Karlis Podnieks

David Hume’s skeptical solution to the problem of induction was grounded in his belief that we learn by means of custom. We consider here how a form of reinforcement learning like custom may allow an agent to learn how to learn in other ways as well. Specifically, an agent may learn by simple reinforcement to adopt new forms of learning that work better than simple reinforcement in the context of specific tasks. We will consider how such a bootstrapping process may lead to a system that includes trial-and-error forms of learning like win-stay/lose-shift, probe and adjust, and simple reinforcement itself together with higher-rationality inferential tools.

This chapter defends a deflationary, or ‘quietist’ account of causation in science. It begins by laying out the elements of four central philosophical theories of causation, namely the regularity, counterfactual, probabilistic and process accounts. It then proceeds to briefly criticise them. While the criticisms are essentially renditions of arguments that are well-known in the literature, the conclusion that is derived from these is new. It is argued that the limitations of each of the theories point to a deflationary or quietist approach to causation in science. The practical focus is on interventionist methods for causal inference in physics and biology, and on the extant debates within the philosophy of each discipline as to how best to approach such methods. It is then argued that none of the four central theories proposed can completely explain or reduce the methods of causal inference that appear in each science. Yet, each theory provides partial insights into some of the grounds of causal inference and causal discovery. It is consequently suggested that we should retain the various methodological lessons without committing expressly to any metaphysics of causation.

Gibbsian statistical mechanics (GSM) is the most widely used version of statistical mechanics among working physicists. Yet a closer look at GSM reveals that it is unclear what the theory actually says and how it bears on experimental practice. The root cause of the difficulties is the status of the averaging principle, the proposition that what we observe in an experiment is the ensemble average of a phase function. We review different stances toward this principle, and eventually present a coherent interpretation of GSM that provides an account of the status and scope of the principle.

Philosophers of science have had little to say about ‘middle-range theory’ although much of what is done in science and of what drives its successes falls under that label. These lectures aim to spark an interest in the topic and to lay groundwork for further research on it. ‘Middle’ in ‘middle range’ is with respect to the level both of abstraction and generality. Much middle-range theory is about things that come under the label ‘mechanism’. The lectures explore three different kinds of mechanism: struc tural mechanisms or underlying systems that afford causal pathways; causal-chain mechanisms that are represented in what in policy contexts are called ‘theories of change’ and for which I give an extended account following the causal process theory of Wesley Salmon; and middle-range-law mechanisms like those discussed by Jon Elster, which I claim are —and rightly are— rampant throughout the social sciences. The theory of the democratic peace, that democracies do not go to war with democracies, serves as a running example. The discussions build up to the start of, first, an argument that reliability in social (and natural) science depends not so much on evidence as it does on the support of a virtuous tangle of practices (without which there couldn’t even be evidence), and second, a defence of a community-practice centred instrumentalist understanding of many of the central basic principles that we use (often successfully) in social (and in natural) science for explanation, prediction and evaluation.

Research on the capacity to understand others' minds has tended to focus on representations ofbeliefs,which are widely taken to be among the most central and basic theory of mind representations. Representations ofknowledge, by contrast, have received comparatively little attention and have often been understood as depending on prior representations of belief. After all, how could one represent someone as knowing something if one does not even represent them as believing it? Drawing on a wide range of methods across cognitive science, we ask whether belief or knowledge is the more basic kind of representation. The evidence indicates that nonhuman primates attribute knowledge but not belief, that knowledge representations arise earlier in human development than belief representations, that the capacity to represent knowledge may remain intact in patient populations even when belief representation is disrupted, that knowledge (but not belief) attributions are likely automatic, and that explicit knowledge attributions are made more quickly than equivalent belief attributions. Critically, the theory of mind representations uncovered by these various methods exhibits a set of signature features clearly indicative of knowledge: they are not modality-specific, they are factive, they are not just true belief, and they allow for representations of egocentric ignorance. We argue that these signature features elucidate the primary function of knowledge representation: facilitating learning from others about the external world. This suggests a new way of understanding theory of mind – one that is focused on understanding others' minds in relation to the actual world, rather than independent from it.