Jan Sprenger’s research while affiliated with University of Turin and other places

This paper generalizes the preface paradox beyond the conjunctive aggregation of beliefs and constructs an analogous paradox for deontic reasoning. The analysis of the deontic case suggests a systematic restriction of intuitive rules for reasoning with obligations. This proposal can be transferred to the epistemic case: It avoids the preface and the lottery paradox and saves one of the two directions of the Lockean Thesis (i.e. high credence is sufficient, but not necessary for rational belief). The resulting account compares favorably to competing proposals; in particular, we can formulate the rules of correct doxastic reasoning without reference to probabilistic features of the involved propositions.

The explanation of psychological phenomena is a central aim of psychological science. However, the nature of explanation and the processes by which we evaluate whether a theory explains a phenomenon are often unclear. Consequently, it is often unknown whether a given psychological theory indeed explains a phenomenon. We address this shortcoming by proposing a productive account of explanation: a theory explains a phenomenon to some degree if and only if a formal model of the theory produces the statistical pattern representing the phenomenon. Using this account, we outline a workable methodology of explanation: (a) explicating a verbal theory into a formal model, (b) representing phenomena as statistical patterns in data, and (c) assessing whether the formal model produces these statistical patterns. In addition, we provide three major criteria for evaluating the goodness of an explanation (precision, robustness, and empirical relevance), and examine some cases of explanatory breakdowns. Finally, we situate our framework within existing theories of explanation from philosophy of science and discuss how our approach contributes to constructing and developing better psychological theories.

Indicative conditionals and tendency causal claims are closely related (e.g., Frosch and Byrne, 2012), but despite these connections, they are usually studied separately. A unifying framework could consist in their dependence on probabilistic factors such as high conditional probability and statistical relevance (e.g., Adams, 1975; Eells, 1991; Douven, 2008, 2015). This paper presents a comparative empirical study on differences between judgments on tendency causal claims and indicative conditionals, how these judgments are driven by probabilistic factors, and how these factors differ in their predictive power for both causal and conditional claims.

In conditional logics the law of Import-Export states the equivalence between right-nested sentences of the form “if A, then if B, then C” and their simplification “if A and B, then C”. While Import-Export appears as a plausible principle of conditional reasoning, Allan Gibbard proved that under certain assumptions, a conditional operator cannot satisfy this principle without collapsing to the material conditional of two-valued logic. Gibbard’s result is often taken to pose a dilemma for a semantic account of indicative conditionals: either give up Import-Export, or other plausible properties such as supraclassicality or the logical implication from the indicative to the material conditional. This chapter examines how this dilemma may be averted in trivalent logics based on Reichenbach’s and de Finetti’s idea that a conditional with a false antecedent is undefined.KeywordsIndicative conditionalMaterial conditionalLogics of conditionalsTrivalent logicGibbardian collapseImport-Export

Causal Modeling Semantics (CMS, e.g., Galles and Pearl 1998; Pearl 2000; Halpern 2000) is a powerful framework for evaluating counterfactuals whose antecedent is a conjunction of atomic formulas. We extend CMS to an evaluation of the probability of counterfactuals with disjunctive antecedents, and more generally, to counterfactuals whose antecedent is an arbitrary Boolean combination of atomic formulas. Our main idea is to assign a probability to a counterfactual (A v B) > C at a causal model M as a weighted average of the probability of C in those submodels that truthmake A v B (Briggs 2012; Fine 2016, 2017). The weights of the submodels are given by the inverse distance to the original model M, based on a distance metric proposed by Eva, Stern, and Hartmann (2019). Apart from solving a major problem in the epistemology of counterfactuals, our paper shows how work in semantics, causal inference and formal epistemology can be fruitfully combined.

A tradition that goes back to Sir Karl R. Popper assesses the value of a statistical test primarily by its severity: was there an honest and stringent attempt to prove the tested hypothesis wrong? For "error statisticians" such as Mayo (1996, 2018), and frequentists more generally, severity is a key virtue in hypothesis tests. Conversely, failure to incorporate severity into statistical inference, as allegedly happens in Bayesian inference, counts as a major methodological shortcoming. Our paper pursues a double goal: First, we argue that the error-statistical explication of severity has substantive drawbacks; specifically, the neglect of research context and the specificity of the predictions of the hypothesis. Second, we argue that severity matters for Bayesian inference via the value of specific, risky predictions: severity boosts the expected evidential value of a Bayesian hypothesis test. We illustrate severity-based reasoning in Bayesian statistics by means of a practical example and discuss its advantages and potential drawbacks.

Research on indicative conditionals usually aims either at determining their truth conditions, or at explaining how we should reason with them and when we can assert them. This paper integrates these semantic and epistemological projects by means of articulating trivalent, truth-functional truth conditions for indicative conditionals. Based on this framework, we provide a non-classical account of the probability of conditionals, and two logics of conditional reasoning: (i) a logic C of inference from certain premises that generalizes deductive reasoning; and (ii) a logic U of inference from uncertain premises that generalizes defeasible reasoning. Both logics are highly attractive in their domain. They provide a unified framework for conditional reasoning, generalize existing theories (e.g., Adams's logic of "reasonable inference") and yield an insightful analysis of the controversies about the validity of Modus Ponens, Import-Export, and other principles of conditional logic.

The finding that intuitions about the reference of proper names vary cross-culturally (Machery et al. Cognition 92: 1–12. 2004) was one of the early milestones in experimental philosophy. Many follow-up studies investigated the scope and magnitude of such cross-cultural effects, but our paper provides the first systematic meta-analysis of studies replicating (Machery et al. Cognition 92: 1–12. 2004). In the light of our results, we assess the existence and significance of cross-cultural effects for intuitions about the reference of proper names.