Jan Sprenger

Belnap (1970, 1973) proposed to formalize the restriction of first-order quantifiers ∀ and ∃ by means of a single sentential connective: a trivalent conditional that takes the semantic value ‘void’ when the antecedent is false. Thus, ‘every A is B’ is represented as ∀x(Ax → Bx) and ‘some A is B’ is represented as ∃x(Ax → Bx)—a notable unification compared to the standard representation in first-order logic that uses distinct connectives. This paper implements Belnap’s program, henceforth called Conditional Reduction, in full generality and applies it to all conservative ⟨1, 1⟩ quantifiers. By combining Conditional Reductions with the Conservativity Theorem, due to Keenan and Stavi, any conservative type ⟨1, 1⟩ quantifier turns out to be equivalent to Boolean combinations of three basic type ⟨1⟩ quantifiers (‘everything’, ‘at least λ many things’ and the possessives) scoping over a trivalent conditional.

Belnap (1970, 1973) proposed to formalize the restriction of first-order quantifiers ∀ and ∃ by means of a single sentential connective: a trivalent conditional that takes the semantic value ‘void’ when the antecedent is false. Thus, ‘every A is B’ is represented as ∀x(Ax → Bx) and ‘some A is B’ is represented as ∃x(Ax → Bx)—a notable unification compared to the standard representation in first-order logic that uses distinct connectives. This paper implements Belnap’s program, henceforth called Conditional Reduction, in full generality and applies it to all conservative ⟨1, 1⟩ quantifiers. By combining Conditional Reductions with the Conservativity Theorem, due to Keenan and Stavi, any conservative type ⟨1, 1⟩ quantifier turns out to be equivalent to Boolean combinations of three basic type ⟨1⟩ quantifiers (‘everything’, ‘at least λ many things’ and the possessives) scoping over a trivalent conditional.

This paper aims at an integrated account of truth, probability and acceptance conditions of counterfactuals. The basic idea is that truth conditions are an abstract semantic device: they are not directly tested against semantic judgments, but determine the probability of a sentence, and via probabilistic acceptance conditions, they generate predictions of semantic judgments. The proposed account has two notable features: first, the probability of counterfactuals can be understood in a purely subjective sense, without reference to objective chance, and second, Lewis-style truth conditions for counterfactuals that quantify over multiple scenarios can be interpreted as describing their acceptance conditions.

Group judgments are often influenced by their members’ individual expertise. It is less clear, though, how individual expertise should affect the group judgments. This chapter surveys a wide range of models of opinion aggregation and group judgment: models where all group members have the same impact on the group judgment, models that take into account differences in individual accuracy, and models where group members revise their beliefs as a function of their mutual respect. The scope of these models covers the aggregation of propositional attitudes, probability functions, and numerical estimates. By comparing these different kinds of models and contrasting them with findings in psychology, management science, and the expert judgment literature, the chapter provides a better understanding of the role of expertise in group agency, both from a theoretical and from an empirical perspective.

Trivalent theories of indicative conditionals are thought to be at odds with trivalent theories of presupposition, and in particular, with an adequate analysis of the presuppositions of conditionals. We address this challenge based on (i) a semantics for conditionals and modals that combines trivalence with context updates, and (ii) a principle for felicitous assertion that we call Avoid Void: do not assert a sentence that takes the value void everywhere in the context. This account highlights the semantic differences between conditionals and other presupposition carriers, and yields the intuitively correct presuppositions of conditionals. Finally, we develop a trivalent logic C# ("C-sharp"), which implements Avoid Void at the level of reasoning. This yields a Strawsonian notion of entailment, on which false antecedents as well as contradictory premises do not allow one to infer arbitrary information.

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.

This paper provides a new framework for formalizing conditional obligations in natural language: it pairs a unary deontic operator with trivalent semantics for the indicative conditional and Kratzer's assumption that the antecedents of conditionals restrict the scope of modals in the consequent. Combining these three ideas, we obtain a fully compositional theory of "if" and "ought'" that validates plausible principles for deontic reasoning. Moreover, it resolves classical challenges such as the "if A then ought A" problem, the paradox of the miners and the modeling of contrary-to-duty obligations (e.g., Chisholm's quartet). All in all, our proposal is attractive from a logical point of view and squares well with general theories of natural language reasoning.

This paper extends a trivalent semantics for indicative conditionals to a language including the modal operators "might" and "must". Specifically, we combine Cooper's (1968) truth-functional, trivalent analysis of the conditional connective with Kratzer's (1986, 2012) idea that if-clauses restrict modal operators. By hard-wiring both trivalence and the restriction operation into the truth conditions of conditional-modal expressions, we obtain an attractive theory that yields plausible predictions for the interaction of conditionals and modals, explains the intuitive appeal of the Restrictor View and avoids the typical problems of Kratzer-style accounts, especially regarding the probability of conditional-modal expressions.

