Robert Gruber

There is great injustice in the distribution of wellbeing among humans. But the situation may appear even worse once we expand our outlook to include other animals. Even a human who has a decent but not very good life plausibly has a much better life than the life of a mouse or an ant. And yet, if nonhuman animals are typically worse off than humans, does that mean that we should favor distributions that prioritize their wellbeing over the wellbeing of humans? Many of the popular principles of distributive justice imply that we should, and some philosophers view this as a problem. I carefully specify this alleged problem, reject two popular approaches that have been taken to resolve it, and I then explore how a desertist approach to distributive justice handles the problem. I suggest that desertism about distributive justice sometimes favors human over nonhuman wellbeing in distributions. However, I then show that the desertist principle that I advance offers only a very limited justification for favoring human wellbeing over the wellbeing of other animals.

I present the mismatch problem for Act Consequentialism, and I critically evaluate some popular solutions before offering my own solution to a specific version of the problem. The mismatch problem arises for Act Consequentialism when a group could have done better, but no individual in the group had an alternative with a better outcome. In such cases, the theory delivers mismatched verdicts: it condemns what the group does, but it cannot condemn any of the individual acts. In the first chapter of the dissertation, I explain exactly how this problem works. In the next four chapters, I identify a variety of cases that give rise to the mismatch problem, and I explain why the most popular strategies for modifying Act Consequentialism do not get around it. In the final chapter, I introduce a novel taxonomy of problem cases, and I introduce a 'cautious' version of Act Consequentialism that doesn't encounter the mismatch problem for a certain class of case.