Events can be modeled through a geometric approach, representing event structures in terms of spaces and mappings between spaces. At least two spaces are needed to describe an event, an action space and a result space. In this article, we invoke general mathematical structures in order to develop this geometric perspective. We focus on three cognitive processes that are crucially involved in events: causal thinking, control of action and learning by generalization. These cognitive processes are supported by three corresponding mathematical (...) properties: monotonicity (that we relate to qualitative causal thinking and allows extrapolation); continuity (that plays a key role in our activities of action control); and convexity (that facilitates generalization and the categorization of events, and enables interpolation). We define how such properties constrain events representations and relate them to thinking about events. We discuss the relevance of the three constraints for event segmentation and explore the implications of such constraints for semantics. We conclude by a discussion that relates our approach to other accounts of events. (shrink)

Dispute types can roughly be divided in two classes. One class in whichthe notion of justification is fundamental, and one in which thenotion of opposition is fundamental. Further, for every singledispute type there exist various types of protocols to conduct such adispute. Some protocols permit local search (a process in which oneis allowed to justify claims partially, with the possibility to extendjustifications on request later), while other protocols rely on globalsearch (a process in which only entire arguments count as justifications).This (...) paper integrates the two above-mentioned types of dispute withthe use of a protocol that permits local search. The locality aspect isrelatively new to computer scientists, while the detailed computationalelaboration of the approach is relatively new to philosophical logicians.The proposed protocol is demonstrated with the help of eight benchmarks.These benchmarks are centered around the problem that co-concludingarguments sometimes accrue, and sometimes do not. (shrink)

Biological and epidemiological phenomena are often measured with error or imperfectly captured in data. When the true state of this imperfect measure is a confounder of an outcome exposure relationship of interest, it was previously widely believed that adjustment for the mismeasured observed variables provides a less biased estimate of the true average causal effect than not adjusting. However, this is not always the case and depends on both the nature of the measurement and confounding. We describe two sets of (...) conditions under which adjusting for a non-deferentially mismeasured proxy comes closer to the unidentifiable true average causal effect than the unadjusted or crude estimate. The first set of conditions apply when the exposure is discrete or continuous and the confounder is ordinal, and the expectation of the outcome is monotonic in the confounder for both treatment levels contrasted. The second set of conditions apply when the exposure and the confounder are categorical. In all settings, the mismeasurement must be non-differential, as differential mismeasurement, particularly an unknown pattern, can cause unpredictable results. (shrink)

This article presents an architecture for natural language generation of biomedical argumentation. The goal is to reconstruct the normative arguments that a domain expert would provide, in a manner that is transparent to a lay audience. Transparency means that an argument's structure and functional components are accessible to its audience. Transparency is necessary before an audience can fully comprehend, evaluate or challenge an argument, or re-evaluate it in light of new findings about the case or changes in scientific knowledge. The (...) architecture has been implemented and evaluated in the Genetics Information Expression Assistant, a prototype system for drafting genetic counselling patient letters. Argument generation makes use of abstract argumentation schemes. Derived from the analysis of arguments used in genetic counselling, these mainly causal argument patterns refer to abstract properties of qualitative causal domain models. (shrink)