Katie Steele

Summary 1. Ecologists and conservation biologists consider many issues when designing a field study, such as the expected value of the data, the interests of the study species, the welfare of individual organisms and the cost of the project. These different issues or values often conflict; however, neither animal ethics nor environmental ethics provides practical guidance on how to assess trade-offs between them. 2. We developed a decision framework for considering trade-offs between values in ecological research, drawing on the field of ecological ethics. We used a case study of the population genetics of three frog species, in which a researcher must choose between four methods of sampling DNA from the study animals. We measured species welfare as the reduction in population growth rate following sampling, and assessed individual welfare using two different definitions: (i) the level of suffering experienced by an animal, and (ii) the level of suffering combined with loss of future life. 3. Tipping the tails of tadpoles ranked as the best sampling method for species welfare, while collecting whole tadpoles and buccal swabbing of adult frogs ranked best for the first and second definitions of individual welfare, respectively. Toe clipping of adult frogs ranked as the worst sampling method for species welfare and the first definition of individual welfare, and equal worst for the second definition of individual welfare. 4. When considering species and individual welfare simultaneously, toe clipping was clearly inferior to the other sampling methods, but no single sampling method was clearly superior to the other three. Buccal swabbing, collecting tadpoles and tail tipping were all preferred options, depending on the definition of individual welfare and the level of precision with which we assessed species welfare. 5.Synthesis and applications. The decision framework we present can be used by ecologists to assess ethical and other trade-offs when planning field studies. A formal decision analysis makes transparent how a researcher might negotiate competing ethical, financial and practical objectives. Defining the components of the decision in this way can help avoid errors associated with human judgement and linguistic uncertainty.

Towards the end of Decision Theory with a Human Face, Richard Bradley discusses various ways a rational yet human agent, who, due to lack of evidence, is unable to make some fine-grained credibility judgments, may nonetheless make systematic decisions. One proposal is that such an agent can simply “reach judgments” on the fly, as needed for decision making. In effect, she can adopt a precise probability function to serve as proxy for her imprecise credences at the point of decision, and then subsequently abandon the proxy as she proceeds to learn more about the world. Contra Bradley, I argue that an agent who employs this strategy does not necessarily act like a precise Bayesian, since she is not necessarily immune to sure loss in diachronic, as well as synchronic, settings. I go on to suggest a method for determining a proxy probability function whereby the agent does act like a precise Bayesian, so understood.

This chapter considers the controversial relationship between dynamic choice models, which depict a series of choices over time, and the more familiar static choice models, which depict a single ‘one-shot-only’ decision. An initial issue concerns how to reconcile the normative advice of these two models: Should an agent take account of the broader dynamic context when making a decision, and if so, in a sophisticated manner, or rather in a resolute manner? Further controversies concern what the dynamic implications of an agent’s preferences reveal about the rationality of these preferences.

This article argues that common intuitions regarding (a) the specialness of ‘use-novel’ data for confirmation and (b) that this specialness implies the ‘no-double-counting rule’, which says that data used in ‘constructing’ (calibrating) a model cannot also play a role in confirming the model’s predictions, are too crude. The intuitions in question are pertinent in all the sciences, but we appeal to a climate science case study to illustrate what is at stake. Our strategy is to analyse the intuitive claims in light of prominent accounts of confirmation of model predictions. We show that on the Bayesian account of confirmation, and also on the standard classical hypothesis-testing account, claims (a) and (b) are not generally true; but for some select cases, it is possible to distinguish data used for calibration from use-novel data, where only the latter confirm. The more specialized classical model-selection methods, on the other hand, uphold a nuanced version of claim (a), but this comes apart from (b), which must be rejected in favour of a more refined account of the relationship between calibration and confirmation. Thus, depending on the framework of confirmation, either the scope or the simplicity of the intuitive position must be revised. 1 Introduction2 A Climate Case Study3 The Bayesian Method vis-à-vis Intuitions4 Classical Tests vis-à-vis Intuitions5 Classical Model-Selection Methods vis-à-vis Intuitions 5.1 Introducing classical model-selection methods 5.2 Two cases6 Re-examining Our Case Study7 Conclusion.

According to Craig Callender [2022], the ‘received view’ across the social sciences is that, when it comes to time and preference, only exponential time discounting is rational. Callender argues that this view is false, even pernicious. Here I endorse what I take to be Callender’s main argument, but only in so far as the received view is understood in a particular way. I go on to propose a different way of understanding the received view that makes it true. In short: When time discounting is suitably conceived, the exponential form of the discounting function is indeed uniquely rational.