David Danks

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 rationality can only properly be assessed for pairs of learning and decision processes. We thus present a formal framework for modeling such pairs of cognitive processes, and thereby assessing their adaptive rationality relative to the environment and the agent’s goals. We then use this high-level formal framework on specific cases by demonstrating how natural formal constraints on decision-making can lead to substantive predictions about adaptively rational learning and representation; and characterizing adaptively rational learning for fast-and-frugal one-reason decision-making.

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 scientific ontologies. This ontological pluralism arises simply from our inability to directly know the world’s objects, rather than any particular claims about our cognitive limits, values, or social structures. I then present two case studies in which this possibility actually occurs—one based on simulations and theoretical analyses of constructed causal systems, and one from actual scientific investigations into the proper ontology for ocean regions

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 lead to changes in the relationships under study, but that standard normative accounts of scientific inquiry have assumed away this problem. At the same time, we show that inference and discovery methods can “protect” themselves in various ways against this possibility by using methods with the novel methodological virtue of “diligence.” Unfortunately, this desirable virtue provably is incompatible with other desirable methodological virtues that are central to reliable inquiry. No scientific method can provide every virtue that we might want

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 indicators of causation, and focus on different types of causation (type vs. token). There are certainly debates in the research field, but the theoretical landscape is not as fractured as Newsome suggests, and a potential unifying framework has already emerged using causal Bayes nets. Philosophical work on causal epistemology matters for psychologists, but not in the way Newsome suggests.

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 structure in order to achieve computational tractability. The resulting algorithm is highly reliable at extracting system-timescale structure from undersampled data

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 and causation,1 and accordingly make different inferences from passive observation than from interventions. A few statisticians aside,” so do most of us. To square these views with the demands of computability, increasing numbers of psychologists and others have embraced a particular formalization, causal Bayes nets, as an account of human reasoning about and to causal connections.111 Such structures can be used by rational agents, including humans in so far as they are rational, to have degrees of belief in various conceptual contents, which they use to reason to expectations, which are realized or defeated by sensory inputs, which cause them to change their degrees of belief in other contents in accord with Bayes Rule, or some generalization of it. How is all of this supposed to be carried out? l. Representing Causal Structures The causal Bayes net framework adopted by a growing number of psychologists goes like this: Our representations of causal relations are captured in a graphical causal.

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 briefly review Pascal’s original wager about whether to live a pious life, including two classes of objections that were almost immediately leveled against his argument. I then show that analogues of those objections apply straightforwardly to the modern versions of Pascal’s wager. Mass electronic surveillance might be ethically permissible or even obligatory in some circumstances, but the details matter in ways that are systematically ignored or assumed away in a Pascal’s wager-type argument. Careful consideration of the complexities of our modern situation is required to decide whether consequentialist considerations override deontological principles in this domain

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 of the shared cognitive and mathematical properties of those models.