Higher-Order Defeat and Doxastic Resilience

It seems obvious that when higher-order evidence makes it rational for one to doubt that one’s own belief on some matter is rational, this can undermine the rationality of that belief. This is known as higher-order defeat. However, despite its intuitive plausibility, it has proved puzzling how higher-order defeat works, exactly. To highlight two prominent sources of puzzlement, higher-order defeat seems to defy being understood in terms of conditionalization; and higher-order defeat can sometimes place agents in what seem like epistemic dilemmas. This chapter draws attention to an overlooked aspect of higher-order defeat, namely that it can undermine the resilience of one’s beliefs. The notion of resilience was originally devised to understand how one should reflect the ‘weight’ of one’s evidence. But it can also be applied to understand how one should reflect one’s higher-order evidence. The idea is particularly useful for understanding cases where one’s higher-order evidence indicates that one has failed in correctly assessing the evidence, without indicating whether one has over- or underestimated the degree of evidential support for a proposition. But it is exactly in such cases that the puzzles of higher-order defeat seem most compelling

Epistemic instrumentalism, permissibility, and reasons for belief

Epistemic instrumentalists seek to understand the normativity of epistemic norms on the model practical instrumental norms governing the relation between aims and means. Non-instrumentalists often object that this commits instrumentalists to implausible epistemic assessments. I argue that this objection presupposes an implausibly strong interpretation of epistemic norms. Once we realize that epistemic norms should be understood in terms of permissibility rather than obligation, and that evidence only occasionally provide normative reasons for belief, an instrumentalist account becomes available that delivers the correct epistemic verdicts. On this account, epistemic permissibility can be understood on the model of the wide-scope instrumental norm for instrumental rationality, while normative evidential reasons for belief can be understood in terms of instrumental transmission

Making Conditional Speech Acts in the Material Way

The conventional wisdom about conditionals claims that (1) conditionals that have non-assertive acts in their consequents, such as commands and promises, cannot be plausibly interpreted as assertions of material implication; (2) the most promising hypothesis about those sentences is conditional-assertion theory, which explains a conditional as a conditional speech act, i.e., a performance of a speech act given the assumption of the antecedent. This hypothesis has far-reaching and revisionist consequences, because conditional speech acts are not synonymous with a proposition with truth conditions. This paper argues against this view in two steps. First, it presents a battery of objections against conditional-assertion theory. Second, it argues that those examples can be convincingly interpreted as assertions of material implication

Unbiased split selection for classification trees based on the Gini Index

The Gini gain is one of the most common variable selection criteria in machine learning. We derive the exact distribution of the maximally selected Gini gain in the context of binary classification using continuous predictors by means of a combinatorial approach. This distribution provides a formal support for variable selection bias in favor of variables with a high amount of missing values when the Gini gain is used as split selection criterion, and we suggest to use the resulting p-value as an unbiased split selection criterion in recursive partitioning algorithms. We demonstrate the efficiency of our novel method in simulation- and real data- studies from veterinary gynecology in the context of binary classification and continuous predictor variables with different numbers of missing values. Our method is extendible to categorical and ordinal predictor variables and to other split selection criteria such as the cross-entropy criterion