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Shingo Model: Continuous Improvement Principles
Published in Rick L. Edgeman, Complex Management Systems and the Shingo Model, 2019
Empiricism is experientially rooted and hence anchored to data-driven decision making (Chesbrough, 2010), that is, it is a posteriori knowledge derived from observation. The enterprise intent of data-driven decision making is to transform data into knowledge, knowledge into strategy, strategy into activities, and activities into results (Davenport et al., 2001). Empirically acquired knowledge is often complemented by a priori knowledge gained through reason—or rationalism. In other words, perception and cognition, empiricism and rationalism can and should work in concert with one another.
Trapped in the past, dealing with the future
Published in Ali Intezari, David Pauleen, Wisdom, Analytics and Wicked Problems, 2018
Kant’s perspective on knowledge has become one of the most philosophically influential explanations of the sources of epistemological knowledge. He distinguishes between a priori knowledge and a posteriori knowledge. A priori knowledge is independent of experience. In contrast, a posteriori knowledge is developed from one’s experience (Trusted, 1997). The debate is best reflected by three main schools of thought concerning the source of knowledge: Empiricism, Positivism, and Apriorism.
The Evolution and Influence of Knowledge Management
Published in José López Soriano, Maximizing Benefits from IT Project Management, 2016
“A priori” knowledge can be innate or learned, but, in any case, it is independent of any experience and is perceived as universal and necessary truth. “Water extinguishes fire” and “Night will follow day” are common expressions of this kind of knowledge.
Unit speed flocking model with distance-dependent time delay
Published in Applicable Analysis, 2023
Let be the position and velocity configurations of N-particles, where d is a spatial dimension. Then with this distance-dependent delay , the unit-speed flocking model is written as, for t>0, subject to initial data where Here, denotes the communication weight. For simplicity, we assume that The authors in [25] used a Lyapunov functional approach for the zero time delay case. See also [26,27]. The Cucker-Smale models with a constant time delay or processing delay is studied in [13,15]. The Cucker-Smale model with time-varying delay for seasonal effects or aging effects are investigated in [28]. Since the constant or time-varying delay is explicitly defined, the authors can obtain the convergence to consensus for small time-delays using Lyapunov functional. For the nonzero time-delay case, [13] employed an a priori estimate method based on an induction argument. Unlike zero time delay or constant time delay, we cannot use the previous arguments directly because of the implicitly defined time delay.
A mosaic of Chu spaces and Channel Theory I: Category-theoretic concepts and tools
Published in Journal of Experimental & Theoretical Artificial Intelligence, 2019
Chris Fields, James F. Glazebrook
Thus when the sequent’s conditional probability is , say, we have . A priori, one must have to apply in a argument. The probability of the former holding in , is . Then follows from the rule . Probability axioms for a Countable Classical Propositional Logic are developed in Allwein (2004) (cf. Allwein et al., 2004) to which we refer for details. Note that information flow in distributed systems can be interpreted dynamically; this amounts to causation in an informational context, consistent with the Dretskean nature of the theory. In this respect, the relations between information theory and logic are also conducive to understanding certain relations between causation and computation (Collier, 2011; Seligman, 2009).
Dynamic thermoviscoelastic thermistor problem with contact and nonmonotone friction
Published in Applicable Analysis, 2018
Krzysztof Bartosz, Tomasz Janiczko, Paweł Szafraniec, Meir Shillor
In Section 2 we introduce the classical formulation of the model, Problem , which is in the form of a hyperbolic-like system for the displacements that is coupled with a parabolic temperature equation and an elliptic equation for the electric potential. The friction condition leads to a Clarke subdifferential inclusion. Also, as noted above, we include frictional heat generation. The variational formulation of the problem as a hemivariational inequality is presented in Section 3. There, the necessary function spaces and operators are developed. The weak or variational formulation is given in Problem . The assumptions on the problem data are provided, and the existence of a weak solution is stated in Theorem 3.2, which is the main result of this work. The proof of the theorem can be found in Section 4. It is based on time delay in some of the nonlinear terms, a priori estimates and a convergence argument. The steps of the proof are presented in the lemmas. The necessary background material, especially about the Clarke subdifferential, can be found in the Appendix 1.