Pittsburgh, Pennsylvania, United States of America
Areas of Interest
 Epistemology Philosophy of Mind Philosophy of Cognitive Science General Philosophy of Science
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##### The Independence Thesis: When Individual and Social Epistemology Diverge with Conor Mayo-Wilson and Kevin J. S. Zollman Philosophy of Science 78 (4): 653-677. 2011.
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…Read more
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##### Wisdom of the Crowds vs. Groupthink: Learning in Groups and in Isolation with Conor Mayo-Wilson and Kevin Zollman 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
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##### Confirmation in the Cognitive Sciences: The Problematic Case of Bayesian Models (review) with Frederick Eberhardt Minds and Machines 21 (3): 389-410. 2011.
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 tha…Read more
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##### 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
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##### Actual causation: a stone soup essay with Clark Glymour, Bruce Glymour, Frederick Eberhardt, Joseph Ramsey, and Richard Scheines Synthese 175 (2): 169-192. 2010.
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
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##### Explaining norms and norms explained with Frederick Eberhardt Behavioral and Brain Sciences 32 (1): 86-87. 2009.
Oaksford &amp; Chater (O&amp;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&amp;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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##### Reasons as Causes in Bayesian Epistemology with Clark Glymour 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
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##### Linearity Properties of Bayes Nets with Binary Variables with Clark Glymour
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…Read more
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##### 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
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##### Erratum to: Model change and methodological virtues in scientific inference with Erich Kummerfeld Synthese 191 (14): 3469-3472. 2014.
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 d…Read more
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##### Causal discovery algorithms: A practical guide with Daniel Malinsky 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
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##### Biological codes and topological causation with Benjamin Jantzen Philosophy of Science 75 (3): 259-277. 2008.
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 code…Read more
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##### Functions and Cognitive Bases for the Concept of Actual Causation Erkenntnis 78 (1): 111-128. 2013.
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 …Read more
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##### Teaching the normative theory of causal reasoning with Richard Scheines and Matt Easterday In Alison Gopnik & Laura Schulz (eds.), Causal Learning: Psychology, Philosophy, and Computation, Oxford University Press. pp. 119--38. 2007.
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 …Read more
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##### “Trust but Verify”: The Difficulty of Trusting Autonomous Weapons Systems with Heather M. Roff Journal of Military Ethics 17 (1): 2-20. 2018.
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
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##### 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
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##### Online Causal Structure Learning
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…Read more
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##### The supposed competition between theories of human causal inference Philosophical Psychology 18 (2). 2005.
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
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##### 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
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##### Model change and reliability in scientific inference with Erich Kummerfeld 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
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##### Theory Unification and Graphical Models in Human Categorization Causal Learning 173--189. 2010.
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 p…Read more
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##### Data Driven Methods for Nonlinear Granger Causality: Climate Teleconnection Mechanisms with Tianjiao Chu and Clark Glymour
Tianjaou Chu, David Danks, and Clark Glymour. Data Driven Methods for Nonlinear Granger Causality: Climate Teleconnection Mechanisms
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##### 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
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##### 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
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##### 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
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##### Keeping Bayesian models rational: The need for an account of algorithmic rationality with Frederick Eberhardt Behavioral and Brain Sciences 34 (4): 197-197. 2011.
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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##### Mixtures and Psychological Inference with Resting State fMRI with Joseph McCaffrey 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