David Danks

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 structure or parameters, and requires significantly less memory in realistic settings. We show by simulation that the algorithm performs comparably to batch-mode learning when the causal structure is stationary, and significantly better in non-stationary environments.

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 and statistical details of machine learning algorithms and techniques available elsewhere. The present chapter focuses on machine learning as a general way of “thinking about the world,” and provides a high-level characterization of the major goals of machine learning. There are a number of philosophical concerns that have been raised about machine learning, but upon closer examination, it is not always clear whether the objections really speak against machine learning specifically. Many seem rather to be directed towards machine learning as a particular instantiation of some more general phenomenon or process. One of the general morals of this chapter is that machine learning is, in many ways, less unusual or peculiar than is sometimes thought

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 2 investigates possible mechanisms underlying this effect, and finds evidence that people exhibit goal-driven attention allocation, but not goaldriven reasoning. We conclude by examining how frugality can be integrated into Bayesian models of learning

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 criteria for individual and group epistemic rationality, and then prove that the four definitions diverge, in the sense that individuals will be judged rational when groups are not and vice versa. We conclude by explaining implications of the inconsistency thesis for (i) descriptive history and sociology of science and (ii) normative prescriptions for scientific communities.

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 posterior expected utility. Experimental confirmation of the models, however, has been claimed because of groups of agents that probability match the posterior. Probability matching only constitutes support for the Bayesian claim if additional unobvious and untested (but testable) assumptions are invoked. The alternative strategy of weakening the underlying notion of rationality no longer distinguishes the Bayesian model uniquely. A new account of rationality—either for inference or for decision-making—is required to successfully confirm Bayesian models in cognitive science.

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 arise throughout everyday life: Did this pressing of the ‘k’ key cause this change in the pixels on the computer monitor? Did these episodes of smoking cause this lung cancer? Did this studying cause this test score? And so on. These questions all ask about singular causation in a particular situation, in contrast with general causation across multiple cases. They are thus particularly salient in situations in which we care about that specific case, as in many legal contexts, social interactions, physical explanations of anomalous events, and more

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 from many standard views: it is graded, context-sensitive, and extrinsic

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 a variety of conditions. In contrast, one component of the normative theory focuses on learning from a sample drawn from a population under some experimental or observational study regime. Through a virtual Causality Lab that embodies the normative theory of causal reasoning and which allows us to record student behavior, we have begun to systematically explore how best to teach the normative theory. In this paper we explain the overall project and report on pilot studies which suggest that students can quickly be taught to (appear to) be quite rational