This paper defines a precise sense in which the material conditional analysis (MCA) is a successful heuristic for deductive reasoning with a suppositional conditional, interpreted by means of trivalent semantics. Both accounts generate the same theorems and valid deductive inferences in a large fragment of the conditional language. However, the suppositional analysis gives a more attractive treatment of conditional negation and the probability of conditionals. Therefore, this paper inverts Williamson's claim that suppositional reasoning is a heuristic for valid reasoning with conditionals (on his account, given by MCA). Similarly, the logic C2 of Stalnaker's possible-world semantics for conditionals is interpreted as approximating probability-preserving inference with suppositional conditionals.

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 ∨ B) € C at a causal model M as a weighted average of the probability of C in those submodels that truthmake A ∨ 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.

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.

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.

This paper develops a trivalent semantics for the truth conditions and the probability of the natural language indicative conditional. Our framework rests on trivalent truth conditions first proposed by Cooper (1968) and Belnap (1973) and it yields two logics of conditional reasoning: (i) a logic C of certainty-preserving inference; and (ii) a logic U for uncertain reasoning that preserves the probability of the premises. We show systematic correspondences between trivalent and probabilistic representations of inferences in either framework, and we use the distinction between the two systems to cast light on the validity of inferences such as Modus Ponens, Or-To-If, and Conditional Excluded Middle. Specifically, the conditional behaves monotonically in C, but non-monotonically in U; Modus Ponens is valid in C, but valid in U only for non-nested conditionals. The result is a unified account of the semantics and epistemology of indicative conditionals that can be fruitfully applied to analyzing the validity of conditional inferences.

This paper presents a unified theory of the truth conditions and probability of indicative conditionals and their compounds in a trivalent framework. The semantics validates a Reduction Theorem: any compound of conditionals is semantically equivalent to a simple conditional. This allows us to validate Stalnaker's Thesis in full generality and to use Adams's notion of $p$-validity as a criterion for valid inference. Finally, this gives us an elegant account of Bayesian update with indicative conditionals, establishing that despite differences in meaning, it is tantamount to learning a material conditional.

In this paper we argue that there is a kind of moral disagreement that survives the Rawlsian veil of ignorance. While a veil of ignorance eliminates sources of disagreement stemming from self-interest, it does not do anything to eliminate deeper sources of disagreement. These disagreements not only persist, but transform their structure once behind the veil of ignorance. We consider formal frameworks for exploring these differences in structure between interested and disinterested disagreement, and argue that consensus models offer us a solution concept for disagreements behind the veil of ignorance.

Social epistemology is a relatively new and booming field of research. It studies the social dimension of the pursuit of acquiring true beliefs and requires philosophical as well as sociological and economic expertise. The insights gained in social epistemology are not only of theoretical interest; they also improve our understanding of social and political processes, as the field includes the analysis of group deliberation and group decision-making. However, surprisingly little work has so far been done on the epistemic properties of group deliberation, belief aggregation, and decision-making procedures. To close this gap, the construction and analysis of formal models are especially promising as formal modeling combines representational adequacy with instructive analytical results. This special issue collects papers that follow this strategy from the point of view of different disciplines.

Group judgements are often – implicitly or explicitly – influenced by their members’ individual expertise. However, given that expertise is seldom recognized fully and that some distortions may occur (bias, correlation, etc.), it is not clear that differential weighting is an epistemically advantageous strategy with respect to straight averaging. Our paper characterizes a wide set of conditions under which differential weighting outperforms straight averaging and embeds the results into the multidisciplinary group decision-making literature.

The enduring replication crisis in many scientific disciplines casts doubt on the ability of science to estimate effect sizes accurately, and in a wider sense, to self-correct its findings and to produce reliable knowledge. We investigate the merits of a particular countermeasure—replacing null hypothesis significance testing with Bayesian inference—in the context of the meta-analytic aggregation of effect sizes. In particular, we elaborate on the advantages of this Bayesian reform proposal under conditions of publication bias and other methodological imperfections that are typical of experimental research in the behavioral sciences. Moving to Bayesian statistics would not solve the replication crisis single-handedly. However, the move would eliminate important sources of effect size overestimation for the conditions we study.

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 ∨ B) > C at a causal model M as a weighted average of the probability of C in those submodels that truthmake A∨ 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.

The rational price of the Pasadena and Altadena games, introduced by Nover and Hájek (2004 ), has been the subject of considerable discussion. Easwaran (2008 ) has suggested that weak expectations — the value to which the average payoffs converge in probability — can give the rational price of such games. We argue against the normative force of weak expectations in the standard framework. Furthermore, we propose to replace this framework by a bounded utility perspective: this shift renders the problem more realistic and accounts for the role of weak expectations. In particular, we demonstrate that in a bounded utility framework, all agents, even if they have different value functions and disagree on the price of an individual game, will finally agree on the rational price of a repeated, averaged game. Thus, we explain the intuitive appeal of weak expectations, while avoiding both trivialization of the original paradox and the drawbacks of previous approaches.