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  40.  11
    Psychological Theories of Categorizations as Probabilistic Models.David Danks - unknown
    David Danks. Psychological Theories of Categorizations as Probabilistic Models.
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  41.  2
    Learning Causal Structure Through Local Prediction-Error Learning.Sarah Wellen & David Danks - unknown
    Research on human causal learning has largely focused on strength learning, or on computational-level theories; there are few formal algorithmic models of how people learn causal structure from covariations. We introduce a model that learns causal structure in a local manner via prediction-error learning. This local learning is then integrated dynamically into a unified representation of causal structure. The model uses computationally plausible approximations of rational learning, and so represents a hybrid between the associationist and rational paradigms in causal learning (...)
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  42.  6
    Learning the Causal Structure of Overlapping Variable Sets.David Danks - unknown
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  43.  3
    Constraint-Based Human Causal Learning.David Danks - unknown
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  44.  5
    Learning Integrated Structure From Distributed Databases with Overlapping Variables.David Danks - unknown
  45.  1
    Mesochronal Structure Learning.Sergey Pils, David Danks & Jianyu Yang - unknown
    Standard time series structure learning algorithms assume that the measurement timescale is approximately the same as the timescale of the underlying system. In many scientific contexts, however, this assumption is violated: the measurement timescale can be substantially slower than the system timescale. This assumption violation can lead to significant learning errors. In this paper, we provide a novel learning algorithm to extract systemtimescale structure from measurement data that undersample the underlying system. We employ multiple algorithmic optimizations that exploit the problem (...)
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  46.  1
    Rate-Agnostic Structure Learning.Sergey Pils, David Danks, Cynthia Freeman & Vince Calhoun - unknown
    Causal structure learning from time series data is a major scientific challenge. Existing algorithms assume that measurements occur sufficiently quickly; more precisely, they assume that the system and measurement timescales are approximately equal. In many scientific domains, however, measurements occur at a significantly slower rate than the underlying system changes. Moreover, the size of the mismatch between timescales is often unknown. This paper provides three distinct causal structure learning algorithms, all of which discover all dynamic graphs that could explain the (...)
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  47.  1
    Learning with a Purpose: The Influence of Goals.Sarah Wellen & David Danks - unknown
    Most learning models assume, either implicitly or explicitly, that the goal of learning is to acquire a complete and veridical representation of the world, but this view assumes away the possibility that pragmatic goals can play a central role in learning. We propose instead that people are relatively frugal learners, acquiring goal-relevant information while ignoring goal-irrelevant features of the environment. Experiment 1 provides evidence that learning is goal-dependent, and that people are relatively frugal when given a specific, practical goal. Experiment (...)
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  48.  4
    Conceptual Problems in Statistics, Testing and Experimentation.David Danks & Frederick Eberhardt - unknown
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  49.  1
    Learning.David Danks - unknown
    Learning by artificial intelligence systems-what I will typically call machine learning-has a distinguished history, and the field has experienced something of a renaissance in the past twenty years. Machine learning consists principally of a diverse set of algorithms and techniques that have been applied to problems in a wide range of domains. Any overview of the methods and applications will inevitably be incomplete, at least at the level of specific algorithms and techniques. There are many excellent introductions to the formal (...)
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  50.  3
    Causal Learning From Observations and Manipulations.David Danks - unknown
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