• Richer Than Reduction
    In David Danks & Emiliano Ippoliti (eds.), Building Theories: Heuristics and Hypotheses in Sciences, Springer Verlag. 2018.
  •  2
    LPCD framework: Analytical tool or psychological model?
    Behavioral and Brain Sciences 41. 2018.
  •  4
    Building Theories: Heuristics and Hypotheses in Sciences (edited book)
    Springer International Publishing. 2018.
  •  47
    ABSTRACTAutonomous weapons systems pose many challenges in complex battlefield environments. Previous discussions of them have largely focused on technological or policy issues. In contrast, we focus here on the challenge of trust in an AWS. One type of human trust depends only on judgments about the predictability or reliability of the trustee, and so are suitable for all manner of artifacts. However, AWSs that are worthy of the descriptor “autonomous” will not exhibit the required strong predi…Read more
  •  21
    Mixtures and Psychological Inference with Resting State fMRI
    British Journal for the Philosophy of Science. forthcoming.
    In this essay, we examine the use of resting state fMRI data for psychological inferences. We argue that resting state studies hold the paired promises of discovering novel functional brain networks, and of avoiding some of the limitations of task-based fMRI. However, we argue that the very features of experimental design that enable resting state fMRI to support exploratory science also generate a novel confound. We argue that seemingly key features of resting state functional connectivity netw…Read more
  •  63
    Causal discovery algorithms: A practical guide
    Philosophy Compass 13 (1). 2018.
    Many investigations into the world, including philosophical ones, aim to discover causal knowledge, and many experimental methods have been developed to assist in causal discovery. More recently, algorithms have emerged that can also learn causal structure from purely or mostly observational data, as well as experimental data. These methods have started to be applied in various philosophical contexts, such as debates about our concepts of free will and determinism. This paper provides a “user's …Read more
  •  23
    Amalgamating evidence of dynamics
    with Sergey Plis
    Synthese 196 (8): 3213-3230. 2019.
    Many approaches to evidence amalgamation focus on relatively static information or evidence: the data to be amalgamated involve different variables, contexts, or experiments, but not measurements over extended periods of time. However, much of scientific inquiry focuses on dynamical systems; the system’s behavior over time is critical. Moreover, novel problems of evidence amalgamation arise in these contexts. First, data can be collected at different measurement timescales, where potentially non…Read more
  • The Epistemology of Causal Judgment
    Dissertation, University of California, San Diego. 2001.
    We make constant use of causal beliefs in our everyday lives without giving much thought to the source of those beliefs, even for situations about which we have no specific prior causal knowledge. We can ask two distinct types of questions about these causal judgments: descriptive questions and normative questions . The primary goal of this dissertation is to apply normative research on causal judgment to our descriptive theories. ;I begin this dissertation by describing the primary results of r…Read more
  •  451
    Wisdom of the Crowds vs. Groupthink: Learning in Groups and in Isolation
    International Journal of Game Theory 42 (3): 695-723. 2013.
    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, commo…Read more
  •  26
    Dynamical Causal Learning
    with Thomas L. Griffiths and Joshua B. Tenenbaum
    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
  •  150
    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…Read more
  •  27
    Tianjaou Chu, David Danks, and Clark Glymour. Data Driven Methods for Nonlinear Granger Causality: Climate Teleconnection Mechanisms
  •  161
    Scientific coherence and the fusion of experimental results
    British Journal for the Philosophy of Science 56 (4): 791-807. 2005.
    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 qu…Read more
  •  13
    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…Read more
  •  66
    Goal-dependence in ontology
    Synthese 192 (11): 3601-3616. 2015.
    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 goal-dependent pluralism in our…Read more
  •  23
    Adaptively Rational Learning
    with Sarah Wellen
    Minds and Machines 26 (1-2): 87-102. 2016.
    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 ration…Read more
  •  32
    Model change and reliability in scientific inference
    Synthese 191 (12): 2673-2693. 2014.
    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 …Read more
  •  38
    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 mechani…Read more
  •  18
    David Danks. Psychological Theories of Categorizations as Probabilistic Models
  •  10
    Mesochronal Structure Learning
    with Sergey Pils and Jianyu Yang
    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 undersampl…Read more
  •  142
    Explaining norms and norms explained
    Behavioral and Brain Sciences 32 (1): 86-87. 2009.
    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
  •  141
    Reasons as Causes in Bayesian Epistemology
    Journal of Philosophy 104 (9): 464-474. 2007.
    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 betwee…Read more
  •  44
    A Modern Pascal's Wager for Mass Electronic Surveillance
    Télos 2014 (169): 155-161. 2014.
    Debates about the moral permissibility of mass electronic surveillance often turn on whether consequentialist considerations legitimately trump relevant deontological rights and principles. In order to establish such overriding consequences, many proponents of mass surveillance employ a modern analogue of Pascal’s wager: they contend that the consequences of no surveillance are so severe that any probability of such outcomes legitimates the abrogation of the relevant rights. In this paper, I bri…Read more
  •  14
    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 l…Read more
  •  32
    Not different kinds, just special cases
    Behavioral and Brain Sciences 33 (2-3): 208-209. 2010.
    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