Happen-before causal partial orders have been widely used in concurrent program verification and testing. This paper presents a parametric approach to happen-before causal partial orders. Existing variants of happen-before relations can be obtained as instances of the parametric framework. A novel causal partial order, called sliced causality, is then defined also as an instance of the parametr...

We study the question of whether a composite structure of elementary particles, with a length scale 1/Λ, can leave observable effects of non-locality and causality violation at higher energies (but . Λ). We formulate a model-independent approach based on BogoliubovShirkov formulation of causality. We analyze the relation between the fundamental theory (of finer constituents) and the derived the...

This paper introduces the notion of weak causality that models the intra-object parallelism in parallel discrete event simulation. In this setting, a run where events are executed at each object according to their timestamp is a correct run. The weak causality relation allows to deene the largest subset of all runs of a simulation that are equivalent to the timestamp-based run. Finally, we desc...

According to Hermann von Helmholtz, sensations are signs (symbols) that external causes impress on our sense organs; those signs are then used by themind to acquire knowledge of the reality. Helmholtz’s work points out the di›culty of dening a notion of causality suitable for explaining the relation between sensations on the one hand and the physical world on the other. In fact, he states that...

This paper proposes an algorithm for causality inference based on a set of lexical knowledge bases that contain information about such items as event role, is-a hierarchy, relevant relation, antonymy, and other features. These lexical knowledge bases have mainly made use of lexical features and symbols in HowNet. Several types of questions are experimented to test the effectiveness of the algor...

We propose a general dynamic regression framework for partial correlation and causality analysis of functional brain networks. Using the optimal prediction theory, we present the solution of the dynamic regression problem by minimizing the entropy of the associated stochastic process. We also provide the relation between the solutions and the linear dependence models of Geweke and Granger and d...

We study causality in the-calculus. Our notion of causality combines the dependencies given by the syntactic structure of processes with those originated by passing names. It turns out that two transitions not causally related may although occur in a xed ordering in any computation, i.e.,-calculus may express implicitly a priority between actions. Our causality relation still induces the same p...

The epistemic theory of causality maintains that causality is an epistemic relation, so that causality is taken to be a feature of the way a subject reasons about the world rather than a non-epistemological feature of the world. In this paper, we take the opportunity to briefly rehearse some arguments in favour of the epistemic theory of causality, and then present a version of the theory devel...

Illusions of causality occur when people develop the belief that there is a causal connection between two events that are actually unrelated. Such illusions have been proposed to underlie pseudoscience and superstitious thinking, sometimes leading to disastrous consequences in relation to critical life areas, such as health, finances, and wellbeing. Like optical illusions, they can occur for an...