A Cognitive Framework for Knowledge-based Process Design

We propose a framework for redesigning knowledge-intensive business processes that is inspired by the knowledge-based theory of the firm, and based on ideas from cognitive science. It views a business process as a problem-solving task consisting of five phases: recognition, decomposition, planning, action and evaluation. The coordination of these tasks among multiple agents is viewed as distributed cognition. We give some general principles for identifying process improvements based on manipulating these phases

Causal reversibility in Bayesian networks

Abstract. Causal manipulation theorems proposed by Spirtes et al. and Pearl in the context of directed probabilistic graphs, such as Bayesian networks, oŒer a simple and theoretically sound formalism for predicting the eŒect of manipulation of a system from its causal model. While the theorems are applicable to a wide variety of equilibrium causal models, they do not address the issue of reversible causal mechanisms, i.e. mechanisms that are capable of working in several directions, depending on which of their variables are manipulated exogenously. An example involving reversible causal mechanisms is the power train of a car: normally the engine moves the transmission which, in turn, moves the wheels; when the car goes down the hill, however, the driver may want to use the power train to slow down the car, i.e. let the wheels move the transmission, which then moves the engine. Some probabilistic systems can also be symmetric and reversible. For example, the noise introduced by a noisy communication channel does not usually depend on the direction of data transmission. In this paper, we investigate whether Bayesian networks are capable of representing reversible causal mechanisms. Building on the result of Druzdzel and Simon (1993), which shows that conditional probability tables in Bayesian networks can be viewed as descriptions of causal mechanisms, we study the conditions under which a conditional probability table can represent a reversible causal mechanism. Our analysis shows that conditional probability tables are capable of modelling reversible causal mechanisms but only when they ful ® ll the condition of soundness, which is equivalent to injectivity in equations. While this is a rather strong condition, there exist systems where our ® nding and the resulting framework are directly applicable. Keywords: causality, manipulation, causal reversibility, Bayesian networks

Subsidence in the Dutch Wadden Sea

Ground surface dynamics is one of the processes influencing the future of the Wadden Sea area. Vertical land movement, both subsidence and heave, is a direct contributor to changes in the relative sea level. It is defined as the change of height of the Earth's surface with respect to a vertical datum. In the Netherlands, The Normaal Amsterdams Peil (NAP) is the official height datum, but its realisation via reference benchmarks is not time-dependent. Consequently, NAP benchmarks are not optimal for monitoring physical processes such as land subsidence. However, surface subsidence can be regarded as a differential signal: The vertical motion of one location relative to the vertical motion of another location. In this case, the actual geodetic height datum is superfluous. In the present paper, we highlight the processes that cause subsidence, with specific focus on the Wadden Sea area. The focus will be toward anthropogenic causes of subsidence, and how to understand them; how to measure and monitor and use these measurements for better characterisation and forecasting; with some details on the activities in the Wadden Sea that are relevant in this respect. This naturally leads to the identification of knowledge gaps and to the formulation of notions for future research.Mathematical Geodesy and Positionin