•  71
    Environmental Epistemology
    Synthese 203 (81): 1-24. 2024.
    We argue that there is a large class of questions—specifically questions about how to epistemically evaluate environments that currently available epistemic theories are not well-suited for answering, precisely because these questions are not about the epistemic state of particular agents or groups. For example, if we critique Facebook for being conducive to the spread of misinformation, then we are not thereby critiquing Facebook for being irrational, or lacking knowledge, or failing to testify…Read more
  •  121
    Normative moral theories are frequently invoked to serve one of two distinct purposes: (1) explicate a criterion of rightness, or (2) provide an ethical decision-making procedure. Although a criterion of rightness provides a valuable theoretical ideal, proposed criteria rarely can be (nor are they intended to be) directly translated into a feasible decision-making procedure. This paper applies the computational framework of bounded rationality to moral decision-making to ask: how ought a bounded…Read more
  •  5
    Causal Search, Causal Modeling, and the Folk
    In Justin Sytsma & Wesley Buckwalter (eds.), A Companion to Experimental Philosophy, Wiley. 2016.
    Causal models provide a framework for precisely representing complex causal structures, where specific models can be used to efficiently predict, infer, and explain the world. At the same time, we often do not know the full causal structure a priori and so must learn it from data using a causal model search algorithm. This chapter provides a general overview of causal models and their uses, with a particular focus on causal graphical models (the most commonly used causal modeling framework) and …Read more
  •  125
    A Theory of Causal Learning in Children: Causal Maps and Bayes Nets
    with Alison Gopnik, Clark Glymour, Laura Schulz, and Tamar Kushnir
    Psychological Review 111 (1): 3-32. 2004.
    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. Experimen…Read more
  •  263
    Algorithmic bias: Senses, sources, solutions
    Philosophy Compass 16 (8). 2021.
    Data‐driven algorithms are widely used to make or assist decisions in sensitive domains, including healthcare, social services, education, hiring, and criminal justice. In various cases, such algorithms have preserved or even exacerbated biases against vulnerable communities, sparking a vibrant field of research focused on so‐called algorithmic biases. This research includes work on identification, diagnosis, and response to biases in algorithm‐based decision‐making. This paper aims to facilitat…Read more
  •  15
    Artificial intelligence and humanitarian obligations
    with Daniel Trusilo
    Ethics and Information Technology 25 (1): 1-5. 2023.
    Artificial Intelligence (AI) offers numerous opportunities to improve military Intelligence, Surveillance, and Reconnaissance operations. And, modern militaries recognize the strategic value of reducing civilian harm. Grounded in these two assertions we focus on the transformative potential that AI ISR systems have for improving the respect for and protection of humanitarian relief operations. Specifically, we propose that establishing an interface for humanitarian organizations to military AI I…Read more
  •  62
    Algorithmic Fairness and the Situated Dynamics of Justice
    Canadian Journal of Philosophy 52 (1): 44-60. 2022.
    Machine learning algorithms are increasingly used to shape high-stake allocations, sparking research efforts to orient algorithm design towards ideals of justice and fairness. In this research on algorithmic fairness, normative theorizing has primarily focused on identification of “ideally fair” target states. In this paper, we argue that this preoccupation with target states in abstraction from the situated dynamics of deployment is misguided. We propose a framework that takes dynamic trajector…Read more
  •  26
    Causal Pluralism in Philosophy: Empirical Challenges and Alternative Proposals
    with Phuong Dinh
    Philosophy of Science 88 (5): 761-772. 2021.
    An increasing number of arguments for causal pluralism invoke empirical psychological data. Different aspects of causal cognition—specifically, causal perception and causal inference—are thought to involve distinct cognitive processes and representations, and they thereby distinctively support transference and dependency theories of causation, respectively. We argue that this dualistic picture of causal concepts arises from methodological differences, rather than from an actual plurality of conc…Read more
  •  1277
    Causation: Empirical Trends and Future Directions
    with David Rose
    Philosophy Compass 7 (9): 643-653. 2012.
    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 causa…Read more
  •  1277
    In Defense of a Broad Conception of Experimental Philosophy
    with David Rose
    Metaphilosophy 44 (4): 512-532. 2013.
    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 …Read more
  •  601
    Diversity in Representations; Uniformity in Learning
    with David Rose
    Behavioral and Brain Sciences 33 (2-3): 330-331. 2010.
    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)
  •  1123
    Demoralizing causation
    Philosophical Studies (2): 1-27. 2013.
    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 …Read more
  • Richer Than Reduction
    In David Danks & Emiliano Ippoliti (eds.), Building Theories: Heuristics and Hypotheses in Sciences, Springer International Publishing. 2018.
  •  6
    LPCD framework: Analytical tool or psychological model?
    Behavioral and Brain Sciences 41. 2018.
  •  32
    Building Theories: Heuristics and Hypotheses in Sciences (edited book)
    Springer International Publishing. 2018.
    This book explores new findings on the long-neglected topic of theory construction and discovery, and challenges the orthodox, current division of scientific development into discrete stages: the stage of generation of new hypotheses; the stage of collection of relevant data; the stage of justification of possible theories; and the final stage of selection from among equally confirmed theories. The chapters, written by leading researchers, offer an interdisciplinary perspective on various aspect…Read more
  •  80
    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
  •  39
    Mixtures and Psychological Inference with Resting State fMRI
    British Journal for the Philosophy of Science 73 (3): 583-611. 2022.
    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
  •  102
    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
  •  33
    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
  •  1
    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
  •  79
    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
  •  36
    The Psychology of Causal Perception and Reasoning
    In Helen Beebee, Christopher Hitchcock & Peter Menzies (eds.), The Oxford Handbook of Causation, Oxford University Press. 2009.
  •  31
    Even if one can experiment on relevant factors, learning the causal structure of a dynamical system can be quite difficult if the relevant measurement processes occur at a much slower sampling rate than the “true” underlying dynamics. This problem is exacerbated if the degree of mismatch is unknown. This paper gives a formal characterization of this learning problem, and then provides two sets of results. First, we prove a set of theorems characterizing how causal structures change under undersa…Read more