Niki Pfeifer

Traditionally, syllogisms are arguments with two premises and one conclusion which are constructed by propositions of the form “All… are…” and “At least one… is…” and their respective negated versions. Unfortunately, the practical use of traditional syllogisms is quite restricted. On the one hand, the “All…” propositions are too strict, since a single counterexample suffices for falsification. On the other hand, the “At least one …” propositions are too weak, since a single example suffices for verification. The present contribution studies algebraic interpretations of syllogisms with comparative quantifiers (e.g., “Most… are…”) and quantitative quantifiers (e.g., “n/m… are…”, “all, except n… are…”). This modern version of syllogistics is intended to be a more adequate framework for argumentation theory than traditional syllogistics.

Nonmonotonic conditionals (A |∼ B) are formalizations of common sense expressions of the form “if A, normally B”. The nonmonotonic conditional is interpreted by a “high” coherent conditional probability, P(B|A) > .5. Two important properties are closely related to the nonmonotonic conditional: First, A |∼ B allows for exceptions. Second, the rules of the nonmonotonic system p guiding A |∼ B allow for withdrawing conclusions in the light of new premises. This study reports a series of three experiments on reasoning with inference rules about nonmonotonic conditionals in the framework of coherence. We investigated the cut, and the right weakening rule of system p. As a critical condition, we investigated basic monotonic properties of classical (monotone) logic, namely monotonicity, transitivity, and contraposition. The results suggest that people reason nonmonotonically rather than monotonically. We propose nonmonotonic reasoning as a competence model of human reasoning.

This chapter presents a probability logical approach to fallacies. A special interpretation of (subjective) probability is used, which is based on coherence. Coherence provides not only a foundation of probability theory, but also a normative standard of reference for distinguishing fallacious from non-fallacious arguments. The violation of coherence is sufficient for an argument to be fallacious. The inherent uncertainty of everyday life argumentation is captured by attaching degrees of belief to the premises. Probability logic analyzes the structure of the argument and deduces the uncertainty of the conclusion from the premises. The approach is illustrated by prominent examples of fallacies, like the argumentum ad ignorantiam, affirming the consequent and the conjunction fallacy

According to probabilistic theories of reasoning in psychology, people's degree of belief in an indicative conditional `if A, then B' is given by the conditional probability, P(B|A). The role of language pragmatics is relatively unexplored in the new probabilistic paradigm. We investigated how consequent relevance aects participants' degrees of belief in conditionals about a randomly chosen card. The set of events referred to by the consequent was either a strict superset or a strict subset of the set of events referred to by the antecedent. We manipulated whether the superset was expressed using a disjunction or a hypernym. We also manipulated the source of the dependency, whether in long-term memory or in the stimulus. For subset-consequent conditionals, patterns of responses were mostly conditional probability followed by conjunction. For superset-consequent conditionals, conditional probability responses were most common for hypernym dependencies and least common for disjunction dependencies, which were replaced with responses indicating inferred consequent irrelevance. Conditional probability responses were also more common for knowledge-based than stimulus-based dependencies. We suggest

In this work we survey reports on selected severe storms of the 17th century. Specifically, we investigate a severe storm which was accompanied by a ball lightning phenomenon in Cornwall (UK) in 1640. The “fiery Ball”, which reportedly made a “ter[r]ible sound”, entered the church, broke stones and smashed windows. It made holes in stone walls and injured about 14 people. Furthermore, we report on a 1672 storm in Bedford (UK) that tore down houses, blew down stone walls and uprooted trees. We also examine two severe thunderstorms that tore off roofs and uprooted trees in Oxfordshire (UK) and Blois (F) in 1680. In Oxfordshire, hailstone killed farm animals, and later lightning caused a fire, which damaged houses and burned down barns. In Blois, houses were torn down by the wind, eight parishes were ruined by hail (hailstone were the size of a “man’s fist”). Furthermore, houses were damaged and glass windows were shattered. Based on various primary sources, we discuss the impact of these severe storms on society. Moreover, we briefly discuss how people perceived atmospheric phenomena like storms, tornadoes, and hail. Finally, we discuss selected key issues of investigating historical severe storms.

An important field of probability logic is the investigation of inference rules that propagate point probabilities or, more generally, interval probabilities from premises to conclusions. Conditional probability logic (CPL) interprets the common sense expressions of the form “if . . . , then . . . ” by conditional probabilities and not by the probability of the material implication. An inference rule is probabilistically informative if the coherent probability interval of its conclusion is not necessarily equal to the unit interval [0, 1]. Not all logically valid inference rules are probabilistically informative and vice versa. The relationship between logically valid and probabilistically informative inference rules is discussed and illustrated by examples such as the modus ponens or the affirming the consequent. We propose a method to evaluate the strength of CPL inference

Conditionals are basic for human reasoning. In our paper, we present two experiments, which for the first time systematically compare how people reason about indicative conditionals (Experiment 1) and counterfactual conditionals (Experiment 2) in causal and non-causal task settings (N = 80). The main result of both experiments is that conditional probability is the dominant response pattern and thus a key ingredient for modeling causal, indicative, and counterfactual conditionals. In the paper, we will give an overview of the main experimental results and discuss their relevance for understanding how people reason about conditionals