Jonathan Matheson

The philosophical significance of attitudinal autonomy—viz., the autonomy of attitudes such as beliefs—is widely discussed in the literature on moral responsibility and free will. Within this literature, a key debate centres around the following question: is the kind of attitudinal autonomy that’s relevant to moral responsibility at a given time determined entirely by a subject’s present mental structure at that time? Internalists say ‘yes’, externalists say ’no’. In this essay, I motivate a kind of distinctly epistemic attitudinal autonomy, attitudinal autonomy that is relevant to knowledge. I argue that regardless of whether we are externalists or internalists about the kind of attitudinal autonomy that is relevant for moral responsibility, we should be externalists about the kind of autonomy that a belief must have to qualify as knowledge.

The philosophical significance of attitudinal autonomy—viz., the autonomy of attitudes such as beliefs—is widely discussed in the literature on moral responsibility and free will. Within this literature, a key debate centres around the following question: is the kind of attitudinal autonomy that’s relevant to moral responsibility at a given time determined entirely by a subject’s present mental structure at that time? Internalists say ‘yes’, externalists say ’no’. In this essay, I motivate a kind of distinctly epistemic attitudinal autonomy, attitudinal autonomy that is relevant to knowledge. I argue that regardless of whether we are externalists or internalists about the kind of attitudinal autonomy that is relevant for moral responsibility, we should be externalists about the kind of autonomy that a belief must have to qualify as knowledge.

What is the aim of inquiry? One notable answer in recent epistemology answers this question with “understanding,” rather than just with true belief or knowledge. A common rationale is that true belief and knowledge can be gained by “offloading” cognitive work to others, where offloading cognitive work prevents one from satisfying curiosity of the sort that is needed to properly “close” inquiry. If this is right, then it looks like the very idea that understanding is the aim of inquiry seems to fit closely with the related idea that the aim of inquiry is one we attain not through outsourcing or epistemic dependence, but just through our own efforts. This chapter challenges this association by bringing together work on the nature of understanding in epistemology with recent work on transactive memory and cognitive enhancement. It is shown that even if we grant the premise that only through the satisfaction of curiosity is the aim of a given inquiry properly closed, this aim can be attained through (even extensive) offloading; the argument develops appeals to (i) via offloading in the case of interactive cueing in the case of transactive memory systems and (ii) understanding via offloading in the case of biomedical cognitively enhanced understanding. The result is a new space for vindicating understanding as the aim of inquiry that is not essentially tied to the idea that to play this role understanding must stand in tension with (various kinds of) offloading, outsourcing, and dependence.

We describe a broad class of problems, called “uncompromising problems,” which are characterized by the requirement that solutions must perform optimally on each of many test cases. Many of the problems that have long motivated genetic programming research, including the automation of many traditional programming tasks, are uncompromising. We describe and analyze the recently proposed “lexicase” parent selection algorithm and show that it can facilitate the solution of uncompromising problems by genetic programming. Unlike most traditional parent selection techniques, lexicase selection does not base selection on a fitness value that is aggregated over all test cases; rather, it considers test cases one at a time in random order. We present results comparing lexicase selection to more traditional parent selection methods, including standard tournament selection and implicit fitness sharing, on four uncompromising problems: 1) finding terms in finite algebras; 2) designing digital multipliers; 3) counting words in files; and 4) performing symbolic regression of the factorial function. We provide evidence that lexicase selection maintains higher levels of population diversity than other selection methods, which may partially explain its utility as a parent selection algorithm in the context of uncompromising problems.